At the nexus of learning and innovation

Why learning from teaching works - and when not

Learning by teaching is one of the most prevalent contemporary educational practices, but we still don't understand when it works, and why. 

What the research says

Learning by teaching is one of the most prevalent contemporary educational practices, including peer-assisted learning (Healey, Flint, & Harrington, 2014; Stoddard, Rieser, Andersson, & Friman, 2012), peer tutoring (King, 1998), problem-based learning (Leary, Walker, Shelton, & Fitt, 2013),  cooperative classrooms (Slavin, 1995), on-line learning (Jopling, 2012), and computer-supported collaborative learning (Dillenbourg, Baker, Blaye, & O'Malley, 1995).  While the practices of peer teaching and tutoring vary widely (Topping, 2005), there is reliable and representative empirical evidence for benefits to both tutees (or pupils), and tutors. For instance, a meta-analysis of 81 peer tutoring studies in elementary school (Rohrbeck, Ginsburg-Block, Fantuzzo, & Miller, 2003) found a positive effect size of 0.33 for peer tutoring compared to control groups. In another widely cited meta-analysis, coving 65 studies, the effect size for pupils (tutees) was 0.4, and the learning gains for tutors was 0.38 (Cohen, Kulik, & Kulik, 1982). A more recent review (Roscoe & Chi, 2007) estimates the average tutor effect size to be around 0.35, combining tutor and pupil learning.

While there is thus considerable evidence that teaching can enhance one’s own learning as well as that of the pupil(s), it is still largely unclear how the different components of the teaching or tutoring process influence learning. Is it the preparation for teaching (Fiorella & Mayer, 2013), and/or the act of teaching/tutoring (monitoring the pupil, questioning, providing explanations and feedback Roscoe, 2014), and/or other, more generic features of dialogue (such as answering questions Graesser, Person, & Magliano, 1995) that account for the learning? Further, the tutoring processes and related learning processes are affected by the kind of tutoring (e.g., cross-age, reciprocal, technology-mediated Topping, 2005), by the domain (e.g., conceptual versus procedural knowledge, math, science or literacy discipline Roscoe & Chi, 2007), and by various characteristics of tutor and tutee, such as age, domain knowledge, motivational dispositions, and epistemic beliefs.

Solely outcome-oriented research, where typically features of a tutoring program, or characteristics of tutors, are set in relation to learning gains of either pupil, tutors, or both, can on their own not fully clarify why tutoring works, and hence also not how to make it work better. The average learning gains around an effect size of .35 must be considered weak to moderate (Roscoe & Chi, 2007). This is the more astonishing as learning from tutoring ‘ticks all the boxes’: It is active, constructive, socially distributed, and the tutor:pupil dialogue offers plenty of learning opportunities for students in either role. We know, however, that under certain circumstances, the effect can be much stronger, but do not have a good grasp of the reasons for the variation.  An interesting finding is that whereas the amount of tutor training seems to have no noticeable effect on the quality and outcomes of tutoring,  the kind of training or structure is critical (Roscoe & Chi, 2007). This suggests that research on tutor training should experiment with structural variations, instead of looking for general quantitative effects. However, few of such studies exist. The main exception is the training of tutors for problem-based learning, typically in medical education,  (e.g., Leary et al., 2013; Mayo, 1995; Wilkerson, 1997), and training for online tutors (Smet, Keer, Wever, & Valcke, 2010). However, most of these studies are not specific to peer tutors, but to tutors hired as casual staff. Hence, the tutor learning effect is not measured.

Regarding online tutoring, while this has been an important topic, in particular in the distance education research literature (Jopling, 2012), there has been little connection with research (in educational  psychology and the learning sciences) on learning from tutoring, such as consideration for the  knowledge telling bias (Roscoe & Chi, 2007). Instead, research in online learning is either on students’ (in the pupil role) experience,  or on the design and effectiveness of tutor training programs (Price, Richardson, & Jelfs, 2007).

