research about peer teaching

• Original article
• Open access
• Published: 09 April 2020

Why does peer instruction benefit student learning?

• Jonathan G. Tullis 1 &
• Robert L. Goldstone 2  

Cognitive Research: Principles and Implications volume  5 , Article number:  15 ( 2020 ) Cite this article

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In peer instruction, instructors pose a challenging question to students, students answer the question individually, students work with a partner in the class to discuss their answers, and finally students answer the question again. A large body of evidence shows that peer instruction benefits student learning. To determine the mechanism for these benefits, we collected semester-long data from six classes, involving a total of 208 undergraduate students being asked a total of 86 different questions related to their course content. For each question, students chose their answer individually, reported their confidence, discussed their answers with their partner, and then indicated their possibly revised answer and confidence again. Overall, students were more accurate and confident after discussion than before. Initially correct students were more likely to keep their answers than initially incorrect students, and this tendency was partially but not completely attributable to differences in confidence. We discuss the benefits of peer instruction in terms of differences in the coherence of explanations, social learning, and the contextual factors that influence confidence and accuracy.

Significance

Peer instruction is widely used in physics instruction across many universities. Here, we examine how peer instruction, or discussing one’s answer with a peer, affects students’ decisions about a class assignment. Across six different university classes, students answered a question, discussed their answer with a peer, and finally answered the question again. Students’ accuracy consistently improved through discussion with a peer. Our peer instruction data show that students were hesitant to switch away from their initial answer and that students did consider both their own confidence and their partner’s confidence when making their final decision, in accord with basic research about confidence in decision making. More broadly, the data reveal that peer discussion helped students select the correct answer by prompting them to create new knowledge. The benefit to student accuracy that arises when students discuss their answers with a partner is a “process gain”, in which working in a group yields better performance than can be predicted from individuals’ performance alone.

Peer instruction is specific evidence-based instructional strategy that is well-known and widely used, particularly in physics (Henderson & Dancy, 2009 ). In fact, peer instruction has been advocated as a part of best methods in science classrooms (Beatty, Gerace, Leonard, & Dufresne, 2006 ; Caldwell, 2007 ; Crouch & Mazur, 2001 ; Newbury & Heiner, 2012 ; Wieman et al., 2009 ) and over a quarter of university physics professors report using peer instruction (Henderson & Dancy, 2009 ). In peer instruction, instructors pose a challenging question to students, students answer the question individually, students discuss their answers with a peer in the class, and finally students answer the question again. There are variations of peer instruction in which instructors show the class’s distribution of answers before discussion (Nielsen, Hansen-Nygård, & Stav, 2012 ; Perez et al., 2010 ), in which students’ answers are graded for participation or for correctness (James, 2006 ), and in which instructors’ norms affect whether peer instruction offers opportunities for answer-seeking or for sense-making (Turpen & Finkelstein, 2007 ).

Despite wide variations in its implementation, peer instruction consistently benefits student learning. Switching classroom structure from didactic lectures to one centered around peer instruction improves learners’ conceptual understanding (Duncan, 2005 ; Mazur, 1997 ), reduces student attrition in difficult courses (Lasry, Mazur, & Watkins, 2008 ), decreases failure rates (Porter, Bailey-Lee, & Simon, 2013 ), improves student attendance (Deslauriers, Schelew, & Wieman, 2011 ), and bolsters student engagement (Lucas, 2009 ) and attitudes to their course (Beekes, 2006 ). Benefits of peer instruction have been found across many fields, including physics (Mazur, 1997 ; Pollock, Chasteen, Dubson, & Perkins, 2010 ), biology (Knight, Wise, & Southard, 2013 ; Smith, Wood, Krauter, & Knight, 2011 ), chemistry (Brooks & Koretsky, 2011 ), physiology (Cortright, Collins, & DiCarlo, 2005 ; Rao & DiCarlo, 2000 ), calculus (Lucas, 2009 ; Miller, Santana-Vega, & Terrell, 2007 ), computer science (Porter et al., 2013 ), entomology (Jones, Antonenko, & Greenwood, 2012 ), and even philosophy (Butchart, Handfield, & Restall, 2009 ). Additionally, benefits of peer instruction have been found at prestigious private universities, two-year community colleges (Lasry et al., 2008 ), and even high schools (Cummings & Roberts, 2008 ). Peer instruction benefits not just the specific questions posed during discussion, but also improves accuracy on later similar problems (e.g., Smith et al., 2009 ).

One of the consistent empirical hallmarks of peer instruction is that students’ answers are more frequently correct following discussion than preceding it. For example, in introductory computer science courses, post-discussion performance was higher on 70 out of 71 questions throughout the semester (Simon, Kohanfars, Lee, Tamayo, & Cutts, 2010 ). Further, gains in performance from discussion are found on many different types of questions, including recall, application, and synthesis questions (Rao & DiCarlo, 2000 ). Performance improvements are found because students are more likely to switch from an incorrect answer to the correct answer than from the correct answer to an incorrect answer. In physics, 59% of incorrect answers switched to correct following discussion, but only 13% of correct answers switched to incorrect (Crouch & Mazur, 2001 ). Other research on peer instruction shows the same patterns: 41% of incorrect answers are switched to correct ones, while only 18% of correct answers are switched to incorrect (Morgan & Wakefield, 2012 ). On qualitative problem-solving questions in physiology, 57% of incorrect answers switched to correct after discussion, and only 7% of correct answers to incorrect (Giuliodori, Lujan, & DiCarlo, 2006 ).

There are two explanations for improvements in pre-discussion to post-discussion accuracy. First, switches from incorrect to correct answers may be driven by selecting the answer from the peer who is more confident. When students discuss answers that disagree, they may choose whichever answer belongs to the more confident peer. Evidence about decision-making and advice-taking substantiates this account. First, confidence is correlated with correctness across many settings and procedures (Finley, Tullis, & Benjamin, 2010 ). Students who are more confident in their answers are typically more likely to be correct. Second, research examining decision-making and advice-taking indicates that (1) the less confident you are, the more you value others’ opinions (Granovskiy, Gold, Sumpter, & Goldstone, 2015 ; Harvey & Fischer, 1997 ; Yaniv, 2004a , 2004b ; Yaniv & Choshen-Hillel, 2012 ) and (2) the more confident the advisor is, the more strongly they influence your decision (Kuhn & Sniezek, 1996 ; Price & Stone, 2004 ; Sah, Moore, & MacCoun, 2013 ; Sniezek & Buckley, 1995 ; Van Swol & Sniezek, 2005 ; Yaniv, 2004b ). Consequently, if students simply choose their final answer based upon whoever is more confident, accuracy should increase from pre-discussion to post-discussion. This explanation suggests that switches in answers should be driven entirely by a combination of one’s own initial confidence and one’s partner’s confidence. In accord with this confidence view, Koriat ( 2015 ) shows that an individual’s confidence typically reflects the group’s most typically given answer. When the answer most often given by group members is incorrect, peer interactions amplify the selection of and confidence in incorrect answers. Correct answers have no special draw. Rather, peer instruction merely amplifies the dominant view through differences in the individual’s confidence.

In a second explanation, working with others may prompt students to verbalize explanations and verbalizations may generate new knowledge. More specifically, as students discuss the questions, they need to create a common representation of the problem and answer. Generating a common representation may compel students to identify gaps in their existing knowledge and construct new knowledge (Schwartz, 1995 ). Further, peer discussion may promote students’ metacognitive processes of detecting and correcting errors in their mental models. Students create more new knowledge and better diagnostic tests of answers together than alone. Ultimately, then, the new knowledge and improved metacognition may make the correct answer appear more compelling or coherent than incorrect options. Peer discussion would draw attention to coherent or compelling answers, more so than students’ initial confidence alone and the coherence of the correct answer would prompt students to switch away from incorrect answers. Similarly, Trouche, Sander, and Mercier ( 2014 ) argue that interactions in a group prompt argumentation and discussion of reasoning. Good arguments and reasoning should be more compelling to change individuals’ answers than confidence alone. Indeed, in a reasoning task known to benefit from careful deliberation, good arguments and the correctness of the answers change partners’ minds more than confidence in one’s answer (Trouche et al., 2014 ). This explanation predicts several distinct patterns of data. First, as seen in prior research, more students should switch from incorrect answers to correct than vice versa. Second, the intrinsic coherence of the correct answer should attract students, so the likelihood of switching answers would be predicted by the correctness of an answer above and beyond differences in initial confidence. Third, initial confidence in an answer should not be as tightly related to initial accuracy as final confidence is to final accuracy because peer discussion should provide a strong test of the coherence of students’ answers. Fourth, because the coherence of an answer is revealed through peer discussion, student confidence should increase more from pre-discussion to post-discussion when they agree on the correct answers compared to agreeing on incorrect answers.

Here, we examined the predictions of these two explanations of peer instruction across six different classes. We specifically examined whether changes in answers are driven exclusively through the confidence of the peers during discussion or whether the coherence of an answer is better constructed and revealed through peer instruction than on one’s own. We are interested in analyzing cognitive processes at work in a specific, but common, implementation of classroom-based peer instruction; we do not intend to make general claims about all kinds of peer instruction or to evaluate the long-term effectiveness of peer instruction. This research is the first to analyze how confidence in one’s answer relates to answer-switching during peer instruction and tests the impact of peer instruction in new domains (i.e., psychology and educational psychology classes).

Participants

Students in six different classes participated as part of their normal class procedures. More details about these classes are presented in Table  1 . The authors served as instructors for these classes. Across the six classes, 208 students contributed a total of 1657 full responses to 86 different questions.

The instructors of the courses developed multiple-choice questions related to the ongoing course content. Questions were aimed at testing students’ conceptual understanding, rather than factual knowledge. Consequently, questions often tested whether students could apply ideas to new settings or contexts. An example of a cognitive psychology question used is: Which is a fixed action pattern (not a reflex)?

Knee jerks up when patella is hit

Male bowerbirds building elaborate nests [correct]

Eye blinks when air is blown on it

Can play well learned song on guitar even when in conversation

The procedures for peer instruction across the six different classes followed similar patterns. Students were presented with a multiple-choice question. First, students read the question on their own, chose their answer, and reported their confidence in their answer on a scale of 1 “Not at all confident” to 10 “Highly confident”. Students then paired up with a neighbor in their class and discussed the question with their peer. After discussion, students answered the question and reported the confidence for a second time. The course instructor indicated the correct answer and discussed the reasoning for the answer after all final answers had been submitted. Instruction was paced based upon how quickly students read and answered questions. Most student responses counted towards their participation grade, regardless of the correctness of their answer (the last question in each of the cognitive psychology classes was graded for correctness).

There were small differences in procedures between classes. Students in the cognitive psychology classes input their responses using classroom clickers, but those in other classes wrote their responses on paper. Further, students in the cognitive psychology classes explicitly reported their partner’s answer and confidence, while students in other classes only reported the name of their partner (the partners’ data were aligned during data recording). The cognitive psychology students then were required to mention their own answer and their confidence to their partner during peer instruction; students in other classes were not required to tell their answer or their confidence to their peer. Finally, the questions appeared at any point during the class period for the cognitive psychology classes, while the questions typically happened at the beginning of each class for the other classes.

Analytic strategy

Data are available on the OpenScienceFramework: https://mfr.osf.io/render?url=https://osf.io/5qc46/?action=download%26mode=render .

For most of our analyses we used linear mixed-effects models (Baayen, Davidson, & Bates, 2008 ; Murayama, Sakaki, Yan, & Smith, 2014 ). The unit of analysis in a mixed-effect model is the outcome of a single trial (e.g., whether or not a particular question was answered correctly by a particular participant). We modeled these individual trial-level outcomes as a function of multiple fixed effects - those of theoretical interest - and multiple random effects - effects for which the observed levels are sampled out of a larger population (e.g., questions, students, and classes sampled out of a population of potential questions, students, and classes).

Linear mixed-effects models solve four statistical problems involved with the data of peer instruction. First, there is large variability in students’ performance and the difficulty of questions across students and classes. Mixed-effect models simultaneously account for random variation both across participants and across items (Baayen et al., 2008 ; Murayama et al., 2014 ). Second, students may miss individual classes and therefore may not provide data across every item. Similarly, classes varied in how many peer instruction questions were posed throughout the semester and the number of students enrolled. Mixed-effects models weight each response equally when drawing conclusions (rather than weighting each student or question equally) and can easily accommodate missing data. Third, we were interested in how several different characteristics influenced students’ performance. Mixed effects models can include multiple predictors simultaneously, which allows us to test the effect of one predictor while controlling for others. Finally, mixed effects models can predict the log odds (or logit) of a correct answer, which is needed when examining binary outcomes (i.e., correct or incorrect; Jaeger, 2008 ).

We fit all models in R using the lmer() function of the lme4 package (Bates, Maechler, Bolker, & Walker, 2015 ). For each mixed-effect model, we included random intercepts that capture baseline differences in difficulty of questions, in classes, and in students, in addition to multiple fixed effects of theoretical interest. In mixed-effect models with hundreds of observations, the t distribution effectively converges to the normal, so we compared the t statistic to the normal distribution for analyses involving continuous outcomes (i.e., confidence; Baayen, 2008 ). P values can be directly obtained from Wald z statistics for models with binary outcomes (i.e., correctness).

Does accuracy change through discussion?

