research project on attitudes towards study listening

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IELTS Listening Test 19

Part 1: Questions 1-10 Complete the notes below. Write NO MORE THAN TWO WORDS OR A NUMBER for each answer.

Health club customer research Name: Selina Thompson

Occupation: (1) Age group: (2) Type of membership: (3) Length of membership: (4)  years Why joined: recommended by a (5) Visits to club per month: Eight (on average) Facility used most: (6) Facility not used (if any): tennis courts (because reluctant to (7)  ) Suggestions for improvements: have more (8) Install (9)  in the gym Open (10)  later at weekends

Part 2: Questions 11-16 Complete the flow chart below. Choose SIX answers from the box and write the correct letter A-G next to questions 11-16.

A air B ash C earth D grass E sticks F stones G water

Making a steam pit • Dig a pit • Arrange a row of (11)  over the pit • Place (12)  on top • Light the wood and let it burn out • Remove (13) • Insert a stick • Cover the pit with (14) • Place wrapped food on top and cover it with (15) • Remove the stick and put (16)  into the hole

Questions 17-18 Choose TWO letters, A-E.

Which TWO characteristics apply to the bamboo oven?

A It’s suitable for windy weather. B The fire is lit below the bottom end of the bamboo. C The bamboo is cut into equal lengths. D The oven hangs from a stick. E It cooks food by steaming it.

Questions 19-20 Choose TWO letters, A-E.

Which TWO pieces of advice does the speaker give about eating wild fungi?

A Cooking doesn’t make poisonous fungi edible. B Edible wild fungi can be eaten without cooking. C Wild fungi are highly nutritious. D Some edible fungi look very similar to poisonous varieties. E Fungi which cannot be identified should only be eaten in small quantities.

Part 3: Questions 21-25 Choose the correct letter, A, B or C.

Research project on attitudes towards study

21. Phoebe’s main reason for choosing her topic was that A her classmates had been very interested in it. B it would help prepare her for her first teaching post. C she had been inspired by a particular book.

22. Phoebe’s main research question related to A the effect of teacher discipline. B the variety of learning activities. C levels of pupil confidence.

23. Phoebe was most surprised by her finding that A gender did not influence behaviour significantly. B girls were more negative about school than boys. C boys were more talkative than girls in class.

24. Regarding teaching, Phoebe says she has learned that A teachers should be flexible in their lesson planning. B brighter children learn from supporting weaker ones. C children vary from each other in unpredictable ways.

25. Tony is particularly impressed by Phoebe’s ability to A recognise the limitations of such small-scale research. B reflect on her own research experience in an interesting way. C design her research in such a way as to minimise difficulties.

Questions 26-30 What did Phoebe find difficult about the different research techniques she used?

Choose FIVE answers from the box and write the correct letter A-G, next to questions 26-30.

Difficulties A Obtaining permission B Deciding on a suitable focus C Concentrating while gathering data D Working collaboratively E Processing data she had gathered F Finding a suitable time to conduct the research G Getting hold of suitable equipment

Research techniques 26. Observing lessons 27. Interviewing teachers 28. Interviewing pupils 29. Using questionnaires 30. Taking photographs

Part 4: Questions 31-40 Complete the sentences below. Write NO MORE THAN TWO WORDS for each answer.

Saving the juniper plant

31. Juniper was one of the first plants to colonise Britain after the last 32. Its smoke is virtually  so juniper wood was used as fuel in illegal activities. 33. Oils from the plant were used to prevent  spreading. 34. Nowadays, its berries are widely used to  food and drink

35. Juniper plants also support several species of insects and

36. In current juniper populations, ratios of the  are poor. 37. Many of the bushes in each group are of the same age so  of whole populations is rapid.

38. Plantlife is trialling novel techniques across  areas of England. 39. One measure is to introduce  for seedlings. 40. A further step is to plant  from healthy bushes.

1. accountant 2. over 50 3. family 4. 9 5. doctor 6. (swimming) pool 7. pay (extra) 8. social events 9. air conditioning 10. restaurant 11. E 12. F 13. B 14. D 15. C 16. G 17. B 18. E 19. A 20. D 21. C 22. A 23. B 24. A 25. B 26. E 27. G 28. A 29. D 30. B 31. ice age 32. invisible 33. infections 34. flavor 35. fungi 36. sexes 37. extinction 38. lowland 39. shelter 40. cuttings

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Can high quality listening predict lower speakers' prejudiced attitudes? ☆

Guy itzchakov.

a University of Haifa, Israel

Netta Weinstein

b University of Reading, United Kingdom

Nicole Legate

c Illinois Institute of Technology, United States of America

d Ono Academic College, Israel

Associated Data

Theorizing from humanistic and motivational literatures suggests attitude change may occur because high quality listening facilitates the insight needed to explore and integrate potentially threatening information relevant to the self. By extension, self-insight may enable attitude change as a result of conversations about prejudice. We tested whether high quality listening would predict attitudes related to speakers' prejudices and whether self-insight would mediate this effect. Study 1 (preregistered) examined scripted conversations characterized by high, regular, and poor listening quality. In Study 2, we manipulated high versus regular listening quality in the laboratory as speakers talked about their prejudiced attitudes. Finally, Study 3 (preregistered) used a more robust measure of prejudiced attitudes to test whether perceived social acceptance could be an alternative explanation to Study 2 findings. Across these studies, the exploratory (pilot study and Study 2) and confirmatory (Studies 1 & 3) findings were in line with expectations that high, versus regular and poor, quality listening facilitated lower prejudiced attitudes because it increased self-insight. A meta-analysis of the studies ( N  = 952) showed that the average effect sizes for high quality listening (vs. comparison conditions) on self-insight, openness to change and prejudiced attitudes were, d s = 1.19, 0.46, 0.32 95% CIs [0.73, 1.51], [0.29, 0.63] [0.12, 0.53], respectively. These results suggest that when having conversations about prejudice, high-quality listening modestly shapes prejudice following conversations about it, and underscore the importance of self-insight and openness to change in this process.

• • Little is known about whether the experience of listening influences speakers' prejudice.
• • We tested whether the experience of high quality listening reduces moderate levels of prejudice for speakers.
• • High quality listening, relative to regular and poor listening quality, predicted lower speakers' prejudice.
• • The effect was mediated through increasing self-insight and openness to change.
• • Findings inform the nascent literature on intrapersonal outcomes of listening.

High quality listening is the focal strategy of most therapeutic interventions ( Friedman, 2005 ). In offering such listening the therapist aims to increase client introspection ( Gilbert, 2010 ; Perls et al., 1951 ; Rogers, 1951 ; Vargas, 1954 ), and it may be one of the primary reasons why, regardless of the specific modality, therapy generally helps people to change in a positive direction ( Lambert & Barley, 2001 ). Outside of the therapy context, however, we know less about the benefits of high-quality listening for helping people to change. The present paper explores the benefits of high quality listening outside of the therapeutic context to test the possibility that high quality listening might catalyze changes in one's attitudes – specifically lowering prejudiced attitudes. We posited that high quality listening can influence prejudiced attitudes by allowing individuals to introspect in an open-minded manner on the views they hold without fear of judgment, thus making it easier to be open to changing or modifying attitudes. The hypotheses are informed by the humanistic approach of Carl Rogers ( Rogers, 1951 , Rogers, 1980 ), Self-Determination Theory ( Deci & Ryan, 2011 ), and the nascent high quality listening literature (e.g., Itzchakov et al., 2017 ; Van Quaquebeke & Felps, 2018 ). Overall, this literature converges on a definition of high quality listening as listening that offers empathy (an understanding of the speaker's point of view), interest-taking, and unconditional regard (caring for the speaker, independent of expressed content and a non-judgmental stance). This operationalization of listening is aligned with the constructs of active and reflective listening ( Gordon, 1975 ; Levitt, 2001 ), and therapeutic listening ( Kemper, 1992 ), all of which broadly share these same supporting features.

1. High quality listening fosters self-insight

High quality listening (aka reflective, active, or therapeutic listening) is thought to be key for facilitating change by encouraging speakers' self-insight. This is because the listener offers a non-judgmental climate in which hidden and contradictory experiences can be safely explored ( Rogers, 1951 ). Self-insight is defined as a deeper reflection and understanding about how one relates to the topic under discussion – and this downstream consequence is a primary goal of many psychotherapies seen to underlie behavioral and attitudinal change (e.g., Bennett-Levy & Thwaites, 2007 ; Connolly Gibbons et al., 2007 ). However, self-insight is important to differentiate from seemingly similar constructs present in the literature. Insight has been studied at the dispositional level, though it is measured in terms of felt confusion about one's experiences alongside self-understanding ( Grant et al., 2002 ). This formulation is sensible for dispositional measures where insight reflects a sense of clarity rather than disorder around the self ( Campbell, Trapnell, Heine, Katz, Lavallee, & Lehman, 1996 ; Morrison & Wheeler, 2010 ), but less relevant when the insight relates to exploring currently held biases, where the absence of the previous self-reflection, rather than confusion, per se, is likely driving biases ( Verplanken et al., 2007 ). Said another way, in the context of attitude change, self-insight matters insofar as it reflects a process of learning about oneself.

Consistent with this is theorizing within self-determination theory (SDT; Deci & Ryan, 2011 ) that views high quality listening as an integral aspect of providing autonomy support – the support for people to act according to their values and beliefs. Autonomy support is similar to high quality listening as conceptualized within the humanistic literature in that it involves demonstrating empathy and perspective-taking, encouraging interest taking in others' self-revelations, and providing unconditional regard ( Deci & Ryan, 2011 ). Though high quality listening is a necessary ingredient of autonomy support, it has not been isolated from other qualities of providing autonomy support (such as providing people choices or a meaningful rationale; Ryan & Deci, 2017 ). Yet SDT-based research offers evidence to support the expectation that high quality listening would promote self-insight in that autonomy support has been consistently shown to reduce defensiveness and increase introspection ( Caprariello & Reis, 2011 ; Hodgins & Knee, 2002 ; Hodgins et al., 2006 ; Pavey and Sparks, 2008 , Pavey and Sparks, 2012 ; Reis et al., 2018 ; Reis et al., 2017 ).

Furthermore, clients in therapy recognize these therapist qualities – providing a climate of empathic understanding and non-judgment – as the primary reason they are able to explore and self-disclose difficult material ( Bachelor, 1995 ; Bedi, 2006 ). Especially salient for attitude change, high quality listening fosters openness and lowers defensiveness, which helps individuals gain insights about themselves, their emotions, cognitions, and values ( Abbass & Town, 2013 ; Lacewing, 2014 ).

Furthermore, studies on mindful attention ( Haddock et al., 2017 ) describe that high attention is required for self-insight but place less focus on the learning and discovery qualities of interest here. We furthermore differentiate self-insight from insight about events, more broadly. Self-insight has to do with knowing oneself, understanding one's own internal emotional and cognitive responses ( Dunning, 2012 ), and with how the interpersonal exchanges foster this form of self-understanding ( Castonguay & Hill, 2007 ). Furthermore, although self-insight has an interpersonal component in this project, in that it is expectedly elicited through conversation with a partner who is empathic, it is distinct from broader experiences of interpersonal comforts, such as psychological safety - feeling safe for interpersonal risk-taking ( Edmondson, 1999 ), or identity security ( Tyler & Blader, 2003 ), which do not involve self-reflection and self-learning.

2. High quality listening during difficult conversations

Even outside of the context of psychotherapy, expressing difficult content such as negative attitudes, experiences, and emotions should also be sensitive to high quality listening because individuals may feel vulnerable, exposed, or judged. High quality listening is likely to reduce the defensiveness that can naturally arise when discussing negative attitudes and experiences ( Itzchakov & Kluger, 2018 ). In the absence of such listening, the result is too often an attitude change in the opposite direction. In this case, speakers become more firmly entrenched in their original stance (boomerang effect; Heller et al., 1973 ) because of the perceived threat ( Brehm, 1972 ), which prompts processing of information in a defensive manner ( Kunda, 1990 ). In the interest of self-protection, while each party is talking, the other party is mulling counter-arguments that would win the argument. On the contrary, when individuals experience high quality listening, they become more open-minded and process information in a less defensive and self-serving manner ( Itzchakov et al., 2017 ).

Furthermore, difficult and negative experiences, attitudes, and emotions may be particularly challenging to integrate or assimilate into self-knowledge or identity, because this knowledge itself elicits a defensive response and enters into a direct contradiction with other more positive content regarding the self (e.g., I am a caring person; Freud, 1936 ; Kegan, 1982 ; Shedler, 2010 ; Weinstein et al., 2011 ). It has been theorized that for the integration of difficult and negative content to take place, individuals must have a willingness to take ownership, which is less likely to happen when defenses are high ( Weinstein et al., 2013 ). Self-insight is necessary for owning or integrating this new information into the self-structure ( Pennebaker et al., 1988 ; Weinstein et al., 2013 ). Because negative attitudes and experiences may evoke people's defenses, it is important to reduce defensiveness in order to process and integrate potentially negative and threatening information.

Thus, lowering defensiveness is key for fostering self-insight ( Stotland et al., 1959 ). When individuals feel defensive, they seek information to support their initial attitude ( Kunda, 1990 ), reject and ignore new information ( Frey, 1986 ; Jemmott et al., 1986 ), process information in a biased manner ( Itzchakov et al., 2020 ; Itzchakov & Van Harreveld, 2018 ) and avoid the associated unpleasant emotions ( Weinstein & Hodgins, 2009 ) in the service of self-protection. Fundamentally, the defensive process is inherently aimed at and always prepared to manage perceived threats to the self ( Sherman & Cohen, 2002 ).

Defensive processes have also been closely linked to extreme views, such as those that may characterize prejudice ( Maio et al., 2010 ). For this reason, it seems important to reduce defensiveness in order to allow people to reflect on their prejudices. Indirect evidence comes from research on mindfulness, which is conceptualized as non-judgmental awareness of the present ( Kabat-Zinn, 2015 ). Specifically, when individuals are mindfully aware of conflictual affect and self-relevant information they show more emotional differentiation ( Hill & Updegraff, 2012 ), better self-regulation ( Erbas et al., 2014 ), and respond more positively to situations of uncertainty ( Haddock et al., 2017 ). Mindful attention has also been linked to changing stereotypes and prejudice (e.g., Djikic et al., 2008 ; Lillis & Hayes, 2007 ; Lueke & Gibson, 2015 ).