First, we examined how correctness changed across peer discussion. A logit model predicting correctness from time point (pre-discussion to post-discussion) revealed that the odds of correctness increased by 1.57 times (95% confidence interval (conf) 1.31–1.87) from pre-discussion to post-discussion, as shown in Table  2 . In fact, 88% of students showed an increase or no change in accuracy from pre-discussion to post-discussion. Pre-discussion to post-discussion performance for each class is shown in Table  3 . We further examined how accuracy changed from pre-discussion to post-discussion for each question and the results are plotted in Fig.  1 . The data show a consistent improvement in accuracy from pre-discussion to post-discussion across all levels of initial difficulty.

The relationship between pre-discussion accuracy (x axis) and post-discussion accuracy (y axis). Each point represents a single question. The solid diagonal line represents equal pre-discussion and post-discussion accuracy; points above the line indicate improvements in accuracy and points below represent decrements in accuracy. The dashed line indicates the line of best fit for the observed data

We examined how performance increased from pre-discussion to post-discussion by tracing the correctness of answers through the discussion. Figure  2 tracks the percent (and number of items) correct from pre-discussion to post-discussion. The top row shows whether students were initially correct or incorrect in their answer; the middle row shows whether students agreed or disagreed with their partner; the last row show whether students were correct or incorrect after discussion. Additionally, Fig. 2 shows the confidence associated with each pathway. The bottow line of each entry shows the students’ average confidence; in the middle white row, the confidence reported is the average of the peer’s confidence.

The pathways of answers from pre-discussion (top row) to post-discussion (bottom row). Percentages indicate the portion of items from the category immediately above in that category, the numbers in brackets indicate the raw numbers of items, and the numbers at the bottom of each entry indicate the confidence associated with those items. In the middle, white row, confidence values show the peer’s confidence. Turquoise indicates incorrect answers and yellow indicates correct answers

Broadly, only 5% of correct answers were switched to incorrect, while 28% of incorrect answers were switched to correct following discussion. Even for the items in which students were initially correct but disagreed with their partner, only 21% of answers were changed to incorrect answers after discussion. However, out of the items where students were initially incorrect and disagreed with their partner, 42% were changed to the correct answer.

Does confidence predict switching?

Differences in the amount of switching to correct or incorrect answers could be driven solely by differences in confidence, as described in our first theory mentioned earlier. For this theory to hold, answers with greater confidence must have a greater likelihood of being correct. To examine whether initial confidence is associated with initial correctness, we calculated the gamma correlation between correctness and confidence in the answer before discussion, as shown in the first column of Table  4 . The average gamma correlation between initial confidence and initial correctness (mean (M) = 0.40) was greater than zero, t (160) = 8.59, p  < 0.001, d  = 0.68, indicating that greater confidence was associated with being correct.

Changing from an incorrect to a correct answer, then, may be driven entirely by selecting the answer from the peer with the greater confidence during discussion, even though most of the students in our sample were not required to explicitly disclose their confidence to their partner during discussion. We examined how frequently students choose the more confident answer when peers disagree. When peers disagreed, students’ final answers aligned with the more confident peer only 58% of the time. Similarly, we tested what the performance would be if peers always picked the answer of the more confident peer. If peers always chose the more confident answer during discussion, the final accuracy would be 69%, which is significantly lower than actual final accuracy (M = 72%, t (207) = 2.59, p  = 0.01, d  = 0.18). While initial confidence is related to accuracy, these results show that confidence is not the only predictor of switching answers.

Does correctness predict switching beyond confidence?

Discussion may reveal information about the correctness of answers by generating new knowledge and testing the coherence of each possible answer. To test whether the correctness of an answer added predictive power beyond the confidence of the peers involved in discussion, we analyzed situations in which students disagreed with their partner. Out of the instances when partners initially disagreed, we predicted the likelihood of keeping one’s answer based upon one’s own confidence, the partner’s confidence, and whether one’s answer was initially correct. The results of a model predicting whether students keep their answers is shown in Table  5 . For each increase in a point of one’s own confidence, the odds of keeping one’s answer increases 1.25 times (95% conf 1.13–1.38). For each decrease in a point of the partner’s confidence, the odds of keeping one’s answer increased 1.19 times (1.08–1.32). The beta weight for one’s confidence did not differ from the beta weight of the partner’s confidence, χ 2  = 0.49, p  = 0.48. Finally, if one’s own answer was correct, the odds of keeping one’s answer increased 4.48 times (2.92–6.89). In other words, the more confident students were, the more likely they were to keep their answer; the more confident their peer was, the more likely they were to change their answer; and finally, if a student was correct, they were more likely to keep their answer.

To illustrate this relationship, we plotted the probability of keeping one’s own answer as a function of the difference between one’s own and their partner’s confidence for initially correct and incorrect answers. As shown in Fig.  3 , at every confidence level, being correct led to equal or more frequently keeping one’s answer than being incorrect.

The probability of keeping one’s answer in situations where one’s partner initially disagreed as a function of the difference between partners’ levels of confidence. Error bars indicate the standard error of the proportion and are not shown when the data are based upon a single data point

As another measure of whether discussion allows learners to test the coherence of the correct answer, we analyzed how discussion impacted confidence when partners’ answers agreed. We predicted confidence in answers by the interaction of time point (i.e., pre-discussion versus post-discussion) and being initially correct for situations in which peers initially agreed on their answer. The results, displayed in Table  6 , show that confidence increased from pre-discussion to post-discussion by 1.08 points and that confidence was greater for initially correct answers (than incorrect answers) by 0.78 points. As the interaction between time point and initial correctness shows, confidence increased more from pre-discussion to post-discussion when students were initially correct (as compared to initially incorrect). To illustrate this relationship, we plotted pre-confidence against post-confidence for initially correct and initially incorrect answers when peers agreed (Fig.  4 ). Each plotted point represents a student; the diagonal blue line indicates no change between pre-confidence and post-confidence. The graph reflects that confidence increases more from pre-discussion to post-discussion for correct answers than for incorrect answers, even when we only consider cases where peers agreed.

The relationship between pre-discussion and post-discussion confidence as a function of the accuracy of an answer when partners agreed. Each dot represents a student

If students engage in more comprehensive answer testing during discussion than before, the relationship between confidence in their answer and the accuracy of their answer should be stronger following discussion than it is before. We examined whether confidence accurately reflected correctness before and after discussion. To do so, we calculated the gamma correlation between confidence and accuracy, as is typically reported in the literature on metacognitive monitoring (e.g., Son & Metcalfe, 2000 ; Tullis & Fraundorf, 2017 ). Across all students, the resolution of metacognitive monitoring increases from pre-discussion to post-discussion ( t (139) = 2.98, p  = 0.003, d  = 0.24; for a breakdown of gamma calculations for each class, see Table 4 ). Confidence was more accurately aligned with accuracy following discussion than preceding it. The resolution between student confidence and correctness increases through discussion, suggesting that discussion offers better coherence testing than answering alone.

To examine why peer instruction benefits student learning, we analyzed student answers and confidence before and after discussion across six psychology classes. Discussing a question with a partner improved accuracy across classes and grade levels with small to medium-sized effects. Questions of all difficulty levels benefited from peer discussion; even questions where less than half of students originally answered correctly saw improvements from discussion. Benefits across the spectrum of question difficulty align with prior research showing improvements when even very few students initially know the correct answer (Smith et al., 2009 ). More students switched from incorrect answers to correct answers than vice versa, leading to an improvement in accuracy following discussion. Answer switching was driven by a student’s own confidence in their answer and their partner’s confidence. Greater confidence in one’s answer indicated a greater likelihood of keeping the answer; a partner’s greater confidence increased the likelihood of changing to their answer.

Switching answers depended on more than just confidence: even when accounting for students’ confidence levels, the correctness of the answer impacted switching behavior. Across several measures, our data showed that the correctness of an answer carried weight beyond confidence. For example, the correctness of the answer predicted whether students switched their initial answer during peer disagreements, even after taking the confidence of both partners into account. Further, students’ confidence increased more when partners agreed on the correct answer compared to when they agreed on an incorrect answer. Finally, although confidence increased from pre-discussion to post-discussion when students changed their answers from incorrect to the correct ones, confidence decreased when students changed their answer away from the correct one. A plausible interpretation of this difference is that when students switch from a correct answer to an incorrect one, their decrease in confidence reflects the poor coherence of their final incorrect selection.

Whether peer instruction resulted in optimal switching behaviors is debatable. While accuracy improved through discussion, final accuracy was worse than if students had optimally switched their answers during discussion. If students had chosen the correct answer whenever one of the partners initially chose it, the final accuracy would have been significantly higher (M = 0.80 (SD = 0.19)) than in our data (M = 0.72 (SD = 0.24), t (207) = 6.49, p  < 0.001, d  = 0.45). While this might be interpreted as “process loss” (Steiner, 1972 ; Weldon & Bellinger, 1997 ), that would assume that there is sufficient information contained within the dyad to ascertain the correct answer. One individual selecting the correct answer is inadequate for this claim because they may not have a compelling justification for their answer. When we account for differences in initial confidence, students’ final accuracy was better than expected. Students’ final accuracy was better than that predicted from a model in which students always choose the answer of the more confident peer. This over-performance, often called “process gain”, can sometimes emerge when individuals collaborate to create or generate new knowledge (Laughlin, Bonner, & Miner, 2002 ; Michaelsen, Watson, & Black, 1989 ; Sniezek & Henry, 1989 ; Tindale & Sheffey, 2002 ). Final accuracy reveals that students did not simply choose the answer of the more confident student during discussion; instead, students more thoroughly probed the coherence of answers and mental models during discussion than they could do alone.

Students’ final accuracy emerges from the interaction between the pairs of students, rather than solely from individuals’ sequestered knowledge prior to discussion (e.g. Wegner, Giuliano, & Hertel, 1985 ). Schwartz ( 1995 ) details four specific cognitive products that can emerge through working in dyads. Specifically, dyads force verbalization of ideas through discussion, and this verbalization facilitates generating new knowledge. Students may not create a coherent explanation of their answer until they engage in discussion with a peer. When students create a verbal explanation of their answer to discuss with a peer, they can identify knowledge gaps and construct new knowledge to fill those gaps. Prior research examining the content of peer interactions during argumentation in upper-level biology classes has shown that these kinds of co-construction happen frequently; over three quarters of statements during discussion involve an exchange of claims and reasoning to support those claims (Knight et al., 2013 ). Second, dyads have more information processing resources than individuals, so they can solve more complex problems. Third, dyads may foster greater motivation than individuals. Finally, dyads may stimulate the creation of new, abstract representations of knowledge, above and beyond what one would expect from the level of abstraction created by individuals. Students need to communicate with their partner; to create common ground and facilitate discourse, dyads negotiate common representations to coordinate different perspectives. The common representations bridge multiple perspectives, so they lose idiosyncratic surface features of individuals’ representation. Working in pairs generates new knowledge and tests of answers that could not be predicted from individuals’ performance alone.

More broadly, teachers often put students in groups so that they can learn from each other by giving and receiving help, recognizing contradictions between their own and others’ perspectives, and constructing new understandings from divergent ideas (Bearison, Magzamen, & Filardo, 1986 ; Bossert, 1988-1989 ; Brown & Palincsar, 1989 ; Webb & Palincsar, 1996 ). Giving explanations to a peer may encourage explainers to clarify or reorganize information, recognize and rectify gaps in understandings, and build more elaborate interpretations of knowledge than they would have alone (Bargh & Schul, 1980 ; Benware & Deci, 1984 ; King, 1992 ; Yackel, Cobb, & Wood, 1991 ). Prompting students to explain why and how problems are solved facilitates conceptual learning more than reading the problem solutions twice without self-explanations (Chi, de Leeuw, Chiu, & LaVancher, 1994 ; Rittle-Johnson, 2006 ; Wong, Lawson, & Keeves, 2002 ). Self-explanations can prompt students to retrieve, integrate, and modify their knowledge with new knowledge; self-explanations can also help students identify gaps in their knowledge (Bielaczyc, Pirolli, & Brown, 1995 ; Chi & Bassock, 1989 ; Chi, Bassock, Lewis, Reimann, & Glaser, 1989 ; Renkl, Stark, Gruber, & Mandl, 1998 ; VanLehn, Jones, & Chi, 1992 ; Wong et al., 2002 ), detect and correct errors, and facilitate deeper understanding of conceptual knowledge (Aleven & Koedinger, 2002 ; Atkinson, Renkl, & Merrill, 2003 ; Chi & VanLehn, 2010 ; Graesser, McNamara, & VanLehn, 2005 ). Peer instruction, while leveraging these benefits of self-explanation, also goes beyond them by involving what might be called “other-explanation” processes - processes recruited not just when explaining a situation to oneself but to others. Mercier and Sperber ( 2019 ) argue that much of human reason is the result of generating explanations that will be convincing to other members of one’s community, thereby compelling others to act in the way that one wants.

Conversely, students receiving explanations can fill in gaps in their own understanding, correct misconceptions, and construct new, lasting knowledge. Fellow students may be particularly effective explainers because they can better take the perspective of their peer than the teacher (Priniski & Horne, 2019 ; Ryskin, Benjamin, Tullis, & Brown-Schmidt, 2015 ; Tullis, 2018 ). Peers may be better able than expert teachers to explain concepts in familiar terms and direct peers’ attention to the relevant features of questions that they do not understand (Brown & Palincsar, 1989 ; Noddings, 1985 ; Vedder, 1985 ; Vygotsky, 1981 ).

Peer instruction may benefit from the generation of explanations, but social influences may compound those benefits. Social interactions may help students monitor and regulate their cognition better than self-explanations alone (e.g., Jarvela et al., 2015 ; Kirschner, Kreijns, Phielix, & Fransen, 2015 ; Kreijns, Kirschner, & Vermeulen, 2013 ; Phielix, Prins, & Kirschner, 2010 ; Phielix, Prins, Kirschner, Erkens, & Jaspers, 2011 ). Peers may be able to judge the quality of the explanation better than the explainer. In fact, recent research suggests that peer instruction facilitates learning even more than self-explanations (Versteeg, van Blankenstein, Putter, & Steendijk, 2019 ).

Not only does peer instruction generate new knowledge, but it may also improve students’ metacognition. Our data show that peer discussion prompted more thorough testing of the coherence of the answers. Specifically, students’ confidences were better aligned with accuracy following discussion than before. Improvements in metacognitive resolution indicate that discussion provides more thorough testing of answers and ideas than does answering questions on one’s own. Discussion facilitates the metacognitive processes of detecting errors and assessing the coherence of an answer.

Agreement among peers has important consequences for final behavior. For example, when peers agreed, students very rarely changed their answer (less than 3% of the time). Further, large increases in confidence occurred when students agreed (as compared to when they disagreed). Alternatively, disagreements likely engaged different discussion processes and prompted students to combine different answers. Whether students weighed their initial answer more than their partner’s initial answer remains debatable. When students disagreed with their partner, they were more likely to stick with their own answer than switch; they kept their own answer 66% of the time. Even when their partner was more confident, students only switched to their partner’s answer 50% of the time. The low rate of switching during disagreements suggests that students weighed their own answer more heavily than their partner’s answer. In fact, across prior research, deciders typically weigh their own thoughts more than the thoughts of an advisor (Harvey, Harries, & Fischer, 2000 ; Yaniv & Kleinberger, 2000 ).

Interestingly, peers agreed more frequently than expected by chance. When students were initially correct (64% of the time), 78% of peers agreed. When students were initially incorrect (36% of the time), peers agreed 43% of the time. Pairs of students, then, agree more than expected by a random distribution of answers throughout the classroom. These data suggest that students group themselves into pairs based upon likelihood of sharing the same answer. Further, these data suggest that student understanding is not randomly distributed throughout the physical space of the classroom. Across all classes, students were instructed to work with a neighbor to discuss their answer. Given that neighbors agreed more than predicted by chance, students seem to tend to sit near and pair with peers that share their same levels of understanding. Our results from peer instruction reveal that students physically locate themselves near students of similar abilities. Peer instruction could potentially benefit from randomly pairing students together (i.e. not with a physically close neighbor) to generate the most disagreements and generative activity during discussion.

Learning through peer instruction may involve deep processing as peers actively challenge each other, and this deep processing may effectively support long-term retention. Future research can examine the persistence of gains in accuracy from peer instruction. For example, whether errors that are corrected during peer instruction stay corrected on later retests of the material remains an open question. High and low-confidence errors that are corrected during peer instruction may result in different long-term retention of the correct answer; more specifically, the hypercorrection effect suggests that errors committed with high confidence are more likely to be corrected on subsequent tests than errors with low confidence (e.g., Butler, Fazio, & Marsh, 2011 ; Butterfield & Metcalfe, 2001 ; Metcalfe, 2017 ). Whether hypercorrection holds for corrections from classmates during peer instruction (rather than from an absolute authority) could be examined in the future.

The influence of partner interaction on accuracy may depend upon the domain and kind of question posed to learners. For simple factual or perceptual questions, partner interaction may not consistently benefit learning. More specifically, partner interaction may amplify and bolster wrong answers when factual or perceptual questions lead most students to answer incorrectly (Koriat, 2015 ). However, for more “intellective tasks,” interactions and arguments between partners can produce gains in knowledge (Trouche et al., 2014 ). For example, groups typically outperform individuals for reasoning tasks (Laughlin, 2011 ; Moshman & Geil, 1998 ), math problems (Laughlin & Ellis, 1986 ), and logic problems (Doise & Mugny, 1984; Perret-Clermont, 1980 ). Peer instruction questions that allow for student argumentation and reasoning, therefore, may have the best benefits in student learning.

The underlying benefits of peer instruction extend beyond the improvements in accuracy seen from pre-discussion to post-discussion. Peer instruction prompts students to retrieve information from long-term memory, and these practice tests improve long-term retention of information (Roediger III & Karpicke, 2006 ; Tullis, Fiechter, & Benjamin, 2018 ). Further, feedback provided by instructors following peer instruction may guide students to improve their performance and correct misconceptions, which should benefit student learning (Bangert-Drowns, Kulik, & Kulik, 1991 ; Thurlings, Vermeulen, Bastiaens, & Stijnen, 2013 ). Learners who engage in peer discussion can use their new knowledge to solve new, but similar problems on their own (Smith et al., 2009 ). Generating new knowledge and revealing gaps in knowledge through peer instruction, then, effectively supports students’ ability to solve novel problems. Peer instruction can be an effective tool to generate new knowledge through discussion between peers and improve student understanding and metacognition.

Availability of data and materials

As described below, data and materials are available on the OpenScienceFramework: https://mfr.osf.io/render?url=https://osf.io/5qc46/?action=download%26mode=render .

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The Power of Peer Learning

Fostering Students’ Learning Processes and Outcomes

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• Discusses cutting-edge pedagogical and technological developments to foster peer learning processes and outcomes
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Table of contents (17 chapters), front matter, conceptual contributions on peer learning, the four pillars of peer assessment for collaborative teamwork in higher education.

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Learning Analytics for Peer Assessment: A Scoping Review

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Support Student Integration of Multiple Peer Feedback on Research Writing in Thesis Circles

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Methodological Contributions on Peer Learning

Peer assessment using criteria or comparative judgement a replication study on the learning effect of two peer assessment methods.

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Using Stochastic Actor-Oriented Models to Explain Collaboration Intentionality as a Prerequisite for Peer Feedback and Learning in Networks

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Comparing Expert and Peer Assessment of Pedagogical Design in Integrated STEAM Education

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Technological Contributions on Peer Learning

Constructing computer-mediated feedback in virtual reality for improving peer learning: a synthesis of the literature in presentation research.

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Web-Based Peer Assessment Platforms: What Educational Features Influence Learning, Feedback and Social Interaction?

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Feed-Back About the Collaboration Process from a Group Awareness Tool. Potential Boundary Conditions for Effective Regulation

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Viewbrics: A Technology-Enhanced Formative Assessment Method to Mirror and Master Complex Skills with Video-Enhanced Rubrics and Peer Feedback in Secondary Education

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Empirical Contributions on Peer Learning

Peerteach: teaching learners to do learner-centered teaching.

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A Thematic Analysis of Factors Influencing Student’s Peer-Feedback Orientation

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Giving Feedback to Peers in an Online Inquiry-Learning Environment

• Natasha Dmoshinskaia, Hannie Gijlers

Peer Interaction Types for Social and Academic Integration and Institutional Attachment in First Year Undergraduates

• Emmeline Byl, Keith J. Topping, Katrien Struyven, Nadine Engels

How to Make Students Feel Safe and Confident? Designing an Online Training Targeting the Social Nature of Peer Feedback

• Morgane Senden, Dominique De Jaeger, Tijs Rotsaert, Fréderic Leroy, Liesje Coertjens

This open access book explores new developments in various aspects of peer learning processes and outcomes. It brings together research studies examining how peer feedback, peer assessment, and small group learning activities can be designed to maximize learning outcomes in higher, but also secondary, education.

Conceptual models and methodological frameworks are presented to guide teachers and educational designers for successful implementation of peer learning activities with the hope of maximizing the effectiveness of peer learning in real educational classrooms.

By providing empirical studies from different peer learning initiatives, both teachers and students in academic and professional contexts are informed about the state of the art developmentsof peer learning.

This book contributes to the understanding of peer learning challenges and solutions in all level of education and provide avenues for future research. It includes theoretical, methodological, and empirical chapters which makes it a useful tool for both teaching and research.

• Peer Learning in Higher Education
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• Peer Learning and Students’ Motivation
• Peer Learning Processes and Outcomes
• Peer Feedback and Peer Feedforward
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• Peer Learning and Students’ Individual Characteristics
• Peer Learning and Students’ Learning Perception
• Peer Learning and Students’ Emotional Responses
• Peer Learning and its Challenges

Education and Learning Sciences, Wageningen University & Research, Wageningen, The Netherlands

Omid Noroozi

Bram De Wever

Omid Noroozi (PhD, 2013) is associate professor at the Education and Learning Sciences group at Wageningen University and Research, the Netherlands. He has fostered an interest in understanding the relations among technology, pedagogy, and learning higher-order skills (e.g. critical thinking, reasoning, problem-solving, communication, collaboration, self-regulation, entrepreneurial thinking) with a specific focus on students’ argumentation competence development in higher education.

Bram De Wever (PhD, 2006) is associate professor at the Department of Educational Studies at Ghent University, Belgium and head of the research group TECOLAB at that department. His research is focusing on technology enhanced learning and instruction, peer assessment and feedback, computer-supported collaborative learning activities, inquiry learning and argumentative writing. Research settings include mostly secondary, higher, and adult education.

Book Title : The Power of Peer Learning

Book Subtitle : Fostering Students’ Learning Processes and Outcomes

Editors : Omid Noroozi, Bram De Wever

Series Title : Social Interaction in Learning and Development

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eBook ISBN : 978-3-031-29411-2 Published: 20 June 2023

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Number of Pages : XIV, 392

Number of Illustrations : 24 b/w illustrations, 44 illustrations in colour

Topics : Study and Learning Skills , Educational Psychology , Education, general

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School-based peer education interventions to improve health: a global systematic review of effectiveness

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• Ruth Simmonds 5 ,
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Introduction

Peer education, whereby peers (‘peer educators’) teach their other peers (‘peer learners’) about aspects of health is an approach growing in popularity across school contexts, possibly due to adolescents preferring to seek help for health-related concerns from their peers rather than adults or professionals. Peer education interventions cover a wide range of health areas but their overall effectiveness remains unclear. This review aims to summarise the effectiveness of existing peer-led health interventions implemented in schools worldwide.

Five electronic databases were searched for eligible studies in October 2020. To be included, studies must have evaluated a school-based peer education intervention designed to address the health of students aged 11–18-years-old and include quantitative outcome data to examine effectiveness. The number of interventions were summarised and the impact on improved health knowledge and reductions in health problems or risk-taking behaviours were investigated for each health area separately, the Mixed Methods Appraisal Tool was used to assess quality.

A total of 2125 studies were identified after the initial search and 73 articles were included in the review. The majority of papers evaluated interventions focused on sex education/HIV prevention ( n  = 23), promoting healthy lifestyles ( n  = 17) and alcohol, smoking and substance use ( n  = 16). Papers mainly reported peer learner outcomes (67/73, 91.8%), with only six papers (8.2%) focussing solely on peer educator outcomes and five papers (6.8%) examining both peer learner and peer educator outcomes. Of the 67 papers reporting peer learner outcomes, 35/67 (52.2%) showed evidence of effectiveness, 8/67 (11.9%) showed mixed findings and 24/67 (35.8%) found limited or no evidence of effectiveness. Of the 11 papers reporting peer educator outcomes, 4/11 (36.4%) showed evidence of effectiveness, 2/11 (18.2%) showed mixed findings and 5/11 (45.5%) showed limited or no evidence of effectiveness. Study quality varied greatly with many studies rated as poor quality, mainly due to unrepresentative samples and incomplete data.

School-based peer education interventions are implemented worldwide and span a wide range of health areas. A number of interventions appear to demonstrate evidence for effectiveness, suggesting peer education may be a promising strategy for health improvement in schools. Improvement in health-related knowledge was most common with less evidence for positive health behaviour change. In order to quantitatively synthesise the evidence and make more confident conclusions, there is a need for more robust, high-quality evaluations of peer-led interventions using standardised health knowledge and behaviour measures.

Peer Review reports

Ensuring good health and wellbeing amongst school-aged children is a global public health priority and the contribution schools can make to this goal is increasingly recognised [ 1 ]. Worldwide, we have seen a rise in peer education interventions over recent decades [ 2 ]. For example, a survey in England revealed that 62% of primary and secondary schools had offered a peer-led intervention in 2009 [ 3 ]. Peer-led interventions within school settings are popular for many reasons, including the important role peers play within the lives of young people, a perception that this approach involves relatively few resources, and the more even balance of authority than in teacher-led lessons [ 4 ]. The use of peer educators for health improvement has also been linked with the importance of peer influence in adolescence [ 5 ]. This is a time of increased social development and peer attachments are central to young people’s development, particularly during adolescence [ 5 , 6 ]. Further, there is evidence that young people are more likely to seek help from informal sources of support such as friends in comparison to adults [ 7 ], and of older students being perceived as role models by their younger peers [ 8 ]. Benefits are also likely to exist for peer educators themselves, including opportunities to develop confidence and leadership skills, as well as many schools rewarding peer educators with a qualification or endorsement for their participation [ 9 ].

Existing peer education interventions cover a wide range of health areas, including mental health, physical health, sexual health, and a general promotion of healthy lifestyles including eating habits and smoking prevention [ 10 , 11 , 12 , 13 ]. There is also variation in the format or delivery of peer-led interventions including 1:1 peer mentoring, peer buddy initiatives, peer counselling, and peer education [ 14 , 15 , 16 , 17 ]. This review focuses specifically on peer education, which typically involves the selection and training of ‘peer educators’ or ‘leaders’, who subsequently relay health related information or skills to younger or similar aged students in their school, known as ‘peer learners’ or ‘recipients’.