Of importance to the present research is recent empirical evidence showing that lower defensiveness could account for the associations between high quality listening and (reduced) attitude extremity ( Itzchakov et al., 2017 ). However, this particular study did not examine negative attitudes such as prejudice. The SDT literature suggests that when autonomy support is low, individuals have more prejudiced attitudes towards out-groups and show less desire to be in contact with them ( Fousiani et al., 2016 ) and this can be explained by increasing feelings of defensiveness ( Weinstein et al., 2012 ). Experimental evidence also supports the notion that autonomy-supportive contexts can reduce prejudice ( Legault et al., 2011 ), though this support did not involve interpersonal interactions or listening. Here, we investigated whether high quality listening allows individuals to introspect in a non-defensive, open-minded manner and therefore change prejudiced attitudes. We expected prejudice to be affected by listening largely because high quality listening in a conversation about prejudice allows people to self-reflect on their beliefs without fear of judgment. As prejudiced attitudes counter most people's deeply-held values for equality and inclusivity ( Amiot et al., 2012 ), SDT theorizing would expect those deep-rooted core values to prevail over prejudiced attitudes that are not as well-internalized ( Assor, 2012 ; Ryan & Deci, 2017 ). Thus, when given space for self-reflection that is free of judgment and consequences, as is the case when providing high-quality listening, people will naturally be less inclined to hold onto prejudiced attitudes.

3. The present research

It has been suggested that individuals must be willing to explore, recognize, and challenge their beliefs for their attitudes to change ( Zúñiga et al., 2002 ). High quality listening may be a key to this type of self-insight ( Itzchakov et al., 2017 ; Rogers, 1951 , Rogers, 1980 ). Recent work has found that a non-judgmental exchange of narratives in interpersonal conversations was more impactful in reducing exclusionary attitudes than providing arguments ( Kalla & Broockman, 2020 ). However, to the best of our knowledge, there has been no research isolating the effects of high quality listening on speakers' prejudiced attitudes, and very little work associating listening to attitude change of any kind.

It is important to distinguish the present study from research that has examined the effects of perspective-taking on prejudice because perspective-taking and high quality listening might at first glance appear similar. Perspective-taking is defined as a process where people try to adopt others' viewpoints to understand their needs, values, and preferences ( Parker & Axtell, 2001 ). Previous work has found that encouraging people to take others' perspectives reduces the perspective takers' prejudice ( Galinsky & Ku, 2004 ). By contrast, the focus of the present study is on the person who expresses prejudiced attitudes, not the perspective-taker. Said differently, perspective-taking research focuses on the effects on the listener (i.e., the perspective-taker) and is other-focused. In contrast, the present study focuses on the effects on the speaker (i.e., the perspective-giver) and the role that listening has on an inner focus through facilitating self-insight. Furthermore, previous work has found that perspective-giving reduces prejudiced attitudes for members of the group with lower power when a member of the group with the higher power listens to them ( Bruneau & Saxe, 2012 ). The present research differs that the listener, whether imagined (Study 1) or real (Studies 2 & 3) was not a member of an outgroup. For these two reasons, the present studies are fundamentally different from other studies on the effects of intergroup contact on prejudice.

Furthermore, studies have not tested whether self-insight plays an exploratory role in explicating why listening may affect attitude change. To address this gap, the present research evaluated three hypotheses to explore the associations between high quality listening, self-insight, and prejudiced attitudes (see Fig. 1 ).

As compared to poor (Study 1) and regular (all studies) listening, high quality listening will increase speakers' self-insight.

As compared to poor (Study 1) and regular (all studies) listening, high quality listening will increase speakers' openness to change their prejudiced attitudes by increasing self-insight.

As compared to regular listening (in Studies 2 and 3), high quality listening will predict increased speakers' attitude favorability (i.e., lower prejudiced attitudes) towards the outgroup by encouraging self-insight and openness to change.

A serial-mediation model of the effect of high quality listening on speakers' attitude favorability towards outgroup.

4. Overview of the studies

We conducted a pilot study (reported in the supplementary material), and three experiments to empirically test these hypotheses using experimental paradigms that would allow largely causal interpretations of the data. Building off of initial development of the paradigm in the pilot study, in Study 1 (preregistered) scenarios instructed participants to imagine having a conversation about a negative bias that they have towards a particular group of their choosing, where their conversation partner demonstrated high quality listening behavior, regular listening behavior, poor listening, depending on assignment to conditions. In Study 2 we increased the ecological validity of the experiments with a live conversation partner, contrasting high quality and regular listening. Participants wrote about the group about which they had a negative bias and conversed about it in front of a listener who exhibited either good or regular listening behavior. Finally, Study 3 was a preregistered conceptual replication and expansion of Study 2. Specifically, participants rated their attitude towards five specific groups, wrote about the group towards which they had the strongest bias, and then conversed about it with either a good or regular listener.

All manipulations and exclusions in the studies are disclosed, as well as the method of determining the final sample size. Data collection did not continue after data analysis. Studies 2 and 3 included other measures for separate work on well-being and self-determination theory (i.e., self-esteem, psychological need satisfaction).

Study 1 was a preregistered ( http://aspredicted.org/blind.php?x=n8ab67 ) test of the three hypotheses summarized above. We Used a confirmatory approach to test the effects of high quality listening as compared to a regular listening condition on measures of openness to change and self-insight. Because the self-insight measure was new to this project (first tested in the pilot study), to substantiate the construct validity, we added an existing measure of reflective self-awareness ( Trapnell & Campbell, 1999 ) as an additional assessment of self-insight.

5.1. Method

5.1.1. participants.

We recruited 461 Israelis through an online platform similar to the one used in the pilot study. 2 Of the initial sample, 14 participants provided meaningless answers to the question about their bias, and 62 failed to answer the awareness question, which we added to this study (i.e. “On this question, mark number 5”), and we excluded their responses. Therefore, the final sample size was N  = 385. Power analysis using Gpower ( Faul et al., 2007 ) indicated that this sample size has a power above 0.80 to detect the effect size on openness to change which was obtained in the pilot study; namely, Cohen's f  = 0.22 (converted from Cohen's d  = 0.44). The sensitivity analysis indicated that the weakest effect size detectable with this sample size and power of 0.80 was Cohen's f  = 0.15.

5.1.2. Procedure

After completing consent forms, participants were asked to think about a negative bias (prejudice) they have towards a specific group. For this purpose, all participants received the following instruction (translated from Hebrew): “Please take a few minutes to think about any negative bias you may have or have had in the past towards a particular social group. Most people will feel some kind of bias throughout their lives. Bias is defined as negative feelings and thoughts about a group of people with a common characteristic.” Examples were provided to help orient participants to the kind of bias examined in this study (namely, prejudice towards out-groups), and to encourage participants to think concretely about what this bias might mean to them. Afterward, participants were asked to write a short description of the bias they described.

Subsequently, participants read a scenario asking them to imagine having a conversation about the group they wrote about with another person. Participants were randomly assigned to read a scenario describing their conversation partner as a high quality listener ( n  = 122), a poor quality listener ( n  = 149), or a regular listener as a comparison ( n  = 123). We asked participants to read the scenario twice. To ensure that participants did not skip the manipulation, survey software ensured they spent at least 50 s on the page before they were able to progress to the next page.

Participants in the high quality listening condition read the following scenario (translated from Hebrew), which have been used in past experiments of high quality listening ( Itzchakov et al., 2018 ) and included elements of empathic listening, unconditional positive regard, and interest-taking based on humanistic, motivational, and social psychological theorizing ( Deci & Ryan, 2011 ; Rogers, 1951 ; Van Quaquebeke & Felps, 2018 ): “Imagine that you are talking about the negative bias you mentioned on the previous page with a person who has a neutral (neither positive nor negative) attitude towards this group. During the conversation, you feel that your conversation partner is really trying to understand your views and experiences relating to your negative bias in a non-judgmental way. Moreover, his reactions, questions, and comments show you that he takes a genuine interest in you and your experiences – in what you have to say. During the conversation, your conversation partner seems empathic; he is attuned to your feelings behind the negative bias and shows an understanding of how difficult it can be to talk about this issue and the feelings and thoughts associated with it.”

Participants in the poor listening condition read the following scenario:

“Imagine that you are talking about the negative bias that you mentioned on the previous page with a person who has a neutral attitude (neither positive nor negative) towards this group. During the conversation, you feel that your conversation partner is not trying to understand your views or experiences relating to your negative bias and is judgmental about the things you are saying. His reactions, questions, and comments show you that he does not take any interest in you and your experiences - in what you have to say about the bias. During the conversation, your conversation partner does not convey any empathy; he is not attuned to your feelings behind the negative bias and does not show he understands how difficult it is to talk about this issue and the feelings and thoughts that are associated with it.”

In the regular listening condition, participants read the following scenario: “Imagine that you are talking about the negative bias that you mentioned on the previous page with a person who has a neutral (neither positive nor negative) attitude towards this group. During the conversation, you talk about several different features of your bias. You did not feel it was an eventful conversation one way or another. Overall you felt it was an ordinary conversation.”

Finally, participants responded to measures of perceived listening as a manipulation check, self-insight, and perceived attitude change were debriefed and compensated.

5.1.3. Measures

All measures were anchored on a 7-point Likert type scale (1 = ‘ not at all ’; 4 = ‘ moderately ’; 7 = ‘ very much ’) as described in the pilot study materials (see supplementary materials).

Listening perception ( manipulation check ). Speakers' listening perception was assessed on the 10-item Layperson-Based Listening Scale (α = 0.98; Lipetz et al., 2018 ). An example item is: “To which extent did you feel that your conversation partner showed interest in what you had to say?”

Self-insight . A five-item scale that included the following items: “how much do you feel this conversation: “Helped to understand yourself better?”, “Made you think more deeply about the topic?” “Helped you to discover new or different insights about yourself?” “Helped you to reflect about your attitudes?” and “Helped you think about things in a different way?” (α = 0.92).

Openness to change . Openness to change with regard to the prejudiced attitude was adapted from previous research ( Omoto & Snyder, 1995 ). Specifically, it read: “To which extent do you feel that the conversation changed your attitude about the bias?”

Reflective self-awareness was used as an additional assessment for self-insight. Six items from Trapnell and Campbell (1999) were adapted to the present setting (α = 0.80). Example items were (translated from Hebrew): “during the conversation, I explored my inner-self”, “during the conversation I analyzed my bias”, and “I don't feel that this conversation prompted me to introspect about my bias” ( reverse coded ).

5.2. Results and discussion

Table 1 presents the descriptive statistics and the correlations between variables.

Study 1: Descriptive statistics and correlations between the variables.

Notes. Experimental condition was coded: −1 = poor listening, 0 = regular listening, 1 = high quality listening; ⁎⁎ p  < .01.

5.2.1. Main effects

Listening perception ( manipulation check ). An analysis of variance ( ANOVA ) indicated a main effect of the listening manipulation on perception of high quality listening, F (2,382) = 442.37, p  < .001, ŋ 2 p  = 0.70, Cohen's f  = 1.52. Post-hoc LSD tests indicated that these effects differed across all the experimental conditions. Specifically, in line with the nature of the manipulations, participants in the high quality listening condition ( M  = 5.82, SD  = 0.98) perceived higher quality listening than participants in the neutral ( M  = 4.51, SD  = 1.15), M difference  = 1.32 SE  = 0.14, p  < .001, 95% CI [1.05, 1.58], and poor listening conditions ( M  = 1.99, SD  = 1.06), M difference  = 3.84, SE  = 0.13, p  < .001, 95% CI [3.58, 4.10]. In addition, participants in the regular listening condition perceived higher quality listening more than participants in the poor listening condition, M difference  = 2.52 SE  = 0.13, p  < .001, 95% CI [2.26, 2.78].

Self-insight . An ANOVA indicated a main effect of the listening manipulation on self-insight, F (2,382) = 120.63, p  < .001, ŋ 2 p  = 0.39, Cohen's f  = 0.80. Post-hoc LSD tests indicated a significant difference where participants in the high quality listening condition ( M  = 4.93, SD  = 1.19) reported higher self-insight than participants in the regular listening condition ( M  = 4.24, SD  = 1.35), M difference  = 0.69 SE  = 0.17, p  < .001, 95% CI [0.36, 1.02], and participants in the poor listening condition ( M  = 2.52, SD  = 1.35), M difference  = 2.41, SE  = 0.16, p  < .001, 95% CI [2.09, 2.73]. Participants in the regular listening condition reported greater self-insight than participants in the poor listening condition, M difference  = 1.72, SE  = 0.16, p  < .001, 95% CI [1.40, 2.04]. Thus, Hypothesis 1 , that listening would affect self-insight, was supported in Study 1.

Reflective self-awareness . Complementing findings for the measure of self-insight, a significant main effect of the listening manipulation on reflective self-awareness was found, F (2,382) = 33.17, p  < .001, ŋ 2 p  = 0.15, Cohen's f  = 0.42. Specifically, participants in the high quality listening condition ( M  = 4.73, SD  = 1.23) reported higher self-awareness than participants in the regular listening condition ( M  = 4.29, SD  = 1.29), M difference  = 0.44, SE  = 0.16, p  = .007, 95% CI [0.12, 0.76], and participants in the poor listening condition ( M  = 3.47, SD  = 1.29), M difference  = 1.26, SE  = 0.16, p  < .001, 95% CI [0.95, 1.57]. Participants in the regular listening condition reported higher reflective self-awareness than participants in the poor listening condition, M difference  = 0.81, SE  = 0.16, p  < .001, 95% CI [0.51, 1.124].

Openness to change . An ANOVA indicated a main effect of the listening manipulation on openness to change with regard to their bias, F (2,382) = 15.46, p  < .001, ŋ 2 p  = 0.08, Cohen's f  = 0.29, supporting Hypothesis 2 that listening quality would affect openness to change. LSD tests indicated a significant difference between all the listening conditions. Specifically, participants in the high quality listening condition reported the greatest attitude change ( M  = 3.35, SD  = 1.76) relative to participants in the regular listening condition ( M  = 2.86, SD  = 1.60), M difference  = 0.49 SE  = 0.21, p  = .019, 95% CI [0.08, 0.90] and participants in the poor listening condition ( M  = 2.24, SD  = 1.54), M difference  = 1.12, SE  = 0.20, p  < .001, 95% CI [0.72, 1.51]. In addition, participants in the regular listening condition reported a more positive attitude change in comparison to participants in the poor listening condition, M difference  = 0.63, SE  = 0.20, p  = .002, 95% CI [0.23, 1.02].