Summary of related reviews

The current literature on peer education indicates a mixed evidence base regarding its effectiveness.

Ten previous reviews were found concerning health-related peer education among young people [ 10 , 12 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Of these, six concerned sexual health/HIV prevention, two concerned health promotion/education more broadly, one focused on substance abuse and one focused on mental health.

Kim and Free’s review concerning sexual health [ 21 ] found no overall effect of peer education on condom use, mixed findings on sexually transmitted infection (STI) prevention, and positive findings regarding improvements in knowledge, attitudes and intentions. Siddiqui et al. [ 20 ] reviewed peer education programmes for promoting the sexual and reproductive health of young people in India, revealing large variations in the way peer education is implemented as well as mixed effectiveness findings and limited effects of behaviour relative to knowledge. Maticka-Tyndale and Barnet [ 22 ] compiled a review into peer-led interventions to reduce HIV risk among youth using a narrative synthesis, and found that peer interventions led to positive change in knowledge and condom use, and had some success in changing community attitudes and norms, but no significant findings for effects on other sexual behaviours and STI rates. By comparison, Tolli’s review [ 12 ] regarding the effectiveness of peer education interventions for HIV prevention found no clear evidence of peer education effectiveness for HIV prevention, adolescent pregnancy prevention or sexual health promotion in young people of member countries of the European Union.

Mellanby et al. [ 23 ] reviewed the literature comparing peer-led and adult-led school health education and identified eleven studies. Seven of these studies found peer-led to be more effective for health behaviour change than adult-led and three of these studies found peer-led to me more effective for change in knowledge and attitudes. Harden et al. [ 24 ] identified 64 peer-delivered health interventions for young people aged 11 to 24 in any setting (i.e. not restricted to school settings), with only 12 evaluations judged to be methodologically sound. Of these 12, 7 studies (58%) showed a positive effect on at least one behavioural outcome. This review concluded an unclear evidence base for peer-delivered health promotion for young people.

MacArthur et al’s [ 19 ] investigation of peer-led interventions to prevent tobacco, alcohol and/or drug use among young people aged 11–21, comprised a meta-analysis, pooling 10 studies on tobacco use, and found lower prevalence of smoking among those receiving the peer-led interventions compared with controls. The authors also found that peer-led interventions were associated with benefit in relation to alcohol use, and three studies suggested an association with lower odds of cannabis use.

A recent systematic review by King and Fazel of 11 school-based peer-led mental health interventions studies revealed mixed effectiveness [ 10 ]. Some studies showed significant improvements in peer educator self-esteem and social stress [ 25 ], but one study showed an increase in guilt in peer educators [ 26 ]. Two studies also found improvements in self-confidence [ 27 ], and quality of life in peer learners [ 28 ], but one study found an increase in learning stress and decrease in overall mental health scores [ 26 ]. The review concluded there is better evidence if benefits for peer educators compared to peer learners. The summary above of previous systematic assessments of the peer education approach reveals a limited evidence base for school-based peer education interventions. Only two reviews were included regarding school-based peer education, one of which occurred over 20 years ago [ 23 ], while the other [ 10 ] was more narrowly concerned with mental health outcomes.

Despite the widespread use of peer-led interventions, the evidence base across all health areas still remains limited and little is known regarding their overall effectiveness in terms of changing behaviours or increasing health-related knowledge and/or attitudes. Due to the limited evidence base of peer education interventions, this review is broad in scope and will cover global peer education interventions covering all health areas. Although some peer education interventions are targeted towards specific populations, this review focuses on universal interventions available to an entire cohort of students (for example whole class or whole year group). The review aims to summarise the effectiveness of existing peer-led health interventions in schools. This is a review of quantitative data; the qualitative peer education literature will be published in a separate review.

We followed the PICO (Population, Intervention, Comparator and Outcome) format to develop our research question. We completed the systematic review in accordance with the 2009 PRISMA statement [ 29 ] and registered it with PROSPERO (CRD42021229192).

Search strategy and selection criteria

Five electronic databases were searched for eligible studies: CINAHL, Embase, ERIC, MEDLINE and PsycINFO. The list of search terms (see Supplementary Materials ) were developed after scanning relevant literature for key terms. Searches took place during October 2020.

Once the search terms had been agreed amongst the study team, pilot searches were run to check that key texts were appearing. Search terms were subsequently refined and this process was repeated until all key texts appeared. Search strategies such as truncations were used to maximise results. No restrictions were placed on publication date, country or language.

Inclusion/exclusion criteria

To be included studies had to be concerned with school-based peer education interventions designed to address aspects of the health of pupils aged 11–18 years old. We are interested in this age group in particular as it is a period when peers take on a particularly important role in young people’s lives. Peer education interventions concerned with health are defined here as interventions in which school-aged children deliver the education of other pupils for the purposes of improving health outcomes or awareness/literacy relating to health, including knowledge, behaviours and attitudes. Interventions must have taken place within a school, during school hours and must be universal, i.e. not targeted towards a specific sub-group of students or students with a particular health condition.

Where comparators/controls existed, they had to include non-exposure to the interventions concerned, exposure to a differing version of the same intervention, or exposure to the intervention within a substantially differing context.

Papers were excluded from data synthesis if they satisfied any of the following criteria:

Peer education interventions only concerned academic outcomes (e.g., reading and writing achievement).

Interventions concerning anger management, behavioural problems, or social skills.

Interventions concerning traffic safety, health and safety, avoidance of injuries, or first aid.

Interventions concerning cultural, social or political awareness (e.g., media literacy).

Interventions in which health outcomes are secondary to other outcomes (e.g., interventions focused on reading that indirectly improve self-esteem).

One-to-one mentoring interventions.

Conference abstracts, research briefings, commentaries, editorials, study protocol papers and pre-prints.

Primary outcome(s)

Improvements in health, including health awareness and understanding as indicated by responses to questionnaires.

Reductions in health problems or risk-taking behaviours.

These outcomes may concern the peer educators and/or peer learners.

Data extraction, selection and coding

Two reviewers independently screened all papers according to the inclusion criteria above using the Rayyan online review platform. In cases where the reviewers were uncertain, or where the decision was disputed, the decision was discussed and agreed among the wider research team. Two reviewers (SD and EW) then divided the papers between them and independently extracted the data, discussing and queries that arose with each other and the wider team.

Data extraction included the following:

Bibliographic details – authors, year of publication, nation in which intervention was carried out

Aims of the study

Description of study design

Sample size and demographic characteristics.

Context into which the intervention is introduced (characteristics of the school involved, the area in which the school is located, characterisations of the student body, relevant policy considerations).

Description of intervention (including duration of intervention).

Outcome measures (measurement tools, time points of data collection).

Data concerning improvements in health.

Quality appraisal

We used the Mixed Methods Appraisal Tool (MMAT) to assess quality of reporting procedures. This tool consists of five specific quality rating items depending on study design (qualitative, quantitative randomized, quantitative non-randomized, quantitative descriptive and quantitative mixed methods). There are 5 quality questions specific to each study design, so all papers are rated between 0 to 5. The following ratings were used to summarise study quality; 0–1 indicating poor quality, 2–3 indicating average quality and 4–5 indicating high quality. Two reviewers (SD and EW) completed quality ratings on each paper and discussed any discrepancies between them.

Examples of randomized design quality questions included items such as: “ Is randomization appropriately performed ? And “ Are the groups comparable at baseline ?” Examples of non-randomized design quality questions included items such as: “ Are the participants representative of the target population?” and “Are there complete outcome data?”

Effectiveness summary

EW and SD completed data synthesis. Due to the volume of studies, and the large number and heterogeneity of outcome measures, in order to summarise effectiveness, we created the following scoring system to indicate effectiveness:

Significant effects are effects where there was an improvement in health-related outcomes either after the peer education intervention, or when compared to a control group, with a p value of <0.05. Due to the volume of studies and varied follow-up periods, we looked at effectiveness at first follow-up, which in the majority of papers was immediately post-intervention.

A total of 2125 articles were identified after the initial search and 73 articles were eligible for inclusion (see Fig. 1 for a flow diagram of the search). Study designs of the 73 articles were as follows: 23 were controlled trial designs (15 cluster or group randomised, 6 randomised controlled and 2 non-randomised). 15 used randomisation methods but were not controlled trials and the remaining 35 studies used uncontrolled non-randomised methods comparing intervention with a comparison group or using a pre-post survey.

Prisma flow diagram of included studies

Health and geographical areas

The 73 quantitative papers included in this review demonstrated a wide range of health areas. The majority of papers evaluated interventions aimed at sex education/HIV prevention ( n  = 23), promoting healthy lifestyles ( n  = 17) and reducing alcohol, smoking and substance use ( n  = 16). Fig. 2 illustrates number of papers per health area by peer learner or peer educator outcome focus and Table 2 illustrates a summary of proportion of health areas, overall effectiveness and quality ratings.

Number of papers by health area. NB See Supplementary Materials for full description of study designs and outcomes

Papers mainly focussed on peer learner outcomes (67/73, 91.8%), with only six papers (8.2%) focussing solely on peer educator outcomes and only five papers (6.8%) reporting on both peer learner and peer educator outcomes. The majority of papers that focussed on peer educator outcomes were those concerned with sex education (n = 4) and mental health (n = 3).

Papers typically reported knowledge, attitude and/or behavioural outcomes. Of the 73 papers, 42/73 (57.5%) reported knowledge outcomes, 43/73 (58.9%) reported attitude outcomes, 35/73 (47.9%) reported behavioural outcomes and 13/73 (17.8%) reported behavioural intentions.

As well as a broad range of health areas, the papers included in the review also spanned several different countries (Fig. 3 ).

Summary of number of papers by country

We have summarised the results first by student type and then by health area.

Results by student type

Summary of peer learner outcomes.

Of the 67 papers reporting peer learner health outcomes, 35/67 (52.2%) showed evidence of effectiveness (as per our thresholds shown in Table 1 ), 8/67 (11.9%) showed mixed findings and 24/67 (35.8%) found limited or no evidence of effectiveness.

Of the 35 papers that demonstrated effectiveness, 9/35 studies (25.7%) were rated as high quality. Therefore only 9/67 (13.4%) of the total papers showed evidence of effectiveness and were rated as high quality.

Twenty-one papers (31.3%) reported controlled trial designs (including 14 cluster or group randomised, and 5 randomised controlled and 2 non-randomised). Thirteen papers used randomisation methods but were not controlled trials and the remaining 33 papers used uncontrolled non-randomised methods comparing intervention with a comparison group or using a pre-post survey design.

Summary of peer educator outcomes

Of the 11 papers reporting on peer educator health outcomes, 4/11 (36.4%) showed evidence of effectiveness, 2/11 (18.1%) showed mixed findings and 5/11 (45.5%) showed limited or no evidence of effectiveness. Of the 4 papers showing evidence for effectiveness, 2 studies (50%) were rated as high quality.

Four papers had a randomised design comparing intervention vs. control or ‘peer educators vs. classmates’ one of which was a cluster randomised controlled trial. The remaining 7 papers used non-randomised intervention vs. control ( n  = 2) or pre-post survey designs ( n  = 5).

A full table of included studies, outcomes and effectiveness and quality ratings can be found in Supplementary Material 1 .

Results by health area

Sex education/hiv prevention.

Twenty-three studies concerned sex education/HIV prevention [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. 9/23 studies had a randomised design with the 8 studies comparing peer-led to teacher-led or ‘lessons as usual’ and one study comparing peer-led with nurse-led. 14/23 involved non-randomised designs comparing intervention vs. control or a pre-post survey design. Studies covered a wide geographical range, among which there were 7 US studies, but also studies from Canada, UK, Africa, South Africa, Turkey and Greece.

Of the twenty-three papers, 21 reported peer learner outcomes, 4 papers reported peer educator outcomes, with 2 papers reporting on both peer educator and peer learner outcomes. The mean number of participants across the studies was 2033 (range: n  = 106–9000).

8/23 (34.8%) of studies showed evidence of effectiveness, and all studies demonstrating effectiveness consisted of knowledge and attitude outcomes rather than behavioural change.

Only 4/23 studies were rated high in quality (two of which showed evidence of effectiveness), whilst the majority of studies were rated medium quality (15/23) and 4/23 rated as low quality.

Healthy lifestyles (exercise, nutrition, oral health, health information)

Seventeen studies reported interventions addressing healthy lifestyles [ 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ]. Of these papers, ten used a randomised controlled trial design primarily comparing peer-led vs. teacher-led or ‘lessons as usual’, but two oral health papers also used a dentist-led condition. Seven papers used non-randomised research designs comparing intervention vs. control or a pre-post survey design.

The most common focus was nutrition and exercise, but interventions also covered oral health, accessing health information online and interventions taking a more general approach to health improvement. Regarding geographical spread, 5/17 papers reported interventions carried out in the USA, with Australia, China, India and UK represented by two papers per country.

Sixteen of the seventeen papers reported peer learner outcomes, and only one reported peer educator outcomes. The mean number of participants per intervention was 1245 (range: n  = 76–4576).

7/17 papers in this health area were shown to be effective, 8/17 were found to be ineffective, and 2/17 showed mixed results. In other words, less than half (41.1%) showed evidence of effectiveness. Of the studies demonstrating effectiveness, the outcomes largely centred around knowledge and attitudes, but one study did demonstrate positive behaviour change [ 62 ].

Over half of the studies (9/17) were rated as high quality, 4/17 were rated medium quality and 4/17 low quality. Of the studies showing evidence for effectiveness, 4/7 (57.1%) were rated as high quality.

Alcohol, smoking, substance use

Sixteen papers were classified within the category of alcohol, smoking and substance use [ 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 ]. Ten of these papers had a randomised design (including 3 cluster randomised controlled trials) comparing peer-led (intervention) vs. teacher-led (control). Six papers were non-randomised and used either a pre-post survey design or intervention vs. control. The 16 papers varied in quality with six rated ‘high quality’, seven rated ‘medium quality’, and three rated ‘low quality’. Studies took place across more than 10 countries with one study being conducted internationally. The mean number of participants across all studies was 2165 (range: n = 105–10,730).

Fifteen papers evaluated the effect of the intervention on peer learner outcomes and only one paper evaluated the effect of the intervention on peer educator outcomes. 8/16 (50%) papers showed evidence of effectiveness. 2/16 (12.5%) papers showed mixed findings and 6/16 (37.5%) showed little to no evidence for effectiveness, including the peer educator outcome paper. Of the eight papers demonstrating evidence for effectiveness, only four (50%) were rated as high quality.

Of the studies demonstrating effectiveness, there was a combination of knowledge, attitude and behavioural outcomes, but more evidence for positive changes in knowledge and attitude.

Mental health and well-being

Six studies assessed mental health and well-being [ 27 , 86 , 87 , 88 , 89 , 90 ]. This category was inclusive of common mental health problems, self-harm and suicide prevention as well as broader topics such as self-esteem and social connectedness. Four of the six studies used non-randomised pre-post survey designs and two studies used randomised design, one of which was a cluster randomised controlled trial.

Of the six studies, 5/6 explored peer learner outcomes, 3/6 explored peer educator outcomes, 2 of which explored both peer learner and peer educator outcomes. The average sample size across the seven mental health studies was 1118 (range: n  = 50–4128).

Study quality was mixed, with two studies rated as high quality, three medium quality and one low quality. Outcome measures largely consisted of knowledge and attitude questionnaires, help-seeking behaviour and help-seeking confidence as well as condition-specific measures including body satisfaction and self-report of emotional and behavioural difficulties.

The majority of mental health studies (5/6) were rated as showing evidence for effectiveness and one study was rated ineffective. Of the studies demonstrating effectiveness, only one reported positive behaviour change (help-seeking behaviours) and this behaviour changed was observed in peer educators as opposed to peer learners [ 86 ].

Disease prevention

Four studies assessed outcomes relating to disease prevention [ 91 , 92 , 93 , 94 ] which included hepatitis, tuberculosis, cervical cancer and blood borne diseases. All four studies focused on peer learner outcomes and one study also included peer educator outcomes. Three of the four studies were non-randomised pre-post survey designs and one study was randomised. The average sample size across the four studies was 2116 (range: 1265–2930).

Three out of the four studies (75%) showed evidence for effectiveness and one study showed mixed results. No studies were rated as high quality, three were rated medium and one was rated low.

Outcomes were largely knowledge or intention based. Studies showing effectiveness mostly related to knowledge, intentions and attitudes and one study did find a positive change in behaviour [ 93 ].

Five included studies assessed asthma interventions [ 95 , 96 , 97 , 98 , 99 ]. 4/5 of these were randomised trials and one study used a non-randomised pre-post survey design. Average sample size across all studies was 427 (range: n  = 203–935). Three studies took place in Australia and two in the US. All papers evaluated the impact of the intervention on peer learner outcomes with none focussing on peer educator outcomes.

4/5 studies showed evidence for effectiveness with only one study showing no evidence for effectiveness. All studies were rated as medium quality. Measures ranged from asthma knowledge, quality of life, school absenteeism, asthma attacks at school and asthma tests. Effectiveness was largely observed for knowledge outcomes, there was less evidence for asthma attacks or symptoms.

Two studies conducted in Italy assessed bullying by evaluating the ‘NoTrap!’ anti-bullying intervention [ 100 , 101 ]. The first study rated as high quality, evaluated two independent trials and focussed on peer learner outcomes ( n  = 622; n  = 461). This study found significant reductions in victimization, bullying, cybervictimization and cyberbullying and was rated as high quality. The second study, rated as medium quality, focussed on peer educator outcomes ( n  = 524) and used a non-randomised, pre-post survey design but overall, only showed some evidence of effectiveness amongst males in terms of reduced victimization and increased prosocial behaviour and social support. No evidence was found for effectiveness among females.

Peer education interventions to improve student health cover a wide variety of topics and are used globally. This review aimed to summarise the results from peer education health interventions in secondary school students (aged 11–18-years-old), which were universal (rather than targeted interventions of sub-groups of students) and carried out at school.

Due to the heterogeneity of findings, range of health areas, types of studies and diversity of outcome measurements used, it was not possible to perform a meta-analysis or formal data synthesis to assess effectiveness. However, some broad conclusions can be made. A number of interventions appear to demonstrate evidence for effectiveness which indicates that peer education interventions can be an important school-based intervention for health improvement. Asthma interventions appeared to be particularly effective. In terms of outcome measures, the strongest evidence was for a positive change in knowledge and attitude measures, but there was less evidence overall for health behaviour outcomes which supports previous findings [ 20 , 22 ].

Although many studies did demonstrate positive results, findings overall were very mixed and several studies were of poor quality. In addition to the shortcomings picked up on by our quality appraisal, many papers lacked methodological detail and clarity regarding the intervention procedure, particularly in regard to how peer educators were selected and trained, which seems to be an important factor in those studies that found positive results and was also emphasised in a previous review [ 10 ]. Further, there were widespread problems of data reporting including noting ‘significant’ results without providing any measure of effect size or between-study variability. Other problems included selective reporting of results, such as selective emphasis on anomalous positive results, or only revealing measures of statistical significance in the case of positive effects. Interestingly, there did not appear to be a relationship between study quality and findings, given that several studies rated as effective were rated both high and low quality with a similar picture for studies showing mixed effectiveness and ineffectiveness.

In terms of frequency of health areas covered, our findings are similar to a recent ‘review of reviews’ of peer education for health and wellbeing which found that the majority of reviews focused on sexual health and HIV/AIDS interventions [ 13 ]. This previous review focused on both children and adults, however, in line with our findings, it found mixed effectiveness and considerable diversity in methods, findings and rigour of evaluation. It was particularly noted that details of peer educator training were rarely provided in HIV/AIDS interventions which supports our findings. Notably, however, the quality of studies was actually highest for peer education programs in HIV/AIDS, which differed to our review which found few studies rated as high quality. This discrepancy may be due to the different measures used to assess quality. Like our study, this review concluded that each health area showed some promising results, but also pointed to a need for higher levels of quality and rigour in future evaluations.

Despite the rising prevalence in mental health difficulties, there were relatively few studies focused on mental health outcomes, particularly more general preventative approaches to mental health and well-being, with many of the included studies focusing on suicide prevention, self-harm or specific disorders. However, many of mental health studies included in this review showed evidence for effectiveness, suggesting peer education approaches for mental health should be further studied and evaluated.

Another key finding of our review is that papers tended to focus more on peer learner outcomes and therefore impacts of peer-led interventions on peer educators themselves appear to be under-explored. This has been reported by previous reviews [ 10 ] and highlights the importance of examining and comparing both peer educators’ and learners’ outcomes within studies. In this context, we found more evidence of peer learners benefitting from the interventions, with 55.2% of studies showing a positive effect, versus only 36.4% for peer educators. This contrasted with a previous review of mental health interventions that concluded peer educators seemed to yield more benefits from participating in the interventions, possibly due to the attention they are given during training and throughout the programmes [ 10 ].

Although common measures existed across studies, including health knowledge, health intentions, and health behaviours, many studies used novel or unvalidated measurements, indicating a need for more standardised health literacy measures and a need for future validation work in this area. This supports two systematic reviews carried out in 2015, firstly a review of health literacy measures which found a lack of comprehensive instruments to measure health literacy and suggested the need for the development of new instruments [ 102 ], and secondly a review of mental health literacy measures which found a number of unvalidated measures and lack of measures that measured all components of mental health literacy concurrently [ 103 ].

Although there are a number of existing reviews summarising the extent to which peer education may improve young peoples health, the literature is still lacking on why peer education is effective within the quantitative literature. It remains unclear which mechanisms involved in peer education lead to its effectiveness (or ineffectiveness). Although many peer education studies are grounded in theory such as Diffusion of Innovation Theory [ 104 ] and Bandura’s Social Cognitive/Social Learning Theory [ 105 , 106 ], the literature is lacking a more nuanced analysis of the mechanisms through which peer education improve young people’s health. This is therefore a key area for future research.

A recent review of peer education and peer counselling for health and well-being highlights how peer education interventions are inherently difficult to quality control and evaluate [ 13 ], partly due to what makes peer education attractive; peer education defies the conventions of traditional formal education and allows young people to learn by more unstructured means, in more ‘real world’ ways, benefiting from meaningful examples and conversations with their peers. Although there are an increasing number of well-designed peer education studies [ 13 ], new evaluation methods may be needed given the complexity and multi-component nature of peer-education approaches (i.e., training, more informal teaching approaches and informal diffusion of knowledge).

Limitations

Despite our review being comprehensive, we acknowledge certain limitations. ‘Peer education’ is a complex and widely contested term and therefore how studies described their approach varied substantially. This may have meant some relevant studies were not picked up from our initial search. A previous review [ 10 ] also noted this potential limitation, with unclear and heterogeneous methods precluding meta-analysis. Therefore, a consensus on how to define ‘peer education’ and using standardised measures to assess effectiveness would facilitate more definitive synthesis of the evidence. Another potential limitation of our approach is that we only searched scientific databases, and therefore could have missed important evidence in the grey literature as we retrieved a relatively small number of initial records ( n  = 2125). Despite this, given the wide variety of study type, age range, health area and country reviewed, this suggests our search strategy was fairly robust, and yielded results that were representative of the breadth in the current literature base.

This review focussed on universal peer education interventions delivered within the secondary school setting during school hours. Further research could explore the effectiveness of varying forms of peer education including 1:1 mentoring, more targeted (not universal) interventions, as well as peer education interventions in other settings including youth clubs or community and local organisations.

Due to the breadth of this review, we did not conduct a detailed comparison between knowledge, attitude and behavioural outcomes, however the studies demonstrating effectiveness tended to show positive change on knowledge and attitude outcomes, but less evidence was seen for positive behavioural change. This is in line with previous reviews which have suggested that peer education better improves health knowledge but often does not lead to behavioural gains [ 13 , 107 ]. To this vein, it remains unclear the differential impact on behavioural intention and actual performance of behaviour, and therefore we urge future researchers to measure outcomes relating to knowledge and attitude, intentions, and actual behaviour in order to synthesise the evidence in a more standardised way. Although the literature is heterogeneous, there is available data to conduct distinct analysis on different outcome measures (knowledge, attitude and behaviour) to create a more nuanced understanding of each health area.

Given the large number of studies and variation in outcome measures (behaviour, knowledge, attitude), this review focussed on findings at first follow-up (usually immediately after intervention) and therefore the effectiveness findings are not likely to represent longer-term effects of peer education interventions, which would require further research. In addition, due to the low number of optimally designed randomised-controlled trials identified, our review could not meaningfully compare results between randomised and non-randomised studies. However, as more high quality trials continue to be published in this growing area of research, a future review could be conducted that looks into the effect of randomisation on young people’s outcomes. Our results also focused on p-values rather than effect sizes due to the large variability in how and what studies measures, future researchers should aim to agree on more standardises ways of measuring outcomes to enable better synthesis.

To conclude, school-based peer education interventions occur worldwide and span a number of health areas. A number of interventions appear to demonstrate evidence for effectiveness, suggesting peer education may be a promising strategy for health improvement in schools. However overall evidence for effectiveness and study quality are mixed. Improvement in health-related knowledge was most common with less evidence for positive health behaviour change. In order to synthesise the evidence and make more confident conclusions, it is imperative that more robust, high-quality evaluations of peer-led interventions are conducted and that studies follow reporting guidelines to describe their methods and results in sufficient detail so that meta-analyses can be conducted. In addition, further research is needed to develop understanding of the intervention mechanisms that lead to health improvement in peer education approaches as well as more focussed work on standardising and validating health literacy and behaviour measurement tools.

Pre-registration

This review was pre-registered on PROSPERO: CRD42021229192. One deviation was made from the original protocol which was the use of a different quality appraisal tool. Initially we had planned to use the Canadian Effective Public Health Project Practice (EPHPP) Quality Assessment Tool for Quantitative Studies and the Critical Appraisals Skills Programme (CASP) checklist for qualitative studies. The authors instead used a combined mixed methods tool (the Mixed Methods Appraisal Tool; MMAT) for both quantitative and qualitative studies. This was due to the large volume and variation of studies which meant there were benefits to using a single brief quality check tool across all included studies, allowing us to standardise scores across study types. The qualitative studies will be discussed in a separate realist review on key mechanisms of peer education interventions.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

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This research study is funded by the National Institute for Health and Care Research (NIHR) School for Public Health Research (project number SPHR PHPES025). The views and opinions expressed in the paper are those of the authors and do not necessarily reflect those of the NIHR. The funding body played no role in the design, analysis, interpretation or writing of the manuscript.