5.2.2. Mediation analysis

We conducted a mediation analysis using Model 4 in PROCESS ( Hayes, 2017 ) using 5000 bootstrapped samples ( Preacher & Hayes, 2008 ) to test Hypothesis 3 , that self-insight would relate to openness to change and mediate the effect of the listening manipulation. We did not assume linearity between the listening conditions; therefore, we created two dummy variables. The variable dummy 1 compared the high quality listening condition (coded as “1”) to the neutral and poor listening conditions (coded as “0”). The variable dummy 2 compared the poor listening condition (coded as “1”) to the supportive and regular listening condition (coded as “0”). We tested mediation for each of the two variables that represented self-insight, controlling for the other dummy code. 3

First, we tested the mediation model with high quality vs. regular and poor quality listening (dummy 1) controlling for dummy 2 (poor listening vs. high and regular quality listening). The indirect effect through self-insight was significant, b  = 0.46, SE  = 0.12, 95% CI [0.24, 0.69], meaning that the high versus regular quality listening condition predicted self-insight, and self-insight, in turn, related to openness to change as hypothesized. The direct effect was not significant, b  = 0.03, SE  = 0.18, t  = 0.15, p  = .88, 95% CI [−0.33, 0.38], indicating that relative to regular listening, good listening did not have an effect on openness to change when controlling for its effects through self-insight (see Fig. 2 a). Similar results were obtained with reflective self-awareness as a mediator; namely, the indirect effect was significant, b  = 0.18, SE  = 0.07, 95% CI [0.05, 0.33], and the direct effect was not significant, b  = 0.31, SE  = 0.20, t  = 1.56, p  = .12, 95% CI [−0.08, 070] (see Fig. 2 a and c). In sum, regardless of which measure was used to test self-insight, this construct appeared to be an underlying factor in explaining the effects of high quality listening on openness to change.

a. Study 1: Mediation analysis for the effect of dummy 1 controlling for dummy 2 to openness to change via self-insight; standard errors in parentheses; ⁎ p  < .05, ⁎⁎ p  < .01.

b. Study 1: Mediation analysis of the effect of dummy 2 Controlling for dummy 1 on attitude change via self-insight; standard errors in parentheses; ⁎ p  < .05, ⁎⁎ p  < .01.

c . Study 1: A mediation analysis of dummy 1 controlling for dummy 2 on attitude change via reflective self-awareness; standard errors in parentheses; ⁎ p  < .05, ⁎⁎ p  < .01.

d. Study 1: A mediation analysis of dummy 2 controlling for dummy 1 on openness to change via reflective self-awareness; standard errors in parentheses; ⁎ p  < .05, ⁎⁎ p  < .01.

The mediation analysis with poor quality listening versus regular and high quality listening (dummy 2) as the independent variable provided additional support for the hypothesized model. The indirect effect from dummy 2 to openness to change through self-insight, when controlling for dummy 1, was significant, b  = −1.16, SE  = 0.14, 95% CI [−1.46, −0.89]. The direct effect was significant as well, b  = 0.53, SE  = 0.19, t  = 2.75, p  = .01, 95% CI [0.15, 0.92]. The mediation pattern was similar when submitting reflective self-awareness as a mediator. The indirect effect was significant, b  = −0.33, SE  = 0.09, 95% CI [−0.51, −0.18]. The direct effect was not significant, b  = −0.30, SE  = 0.20, t  = −1.49, p  = .14, 95% CI [−0.68, 0.09] (see Fig. 2 b and d), suggesting that self-insight provides a good account of the effects of listening on attitudes. In conclusion, these additional mediation analyses further support the hypothesized model. Thus poor listening showed effects in line with those of high quality listening: its impact on openness to change is better understood through its more immediate effects on self-insight.

These findings provided confirmatory evidence for Hypotheses 1 and 2 regarding the main effects of listening on self-insight and openness to change. The results of Study 1 indicated that both high quality listening and independently, poor listening, contribute to self-insight and attitudes as compared to regular listening. This experiment also validated our measure of self-insight and showed that effects were robust for both measures across all main and meditational effects. Specifically, the results indicated that self-insight mediated the effect of high quality listening conditions on openness to change in relation to one's prejudiced attitudes.

However, this study had two important shortcomings. First, although scenario experiments are often used to measure interpersonal listening (e.g., Itzchakov et al., 2018 ), and are a recommended approach to experimental manipulations ( Aguinis & Bradley, 2014 ), they only provided a proxy for an actual interaction. In addition, the way that people imagine they would feel and behave in uncomfortable situations such as when witnessing discrimination may differ from how they actually behave in these situations ( Kawakami et al., 2009 ). Thus, this experiment was limited with regard to its ecological validity, and it remains unclear whether the effects would be replicated in an actual interpersonal encounter. In addition, Hypothesis 3 , regarding the effect of high quality listening on prejudice, was not tested in the present experiment.

The first goal of Study 2 was to increase ecological validity by using an actual conversation partner. Second, we tested whether the listening-induced self-reported attitude change would correspond to lower prejudice. While attitude exploration and change is a crucial aspect of many prejudice reduction efforts ( Paluck & Green, 2009 ), it is possible, though unlikely, and that change can shift towards greater prejudice. To rule out this possibility, we added a widely validated measure of prejudice ( Correll et al., 2010 ), the feeling thermometer, to complement the measure of attitude change.

6.1. Method

6.1.1. participants.

Undergraduates from a British University ( N  = 140 4 ) participated in the study in exchange for course credit. Of these participants, 13 were excluded from analyses because they did not report a bias towards a group as instructed (e.g., “I have never experienced any intense bias in my life and have been very fortunate, and I try extremely hard not to feel bias towards any groups of people”). Hence, the final sample size was 127 individuals ( M age  = 19.30, SD  = 1.33, 89.8% female). Power analysis using Gpower indicated that this sample size has a power of above 0.80 to detect the average effect size on attitude change that was obtained in the pilot study and Study 1 (Cohen's d  = 0.51). Sensitivity analysis showed that the weakest effect size that such a sample could detect with a power of 0.80 was d  = 0.44.

6.1.2. Procedure

Participants entered the laboratory and were seated at a cubicle, which provided privacy from the researcher. The researcher explained that the study consisted of three parts: responding to an online questionnaire, a brief conversation, and responding to another questionnaire afterward. The first questionnaire contained a consent form, demographics, and the following instructions to write about an incident that evoked prejudice:

“ I would like you to take a couple of minutes to think about a specific bias that you may feel or may have felt. Most people will have felt some sort of bias during their lives. This bias can be towards any group of people, for example, older people, people of color, the Opposite gender, or people from different socioeconomic status. In the box below, please write about this bias, how you were feeling, and what your overall experience was .”

Afterward, participants turned their chairs around to face the researcher. The researcher started the conversation with the following preface: “ I would now like you to describe the bias you just wrote about to me . Everything we talk about here is confidential and will not be recorded . When you are ready , please begin ”. The RA allowed the participants to talk for as long as they wanted. Once it was clear that the participant had finished talking, she said: “ Thank you for sharing this with me today , when you are ready , there are a few questionnaires for you to fill out on the computer .” In the regular listening condition ( n  = 64), the researcher merely listened without responding; however, researcher responses included head nodding and minor communications of acknowledgment ( hmmm , I see ) to maintain naturalness and avoid actively alienating the participants.

Participants in the high quality listening condition ( n  = 63) were given the same instructions as participants in the regular listening condition. In the conversation, the researcher responded by nodding and saying specific phrases when prompted. These phrases included questions about the bias and the participant's experience and empathic responses such as “I realize this can be difficult to talk about.” Although the researcher minimized the number of responses to allow sufficient time for listening to occur, responses were directly designed to communicate interest in what the speaker was saying, along with empathy and non-judgment (unconditional regard). Once it was clear that the participant had finished talking, the researcher instructed them to complete the second part of the questionnaire. At the end of the experiment, the participants were debriefed, thanked, and given credit for their participation.

6.1.3. Measures

Listening perception ( manipulation check ). We used the same measure as in Study 1. The items were anchored on a 7-point Likert-type scale (1 = ‘ not at all ’; 7 = ‘ very much ’; α = 0.97).

Self-insight . The measure was the same as the one used in Study 1. Items were anchored on a Likert-type scale (1 = ‘ not at all ’; 5 = ‘ very much ’; α = 0.88).

Openness to change . We used the same item as in Study 1, which was anchored on a Likert-type scale (1 = ‘ not at all ’; 5 = ‘ very much ’).

Attitude favorability towards the prejudiced group . Prejudiced attitudes were measured with a feeling thermometer, which ranged from 0° ( very cold or unfavorable feelings ) to 100° ( very warm or extremely favorable feelings ) with regard to the group they talked about during the conversation. This thermometer is frequently used to examine a variety of prejudiced attitudes (e.g., Correll et al., 2010 ; Haddock and Zanna, 1997 , Haddock and Zanna, 1998 ; Haddock et al., 1993 ). Higher scores indicate less prejudice.

6.2. Results & discussion

Table 2 presents the descriptive statistics and the correlations between variables.

Study 2 : Descriptive statistics and correlations between the variables .

Notes. Experimental condition was coded: 1 = regular listening, 2 = high quality listening; ⁎ p  < .05 ⁎⁎ p  < .01; Higher scores on attitude favorability correspond to lower prejudice.

6.2.1. Main effects

Listening perception . Participants in the high quality listening condition reported better listening ( M  = 5.45, SD  = 1.08) than participants in the regular listening condition ( M  = 3.30, SD  = 1.26), t (125) = 10.27, p  < .001, 95% CI [1.72, 2.56], d  = 1.82; the listening manipulation was therefore successful.

Self-insight . Participants in the high quality listening condition had higher levels of self-insight ( M  = 3.03, SD  = 0.90) than participants in the regular listening condition ( M  = 2.36, SD  = 0.90), t (125) = 4.10, p  < .001, 95% CI [0.34, 0.97], d  = 0.73, supporting Hypothesis 1 regarding the main effects of condition on self-insight and consistent with the findings in Study 1.

Openness to change . As was the case in the pilot study and Study 1, participants in the high quality listening condition ( M  = 2.15, SD  = 1.06) reported higher openness to change with regard to their prejudiced attitude than participants in the regular listening condition ( M  = 1.89, SD  = 0.99). However, unlike the pilot and Study 1, the difference was not significant, t (125) = 1.38, p  = .169, 95% CI [−0.11, 0.61], d  = 0.25.

Attitude favorability towards outgroup . Participants in the high quality listening condition demonstrated more favorable attitudes towards the outgroup ( M  = 56.21, SD  = 23.85) than participants in the regular listening condition ( M  = 46.69, SD  = 21.01), t (125) = 2.39, p  = .018, 95% CI [1.63, 17.41], d  = 0.42, with a significant effect of condition on lower prejudice.

6.2.2. Mediation analysis

To examine how listening shapes attitudes through the facilitation of introspection, we conducted a serial-mediation analysis using Model 6 in PROCESS ( Hayes, 2017 ). Specifically, we tested the effect of the listening manipulation on attitude favorability towards the prejudiced group via increasing self-insight openness to change. We anticipated that as individuals perceived more change in their prejudicial attitudes, they would also report more favorable (less prejudiced) attitudes towards the outgroup they described. Note that although the main effect of the high quality listening manipulation on self-reported attitude change was not significant, mediation can still occur ( Rucker et al., 2011 ).

As can be seen from Fig. 3 , the indirect effect from the listening manipulation to attitude favorability through self-insight and openness to change was significant, b  = 3.38, SE  = 1.43, 95% CI [0.88, 6.46]. Thus, the mediation analysis provided support for Hypothesis 3 , namely, that high quality listening will reduce speakers' prejudiced attitudes towards the outgroup by increasing self-insight and openness to change. However, the direct effect was significant, b  = 11.03, SE  = 4.18, t  = 2.64, p  = .009, 95% CI [2.76, 19.30], suggesting additional variance is still to be explained by other mediating factors. Indirect effects conceptually replicated Study 1, but results for direct effects did not. The final analysis also supported the downstream consequences of openness to change. It meaningfully related to more favorable attitudes towards the outgroup, reinforcing the view that perceptions of change reflected actual attitudes and demonstrated that perceived change was not in the counterproductive direction (i.e., increased negative attitudes).

Study 2: Serial-mediation analysis of the effect of listening on attitude favorability towards prejudiced groups via self-insight and openness to change; standard errors in parentheses;

⁎ p  < .05, ⁎⁎ p  < .01.

Study 2 largely provided support for H3 and increased the ecological validity of the study by using a live interaction. Specifically, the listening manipulation was effective with regard to reducing speakers' prejudiced attitudes. In addition, speakers who experienced high quality listening were able to delve deeper into their biases (i.e., increased self-insight), which in turn resulted in less prejudice or more favorable attitudes towards the group. In addition, the results of the pilot study and Study 1, which recruited participants from Israel, were replicated with participants from Great Britain. The generalization of findings across two cultures speaks to the robustness of the models across samples, which most likely came from different demographic backgrounds and held different biases ( Bond & Gudykunst, 1997 ).

However, despite its merits, the approach used in Study 2, which let participants select any group they wished to discuss as a target of their prejudice, may have resulted in participants selecting socially acceptable groups to discuss. As a result, we cannot be certain that any of the beneficial effects of listening translated into lower prejudice towards any specific outgroups. This is a problem when attempting to generalize effects to real-life conversations, where typically there are specific groups under discussion. In addition, from an attitude strength perspective ( Krosnick & Petty, 1995 ), the participants might have chosen relatively weak attitudes (i.e., ambivalent; DeMarree et al., 2011 ) so that the conversation would be more comfortable or less threatening. Previous work had found that people's attitudes are more likely to respond to change inductions when their initial attitude was weak (i.e., ambivalent; DeMarree et al., 2011 ). We conducted Study 3 to address these issues.

The primary objective of Study 3 was to provide a conceptual preregistered replication of Study 2, once again using an in-person interaction that manipulated listening quality ( https://aspredicted.org/blind.php?x=ej27v9 ). Second, we tested the effect of high quality listening on stronger prejudiced attitudes relative to the previous studies and used a more robust measure of prejudiced attitudes, namely, change in attitude favorability from before to after the conversation.

We also modified the procedure in two ways to account for the possibility that participants would select to discuss weak or socially acceptable attitudes. First, rather than permitting participants to select any group of their choosing, we asked participants to discuss one of five groups that experience the most prejudice in Israel: those who are Black, homeless, immigrants to Israel, gay, and transgender. Of these, they were asked to discuss the group towards which they reported the least favorable attitudes at the start.

In addition, we measured participants' perceived social acceptance of the outgroup attitude directly and tested whether it would moderate the listening manipulation on attitude favorability towards the outgroup. Social acceptance is a basic and motivating need in interpersonal interactions, including those that concern prejudice ( Kunstman et al., 2013 ). In the context of the present research, the desire for social acceptance may influence the content of the discussion such that speakers only describe attitudes they perceive is normative and thus acceptable to express in front of the listener. To explore this, we examined this possible boundary condition to the effects of high quality listening: namely, that the effects of high quality listening on speakers' prejudice depends on the extent that the speaker feels that the expressed attitude is socially acceptable.

7.1. Method

7.1.1. participants.

Undergraduate students from Israel ( N  = 245) 5 participated in this study in exchange for course credit. As specified in the preregistration form, we excluded nine participants who did not report their bias towards a group as instructed. Examples are: “I am gay, out of the closet. I have friends in the community who are transgender, and I see the discrimination towards them, they are treated disrespectfully and are perceived as unusual from the community,”; and “in the past I used to be afraid of homeless people. However, today as I have matured, I now offer them a hot meal and something to drink.” The final sample size was N  = 236 ( M age  = 26.55, SD  = 6.77, 68.2% female). This sample size had a power of above 0.80 to detect the average effect size on attitude favorability that was obtained in Study 2 (Cohen's d  = 0.42). Sensitivity analysis using Gpower ( Faul et al., 2007 ) showed that the weakest effect size that this sample could detect with a power of 0.80 was d  = 0.37.

7.1.2. Procedure

Four research assistants (RAs; three females, one male; M age  = 24.25, SD  = 7.89) participated in the study as listeners. They received listening training and followed a protocol that was written for the present study. Every RA performed both the good and regular listening conditions in a randomized order. The experiment included two stages. First, participants signed a consent form and completed a questionnaire where they indicated, using the thermometer, their attitude towards five social groups, namely those who are Black, homeless, immigrants, gay, and transgender. Of the five groups, 28.4% selected homeless individuals, 27.5% selected immigrants to Israel, 19.1% chose transgender individuals, 12.7% selected Black people, and 12.3% selected gay individuals as the group they held the least favorable attitudes. Afterward, the participants indicated to what extent they thought that it is socially acceptable to have a negative attitude towards each specific group. Subsequently, participants were asked to select the group towards which they had the most negative attitude and write a short description or give an example of their bias. In the second stage, participants were informed that they would talk about the attitude towards the group they wrote about with the person in the lab (i.e., the RA). As in Study 2, participants were randomly assigned to the high quality ( n  = 115) or the regular listening condition ( n  = 121). The behavior of the listeners in both conditions was the same as described in Study 2. Only a single listener-speaker dyad was present in each experimental session to eliminate potential artifacts related to social influence and distraction. After the conversation, participants answered questionnaires that included the outcome variables and were debriefed by the RAs. None of the participants guessed the goals or nature of the study. The most frequent answers regarding the objective of the study were that it was meant to characterize students' attitudes towards minority groups, build knowledge about prejudice in the Israeli society, and examine conversations between people who do not know each other.

7.1.3. Measures

The Likert-type scales ranged from 1 (‘ not at all ’) to 9 (‘ very much ’). We followed best practice recommendations and used a scale with a wider range of anchors to increase validity and capture more variability ( Aguinis et al., 2009 ).

Listening perception 1 . In order to increase the construct validity of the manipulation check, we used the constructive behavior sub-scale from the Facilitating Listening scale ( Kluger & Bouskila-Yam, 2018 ). Previous work has found that this scale has strong correlations with other validated listening measures ( Itzchakov et al., 2014 ). This measure is composed of 10 items, α = 0.95. Example items were: “When my conversation partner listened to me, he or she (a) Tried hard to understand what I was saying, (b) Listened to me attentively, and (c) Asked questions that showed his/her understanding of my opinions.”

Listening perception 2 . As another measure of the listening manipulation, participants responded to the following item: “to what extent would you like to experience the kind of listening you experienced in the conversation again?”. Participants dragged the slider from 0° ( not at all ) to 100° ( very much ).

Self-insight . The measure was the same as the one used in Studies 1 and 2, α = 0.87.

Openness to change . We used the same item as in the previous studies.

Cognitive reappraisal . A new measure of cognitive reappraisal was used here ( Jones & Wirtz, 2006 ). This measure served as an additional indicator of openness to change and was composed of four items: namely: (a) “My conversational partner made me think about the attitude I described during the conversation”, (b) “I feel that I ought to re-evaluate the event now, after the conversation”, (c) “I don't really see the conversation in a different light after the conversation” (reverse-coded), and (d) “I understand the situation better now that I talked about it with my conversation partner”, α = 0.70.

Attitude favorability change towards the outgroup . Attitude favorability towards the prejudiced group was measured twice: before and after the listening manipulation. Hence, the dependent variable was the change in attitude favorability. As in Study 2, prejudiced attitudes were measured with a feeling thermometer that asked about participants' attitudes towards the group they talked about during the conversation. The measure ranged from 0° ( very cold or unfavorable feelings ) to 100° ( very warm or extremely favorable feelings ) with regard to the group they talked about during the conversation. Attitude favorability change (or prejudice reduction) was computed as attitude favorability (post-conversation) – attitude favorability (pre-conversation). The correlation between the participants' pre-listening attitude favorability and post-listening attitude favorability with regard to the group they chose was r  = 0.74, p  < .001.

Perceived social acceptance of prejudice . Participants indicated the extent they perceived that it was socially acceptable to express a negative attitude towards each of the five groups using a feeling thermometer measure. The slider ranged from 0 (‘ not at all acceptable ’) to 100 (‘ completely acceptable ’), M homeless  = 38.74, SD  = 29.90; M Black  = 41.41, SD  = 30.30; M immigrants  = 44.17, SD  = 30.54; M gay  = 40.07, SD  = 31.78; M transgender  = 39.77, SD  = 31.40. This measure was administered in the initial survey before the listening manipulation ( Table 3 ).

Study 3: Descriptive statistics and correlations between the variables.

Notes. Experimental condition was coded: 1 = regular listening, 2 = high quality listening; ⁎ p  < .05 ⁎⁎ p  < .01. Higher scores on attitude favorability change correspond to higher prejudice reduction; Reliabilities in parentheses; a- refers to the outgroup that the participant selected.

7.2. Results & discussion

Table 3 presents the descriptive statistics and the correlations between the variables.

7.2.1. Main effects

Listening perception 1 . Participants in the high quality listening condition reported experiencing better listening ( M  = 8.38, SD  = 0.91) than participants in the regular listening condition ( M  = 6.31, SD  = 2.19), t (234) = 9.38, p  < .001, 95% CI [1.63, 2.50], d  = 1.22.

Listening perception 2 . Participants reported a greater desire to re-experience the type of listening in the high listening condition ( M  = 89.38, SD  = 13.64) than participants in the regular listening condition ( M  = 62.03, SD  = 36.27), t (234) = 7.72, p  < .001, 95% CI [20.82, 34.78], d  = 1.01. Thus, overall, the manipulation was successful.

Self-insight . Participants in the high quality listening condition had higher levels of self-insight ( M  = 5.97, SD  = 1.69) than participants in the regular listening condition ( M  = 4.30, SD  = 2.27), t (234) = 6.40, p  < .001, 95% CI [1.16, 2.18], d  = 0.83.

Openness to change . Participants in the high quality listening condition ( M  = 3.36, SD  = 2.50) reported higher openness to change than participants in the regular listening condition ( M  = 2.72, SD  = 2.30), t (234) = 2.01, p  = .045, 95% CI [0.01, 1.25], d  = 0.26. Note that this effect size is consistent with the effect size that was observed in the live interaction used in Study 2 ( d  = 0.25), suggesting that greater power was needed to detect this smaller effect.

Cognitive reappraisal . Participants in the high quality listening condition ( M  = 5.35, SD  = 2.37) reported higher cognitive reappraisal than participants in the regular listening condition ( M  = 4.11, SD  = 1.90), t (234) = 5.30, p  < .001, 95% CI [0.78, 1.70], d  = 0.69.

Attitude favorability change towards outgroup . We conducted analyses of covariance ( ANCOVA ) with the experimental condition as a predictor of the post-listening prejudice score while controlling for the pre-listening scores on prejudice. Consistent with our prediction, the ANCOVA indicated a significant main effect of condition, F (1,233) = 4.92, p  = .028, η 2 p  = 0.02. Specifically, participants in the high quality listening condition ( M adjusted  = 45.95, SE  = 1.65) reported a more favorable attitude towards the outgroup than participants in the regular listening condition, ( M adjusted  = 40.84, SE  = 1.61). 6 However, the confidence interval for prejudice change within the high quality listening condition crossed 0: 95% CI [−1.06, 5.70] (regular listening condition was: 95% CI [−6.80, 0.78]). Therefore, the high quality listening condition only led to increased attitude favorability towards the outgroup relative to a similar conversation taking place in the context of regular listening.

7.2.2. Mediation analysis

We conducted two serial-mediation analyses using Model 6 in PROCESS ( Hayes, 2017 ). As indicated in the preregistration, we tested two mediation models, specifically, (a) a mediation of the listening manipulation on attitude favorability change via self-insight and openness to change, and (b) a mediation of the listening manipulation on attitude favorability change via self-insight and reappraisal.

As can be seen from Fig. 4 a, the indirect effect from the listening manipulation to attitude favorability change through self-insight and openness to change was significant, b  = 1.41, SE  = 0.67, 95% CI [0.07, 2.71], suggesting high-quality listening promoted openness to change through its effects on self-insight. The direct effect was not significant, b  = 4.45, SE  = 2.77, t  = 1.60, p  = .111, 95% CI [−1.00, 9.90], suggesting self-insight explained substantial variance in the condition - openness to change effect. The reverse indirect effect from the listening manipulation to attitude favorability change via openness to change and introspection was not significant, b  = −0.002, SE  = 0.22, 95% CI [−0.46, 0.47].

a. Study 3: Serial-Mediation analysis of the effect of listening on attitude favorability change towards prejudiced groups via self-insight and openness to change; standard errors in parentheses;

b. Study 3: Serial-Mediation analysis of the effect of listening on attitude favorability change towards prejudiced groups via self-insight and cognitive reappraisal; standard errors in parentheses; ⁎ p  < .05, ⁎⁎ p  < .01.

A similar pattern was observed with reappraisal as the second mediator. As can be seen in Fig. 4 b, the indirect effect from the listening manipulation to attitude favorability change via self-insight and cognitive reappraisal was significant, b  = 2.00, SE  = 0.95, 95% CI [0.20, 3.92]. The direct effect was not significant, b  = 3.05, SE  = 2.77, t  = 1.10, p  = .271, 95% CI [−1.00, 9.90]. The reverse indirect effect from the listening manipulation to attitude favorability change via cognitive reappraisal and introspection was not significant, b  = −0.29, SE  = 0.70, 95% CI [−1.63, 1.15].

7.2.3. Moderation analysis

We examined whether the perception of social acceptance of the prejudiced attitude moderated the effect of the listening manipulation on the change in attitude favorability. The results of Model 1 in PROCESS ( Hayes, 2017 ) indicated that the perceived social acceptance was not a significant moderator, b  = −0.10, SE  = 0.09, t  = −1.20, p  = .231, 95% CI [−0.27, 0.07]. R 2 change  = 0.006, F (1,232) = 1.45, p  = .231. This result hints that perceived social acceptance does not serve as an alternative explanation for the effect of high quality listening on reducing speakers' prejudiced attitudes.

In sum, the results of Study 3 generally supported the research hypotheses. The present study conceptually replicated Study 2 and addressed several of its limitations with four methodological and analytic advances. First, the effect of high quality listening on the dependent variables was replicated using a more conservative procedure, namely, following discussions of attitudes towards one of a small number of pre-specified outgroups. Second, the main effect of the listening manipulation on cognitive reappraisal alongside the significant indirect effect identifying cognitive appraisal as a mediator increased the validity of the model. Third, the use of a change score for attitude favorability provided a more precise measure of prejudice reduction that was directly due to the listening manipulation. Finally, though somewhat underpowered, the lack of moderation effect between the listening quality manipulation and perceived social acceptance predicting attitude favorability change increases the confidence that the benefits of high quality listening found in Studies 2 and 3 were unrelated to perceived social acceptance.

8. Mini meta-analysis

We conducted a random-effects meta-analysis on the pilot study and studies 1–3 ( N =  952, including the pilot study). In Study 3, we included the two listening manipulation checks. In Study 2, we converted the Cohen's f score to a Cohen's d and used the measure of reflective self-awareness as an additional indicator of self-exploration. In Study 3, we used the measure of cognitive reappraisal as an additional indicator of openness to change.

As can be seen in Table 4 , the average effect size of the listening manipulation check across the three experiments was very strong, d  = 2.11, as was the average effect on self-insight, d  = 1.17, p s < .001. Neither of these effects showed evidence of heterogeneity (although the τ for the manipulation check was large, and may not be significant due to low power). The average effect size of openness to change was d  = 0.46, p  < .001, with no evidence of heterogeneity across the experiments. Despite the non-significant main effect of listening on attitude change in Study 2 ( d  = 0.25, p  = .169), this mini meta-analysis suggests that high quality listening, as compared to regular and poor listening, increased openness to change meaningfully across the three experiments and the pilot study. Finally, the average effect size of the listening manipulation on attitude favorability was d  = 0.32, p  = .002, with no evidence of heterogeneity.

A meta-analysis of the variables (including the pilot study; N =  952).

9. General discussion

Experimental studies showed that as compared to poor listening (Study 1) and regular listening (all studies), high quality listening when discussing prejudiced attitudes facilitated speakers' self-insight, and through doing so promoted more positive attitudes. To the best of our knowledge, these studies constitute the first empirical attempt to test how listening shapes discussions of prejudice and explores a promising explanation of why attitudes shifted, namely, higher self-insight. In the present studies, complementary experimental designs allowed for a largely causal interpretation of the downstream consequences of high quality as compared to regular and poor quality listening.

These findings inform theoretical claims related to the self-integrative process by suggesting that when conversations occur in the presence of a supportive listener, the climate facilitates reflection and self-insight about one's experiences ( Rogers, 1951 , Rogers, 1980 ). Often, during the process of reflection, people can reconcile contradictory or ambivalent attitudes. Acknowledging this ambivalence and the complexity of attitudes is what Rogers predicted would result from experiencing high quality listening, and is consistent with the empirically supported therapeutic approach of motivational interviewing that his work inspired. Specifically, high quality listening elicits different and sometimes contradictory views within the speaker, that they must reconcile. This process often leads to behavioral or attitudinal changes in the speaker ( Miller & Rose, 2009 ). Previous work outside of the therapeutic context has shown that high quality listening by a layperson (not a trained clinician) can also have an impact on speakers' attitudes (e.g., Itzchakov et al., 2017 ; Itzchakov & Kluger, 2017 ; Itzchakov et al., 2018 ), though these studies focused on ambivalent attitudes, more generally. The present results provided mixed support for the view that listening can affect attitudes: we did not find compelling evidence that the experience of being listened to reduced prejudice from baseline (tested in Study 3); however, we did find, consistently, that high quality listening led to lower prejudice than regular or poor listening did when speakers talked about their attitudes.

These findings highlight that high quality listening is beneficial when individuals are asked to discuss their prejudiced attitudes. In other words, conversations about such attitudes are best had in the context of listening that conveys empathy, understanding, and support. However, these findings should be understood in the context of their boundary conditions. First, it is plausible that speakers in our studies selected to discuss moderate, rather than extreme, prejudiced attitudes. We cannot be certain that conversations about extreme attitudes would be benefited by the quality of listening similarly to our observed effects. Second, since we elicited self-reflection for the purposes of the study, it is possible that participants had not previously reflected on the attitudes they discussed. In cases where people have previously reflected on their beliefs but continue to express prejudice, high-quality listening might not show the robust benefits over regular listening identified here, as there is little or no ambivalence in beliefs to resolve.