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Steven Dodd & Mark Limmer

Population Health Sciences, University of Bristol, Bristol, UK

Emily Widnall & Judi Kidger

College of Medicine and Health, University of Exeter, Exeter, UK

Abigail Emma Russell

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK

Esther Louise Curtin

Mental Health Foundation, London, UK

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All authors contributed to the design of the systematic review. SD led on designing the search strategy with input from all co-authors. SD carried out the initial searches across four databases. SD and EW led on retrieving papers and screening abstracts and full papers. EW and SD led on data extraction with support from AR. SD and EW drafted the initial manuscript. All co-authors reviewed the manuscript and approved the final version.

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Dodd, S., Widnall, E., Russell, A.E. et al. School-based peer education interventions to improve health: a global systematic review of effectiveness. BMC Public Health 22 , 2247 (2022). https://doi.org/10.1186/s12889-022-14688-3

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Learning by Teaching Others: a Qualitative Study Exploring the Benefits of Peer Teaching

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This research explores how peer-to-peer teaching, a form of collaborative learning, can enhance student learning in non-studio landscape architecture courses by integrating the learning-by-doing model employed and valued in our curricula and profession. We describe a peer-teaching case study, and use qualitative research analysis to explore students’ perceptions of the method’s impact on their learning. Students reported that the peer teaching experience increased their understanding of the subject matter, enabled them to apply course concepts in new settings, and encouraged them to take initiative and be responsible for their own learning. We suggest that peer teaching is a valuable, even critical, experience for students in a professional education program. As this is a single case study utilizing a relatively new pedagogical approach, particularly as it relates to landscape architecture education, we encourage additional research on applications that explore the broader implications for student learning.

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Collaborative Learning

Collaborative learning can occur peer-to-peer or in larger groups. Peer learning, or peer instruction, is a type of collaborative learning that involves students working in pairs or small groups to discuss concepts or find solutions to problems. Similar to the idea that two or three heads are better than one, educational researchers have found that through peer instruction, students teach each other by addressing misunderstandings and clarifying misconceptions.

Why use collaborative learning?

Research shows that educational experiences that are active, social, contextual, engaging, and student-owned lead to deeper learning. The benefits of collaborative learning include:

• Development of higher-level thinking, oral communication, self-management, and leadership skills.
• Promotion of student-faculty interaction.
• Increase in student retention, self-esteem, and responsibility.
• Exposure to and an increase in understanding of diverse perspectives.
• Preparation for real life social and employment situations.

Considerations for using collaborative learning

• Introduce group or peer work early in the semester to set clear student expectations.
• Establish ground rules   for participation and contributions.
• Plan for each stage of group work.
• Carefully explain to your students how groups or peer discussion will operate and how students will be graded.
• Help students develop the skills they need to succeed, such as using team-building exercises or introducing self-reflection techniques.
• Consider using written contracts.
• Incorporate   self -assessment  and   peer  assessment  for group members to evaluate their own and others' contributions.

Getting started with collaborative learning

Shorter in-class collaborative learning activities generally involve a three-step process. This process can be as short as five minutes, but can be longer, depending on the task at hand.

• Introduce the task. This can be as simple as instructing students to turn to their neighbor to discuss or debate a topic.
• Provide students with enough time to engage with the task. Walk around and address any questions as needed.
• Debrief. Call on a few students to share a summary of their conclusions. Address any misconceptions or clarify any confusing points. Open the floor for questions.

For larger group work projects, here are some strategies to help ensure productive group dynamics:

• Provide opportunities for students to develop rapport and group cohesion through   icebreakers , team-building, and reflection exercises.
• Give students time to create a group work plan allowing them to plan for deadlines and divide up their responsibilities.
• Have students   establish ground rules . Students can create a contract for each member to sign. This contract can include agreed-upon penalties for those who fail to fulfill obligations.
• Assign roles to members of each group and change the roles periodically. For example, one student can be the coordinator, another the note-taker, another the summarizer, and another the planner of next steps.
• Allow students to rate each other’s quality and quantity of contributions. Use these evaluations when giving individual grades, but do not let it weigh heavily on a student's final grade. Communicate clearly how peer assessment will influence grades.
• Check in with groups intermittently but encourage students to handle their own issues before coming to you for assistance.

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Peer Learning: Overview, Benefits, and Models

• Classroom Strategies
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How do K-12 teachers facilitate effective learning? The best teachers do more than just read from a textbook. They understand that there are many different techniques, theories, and teaching models that can give students a well-rounded education that’s foundational to a lifetime of success and continual improvement.

Effective learning happens in many ways. Some students learn well directly from a teacher. Others are skilled independent learners. Yet, one of the most effective active learning techniques is that of peer learning. Put simply, peer learning is when students teach each other. This type of learning aids retention and encourages communication and collaboration. 

Learn more about peer learning and how a teaching degree from WGU can prepare you to make a difference in the classroom.

What Is Peer Learning?

Peer learning is an education method that helps students solidify their knowledge by teaching each other. One student tutoring another in a supervised environment can result in better learning and retention. Why? Because to teach another, one must first fully understand a concept themselves. Verbalizing a concept and sharing the information with a peer serves to reinforce the knowledge gained. 

Peer learning is best supported by other learning strategies, including the Constructivism Learning Theory and the Connectivism Learning Theory . 

Constructivist learning suggests that knowledge is constructed by each individual student. The new concepts they learn are built upon their existing knowledge and beliefs. Constructivism also proposes that learning is an active process and a social activity. These concepts tie in well with peer learning. 

Next, there’s Connectivism. Introduced in 2005 by George Siemens, the Connectivism Learning Theory focuses on technology as a critical component of connected learning. Today’s social networks allow rapid information transfer, but not every piece of information is equally helpful or enriching. Siemens suggests that being able to distinguish between important and unimportant information is vital. Even young students today are connected to the world and to each other through online means. An understanding of connectivism is especially helpful for K-12 teachers in the digital age. 

Why Is Peer Learning Important?

To thrive in school, in the workplace, and in society, individuals must be able to learn from others and work with them to achieve mutual success. Below are even more reasons why peer learning is important.

Teamwork:   Peer learning fosters teamwork, cooperation, patience, and better social skills. In a cooperative peer learning environment, each student’s strengths can serve to complement the group and enhance learning. Becoming skilled at working with and learning from one's peers can start at a young age in the classroom. 

Better Feedback :  Often, students are not able to recognize the gaps in their own knowledge. But when they learn with their peers, they can see new processes for answering questions and come up with creative, collaborative solutions. Importantly, they will carry these new perspectives, as well as a willingness to seek and accept feedback, with them as they progress in their education. 

Supports Diversity:   Peer learning fosters diversity and depth in a student’s knowledge and opinions. Learning from peers of different backgrounds, views, and ethnicities fosters an environment of mutual respect, gratitude, and progress. It’s the differences between students that add a richness to the learning environment. Supporting diversity through peer learning is part of culturally responsive teaching .   

What Are the Benefits of Peer Learning?

It’s hard to number all the benefits of peer learning, but some of them include new perspectives, more social interaction, and deepened personal learning. See more information on these specific areas below.

New Perspectives for Students:   If a student learns exclusively from the teacher, they may only gain one new perspective. Learning from their peers can add numerous helpful perspectives, nuances, and layers to a student’s knowledge. 

Social Interaction Makes Studying Fun:   By nature, humans are social beings. We long to make connections and be part of a group. The added element of social interaction in peer learning can be exciting and enriching. Students who may be hesitant to interact with the teacher may be more willing to open up to their peers.

Teaching Others Helps Students Learn:  Nothing requires you to feel confident in your own knowledge quite like teaching what you know to someone else. As mentioned, peer learning can help students learn and solidify their own knowledge. Effective teaching requires a deeper level of knowledge on a subject.

Peer Learning Drawbacks

While there are many benefits to peer learning, there are also some drawbacks, including distraction and lack of respect for feedback.

Working in Groups Can Be Distracting: Learning from your peers can be exciting. However, especially for younger students, that excitement can lead to distraction. When working with their friends, some students can easily get off track, misbehave, and focus on anything but learning.

Students Might Not Respect the Feedback of Their Peers:  If a teacher gives feedback, the student is more likely to listen carefully. After all, the teacher is the authority in the classroom and the resident expert on the subject being taught. On the other hand, if one’s peer gives them feedback, it’s easier to disregard it.

Peer Learning Models

Effective peer learning can take place through many different models and strategies. See some of the tried-and-true ways to encourage peer learning.

Proctor Model:  In the proctor model, an older or more experienced student teaches a younger or less experienced peer. In an elementary school, this might mean that students from a higher grade level come and teach kindergarteners. It could also entail having a more skilled student within the class teach their classmate.  

Discussion Seminars:  Discussion seminars are more common at the university level. They’re often held after students learn the material through a lecture or a weekly reading. Through these discussions, students deepen their knowledge and gain additional perspectives.

Peer Support Groups: Sometimes referred to as private study groups, peer support groups are student-led gatherings that are generally held outside of class without teacher support. Peers might meet up to study for a test together or complete a group project.

Peer Assessment Schemes:  Peer assessment schemes can be common in writing courses. For instance, an AP English Language teacher might have students read one another’s essays to provide informal feedback. 

Collaborative Projects: Assigning students to work on collaborative projects can serve them well for their future endeavors in the workplace and society. These projects teach collaboration, the importance of combining skills, and the need to meet deadlines.

Cascading Groups: Cascading groups is a learning method by which students are split into groups that get either progressively larger or smaller. For instance, students might be encouraged to learn about a distinct topic on their own and then share it with a partner. That partnership would then share their knowledge with another partnership and so forth.

Mentoring: A mentor is someone who has experience in a certain area. They guide a student, training them and teaching them the lessons they once had to learn. Peer tutoring is a form of mentoring. Sometimes students who require extra support are assigned a personal peer mentor who works one-on-one with them to help them succeed.

Reciprocal Teaching: In reciprocal teaching, students must develop the skills of questioning, predicting, summarizing, and clarifying. They teach one another using these techniques. They serve to form a sort of scaffolding for peer-led learning.

Jigsaw Method: In the jigsaw method of peer learning, students are split into groups, with each group given a different topic to study. Then, one student from each group is taken to form a collaborative group where multiple concepts are discussed. If there are eight jigsaw groups, then eight topics will ultimately be discussed in one group.

Discover More Learning Models with WGU

Peer learning is an effective way to facilitate deep learning. It also lends itself to many different approaches. The power of a classroom where students come together is that of collaborative learning. Teachers who implement peer learning strategies in their classroom may see higher levels of student performance, satisfaction, and overall engagement.

If you’re ready to learn new teaching methods and prepare to make a difference in the classroom, check out the WGU School of Education . The programs help teachers learn up-to-date teaching methods for the modern learning environment.  

Ready to Start Your Journey?

HEALTH & NURSING

Recommended Articles

Take a look at other articles from WGU. Our articles feature information on a wide variety of subjects, written with the help of subject matter experts and researchers who are well-versed in their industries. This allows us to provide articles with interesting, relevant, and accurate information. 

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• v.42(11); 2013 Nov

The Peer Education Approach in Adolescents- Narrative Review Article

Fatemeh abdi.

1. Students Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Masoumeh Simbar

2. Dept. of Reproductive Health, Faculty of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Adolescence is an important stage of human life span, which crucial developmental processes occur. Since peers play a critical role in the psychosocial development of most adolescents, peer education is currently considered as a health promotion strategy in adolescents. Peer education is defined as a system of delivering knowledge that improves social learning and provides psychosocial support. As identifying the outcomes of different educational approaches will be beneficial in choosing the most effective programs for training adolescents, the present article reviewed the impact of the peer education approach on adolescents. In this review, databases such as PubMed, EMBASE, ISI, and Iranian databases, from 1999 to 2013, were searched using a number of keywords. Peer education is an effective tool for promoting healthy behaviors among adolescents. The development of this social process depends on the settings, context, and the values and expectations of the participants. Therefore, designing such programs requires proper preparation, training, supervision, and evaluation.

Introduction

Adolescence, an important stage of human life ( 1 ), involves crucial developmental processes ( 2 ) through which a person goes over to adulthood from childhood ( 3 ). These changes may potentially pose pressure on adolescents ( 4 ) and cause multidimensional problems necessitating a holistic approach. The majority of adolescents experience some level of emotional, behavioral, and social difficulties ( 2 , 5 ). On the other hand, adolescents naturally tend to resist any dominant source of authority such as parents and prefer to socialize more with their peers than with their families ( 4 , 6 ). Research suggests that adolescents are more likely to modify their behaviors and attitudes if they receive health messages from peers who face similar concerns and pressures ( 7 ).

A peer is a person whose has equal standing with another as in age, background, social status, and interests. Peers play a critical role in the psychosocial development of most adolescents. They, in fact, provide opportunities for personal relationships, social behaviors, and a sense of belonging. Therefore, peer education is considered as a health promotion strategy in adolescents ( 8 , 9 ).

Adolescents comprise 20% of the world population and live mostly (85%) in developing communities ( 10 ). Moreover, about a quarter (25.1%) of Iran’s population belongs to the age group of 11-14 years old. Unfortunately, more than half of this huge population does not develop healthy life skills. Since peers can effect on each other’s feelings of health, habits, and behaviors ( 11 , 12 ), various studies have indicated peer education to be more effective than traditional methods (e.g. training provision by teachers) when sensitive subjects like sexual relationships and substance abuse are concerned ( 12 ). Studies have also evaluated peer education as a mechanism to promote behavior and attitude modification ( 13 ). Peer education has been shown beneficial in improving knowledge and the intention to change behavior in human immunodeficiency virus infection/acquired immunodeficiency syndrome (HIV/AIDS) prevention programs among high school students ( 14 ). It is, hence, a system of delivering knowledge that promotes social skills ( 15 ).