More generally, it is important to understand boundary conditions before applying any prejudice reduction strategy. As another example, we did not find evidence that a conversation about prejudice with someone who provides high quality listening is the solution to long-standing, pervasive problems of prejudice in societies, but we did observe that it benefited an individual's attitudes in the short-term and when compared to a similar conversation with a regular quality listener. Further work examining the long-term impacts of high-quality listening on both self-insight and attitudes is critical to understand how interventions with listening can be formalized.

It is important to contextualize the present findings within the larger literature on prejudice reduction strategies. While high quality listening constitutes a new strategy in the context of prejudiced attitude change, it aligns with and complements prior work. For example, value consistency is a complementary approach to the one we used here, where participants consider the extent to which their prejudice is inconsistent with other values they hold (e.g., equality). Value consistency has been shown to be effective (e.g., Eisenstadt et al., 2003 ), and may help explain our finding that self-insight predicted lower prejudice; namely, self-insight may allow participants to realize and accept that prejudiced attitudes are inconsistent with other values they hold. Further, the effect of high quality listening studied here was not driven by intergroup contact, which is arguably the most effective of all prejudice-reduction strategies ( Pettigrew & Tropp, 2006 ), because participants did not interact with a member of the outgroup in any of the studies. It seems plausible, however, that experiencing high quality listening when it is provided by an outgroup member could produce even stronger effects on prejudice reduction. Finally, some research has indicated that perspective-taking is an effective prejudice reduction strategy ( Broockman & Kalla, 2016 ; Shih et al., 2009 ), where participants take the perspective of the outgroup. By contrast, the studies here involved participants' experience of feeling heard when they expressed their perspective. It seems plausible that conversations that facilitate the perspective-taking of both parties could be even more effective in reducing prejudice. Future research should test the effectiveness of combining high quality listening with these other documented strategies in the prejudice-reduction literature (e.g., mutual perspective sharing and taking, high quality listening provided by an outgroup member).

These findings further inform the literature on therapeutic interventions and conversations aimed towards attitude change beyond the context of prejudice reduction. Although we focused on prejudiced attitude change in this study, the processes hypothesized here are relevant to difficult conversations dealing with sensitive or potentially defense-inducing topics more broadly, including but not limited to attitude change (e.g., conflicts in romantic relationships). Though there is a substantial emphasis on incorporating high quality listening into therapeutic interventions with couples at present ( Chessick, 1989 ; Graybar & Leonard, 2005 ), it is helpful for researchers and practitioners alike to understand the mechanisms that are involved in linking high quality listening to its beneficial effects, as well as the limits of these effects across domains, subjects, relationships, and clients. Although the present studies merely scratch the surface of these research questions, they provide a launchpad for further explorations on the extent to which high quality listening facilitates the kinds of behavior and attitude change that therapists and other conversation partners hope to see.

The present research has further implications for other healthcare contexts where self-insight is important. A good example is medical and hospital visits. Doctors interrupt their patients, on average, 12 s after their patients start talking ( Rhodes et al., 2001 ). Hence, not surprisingly, one of the patients' main complaints is that their doctors do not listen to them ( Boudreau et al., 2008 ). Interestingly, the lack of self-insight might explain why patients who report that their doctors do not listen to them are less likely to adhere to their recommendations ( Magnus et al., 2013 ).

It is further worth noting that in Study 1, poor listening actively undermined self-insight. Arguably, poor listening reduces the willingness to change attitudes because it elicits defensiveness, which then discourages any positive integration tendencies like self-insight or openness to change. This finding sheds further light on the listening literature, which shows that speakers also suffer from poor listening. These include reducing the quality of speakers' narration and speech fluency ( Bavelas et al., 2000 ), as well as impairing speakers' memory ( Pasupathi et al., 1998 ), psychological safety ( Castro et al., 2016 ), and their creativity ( Castro et al., 2018 ). It is possible that poor listening reduces available cognitive resources, in part, because it puts speakers in a defensive stance, and that poor listening may backfire and increase prejudice among speakers. This backfiring effect has indeed been demonstrated in contexts low in autonomy support, where people are told that they must change without being able to express themselves or have their perspective understood ( Legault et al., 2011 ). Studies 2 and 3 did not employ poor listening because our primary interests were the beneficial effects of listening on attitudes, but future research could examine the detrimental effects of poor listening, particularly on prejudice.

9.1. Limitations and future directions

These findings should be viewed in light of several limitations. First, attitudes were measured through self-reports. Future research should complement them with measures of implicit bias and behavioral indicators of prejudice. As such, the present findings may have been vulnerable to social desirability effects (e.g., Janus, 2010 ). Implicit measures and behavioral observations of prejudice would help to validate measures of self-reported attitudinal change. In a similar vein, we focused our mediational analyses on explanations focused on fairly complex internal processes resulting from being listened to (e.g., self-insight), but the effects of listening on attitudes may be better, and more simply, explained by other proximal of listening, such as interpersonal comfort ( Williams & Irurita, 2004 ), psychological safety ( Carmeli & Gittell, 2009 ; Castro et al., 2016 ; Itzchakov et al., 2016 ), or even the valence of mood. Indeed, it is reasonable to assume that being listened to improves mood, which has downstream effects on attitudes ( Haddock et al., 1994 ).

Although good listening perception increased openness to change relative to regular listening and poor listening, the average rating on this measure was below the midpoint of the scale, even following the listening intervention, indicating the difficulty of nurturing openness. Future work should contrast listening with other interpersonal constructs, including those that have been found to increase open-mindedness in the context of attitude change ( Itzchakov & Reis, 2020 ).

Third, we manipulated several aspects of high quality listening simultaneously, but future work should consider manipulating specific qualities (e.g., careful attention, demonstrations of empathy, unconditional regard) in isolation to examine each of their separate contributions to listening effects on attitudes. At present, we can only assume that each of these contributed to the self-insight and non-prejudiced attitudes reported by our participants, but future work is needed to examine this assumption.

Finally, future research should complement this work with observed and coded data from naturalistic conversations such as those that take place between therapists and clients. Future work could also manipulate high quality listening in the context of a prejudice reduction program such as unconscious bias training, which focuses on information dissemination to reduce prejudice ( Noon, 2018 ). This form of training could be enhanced by having trainers use high quality listening strategies with attendees in helping them to explore their biases. Testing this possibility is important because most research on unconscious bias training programs have reported weak effects at best ( Lai et al., 2016 ), and many workplaces are highly invested in the goal of reducing prejudice in their organization.

10. Conclusion

The present research used experimental paradigms to manipulate listening and examined its consequences on speakers' self-insight and prejudiced attitude change. The findings highlight the importance of high quality listening for productive conversations and the potential detriments of poor listening. They suggest that in therapeutic practice, and presumably in other, informal, daily conversations, listening partners can help facilitate self-insight to promote speakers' awareness and integration of their existing views with implications for witprejudiced attitudes.

Open science statement

This work received an open science badge because the hypotheses, measures, sample size, analytic plan, and exclusion criteria for Study 1 and Study 3 were preregistered. The links for the preregistration forms can be found in the manuscript or the following links:

Study 1- http://aspredicted.org/blind.php?x=n8ab67

Study 3- http://aspredicted.org/blind.php?x=ej27v9

☆ This research was funded by a grant Number 460/18 from the Israeli Science Foundation to Dr. Guy Itzchakov.

2 Those who participated in the pilot study were not eligible to take part in Study 1.

3 Analyses presented above were conducted following recommendations during peer review. Findings based on preregistered models not controlling for the second dummy code produced comparable results, Dummy 1: indirect effect via self-insight, b  = 0.97, SE  = 0.12, 95% CI [0.95, 1.82]; direct effect, b  = −0.14, SE  = 0.17, t  = −0.84, p  = .401, 95% CI [−0.48, 0.18]. Self-awareness as a mediator indirect effect, b  = 0.38, SE  = 0.08, 95% CI [0.23, 0.58]; direct effect: b  = 0.45, SE  = 0.18, t  = 2.52, p  = .012, 95% CI [0.10, 0.80]. Dummy 2: indirect effect via self-insight, b  = −1.39, SE  = 0.15, 95% CI [−1.69, −1.12]; direct effect, b  = 0.52, SE  = 0.18, t  = 2.88, p  = .004, 95% CI [0.17, 0.88]. Self-awareness indirect effect, b  = −0.43, SE  = 0.09, 95% CI [−0.63, −0.27]; direct effect, b  = −0.44, SE  = 0.18, t  = −2.47, p  = .014, 95% CI [−0.78, −0.09]. The figures are reported in the supplementary materials (Figures b–e)

4 Due to a coding error, we did not have the condition assignment of one additional participant. However, additional analyses placing this individual in each of the two conditions produced comparable effects.

5 As stated in preregistration form, we aimed for N  = 328 to have a power of 95% to detect the hypothesized effect. However, because of the outbreak of COVID-19, in-lab experiments were no longer permitted as of March 2020. Therefore we had to stop data collection.

6 Similar results were obtained when using an independent t -test with the measure of attitude favorability change. Participants in the high quality listening ( M pre  = 43.37, SD  = 28.29; M post  = 45.69, SD  = 27.13) condition evidenced more change in their attitude favorability towards the outgroup, M difference  = 2.32, SD  = 18.32, than participants in the regular listening condition ( M pre  = 44.11, SD  = 29.15; M post  = 41.09, SD  = 25.97), M difference  = −3.02, SD  = 21.05, t (234) = 2.07, p  = .039, 95% CI [0.27, 10.41], d  = 0.27. d  = 0.29.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jesp.2020.104022 .

Appendix A. Supplementary data

Supplementary material 1

Supplementary material 2

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Investigating Students’ Attitudes Towards Listening and Speaking in the English Classroom at Al Istiqlal University: An Action Research

• Khaled M S Masood English Department AL-Istiqlal University/ Jericho-Palestine
• Hussam A Qadomi English Department AL-Istiqlal University/ Jericho-Palestine

This study sought to investigate students’ attitudes towards listening and speaking in the English classroom at Al Istiqlal University.  The researchers noticed that freshmen students at Al Istiqlal University were de-motivated and reluctant to get involved in listening and speaking activities in the English classroom. One root cause may be students’ prior experience of learning English in Palestinian state schools where Arabic is the dominant language (Hamdallah, 1999) and where there may be little provision of speaking and listening to English. The researchers sought to experiment with innovative teaching methods to address these problems. These included using group work, giving students clearly defined roles, using technological applications and drawing on students’ personal lives as strategies to motivate students and change their attitudes towards listening and speaking activities. A stratified–purposeful sample consisting of 18 freshman students was selected.  The researchers used a quantitative approach to measure changes. The researchers also used qualitative instrument for the purpose of the study. A questionnaire was administered at the beginning and end of the study to determine changes in students’ attitudes. Also, interviews were organized to decide and get comments on the influence of the strategies which were used.  Results indicated that there was a significant difference in students’ attitudes towards listening and speaking activities in English class. When the researchers used different strategies such as:  role plays cards, group activities, technology and different assessment forms, students expressed positive feelings, enjoyment and fun. The researchers recommend that teachers in similar university-level context should implement different strategies such as: group wok, role plays, cards, videos and some applications i.e. Padlet to motivate students bridge the gap between school life and university life.  It was also found that such strategies help to change students’ attitudes to listening and speaking.

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TUTORLISTENING.COM

(from IELTS TUTOR )

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Research project on attitudes towards study: Đề thi thật IELTS LISTENING Test 15 Section 3 (IELTS Listening Recent Actual Test)

Bên cạnh PHÂN TÍCH ĐỀ THI THẬT TASK 2 (dạng advantages & disadvantages) Some students work while studying. Discuss the advantages and disadvantages of this trend and give your opinion?NGÀY 04/8/2020 IELTS WRITING GENERAL MÁY TÍNH (kèm bài được sửa hs đi thi) , IELTS TUTOR giới thiệu Research project on attitudes towards study: Đề thi thật IELTS LISTENING Test 15 Section 3 (IELTS Listening Recent Actual Test)

I. Kiến thức liên quan

IELTS TUTOR lưu ý:

• Cách học IELTS LISTENING hiệu quả  
• Khắc phục mất tập trung khi nghe IELTS LISTENING
• Tự học IELTS LISTENING từ A-Z

II. Research project on attitudes towards study: Đề thi thật IELTS LISTENING Test 15 Section 3 (IELTS Listening Recent Actual Test)

Questions 21-25

Choose the correct letter, A, B or C.

Research project on attitudes towards study

21. Phoebe’s main reason for choosing her topic was that

A. her classmates had been very interested in it.

B. it would help prepare her for her first teaching post.

C. she had been inspired by a particular book.

22. Phoebe’s main research question related to

A. the effect of teacher discipline.

B. the variety of learning activities.

C. levels of pupil confidence. >> IELTS TUTOR lưu ý: Phân tích"Cyclists and car drivers today share the same road, and this can cause some problems. What are the problems? What can be done to reduce these problems?"(kèm bài được sửa của HS đạt 6.0 đi thi thật ngày 13/6/2020)

23. Phoebe was most surprised by her finding that

A. gender did not influence behaviour significantly.

B. girls were more negative about school than boys.

C. boys were more talkative than girls in class.

24. Regarding teaching, Phoebe says she has learned that

A. teachers should be flexible in their lesson planning.

B. brighter children learn from supporting weaker ones.

C. children vary from each other in unpredictable ways.

25. Tony is particularly impressed by Phoebe’s ability to

A. recognise the limitations of such small-scale research.

B. reflect on her own research experience in an interesting way.

C. design her research in such a way as to minimise difficulties. >> IELTS TUTOR lưu ý: PHÂN TÍCH ĐỀ THI TASK 1 VIẾT THƯ NGÀY 05/7/2020"you are going to take a holiday and your friend agrees to stay at your house. Write a letter to him for"IELTS WRITING GENERAL MÁY TÍNH (kèm bài được sửa HS đạt 6.0 đi thi thật)

Questions 26-30

Choose FIVE answers from the box and write the correct letter A-G, next to questions 26-30.

Difficulties

A. Obtaining permission

B. Deciding on a suitable focus

C. Concentrating while gathering data

D. Working collaboratively

E. Processing data she had gathered

F. Finding a suitable time to conduct the research

G. Getting hold of suitable equipment

Research techniques

26. Observing lessons

27. Interviewing teachers

28. Interviewing pupils

29. Using questionnaires

30. Taking photographs >> IELTS TUTOR lưu ý: PHÂN TÍCH ĐỀ THI THẬT TASK 2 (dạng advantages & disadvantages) Some students work while studying. Discuss the advantages and disadvantages of this trend and give your opinion?NGÀY 04/8/2020 IELTS WRITING GENERAL MÁY TÍNH (kèm bài được sửa hs đi thi)

III. Đáp án Research project on attitudes towards study: Đề thi thật IELTS LISTENING Test 15 Section 3 (IELTS Listening Recent Actual Test)

21. C 22. A 23. B 24. A 25. B

IELTS ONLINE TEST

IELTS LISTENING PRACTICE TESTS

Research Project On Attitudes Towards Study IELTS Listening Answers With Audio, Transcript, And Explanation

Luyện tập đề IELTS Listening Practice với Research Project On Attitudes Towards Study được lấy từ cuốn sách IELTS Practice Test Plus 3 - Test 1 - Section 3 kèm Answer key, list từ vựng IELTS cần học trong bài đọc và Free PDF & Audio Transcript Download với trải nghiệm thi IELTS trên máy và giải thích đáp án chi tiết bằng Linearthinking

👂️ Audio and questions

❓ transcript, 🔥 answer key (đáp án và giải thích), giải thích chi tiết.

IELTS Listening Practice

Gồm làm đề, xem giải thích chi tiết, học từ vựng của những bài thi IELTS Listening phổ biến nhất trên thị trường

Bài viết liên quan

Geography dissertation ielts listening answers with audio, transcript, and explanation.

Luyện tập đề IELTS Listening Practice với Geography Dissertation được lấy từ cuốn sách IELTS Actual Test 5 - Test 5 - Section 3 kèm Answer key, list từ vựng IELTS cần học trong bài đọc và Free PDF & Audio Transcript Download với trải nghiệm thi IELTS trên máy và giải thích đáp án chi tiết bằng Linearthinking

Moving To Banford City IELTS Listening Answers With Audio, Transcript, And Explanation

Luyện tập đề IELTS Listening Practice với Moving To Banford City được lấy từ cuốn sách IELTS Cambridge 13 - Test 3 - Section 1 kèm Answer key, list từ vựng IELTS cần học trong bài đọc và Free PDF & Audio Transcript Download với trải nghiệm thi IELTS trên máy và giải thích đáp án chi tiết bằng Linearthinking

Employment Agency: Possible Jobs IELTS Listening Answers With Audio, Transcript, And Explanation

Luyện tập đề IELTS Listening Practice với Employment Agency: Possible Jobs được lấy từ cuốn sách IELTS Cambridge 15 - Test 3 - Section 1 kèm Answer key, list từ vựng IELTS cần học trong bài đọc và Free PDF & Audio Transcript Download với trải nghiệm thi IELTS trên máy và giải thích đáp án chi tiết bằng Linearthinking

Hazel Analysed Items In Newspapers IELTS Listening Answers With Audio, Transcript, And Explanation

Luyện tập đề IELTS Listening Practice với Hazel Analysed Items In Newspapers được lấy từ cuốn sách IELTS Cambridge 15 - Test 3 - Section 3 kèm Answer key, list từ vựng IELTS cần học trong bài đọc và Free PDF & Audio Transcript Download với trải nghiệm thi IELTS trên máy và giải thích đáp án chi tiết bằng Linearthinking

How The Industrial Revolution Affected Life In Britain IELTS Listening Answers With Audio, Transcript, And Explanation

Luyện tập đề IELTS Listening Practice với How The Industrial Revolution Affected Life In Britain được lấy từ cuốn sách IELTS Cambridge 15 - Test 4 - Section 4 kèm Answer key, list từ vựng IELTS cần học trong bài đọc và Free PDF & Audio Transcript Download với trải nghiệm thi IELTS trên máy và giải thích đáp án chi tiết bằng Linearthinking

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Question 1-6

Choose the correct letter, A, B or C.

Research Project

1. How did John choose the topic of his research project?

A He thought the information would be useful for town planning.

B He has a special interest in the use of public services.

C He read about a similar study which had been done earlier.

2. During his research, John is expecting to find that

A The use of public services has altered very little.

B A group of people has changed its habits.

C The most frequently used facility is the library.

3. What is the problem with using official records?

A They may be lacking in detail.

B They may not be readily available.

C They may be difficult to analyse.

4. What does the tutor think about John using a questionnaire to get information?

A He needs to do a course in questionnaire design first.

B   He should use this method instead of looking at public records.

C   He will find the practice he gets useful the following year.

5. Which new variable does John agree to add to his investigation?

A    Occupation

B     Income group

C     Qualifications

6. How many questionnaires will John distribute?

A         The same number as    in the   previous    study

B         A greater number than he needs for analysis

C       The number recommended  in the project guidelines 

Question 7-8

Choose TWO letters, A-E

Which TWO strategies will John use to encourage people to fill out his questionnaire?

A         Using simple language

B         Delivering the questionnaires in person

C       Making the questionnaires anonymous

D         Providing return envelopes

E         Trialing the questionnaire on friends

Questions 9 and 10

Choose TWO letters, A-E.

Which TWO pieces of advice does the tutor give John about his questionnaire?

A         There should be a mixture of question types.

B         Some questions should elicit personal information.

C         There should be an introduction to explain the survey’s purpose.

D         A telephone number should be provided for queries.

E          The questions should only take a few minutes to answer.

---End of the Test---

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• Published: 27 August 2024

Enhancing students’ attitudes towards statistics through innovative technology-enhanced, collaborative, and data-driven project-based learning

• Andreea Cujba 1 &
• Manoli Pifarré   ORCID: orcid.org/0000-0002-4271-4824 1  

Humanities and Social Sciences Communications volume  11 , Article number:  1094 ( 2024 ) Cite this article

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Given the substantial body of educational research highlighting the significant influence of student attitudes on academic performance, particularly in disciplines like statistics where anxiety is prevalent, there is a need to investigate how innovative methodologies could reshape these attitudes. This paper will capitalize on the advancements from previously uncombined innovative methodologies of teaching statistics, such as project-based learning, data analytics, collaborative work, or the use of technology. Specifically, this paper reports on the design, implementation, and evaluation of innovative technology-enhanced, collaborative, and data-driven project-based learning, aiming to positively impact students’ attitudes towards statistics as a cornerstone to improve statistical knowledge. To achieve this, a quasi-experimental research study involving 174 secondary students was undertaken, with participants divided into an experimental group (EG) and a control group (CG). Results indicate a notable positive shift in attitudes among EG students following the intervention. The EG students decreased their anxiety after the intervention and, increased their affect and positive attitude toward using technology for learning statistics. By contrast, the CG students do not show any positive effect on their attitudes. These findings underscore the potential of the innovative instructional design implemented in this project to not only foster practical statistical problem-solving skills but also cultivate positive attitudes crucial for statistical competence. Educational implications are discussed.

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Introduction.

In our increasingly digital world, technology generates vast quantities of data, and with the rise of artificial intelligence, this influx is set to skyrocket. To effectively harness and make sense of this data, citizens need to be equipped with robust data analytic skills. As evidence-based decision-making becomes increasingly imperative, advanced data analytic abilities will be indispensable. However, a significant challenge lies in the fact that many secondary students lack the positive attitudes necessary to engage with and learn data analytics skills and statistics (Garfield and Ben‐Zvi, 2007 ; Szczygieł and Pieronkiewicz, 2021 ).

Previous educational research has confirmed the role of developing positive attitudes to obtaining better results and meaningful learning of mathematics and statistics (e.g., Albelbisi and Yusop, 2018 ; Dowker et al., 2019 ; Muñoz et al., 2018 ; Silva and Sousa, 2020 ).

In the same vein, Emmioğlu and Capa-Aydin ( 2012 ) point out that positive attitudes towards statistics correlate positively with higher students’ results in statistics courses. Furthermore, several studies indicate that most students and adults do not statistically reason about important issues that affect their lives because they have not acquired the necessary skills (Domu et al., 2023 ; Garfield and Ben‐Zvi, 2007 ; Haddar et al., 2023 ; Özmen and Baki, 2021 ). Therefore, many students do not understand the usefulness or application of statistics in real and daily life and develop negative attitudes, e.g., anxiety, towards statistical content (Gal and Ginsburg, 1994 ; Williams, 2015 ). Rejection towards this subject is also accounted for by the widespread and well-known mathematical anxiety (Szczygieł and Pieronkiewicz, 2021 ) due to the student’s perception that statistics posits a great deal of mathematical content, without a real application and is difficult to understand (Gal and Ginsburg, 1994 ).

This paper capitalizes on the advancements from previously uncombined innovative methodologies of teaching statistics, such as project-based learning, data analytics, collaborative work, or the use of technology; and it designs an innovative instructional design to promote positive students’ attitudes towards statistics. Moreover, the paper reports on the implementation, and evaluation of technology-enhanced, collaborative, and data-driven project-based learning and its impact on students’ attitudes towards statistics via a quasi-experimental study. The paper contributes with an innovative pedagogy that combines and integrates the advancements of already testing teaching methods for engaging students in big data analysis, increasing their positive attitudes towards statistics, a cornerstone to improve students’ statistics skills and learning.

Literature review

Attitudes have been broadly defined as not directly observable, inferred aspects consisting of beliefs, feelings, and behavioural predispositions towards the object to which they are directed (Nolan et al., 2012 ). Although the attitude definition is not consistent in the literature, in accordance with the most frequent definitions in research, an attitude is a psychological tendency that is expressed by evaluating a particular entity with some degree of favour or disfavour (Savelsbergh et al., 2016 ). Hence, this psychological tendency is shaped through experience and determines future behaviours. In this line of argument, an attitude can be seen as a personal characteristic that has an influence on subject’s behaviour (Di Martino and Zan, 2015 ).

In the context of learning, attitudes towards mathematics, and statistics in particular, are profound feelings and emotional reactions shaped by students’ experience in solving statistics tasks and throughout time (Tuohilampi, 2016 ). In other words, attitudes toward statistics can be seen as students’ expectations towards this subject, and according to them, the student will have one reaction or another in statistics class (Batanero and Díaz, 2011 ). Math anxiety is the sensation of concern and worry felt when thinking about mathematics or while doing a mathematics task (Abín et al., 2020 ).

Educational research claims that positive attitudes towards mathematics and statistics can be promoted by implementing innovative teaching methods that include, among others, the following five educational variables: (a) student-centred learning; (b) project-based learning and solving real problems or challenges familiar to students; (c) data analytics (henceforth DA) skills; (d) collaborative learning and e) use of interactive technologies (Chew and Dillon, 2014 ; Savelsbergh et al., 2016 ).

In this line of argument, recently, the growth in the everyday use of digital technologies is creating vast reservoirs of data. These data have huge but largely untapped potential. The economic sector has already considered the necessity to understand the “big data” generated in each sector and turn it into insight and action. Therefore, there is an increasing demand for citizens with the skills and creativity capable to perform data-driven decision making (Frischemeier et al., 2022 ). For example, the Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report (Bargagliotti et al., 2020 ) for the pre-K-12 classroom explicitly emphasize the need for innovative instructional programmes about data analytics to teach students to: formulate questions that can be answered using data, learn to collect data, organize data, create graphs and charts with data to answer their questions. In this context, there is a need for studies that innovate and extend best practices in teaching statistics in schools using data analysis and technology-enhancement through a project-based learning approach (Chew and Dillon, 2014 ; Koparan and Güven, 2014 ). Countless investigations point to the positive impact of technology on students’ attitudes, and so technology-driven teaching becomes a useful pedagogical tool for teaching and learning statistics (Emmioğlu and Capa-Aydin, 2012 ; Ramirez et al., 2012 ).

In this line of research, this paper aims to design, implement, and evaluate a technology-enhanced, project-based intervention that could offer secondary students the statistical and digital skills needed to use data to address real-life problems. Specifically, in this paper, we analyse the effects that this technology-enhanced project-based intervention could have on students’ attitudes toward statistics. Our working hypothesis is that students will improve their positive attitudes towards statistics because the technology-enhanced project-based intervention will create a meaningful and positive learning environment that will raise the student's awareness of the role of data, statistics, and technology in many everyday problems.

In the next sections, we revise previous research on the effects of the four uncombined, innovative educational variables in statistics education, namely: (i) project-based learning, (ii) data analytics approach, (iii) use of technology, and (iv) collaborative work. This will be followed by our research study, the results and discussion of our findings and, finally, the educational implications for statistics education.

Project-based learning and data analytics approach

The use of project-based Learning (henceforth PBL) has been increasingly practised globally in schools. This methodology is characterized by the introduction of the following four educational variables: student-centred learning, problem-solving structured in different research phases, contextualized learning contents in real and open-ended challenges and collaborative work (Haatainen and Aksela, 2021 ). In this line, Batanero and Díaz ( 2011 ) claim the importance of contextualizing the data used in real-life problems when designing PBL in statistics. This aspect encourages, firstly, the student's interest and motivation, even more so if they can choose to tackle the problems they are interested in; secondly, students value the relevance of statistics since it can solve real-life problems and facilitate scientific and economic development. Overall, they adhere to the theory that PBL can improve the students’ attitudes toward statistics. In this same line of argument, Santos ( 2016 ) adds to the equation the influential role of digital technologies in solving collaboratively real-life problems and increasing the positive attitudes towards learning statistics to solve a problem in small groups.

Different quasi-experimental studies have reported the benefits of this innovative methodology on students learning and on students’ attitudes and affect towards statistics (Bateiha et al., 2020 ; Chong et al., 2019 ; Özdemir et al., 2015 ; Markulin et al., 2021 ). In these studies, it is reported that PBL methodology promotes the creation of a creative environment, as most students perceived the project to be an easy and enjoyable activity that favours the learning of mathematical concepts as well as the development of key soft skills such as sense of responsibility, communication skills and ability to work in small groups (Özdemir et al., 2015 ). Besides, PBL encourages students to take a more active role by allowing them to take responsibility for and decisions on their own learning process while the teacher guides them through their learning processes, by taking into account their interests (Moreno-Guerrero et al., 2020 ). These PBL characteristics could have a positive impact on students’ attitudes towards statistics (Özdemir et al., 2015 ), and on students’ affect towards learning statistics (Chong et al., 2019 ).

Recently, along with the appearance of interactive technologies, new ways of engaging with real-life data—notably via interactive data visualizations—have emerged and new ways of thinking and learning from complex data have evolved (Engel, 2017 ; Sutherland and Ridgway, 2017 , Rao et al., 2023 ). In this context, various authors have seen the need to develop studies that introduce the perspective of data analytics when designing PBL in teaching statistics (Kazak et al., 2021 ; Zotou et al., 2020 ). From this perspective, data analytics is seen as a process of engaging students creatively in exploring data to understand our world better, draw conclusions, make decisions and predictions, and critically evaluate present/future courses of action (Fujita et al., 2018 ). Data analytics does not focus on learning mathematical procedures but on understanding and interpreting data to solve a real-life problem (Chew and Dillon, 2014 ). Furthermore, data analytics reinforces the active role of students in learning statistics as they must make the effort to focus on the process of understanding and interpreting data to address a real-life problem. The students are encouraged to solve the problem since the teacher acts only as a guide and will not provide them with a solution.