As the important role of peers in quality of life of adolescents warrants further research on peer education, the present study reviewed the peer education approach in adolescents. Knowing the outcomes of different educational approaches will help choose the most effective programs in training adolescents.

Searching Method

In this narrative literature review, databases of PubMed, EMBASE, ISI, and Iranian databases including IranMedex and SID were searched to review the relevant literature. A comprehensive search was performed through PubMed and Google scholar using the combinations of the following keywords: adolescent, peer, peer group, peer education, peer intervention, peer educator. All published data from 1999 to 2013 were then included in this review.

Results and Discussion

Peer education (pe).

Peer education is known as sharing of information and experiences among individuals with something in common ( 16 , 17 ). It aims to assist young people in developing the knowledge, attitudes, and skills that are necessary for positive behavior modification through the establishment of accessible and inexpensive preventive and psychosocial support. Peer education programs mainly focus on harm reduction information, prevention, and early intervention. The youth have accepted peer education as a preferred strategy to reach unreachable populations such as sex workers and to approach and discuss topics that are insufficiently addressed or considered taboo within other contexts ( 17 – 19 ). Sexual health peer education has been found to significantly increase the use of modern contraceptives and methods to prevent sexually transmitted infections (STIs) ( 20 ). A systematic review of interventions to prevent the spread of STIs among young people indicated that peer-led interventions were more accepted, and thus more successful in improving sexual knowledge, than teacher-led interventions ( 21 ).

Different methods of peer education have been proposed. The audience can be reached through a variety of interactive strategies such as small group presentations, role plays, or games ( 15 ). Formal delivery of peer education in highly structured settings such as class teaching in schools is also possible. Other methods may include informal tutoring in unstructured settings during the course of everyday interactions or individual discussions and counseling. Various methods are adopted based on the intended outcomes of the project (e.g. communicating information, behavior modifi-cation, or development of skills) ( 22 ).

Peer educator

A peer educator is a member of a peer group that receives special training and information and tries to sustain positive behavior change among the group members ( 18 , 23 ). The levels of trust and comfort between the peer educator and his/her peer group will facilitate more open discussions on sensitive topics ( 24 ). Peer educators can in fact act as role models of attitude and behavior for their peers ( 25 ).

Peer educators should receive adequate training enabling them to understand the purpose of the program, be good listeners, provide encouragement, motivation, and support healthy decisions and behaviors. They should also know other sources of information and counseling so as to refer other peers to appropriate help ( 5 ).

More attention to the specific personal characteristics, for instance leadership skills of peer educators is important ( 26 ). Identification and selection of peer educators with sufficient confidence, technical competency, compassion, and communication skills who are accepted by other peers are crucial aspects of program success ( 27 ). Borgia et al. stated that peer educator selection is a crucial and delicate point in the efficacy of peer education interventions ( 28 ).

Peer educators should allow that emotions, feelings, attitudes, and beliefs to be expressed and discussed openly ( 29 ). They should also be aware of the usefulness of jokes and humor in establishing relationships with the target group ( 23 ). Moreover, initiation of trainings at early ages of adolescence will maintain and consolidate a healthy function. Nevertheless, educational outcomes will widely depend on the relationship with peers ( 29 ). Sharing socioeconomic conditions with program participants, peer educators are able to make educational material accessible and credible to participants and hence increase the efficacy of a peer education program ( 15 ). A variety of financial, intellectual, and emotional reasons leads to the attractiveness of youth peer education. In addition, the participation of unpaid volunteers makes peer education inexpensive ( 30 ).

Theories of Peer Education

As a broadly accepted effective behavioral change strategy, peer education relies on several well-known behavioral theories:

The social learning theory asserts that some individuals function as role models of human behavior due to their aptitude for stimulating behavior changes in other individuals ( 31 ).

The theory of reasoned action states that a person’s perception of social norms or beliefs about what people, who are important to the individual, do or think about a particular behavior can affect behavior change ( 32 ). In other words, people’s attitudes toward changing a behavior is strongly influenced by their view of its positive or negative consequences and what their peer educators would think about it ( 7 ).

The diffusion of innovation theory considers an innovation as new information, an attitude, a belief, or a practice that is perceived as novel by an individual and that can be diffused to a particular group. This theory employs ‘opinion leaders’ to propagate information, influence group norms, and finally act as change agents within the population they belong to ( 27 ).

The theory of participatory education has also played a key role in the development of peer education. According to participatory or empo-werment models of education, powerlessness at the community or group level along with socioeconomic conditions caused by the lack of power are major risk factors for poor health ( 7 ).

The social inoculation theory postulates that people may adopt unhealthy behaviors under social pressures ( 33 ).

Other available theories (the role theory, health belief model, and transtheoretical model) imply partnership, ownership, empowerment, and reinforcement as the critical principles of peer education.

Peer education program

Peer education programs have been used as public health strategies to promote various positive health behaviors such as smoking cessation and vio-lence, substance abuse, and HIV/AIDS prevention. Since such programs seek to produce behavior change in a peer group (the unit of change) by the help of a peer educator or facilitator (the agent of change) ( 34 ), they may simultaneously empower the educator and the target group by creating a sense of collective action. In non-hierarchical structure, the management structure of peer education comprises two distinct parallel roles ( 15 ), i.e. peer educators and adult support workers. While the first group are the “bosses” and control the direction of the program, the second group (also known as program facilitators) guide and support the peer educators throughout the process ( 35 , 36 ) ( Fig. 1 ). Peer education programs require careful planning ( 37 ), identification and training of peer educators, and follow-up evaluations.

Management model of peer education program

Peer educator training, as the most important component of a peer education program, involves:

• An introductory meeting to familiarize the peer educators with the concept of peer education and the training needs;
• Training the educators with communication, facilitation, research, and evaluation skills;
• Providing opportunities for personal development;
• Providing access to formal knowledge ( 13 ).

The period between the training and the delivery of knowledge to the target group should not be longer than a few weeks ( 23 ). After the initial training, peer educators will undoubtedly require continuous supervision and opportunities to give feedback about the program ( 38 ).

Peer education strategies engage all five senses and can also improve the participants’ power of thinking and innovation. In fact, the participants will take part in all stages of the program including planning, implementation, and evaluation ( 12 ). Studies with more rigorous designs reported peer education programs to increase knowledge and help-seeking about STIs and condom use to prevent HIV infection and to delay first sexual experience ( 39 ). Youth peer education programs, whose numbers are growing throughout the world, are extensively used to promote reproductive health. These programs require appropriate technical frameworks, particularly training and supervision, to satisfy the needs of the young and adolescent volunteers ( 30 ).

The general approach to peer observation was first described in Bell’s model ( Fig. 2 ) which involved pre-observation meeting, observation, post-observation feedback, and reflection ( 40 ).

Peer observation process (Bell’s model)

Peer education intervention

Peer education interventions are commonly employed to prevent HIV and other STI ( 41 ). By selecting and training peer educators, peer education interventions try to increase the peer group’s knowledge and stimulate behavior change among them. More cost-effective than programs that incorporate highly trained professionals; have been applied in various target populations including the youth, commercial sex workers, and injection drug abusers in developing countries ( 42 , 43 ). A study in 10 African, Asian, and Latin American countries indicated that peer education interventions can be effective strategies in prevention of risky behaviors and increasing self-esteem and psychosocial aspects ( 12 ). According to Merakou and Kourea-Kremastinou, peer education interventions can affect the youth’s behavior about self-protection from HIV infection ( 25 ). Similarly, a systematic review suggested peer learning as an efficient method in improving the standing of health science students in clinical placements ( 44 ).

Peer education interventions can be used in multiple domains including physical activity, mental health, nutrition, HIV/AIDS and STIs, tobacco and alcohol use, and drug abuse. Visser believed that peer education can postpone the onset of sexual activity and hence play a critical role in the prevention of HIV/AIDS among adolescents ( 45 ). Besides, other researchers have identified school-based HIV education as the basis of youth-focused HIV prevention interventions ( 46 ). Studies have found the mean score of knowledge regarding breast self-examination to increase in students who receive peer education about breast cancer prevention through the learning of self-examination ( 29 , 47 ). Rhee et al. showed that a peer-led asthma self-management program can be successfully implemented and absorbed by adolescent learners ( 48 ). In addition, the peer education program designed by Karayurt et al. could increase knowledge about breast cancer, enhance the performance of breast self-examination, and improve perceived health beliefs ( 49 ). Peer mentorship has also been broadly and successfully used to treat alcohol and substance abuse disorders ( 50 ). Finally, some researchers believe that although school-based behavioral interventions which teach sexual health skills can improve the youth’s levels of knowledge and self-efficacy, they may not have great impacts on sexual behavior ( 51 , 52 ).

We briefly reviewed the impacts of the peer education approach on adolescents. Peer education, which is considered as an effective tool in promoting healthy behaviors among adolescents ( 53 ), is a social process affected by the settings, organizational context, key personnel, and the values and expectations of the participants. It requires proper preparation, training, supervision, and evaluation. We found various studies suggesting the success of different peer education programs. We hope that this paper will serve as a starting point in the application of this method in health promotion.

Ethical Considerations

Ethical issues including plagiarism, data falsification, double publication or submission have been completely observed by the authors.

Acknowledgements

We would like to express our appreciation to everyone involved in this project. The authors declare that there is no conflict of interest.

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Augmented reality meets Peer instruction †

First published on 19th April 2024

Peer Instruction (PI), a student-centred teaching method, engages students during class through structured, frequent questioning, facilitated by classroom response systems. The central feature of PI is the ConcepTest, a question designed to help resolve student misconceptions around the subject content. Within our coordination chemistry PI session, we provide students two opportunities to answer each question – once after a round of individual reflection, and then again after a round of augmented reality (AR)-supported peer discussion. The second round provides students with the opportunity to “switch” their original response to a different answer. The percentage of right answers typically increase after peer discussion: most students who answer incorrectly in the individual round switch to the correct answer after the peer discussion. For the six questions posed, we analysed students’ discussions, in addition to their interactions with our AR tool. Furthermore, we analyse students’ self-efficacy, and how this, in addition to factors such as ConcepTest difficulty influence response switching. For this study, we found that students are more likely to switch their responses for more difficult questions, as measured using the approach of Item Response Theory. Students with high pre-session self-efficacy switched from right-to-wrong ( p < 0.05) and wrong-to-different wrong less often, and switched from wrong-to-right more often than students with low self-efficacy. Students with a low assessment of their problem solving and science communication abilities were significantly more likely to switch their responses from right to wrong than students with a high assessment of those abilities. Analysis of dialogues revealed evidence of the activation of knowledge elements and control structures.

Introduction

Within a PI session, time is organised by a sequence of questioning, interactive discussion, and explanation ( Schell and Mazur, 2015 ). The element of peer discussion is arguably the most recognizable feature of the PI model, and works to maximise both the amount of time that students think about key concepts, in addition to the time students spend engaging in self-monitoring of their understanding of the discipline. As students explain their understanding of a ConcepTest, often an epiphany occurs, which takes them further than their individual thinking processes. The body of research on PI, primarily from physics education researchers indicates that PI significantly improves student learning outcomes, such as conceptual understanding and problem-solving ability. As such, implementation of the process outlined in Fig. 1 has provided compelling evidence that PI is associated with substantial improvements in students’ ability to solve conceptual and quantitative problems ( Mazur, 1997 ; Vickrey et al. , 2015 ).

Theoretical approach

Self-efficacy was first developed as an integral part of social cognitive theory (SCT), an agentic perspective to human development, adaptation, and change. As there are different social cognitive theoretical perspectives, the focus of this study is limited to the social cognitive theory proposed by Bandura (1986, 1997, 2001) . SCT posits that learning occurs in a social context with a dynamic and reciprocal interaction of the person, environment, and behaviour ( Bandura, 1986 ). Within this triadic reciprocality, each set of influences on human functioning affects the others, and is in turn affected by them. The pivotal feature of SCT is the importance of social influence, and its emphasis on external and internal social reinforcement.

The construct of self-efficacy within SCT refers to the level of a person's confidence in his or her ability to successfully perform an action. Thus, to support the establishment of perseverance and self-regulated learning within our PI environment, students were randomly organised into groups of 2–3 individuals. This allowed students to support one another, whilst making their thinking explicit through discussion. Social cognitive theorists emphasize that learning is most effective when peers learn from others, who are both similar to themselves, and display high levels of self-efficacy ( Schunk, 2005 ). For example, students who feel competent about performing well in mathematics (high self-efficacy) are apt to engage in effective learning strategies that will benefit their learning (behavioural), as well as demonstrating greater persistence ( Schunk and DiBenedetto, 2016 ; Schunk and Usher, 2019 ).

Meta-Analyses have been conducted on studies with diverse experimental and analytical methodologies applied across diverse spheres of functioning ( Boyer et al. , 2000 ; Moritz et al. , 2000 ; Stajkovic et al. , 2009 ). The accumulated evidence confirms that efficacy beliefs contribute significantly to the quality of human functioning. Cognitively, our intention was that the 3D perspective afforded by ChemFord would help manage working memory load, in addition to providing insight into the structure.