Interactive technologies have been essential in teaching and learning statistics and data analytics. Technologies can provide a creative and interactive environment to represent, visualize and manipulate data in a way that encourages students to think and learn from complex data. In this respect, our educative intervention has designed a technology-enhanced, project-based learning environment that promotes the use of a variety of technological tools for learning key statistical concepts and developing key skills, e.g., explore, understand and interpret data to solve a real problem. In the next section, we will present key studies that have used technology affordances to promote better statistical literacy and positive attitudes toward statistics.

Use of technology to increase the students’ attitudes toward statistics

In the use of technology for teaching mathematics, there is a trend towards constructivist tasks based on research, which supports collaborative approaches, resolution of problems, and the practice of learning by doing. Bray and Tangney ( 2017 ) point this out through a systematic analysis of 139 studies and, in view of the results, conclude that contemporary technologies increase collaboration and allow a practical application of mathematics through visualization, modelling and manipulation. They claim that technologies provide an interactive, dynamic, and contextualized learning of the subject. These technological affordances facilitate experimentation and testing of ideas and manage to change classroom dynamics from the teacher leading the session and transmitting knowledge to more dynamic student-centred research.

Technological tools are also increasingly used in teaching statistics as the means to mediate and promote learning of problem-solving strategies and statistical challenges. Among the affordances of technologies to promote statistical education, Ridgway et al. ( 2017 ) highlight data visualizations as they facilitate interaction with data in a more intuitive, dynamic, and exploratory way. Such software programmes as TinkerPlots (dynamic data exploration, available at https://www.tinkerplots.com/ ) or common online data analysis platform (CODAP, available on http://codap.concord.org ) are widely used to promote statistical literacy and positive attitudes toward statistics. Among the main characteristics of these software programmes, the more salient are the next four: (a) they facilitate modelling activities, in which students can deeply analyse real-world situations through mathematical representations and asking questions, (b) they mediate between conceptual thinking and investigate probability events and identify patterns, (c) they improve intuition about data representation and analysis, and (d) they facilitate the creation of graphs (Gonzalez and Trelles, 2019 ; Kazak et al., 2014 ).

Various authors provide evidence of how the characteristics of technologies such as TinkerPlots, CODAP, and Fathom improve the students’ learning and attitudes. Gonzalez and Trelles ( 2019 ) investigated how a group of 15-year-old students increased their motivation through modelling activities in mathematics through TinkerPlots. In this study, modelling is defined as a learning system that encourages students to ask questions and analyse situations that could be real through mathematics. Other authors agree that the use of technological tools, such as CODAP is essential to develop students’ statistical reasoning (Casey et al., 2020 ; Mojica et al., 2019 ). The ability of CODAP to facilitate working with large data sets makes it easier for students to focus on making decisions about data analysis and reasoning about different forms of data representation, rather than on struggling with computational work, since no programming knowledge is required (Casey et al., 2020 ; Frischemeier et al., 2021 ). In this line, Kazak et al. ( 2014 ) showed how 11-year-olds improved their understanding of statistics with the help of TinkerPlots through collaborative work in small groups. The authors used TinkerPlots as a technology that mediated conceptual thinking to investigate various probability events in statistics and identify patterns. They argued that this software favoured the improvement of the students’ intuition about data representation and analysis and facilitated the creation of graphs.

Many other studies amplify the potential of technology in favouring positive attitudes and learning of mathematics by integrating technology in the classroom along with other teaching and learning strategies that have also proved relevant for improving mathematics learning. Attard and Holmes ( 2020 ) show that new technologies manage to place the student at the centre of the teaching–learning process: technology captures the attention and interest of students by means of immediate instructions and feedback. In addition, technology offers students an additional and different space for communication, beyond the classroom (Attard and Holmes, 2020 ).

The technology-enhanced, project-based study presented in this paper explicitly implements the findings of recent educational research based on supporting classroom dialogue, thinking and collaborative learning. In the next section, we will present these key findings.

Collaborative work

Collaborative work has been embedded in PBL (Fredriksen, 2021 ; Lyons et al., 2021 ; Ozdamli et al., 2013 ; Özdemir et al., 2015 ) and its impact on students’ development of positive attitudes towards mathematical learning is highly reported (Kazak et al., 2014 ; Moreno-Guerrero et al., 2020 ; Özdemir et al., 2015 ). Furthermore, educational research claims that interactive technologies can afford group work and communication and enrich the development of key problem-solving strategies (Kazak et al., 2014 ; Major et al., 2018 ; Noll et al., 2018 ).

Promotion of collaborative learning involves working explicitly on ground rules, interactional processes, and exploratory talk (Mercer, 2019 ). Exploratory talk improves attitudes toward learning as it facilitates the exploration and understanding of content and promotes intersubjectivity between group members when creating jointly new knowledge and understandings (Gómez, 2016 ; Knight and Mercer, 2015 ; Mercer et al., 2019 ). Dialogue is also very important for better organization and management of the group. This aspect is verified by Kazak et al. ( 2014 ) through an intervention based on collaborative work with technology. In this experiment, students were instructed to communicate with their classmates in a dialogical way, following five ground rules: (1) ensuring that all members of the group contribute with ideas; (2) asking classmates for arguments, listening to explanations and making an effort to understand; (3) being interested in what the others think; (4) taking into account different points of view or alternative methods, and (5) trying to reach a consensus before carrying out an action with the computer. This study, whose main objective was to teach key concepts of statistics and probability to 11-year-old students, through qualitative analysis of the dialogues from the groups, concluded that the students improved their communication with and opinions about their classmates. It also proved that their contributions were incorporated and integrated, thus facilitating the consensus of ideas.

Our study aims to contribute to research on the design and application of innovative methods in teaching statistics. To this end, our research took a quasi-experimental approach toward answering the following research question: what are the effects of a collaborative, technology-enhanced and data-driven project-based intervention on students’ attitudes towards statistics? Our general working hypothesis was that the design and implementation of a long-term real-classroom intervention that embeds and combines the three key educative variables for the promotion of statistics education, i.e., collaborative learning, technology-enhanced learning, and project-based learning, would have a positive impact on the students’ attitudes towards statistics. Furthermore, our expectations were that those students who received a collaborative, technology-enhanced project-based intervention would improve their attitudes towards statistics unlike their counterparts who followed a regular standard curriculum.

Our research aims to confirm or reject the next four hypotheses:

H1. Students following the collaborative, technology-enhanced, data-driven project-based intervention (henceforth SPIDAS) will improve their global attitude towards statistics. This increment will be higher than their counterparts who follow a traditional intervention.

H2. Students following the SPIDAS intervention will decrease their anxiety towards statistics, unlike their counterparts who follow a traditional intervention.

H3. Students following the SPIDAS intervention will increase their affect towards statistics more than their counterparts who follow a traditional intervention.

H4. Students following the SPIDAS intervention will improve their attitude towards learning statistics with technology more than their counterparts who follow a traditional intervention.

This research is part of a larger EU ERASMUS+ project called International Strategic Partnership for Innovative in Data Analytics in Schools (SPIDAS henceforth) aiming to innovate and extend best practices in data analytics in schools. In this paper, we will report only on one aspect of the ERASMUS+ project; with an eye on analysing the impact of a SPIDAS educational intervention on students’ attitudes towards learning statistics, a quasi-experimental design was planned in which an experimental group (henceforth EG) followed the SPIDAS instruction, and a control group (henceforth CG) followed the traditional education method.

Readers can learn more about the design, implementation, and multi-method evaluation of the statistics innovative instructional carried out in this Erasmus+ project in Cujba and Pifarré ( 2023 , 2024 ) and in https://spidasproject.org.uk/ web site.

Participants

A total of 174 students from two Spanish private publicly funded schools in the 8th grade (13–14 years old) participated, either as part of the experimental group (EG) or the control group (CG). 110 students belonging to the EG and had a homogeneous gender distribution: 52.7% (58) of them were girls and 47.3% (52) of them were boys. In the CG participated 64 students and the gender distribution was also homogeneous: 53.12% (34) of them were girls, and 46.88% (30) of them were boys. Additionally, both schools had similar medium socioeconomic characteristics, and the sample demonstrated a comparable level of general academic achievement, as evidenced by the results of the National Test of Basic Skills. Several studies showed a significant correlation between socioeconomic status and academic achievement, being negative in schools with lower socioeconomic status backgrounds (Berkowitz et al., 2017 ).

Additionally, the study assessed participants’ prior statistical knowledge, uncovering a notable deficiency in this area (Cujba and Pifarré, 2023 ). For further insights into the beneficial effects of the innovative instructional design detailed in this paper on enhancing students’ statistical knowledge, readers are encouraged to explore the Cujba and Pifarré ( 2023 ) findings.

Materials and procedure

Following previous research in the area (e.g., Nolan et al., 2012 ), this study evaluates the students’ attitudes towards statistics with technology using a questionnaire developed and validated exploratory into Spanish (Cujba and Pifarré, 2024 ) . In synthesis, the validation process consisted of three steps: firstly, the questionnaire development was based on a thorough revision of previous international questionnaires. Secondly, a double back-translation of the original items was carried out and followed a consensus process among expert judges’ methodology (content validity). Thirdly, the questionnaire developed was applied and tested to a sample of 254 13/14-year-old Spanish Secondary Education students. As a result of this process, a three-factor structure (namely anxiety, learning statistics with technology and effect) was found through exploratory factor analysis using the varimax rotation with the SPSS programme. Evidence of internal consistency was provided with an α  = 0.83 (“anxiety” factor α  = 0.83; “learning statistics with technology” factor α  = 0.76; “affect” factor α  = 0.77). The results showed suitable psychometric properties to use the questionnaire to evaluate secondary education students’ attitudes toward statistics with technology in the Spanish language.

The final version of the questionnaire contains 16 items structured along three factors: anxiety, learning statistics with technology, and affect. The questionnaire was applied to the 174 students who participated in this study at two different moments: before and after the educational intervention. The students were tested individually, and their attitudes were evaluated using a Likert scale of 4 options: 1 = Totally disagree, 2 = Disagree, 3 = Agree, and 4 = Totally agree. Scores on all negatively worded items (i.e., anxiety factor items) were reversed prior to data analysis.

Experimental group (EG) intervention: the collaborative, technology-enhanced data-driven project-based intervention—SPIDAS

The EG educational intervention lasted 30 h, distributed over 2 months. Students completed a real-life statistical project on how the weather influences daily activities, a topic of great current interest that requires well-argued, data-based answers. Students worked in small groups of 3–4, combining on-site classroom activities with work outside the classroom. For the outside work, the groups collaborated synchronously on shared documents using the Google Drive platform.

SPIDAS project incorporates and combines innovatively the three key pedagogical axes considered by the literature review as relevant in promoting data analysis skills in students, namely: (1) data-driven project-based learning, (2) collaborative learning and (3) the use of technology to help to learn statistics through visual learning (Frischemeier et al., 2021 ). In our project, we used the open-source software CODAP. This software has the same attributes as TinkerPlots. Both software are easy to use by inexperienced users, allow flexible plot creation, deal with data as a first-order persistent object, support an exploratory and confirmatory analysis, and are very interactive (McNamara, 2018 ). As a disadvantage, TinkerPlots needs previous installation and a payment license per computer.

Next, we describe how the three pedagogical axes were incorporated into the SPIDAS educational intervention. Fig. 1 graphically illustrates how the SPIDAS intervention leverages and combines the advancements of these three innovative methods for teaching statistics. Readers can learn more about the SPIDAS educational intervention in: https://spidasproject.org.uk .

SPIDAS intervention.

Data-driven project-based learning : The SPIDAS intervention explicitly incorporates the four educational variables highlighted in the PBL literature review (e.g., Batanero and Díaz, 2011 ; Bateiha et al., 2020 ; Haatainen and Aksela, 2021 ): (a) structure of students’ learning process in enquiry phases; (b) statistical literacy contextualized in real and daily life; (c) active role of the students and guidance role of the teacher and (d) development of ‘data analytics (DA) cycle’ drawn on PPDAC statistical enquiry cycle (Wild and Pfannkuch, 1999 ), statistical thinking process (Wild et al., 2011 ) and informal statistical inference (Makar and Rubin, 2018 ). In synthesis, the main tasks of the data-driven project are presented in Table 1 . To give more information about the instructional design, Table 1 and Figs. 2 – 5 present examples of activities of one group of students.

Example of Define the problem activity.

Example of students’ graph for Explore data .

Example of Draw conclusions activity.

Example of Make decisions activity. It is presented students’ infographic to communicate their project decisions and conclusions.

Collaborative learning : Students worked in small groups during the whole project and were encouraged to actively create, reflect and evaluate ideas by using effective communication skills and ground-rules. Three specific strategies from “Thinking Together” programme (Mercer et al., 2019 ) for promoting good small-group work and exploratory talk were explicitly taught and these are (a) reflection on group roles, (b) reflection on attitudes and behaviours that promote collaborative learning, and (c) development of effective ground rules.

Technology : The SPIDAS project used two types of technologies: CODAP data analysis software and different applications linked to Google Drive. CODAP software allows graphical visualization of data and supports students understanding and interpretation of their data. Due to the CODAP interactive and manipulative design, students can actively explore their own data and obtain meaningful graphical representations that could help draw data-based conclusions (e.g., Fig. 3 ). Considering the extensive research about the role of interactive technologies (Major et al., 2017 ; Pifarré, 2019 ) to enhance collaborative work and dialogic discussions, some Google Drive applications (such as Docs, Slides) were used. These applications allow the creation of synchronic and multi-user workspaces that in our project fostered four key processes of collaborative work with technology: (a) discussion of shared ideas; (b) co-construction of new ideas, planning and reflection on joint work; (c) support to the development of statistics literacy and (d) enrichment of data analytics strategies such as organization of data, manipulation of data, creation of graphs, analysis of data and making decisions based on data.

The innovative instructional design advocated for student-centred methodologies, wherein teachers adopted a dual role: part lecturer, part coach, fostering collaborative learning within student groups. They facilitated the data analytics projects undertaken by each group, providing guidance throughout the process and supporting them in drawing conclusions and making informed decisions based on their analyses.

Control group (CG) intervention

The CG followed a traditional intervention. It also lasted 2 months. It was a teacher-centred intervention and the teacher mainly used lectures to teach the same statistical concepts taught in the EG. The statistical literacy taught integrates the next concepts: mean, median, mode, range, variability, qualitative and quantitative variables, frequency, proportional reasoning, count, reading graphs, sample, and population. CG students followed the lectures, had a passive role, paid attention to the explanations of the teachers and applied what they had learned by carrying out a series of individual and routine exercises. Unlike the EG, in the CG all the students worked with the same data provided by the teacher. Some of these exercises were carried out outside the classroom, as homework and these exercises were solved individually.