ConcepTest development

Smith et al. (2009) report that students improve the most when asked difficult questions during PI, a trend that was also found by Porter et al. (2011) . In addition, lower learning gains have also been reported for instructors implementing easier ConcepTests ( Rao and DiCarlo, 2000 ; Knight et al. , 2013 ). Hence, empirical evidence suggests that the benefits of PI, especially the effectiveness of student discussions, is very likely influenced by the difficulty of the question posed. In their longitudinal analysis, Crouch and Mazur (2001) found that substantial learning gains following voting in round 2 (post-discussion) occurred when the voting in round 1 was correct for 35–70% of the student base. Below 35%, the concept may still be too alien, requiring the provision of further description ( Simon et al. , 2010 ).

As such, we developed six ConcepTests to probe students’ comprehension of organometallic chemistry concepts (see ESI, † for details of ConcepTests). Throughout the development process, internal validation with experts in the field of inorganic chemistry at UEA was carried out to ensure student attention was focused towards critical concepts key to addressing specific learning goals. To satisfy these requirements, we used the following six criteria for each ConcepTest ( Newbury, 2013 ):

i. Clarity. Students should waste no cognitive resources understanding the requirements of the question.

ii. Context. The question should be appropriate for the learning material.

iii. Learning outcome. The question should allow students to demonstrate that they grasp the concept.

iv. Distractors. Distractors should be plausible solutions to the question.

v. Difficulty. The question should not be too easy or too hard.

vi. Stimulates thoughtful discussion. The question should engage students, and incentivise thoughtful discussion.

Regarding the implementation of AR technology into PI, a very limited number of previous works are reported ( Ravna et al. , 2022 ; Themelis, 2022 ). Although VR is commonly preferred for multiuser collaboration, the role of AR for collaboration is increasing. As such, throughout ConcepTest development, we focused on how the affordances of AR could be leveraged to promote important discussion points. In Fig. 2 , we present our first ConcepTest. To answer the first ConcepTest correctly, there are three conceptual points which, fundamentally, students must understand:

i. Firstly, students must recognise how the axial and equatorial aqua ligands are situated around the chromium metal atom.

ii. Secondly, students must be able to comprehend the shapes and orientations of the five d-orbitals of the chromium metal atom.

iii. Lastly, students must be able to comprehend the consequence of ligand and chromium d-orbital interactions along the three Cartesian axis ( x , y , and z ).

ChemFord affords users the ability to instantiate interactable three-dimensional (3D) representations of the octahedral coordination sphere of the chromium complex, in addition to the 5d-orbitals of the chromium metal atom, to direct peer discussion towards these three conceptual points.

Peer instruction self-efficacy instrument (PISE)

The structure of our PI session is outlined in Fig. 3 . Student response (voting) data for our six ConcepTests were collected through TurningPoint (2022), an audience response system in which students submitted their responses using mobile phones. In parallel, all students’ PI discussions, alongside their interactions with ChemFord, were captured using audio- and screen-recording software installed on a suite of iPads distributed to student groups. This allowed the study of learning from two perspectives:

i. Probing the conceptual understanding of students through the collection of voting data.

ii. Studying the process of conceptual development during AR-supported peer discussion, through recorded conversations.

The research questions investigated were as follows:

Research question 1

Research question 2, research question 3.

Ethical clearance was obtained under the regulations of UEA's School of Science Research Ethics Committee, a sub-committee of the UEA Research Ethics Committee. Participants were informed that their involvement within any aspect of this research was completely voluntary. In addition, Participants were made aware of their right to withdraw from the study, at any part of the research phase, without declaring a reason. Throughout the research period, participants were assured of data anonymity and confidentiality. Identifying information was irrevocably stripped from data documentation, and study codes utilized in their place. All information was stored securely and only accessible to the researcher.

Students are often unaware that they are engaging in a particular epistemic game. As such, the focus of this qualitative analysis is the interaction between students’ AR experiences and the activation of these control structures. We start by examining ConcepTests 2 and 3, as these both showed significant intragroup improvement and high PI efficiency. ConcepTest 2 relates to the identification of a linear complex's crystal field splitting diagram, whereas ConcepTest 3 concerns the geometric [Jahn–Teller] distortion of a non-linear molecular system. These ConcepTests are examples of a productive dialogue in which a change in student thinking, and voting response, are evident.

Throughout discussions relating to ConcepTests 2 and 3, evidence of the Pictorial Analysis epistemic game was apparent ( Fig. 4 ). In the Pictorial Analysis Game, students generate an external spatial representation that specifies the relationship between influences in a problem statement ( Tuminaro and Redish, 2007 ). The epistemic form being a representation that the student generates to guide their inquiry.

The discussion presented ( Table 1 ) was between a pair of students, of which one voted correctly on ConcepTest 2, and the other incorrectly. The second comment from group member (GM) 1 is the first activating statement in this dialogue. GM2 explains the interaction between the d-orbitals of the gold atom, and the two chlorido ligands. Amidst choosing a new representation on the virtual molecule, it is clear that there has been a change in thinking for GM1. This can be interpreted as an activating event, and evidence of the lowest level of resource activating (activation of a knowledge element). As the dialogue progresses, it is clear that GM1 has understood the concept, and is now able to use their knowledge to contribute to the discussion. Combining the video recording, representing the students’ AR experience, with the audio recording of the peer discussion, gave a clear indication of the positive impact that using AR had on supporting students’ thinking and knowledge construction.

As the session progressed to ConcepTest 3, employment of the Pictorial Analysis epistemic game was, again, evident from students’ discussions. The example outlined in Table 2 is a group of three students, in which a single member answered correctly during round 1, and the other two incorrectly. The interplay that is of particular interest is Section 2. GM2 is able to warrant proof of their claim through the use of ChemFord. The distortion of the represented octahedral complex is used as a means of activating the thinking of GM3. GM3 demonstrates activation of a knowledge structure, specifically, support knowledge building. The thinking of GM1 has not changed. Thus, GM2, building on their previous statement, thinks of a new way to persuade GM1 regarding the stabilisation of the z -components. Using ChemFord, GM2 is able to introduce the metal d-orbitals to support their conceptual story. Subsequently, GM1's thinking is activated, and repeats the statement that altered their perspective, reaching the correct conclusion. Both ConcepTests provide evidence of resource activation by means of successful AR-supported dialogue. All three students responded correctly on the second vote.

ConcepTest 1 is an interesting case. Although quantitative response data suggests that a majority of students answered correctly, qualitative data suggests that students may not have demonstrated a clear understanding at the start of the dialogue. As such, there are points of interest in terms of resource activation through utilisation of AR. Below, we present an example of a dialogue from a pair of students for ConcepTest 1. The AR representations employed are shown in Fig. 5 . Both answered correctly before and after discussion:

Lastly, we provide an example from ConcepTest 5. Our data shows that this ConcepTest had the lowest correct response rate, as well as the lowest theoretical (and measured) PI efficiency. Furthermore, it was the only ConcepTest where the correct response rate of students was lower after discussion. Hence, it is important to understand the interactions present throughout discussions of ConcepTest 5, and how these differ from the successful dialogues presented in ConcepTests 1–3.

ConcepTest 5 asked students to use their understanding of pi backbonding to identify which carbonyl ligands ( Fig. 6 ) are most susceptible to electrophilic attack. In all of the transcripts, a common theme was whether or not students could recognise that the two bridging carbonyl ligands are equivalent.

For ConcepTest 5, we also provided representations of the π and π* molecular orbitals of the ligands, in addition to the iron atom d-orbitals in the hope of initiating discussion of electron backdonation. This was noted in some dialogues, in which students responded correctly during round 2:

Several dialogues for ConcepTest 5 provided examples of unproductive discussion, in which little conceptual chemistry was used. A reason for this may be that students were not able to retrieve the required knowledge elements to respond correctly, or that the AR experience did not manage to support resource activation. For group dialogues where the AR virtual objects were not referenced, or used as a driver for supporting the discussion, we found a greater number of incorrect responses after round 2. Evidence of the Recursive Plug-and-Chug epistemic game ( Fig. 7 ) was also observed within ConcepTest 5 dialogues (not optimal). In the Recursive Plug-and-Chug epistemic game, students plug ideas into a problem situation and churn out answers without conceptually understanding the implications of their solution. Evidence of dialogue similar to that expected of Recursive Plug-and-Chug epistemic game was also observed in ConcepTests 4 and 6, but not in ConcepTests 1–3.

ConcepTest difficulty

We employed PI efficiency ( η ) calculations, defined with the help of Hake's standardised gain ( Hake, 1998 ), to examine the effectiveness of each ConcepTest. The proportion of correct answers before, and after, the discussion is denoted by N b and N a respectively. While Hake's gain represents individual learning gain, PI efficiency is considered to reflect the ease of understanding gained through PI ( Table 4 ). The collected response data from our ConcepTests was found to be normally distributed. Hence, we conducted paired-samples t -tests, alongside analysis of effect size, for intragroup comparisons. The theoretical value of N a is expressed as a function of N b ( Nitta, 2010 ), with the theoretical value of η = N b . For this study, the average difference between the measured, and theoretical values of η = 0.061, similar to a value of 0.062 recorded by ( Nitta et al. , 2014 ) when measuring the effectiveness of PI using the Force Concept Inventory. The proportion of correct responses during independent voting in round 1 ranged from 0.290–0.897. The ideal range is reported to be from 0.35–0.70 ( Crouch and Mazur, 2001 ). For ConcepTests 4–6, where correct independent response rates lie at the lower end of this range, students were likely to have had ineffective discussions during round 2. As such, the value of η observed is low.

The normalised proportion of correct responses before, and after, the discussion phase of each ConcepTest is shown in Fig. 8 . We observed statistically significant improvement for correct response rates between the first and second round of voting on ConcepTests 2 and 3. For ConcepTests 1 and 4, this improvement was approaching significance, with the difference between groups greater than 0.2 standard deviations.

Response switching

When switching is measured, it is important to ensure that the data is not confounded with the frequency of correct (or incorrect) responses in round 1 ( Miller et al. , 2015 ). Normalising our response data with respect to students’ answers in round 1 provides an adjusted measure of switching, independent of how many times a student was correct, or incorrect, in round 1.

Coupling these normalised values with the output of our 2PL IRT model allows us to examine switching as a function of ConcepTest difficulty ( Fig. 9 ). A Pearson's correlation showed a strong, positive correlation, r = 0.910, between response switching and ConcepTest difficulty which was statistically significant, p = 0.012. With increasing ConcepTest difficulty, students are more likely to switch their answers from right-to-wrong ( r = 0.754, p = 0.084), and wrong-to-different wrong ( r = 0.829, p = 0.042). In addition, students are less likely to switch their answers from wrong-to-right ( r = −0.771, p = 0.072). A finding consistent with previous studies ( Miller et al. , 2015 ).

It is important for instructors to understand that they have some control over the measure of response switching that occurs throughout PI via the difficulty of the ConcepTests posed. Within our session, we attempted to scaffold this by posing easier ConcepTests first, subsequently building up to more difficult ConcepTests. Research has shown that prefacing more difficult problems with a sequence of related, but more basic conceptual questions, helps students answer harder problems ( Ding et al. , 2011 ). Cognitively, presenting easier questions prior to difficult questions may help students break down concepts into smaller, more manageable chunks when questioning the same concept. As ConcepTests often require students to apply conceptual understanding in new contexts, it is possible that scaffolding difficult ConcepTests may assist with positive switching transitions. A future study of ConcepTest response patterns to a series of scaffolded discussion points would prove interesting in providing further insight into the relationship between switching and ConcepTest difficulty.

Reported self-efficacy

Students with higher pre-session self-efficacy negatively switched less often, and positively switched more often than students with low self-efficacy. As we did not administer a pre-test assessment, we are unable to control for covariates such as prior knowledge, but previous work has indicated that self-efficacy may be more predictive of switching than incoming knowledge ( Zajacova et al. , 2005 ). In addition, students with high self-efficacy positively switched their responses more often than students with low self-efficacy.

Students’ responses to two individual items on the PISE moderately correlated with not switching responses. These statements are: “I usually don’t worry about my ability to solve chemistry problems” , ( p < 0.01); and “I know how to explain my answers to organometallic chemistry questions in a way that helps others understand my answer” , ( p < 0.01). In contrast, these two same items strongly negatively correlated ( p < 0.01) with negatively switching responses. For item 10, students who either disagreed or strongly disagreed negatively switched significantly more than students who agreed or strongly agreed ( p < 0.001). This difference was also observed for item 16 ( p = 0.01). Students with a low assessment of their problem solving and science communication abilities are significantly more likely to negatively switch their responses than students with a high assessment of those abilities.

Following our PI session, median Likert scores on the PISE instrument improved on the following items: “When I come across a tough chemistry problem, I work at it until I solve it” (neutral to agree); “I like hearing about questions that other students have about chemistry” (neutral to agree); “I can communicate science effectively” (neutral to agree, p = 0.04); and “I can communicate chemistry effectively” (neutral to agree, p = 0.025).

Study limitations

Conclusions.

Moreover, we examined the relationship between response switching and reported self-efficacy. Students reporting higher measures of self-efficacy displayed lower levels of switching in a negative direction. Students with a low assessment of their problem solving and science communication abilities were significantly more likely to switch their responses from right to wrong than students with a high assessment of those abilities. Through qualitative analysis, we have provided evidence of Pictorial Analysis through AR-supported PI discussions. Where calculated peer efficiency values for ConcepTests were lower, this was less apparent, with Recursive Plug-and-Chug being the commonly observed control structure. It would be interesting to see what would happen if they were asked the same questions again at a later date – retention. Or a third round of the same question to see how many make 3 different choices.

Conflicts of interest

Acknowledgements, notes and references.

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