Regarding the use of technology, Excel software was used. This technological tool consists of a spreadsheet that allows calculations and graph creation. Working with this software requires being knowledgeable about the mathematical operations necessary to execute the desired parameters. We believe that with Excel, students should invest more time in understanding which calculations and formulas they need to apply and how, rather than lesson analysing and interpreting data. The teachers explained in class the operations that were to be carried out with Excel, so that, at home, the students could carry out most of the activities. These activities contained real-life data, yet lacked contextualization of the problem or daily life situation.

Data analysis

In order to analyse the sample normality a Shapiro–Wilk statistical test was run with SPSS. Due to the sample is not normally distributed, non-parametric tests were used. On one hand, for comparing the intragroup differences between the pre-test and post-test results, the Wilcoxon test was established. On the other hand, Mann–Whitney U test was used to analyse the intergroup differences between the post-test results in both groups (experimental vs. control).

This section will analyse the effects of the technology-enhanced, collaborative and data-driven project-based learning on four variables (or factors) included in the questionnaire on the students’ attitudes towards statistics, namely: (a) global attitude towards statistics learning; (b) anxiety towards statistics; (c) affection towards statistics; and (d) attitude towards statistics with technology. The global attitude score resulted from the sum of the 16 items included in the questionnaire ( Annex ). Similarly, the score for each factor was calculated by adding the ratings of all the items that composed each factor. Therefore, the factors of anxiety (i5, i7, i10, i11, i12) and affection (i1, i3, i8, i14, i15) contained five items each and the statistics learning with the technology factor contained six items (i2, i4, i6, i9, i13, i16). The Likert scale was a four-point scale: 1 (strongly disagree), 2 (disagree), 3 (agree), or 4 (strongly agree).

Intervention effect on intragroup differences

Wilcoxon analyses were carried out to study the effect of the educational interventions on each experimental (EG and CG) group of students’ attitudes toward statistics. The effect size was checked with Cohen’s d statistic. Table 2 summarizes these results. Experimental group students showed significant differences ( α  = 0.05) in their global attitude score. Besides, experimental group students displayed significant scoring differences in the anxiety and technology factors. Although there was a positive trend in the affection factor, no statistical significance was found.

The control group students did not show significant differences neither in global attitude scores nor in any of the three factors analysed.

Intervention effect on intergroup differences

A Mann–Whitney U test analysis was carried out to investigate the differences between the two groups, before and after students’ participation in the SPIDAS intervention. The effect size was checked with Cohen’s d statistic. Table 3 summarizes these results. These analyses display significant differences ( α  = 0.05) between EG and CG students as regards their global attitude score and in three factors of the questionnaire: namely Anxiety, Affection, and use of Technology. Figure 6 displays the mean scores obtained by the two groups in pre- and post-measurements of the different factors of the questionnaire and the significant statistical differences observed in the analysis.

Pretest and posttest results and statistical differences between EG and CG students.

Furthermore, these results firstly show that the experimental group obtained higher mean post-test scores than the control group regarding the overall attitude questionnaire towards statistics (see Fig. 6 ). The experimental group presented a higher global attitude score in the questionnaire both before and after the intervention. Although before the intervention the global attitude of the EG students was already higher than that obtained by the CG, the post-intervention improvement was greater and statistically significant in the EG and not in the CG, whose improvement was hardly perceived. This result allows us to conclude that the SPIDAS intervention had a positive impact on the student's attitudes toward learning statistics with technology.

Secondly, EG students significantly decreased their levels of anxiety toward learning statistics after their participation in the SPIDAS intervention, unlike the CG students, who showed a statistically low decrease in this variable. The difference in the post-measure between both groups was statistically significant, being the EG score higher than that of CG students (Fig. 6 ).

Thirdly, regarding the affection factor, in EG students there was a tendency to improve this factor after the SPIDAS intervention. The post-test value was higher than that of the pre-test in this group. On the other hand, in the CG the post score in the affection factor decreased. Therefore, the impact of the traditional intervention had a negative impact on the student’s perception of their abilities to learn and solve statistical problems. The comparison between the two groups (see Fig. 6 ) yielded statistically significant differences between the EG and the CG, in the pre- and post-measures. In addition, the EG showed a higher score in the post-measure while the CG decreased its post-score. This result increased the differences between the two groups in this variable. These data allow us to conclude that the impact of the SPIDAS intervention also had a positive impact on the affection variable towards learning statistics.

Finally, with respect to technology, EG students significantly improved their attitude toward learning statistics with technology after the SPIDAS intervention, unlike the CG students who hardly showed any improvement. The comparison of post-intervention scores between both groups (see Fig. 6 ) as regards the technology factor shows statistical differences between both groups, namely, EG students obtained higher scores in this factor compared to CG students. Thus, the technology-enhanced intervention positively influenced the students’ attitude towards learning statistics.

Discussion and conclusions

The main objective of this study was to investigate the effects of technology-enhanced, collaborative, and data-driven project-based learning on the students’ attitudes towards statistics. This study distinguishes itself from other PBL studies on statistics because we investigated a long-term intervention in real classrooms and integrated into one intervention the three pedagogical variables that previous research highlighted as relevant in statistics education: (a) the use of technological tools and affordances for analysing and visualizing data, (b) enrichment of collaborative strategies and (c) project-based learning with a data analysis approach.

Results show that the designed SPIDAS intervention had a highly positive impact on the students’ attitudes toward statistics (Hypothesis 1) and on student’s affect towards learning statistics (Hypothesis 3). Our results support those obtained by other authors that indicate that the PBL is an innovative methodology that meets the necessary characteristics to improve students’ attitudes and increase their interest and motivation towards statistics, as the active role of students in investigating real-life questions is notable (Koparan and Güven, 2014 ; Siswono et al., 2018 ). The EG intervention granted students an active role in investigating a problem that both captured their interest and was related to a daily problem, with the added use of technology. According to Siswono et al. ( 2018 ), improvement in learning statistics is greater if the PBL is combined with technology.

Furthermore, our SPIDAS intervention explicitly taught students a series of collaborative strategies, such as the assumption of different roles, drawing up a joint work plan, managing time for solving problems, distributing responsibilities and co-evaluating group work. Previous educational research highlights that the improvement of the organization and management strategies of group work has a positive impact on the better functioning of small group work and on the creation of positive synergy between group members, which in turn has a positive impact on the all-students’ attitudes towards learning (Chang and Brickman, 2018 ; Pai et al., 2015 ). In this respect, our study confirms these results.

Unlike the results obtained by the EG students, the traditional intervention followed by the CG did not improve the students’ attitudes towards statistics. Previous research points out that one of the limitations of traditional teaching is that it does not contextualize statistical concepts with real-life situations and thus, students cannot establish meaningful links with daily life problems. This has a negative impact on the students’ attitudes towards learning statistics (Bateiha et al., 2020 ; Hwa, 2018 ; Özdemir et al., 2015 ). Our CG students learned statistical concepts focusing on mathematical procedures and calculations of sets of data that bore no relation with their real-life context. This makes it difficult for students to build meaningful learning and feel that statistical concepts could be useful for them outside the school context and for solving daily problems (Lalayants, 2012 ). Different studies highlight that the practical use of curricular content in collaboration with peers and with the teacher’s guidance is one of the key elements that can explain students’ learning (Andrade and Chacón, 2018 ; Torrecilla, 2018 ). These elements are emphasized in the experimental intervention and, on the contrary, are not usually part of a more traditional teaching.

Despite the positive results obtained by EG in all the factors of the questionnaire, it was revealed that for both groups—EG and CG—there was a certain lack of interest of the students towards statistics (item 3 → EG p  = 0.007; CG p  = 0.047) and they did not get to enjoy learning statistics (item 1 → EG p  = 0.081; CG p  = 0.104), since these items present lower scores in post-intervention measure than the pre-test measure. Also, technology did not make the learning of statistics more interesting (item 16 → EG p  = 0.976; CG p  = 0.50). Despite this negative perception of learning statistics, the actual improvement in EG compared to CG is remarkable as it is supported by statistically significant differences.

One possible explanation for the slight improvement in the EG students’ affection may be accounted for in that the PBL involves more complex procedures, more effort, and more time in fulfilling the tasks than traditional teaching to learn the content (Koparan and Güven, 2014 ). Being the first experience of the students with a PBL, they may need being involved in further and longer-term PBL experiences to present better and more positive perceptions about learning statistics. Therefore, more long-term studies using innovative methodologies are needed to investigate more about the impact of these methodologies on students’ attitudes and affection in learning statistics (Siswono et al., 2018 ).

Our results also reveal that the SPIDAS instruction had a positive impact on the decrease of the students’ anxiety towards learning statistics (H2). In addition, our results show that the educational intervention followed by the EG had a greater impact than the traditional intervention followed by the control group in reducing students’ anxiety towards learning statistics. These findings are consistent with those found in previous research studies, which claimed a strong relationship between mathematical anxiety, motivation and mathematical achievement (Abín et al., 2020 ; Henschel and Roick, 2017 ; Passolunghi et al., 2016 ).

On the other hand, unlike the results obtained with EG students, the CG students’ anxiety may not have decreased because they learned statistical contents with insufficient context and application to real life. In this line of argument, Lalayants ( 2012 ) claimed in his study that the fear felt by a group of university students towards learning statistics was caused mainly by the lack of connection between their studies and statistics. Basically, they did not understand how to apply the content to real-life situations. The same group of university students declared in the questionnaire that it would have helped them reduce anxiety if they had found any of the following aspects in their statistics classes, i.e., practical real-life problem-solving related to their future profession, teachers who cared about their negative feelings, working sessions with technology, and collaborative work in small groups, as opposed to individual work.

Although students decreased their anxiety towards statistics after their participation in the SPIDAS intervention, students still expressed certain levels of anxiety when doing statistics (item 11 of the questionnaire → EG p  = 0.069; CG p  = 0.661). On this issue, some authors defend that a low level of anxiety toward statistics is not necessarily totally negative. In some cases, a low level of anxiety can motivate students not to give up and continue working to understand the content (Çiftçi, 2015 ).

These results coincide with those found in previous studies that indicate the benefits of implementing a collaborative and student-centred learning methodology (Bateiha et al., 2020 ) and carrying out contextualized activities involving real-life problems (Chong et al., 2019 ) contributed to the students’ increased affection. These characteristics were included in the SPIDAS intervention and, therefore, helped to improve the EG students’ value judgments and motivation in the present study.

Regarding the intensive use of a variety of technological tools to learn key statistical concepts and data analysis skills (Hypothesis 4), our study indicates a statistically significant improvement in the EG students’ attitude towards learning statistics with technology. CG students do not show any progress on this variable. Therefore, the designed SPIDAS intervention confirms that the combination of the use of technology and collaborative work in small groups are powerful pedagogical tools to improve students’ attitudes towards statistics.

These results are consistent with those found by various authors, such as Kazak et al. ( 2014 ), who present the use of the TinkerPlots software as an enabling technology for understanding statistical concepts. Other authors conclude that new technologies encourage collaboration, motivation and facilitate the performance of student-centred activities, a combination that improves students’ attitudes toward learning statistics (Attard and Holmes, 2020 ; Bray and Tangney, 2017 ; Gonzalez and Trelles, 2019 ; Moreno-Guerrero et al., 2020 ).

It is worth noting the higher increase shown by EG in comparison with CG in Item 6 (EG p  = 0.000; CG p  = 0.031). In our view, this result suggests that the use of CODAP throughout the SPIDAS intervention supported the students’ creation of useful data visualizations and students’ learning of data analytics skills. CODAP software facilitates learning, and this has a noticeable positive impact on student attitudes (Woodard et al., 2020 ).

As a final conclusion, this study suggests that implementing technology-enhanced, collaborative, and data-driven project-based learning can provide the basis for an appropriate teaching approach to improve secondary students’ attitudes toward statistics, to have a positive impact on the student's motivation to learn statistics and on the reduction of anxiety in solving problems about this subject. In light of the results of this study, these three educative variables should be considered and included in the design of educative interventions that have the objective to engage students in data analytics in which students are able to select a real problem to investigate, collect and explore appropriate data, make inferences and discuss their conclusions using a data-based approach.

The study shows some limitations that call for further research. Firstly, it was the first interaction of students with statistics, the use of CODAP software in PBL and this learning approach requires a high cognitive implication from students during the learning process (Ge and Chua, 2019 ). This novelty may cause a cognitive overload that could reduce the impact of the innovative intervention on the students’ attitudes toward statistics. Therefore, the design of longer interventions with a longitudinal research approach capable of improving the students’ attitudes over a longer period of time would probably soften the impact of cognitive overload.

Secondly, our study has revealed that despite the innovative intervention, some EG students still feel anxiety towards learning statistics. Despite this, some research claims that a certain level of anxiety can boost students’ positive actions to not give up and, as a result, successfully fulfil a task (Çiftçi, 2015 ). Therefore, it would be interesting to design more qualitative research methods capable of capturing and measuring positive levels of anxiety for learning statistics.

Thirdly, as previous studies noted the relevance of socioeconomic status in academic achievement (Berkowitz et al., 2017 ), for future research, the socioeconomic level of the students will be considered as an independent variable.

Fourthly, the questionnaire used in the study has been useful for evaluating students’ attitudes toward statistics using technology. However, the questionnaire needs further validation to extend the results to other contexts. To this end, as a future research action, we plan to expand the sample, analyse its external validity, and compare the results of the confirmatory factor analysis obtained in this study against other samples (such as students from other courses of Secondary Education, Upper Secondary Education and even university students).

The overall results found in this study are promising for improving students’ data analysis competences with technology and they can be seen as a contribution to the United Nations Education 2030 Agenda which emphasises the need to equip all students with technological and mathematical knowledge. By following this agenda, it is expected that a greater number of students will reach the minimum levels of knowledge in mathematics.

Data availability

The data supporting this study’s findings are available from the corresponding author ([email protected]), upon reasonable request. The data are not publicly available because they contain information that could compromise the privacy of research participants.

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Acknowledgements

This paper has been funded by the Strategic Partnership for the Innovative Application of Data Analytics in Schools (SPIDAS) project, European Union’s Erasmus+, under Grant 2017-1-UK01-KA201-036520. Furthermore, the paper has been partially funded by the Spanish Ministry of Science and Innovation under Grant PDC2022-133203-I00. All views expressed are those of the authors, not the European Commission or the Spanish Ministry. Finally, the authors would like to thank the teachers and the pupils of the schools Claver Raïmat Jesuïtes-Lleida and Maristes Montserrat-Lleida for their participation in the study reported in this paper.

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Cujba, A., Pifarré, M. Enhancing students’ attitudes towards statistics through innovative technology-enhanced, collaborative, and data-driven project-based learning. Humanit Soc Sci Commun 11 , 1094 (2024). https://doi.org/10.1057/s41599-024-03469-5

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