An attempt at an explanatory model

From research in educational psychology and the learning sciences, such as reviewed in (Roscoe & Chi, 2007), I distill the following account of learning from tutoring: Tutor T  and Pupil P form a dynamic system constituted by a knowledge building relation: The more knowledge building opportunities T offers for P (directly, for instance through questions, or indirectly, for instance by posting a problem in the shared environment), the more P will engage in deeper processing and learning. That will, in turn, lead to deeper questions P raises to T, and/or deeper explanations P provides for T for feedback, and/or more interesting problem solutions/mistakes for feedback from T. Which in turn will increase the learning opportunities for T. The two main learning mechanisms underlying knowledge building are accretion—more facts, rules, concepts are learned—and integration: concepts, facts and rules are becoming related to each other and to goals and situational cues; in consequence, explanations become more coherent. For learning to actually occur, a number of conditions must be met: Both T and P must be aware of a learning need, and the learning opportunity (metacognition, and self-regulation (Roscoe, 2014)). Both must be motivated to capitalise on such opportunities, and more generally must be motivated to create them actively. This motivation can be partly due to efficacy beliefs, such as teaching efficacy (Tschannen-Moran & Hoy, 2001), and to epistemic beliefs about learning, teaching, and tutoring (Chinn, Buckland, & Samarapungavan, 2011). Both T and P need to have the cognitive capacity to act on the identified need/learning opportunity. Finally, for learning to occur in T, resources in the tutorial situation, plus T’s not-shared environment, must include the information T needs to close their knowledge gap, accessible in a way that obeys resource constraints, in particular time constraints.

References

Chinn, C.A., Buckland, L., & Samarapungavan, A. (2011). Expanding the Dimensions of Epistemic Cognition: Arguments From Philosophy and Psychology. Educational Psychologist, 46, 141-167.

Cohen, P.A., Kulik, J.A., & Kulik, C-L. (1982). Educational outcomes of peer tutoring: A meta-analysis of findings. American Educational Research Journal, 19, 237-248.

Dillenbourg, P., Baker, M., Blaye, A., & O'Malley, C. (1995). The evolution of research on collaborative learning. In P. Reimann & H. Spada (Eds.), Learning in humans and machines: towards an interdisciplinary learning science (pp. 189-227). Oxford: Elsevier Science Inc.

Fiorella, L., & Mayer, R.E. (2013). The relative benefits of learning by teaching and teaching expectancy. Contemporary Educational Psychology, 38, 281-288.

Graesser, A., Person, N., & Magliano, J. (1995). Collaborative dialogue patterns in naturalistic one-to-one tutoring. Applied Cognitive Psychology, 9, 495-522.

Healey, M., Flint, A., & Harrington, K. (2014). Engagement through partnership: students as partners in learning and teaching in higher education. United Kingdom: The Higher Education Academy.

Jopling, M. (2012). 1:1 online tuition: a review of the literature from a pedagogical perspective. Journal of Computer Assisted Learning, 28, 310-321.

King, Alison. (1998). Transactive Peer Tutoring: Distributing Cognition and Metacognition. Educational Psychology Review, 10, 57-74.

Leary, Heather, Walker, Andrew, Shelton, Brett E, & Fitt, M Harrison. (2013). Exploring the Relationships Between Tutor Background, Tutor Training, and Student Learning: A Problem-based Learning Meta-Analysis. Interdisciplinary Journal of Problem-Based Learning, 7, 3-15.

Mayo, W., Porter, D., Michael, B. & Schwartz, R. W. (1995). Characteristics of the ideal problem-based learning tutor in clinical medicine. Evaluation and the Health Professions. (Vol. 18 (2), pp. 124-136).

Price, L, Richardson, J.T.E, & Jelfs, A. (2007). Face‐to‐face versus online tutoring support in distance education. Studies in Higher Education, 32(1), 1-20.

Rohrbeck, C.A., Ginsburg-Block, M.D., Fantuzzo, J.W., & Miller, T.R. (2003). Peer-assisted learning interventions with elementary school students: A meta-analysis review. Journal of Educational Psychology, 95(2), 240-257.

Roscoe, R.D. (2014). Self-monitoring and knowledge-building in learning by teaching. Instructional Science, 42, 327-351.

Roscoe, R.D., & Chi, M.T.H. (2007). Understanding Tutor Learning: Knowledge-Building and Knowledge-Telling in Peer Tutors' Explanations and Questions. Review of Educational Research, 77, 534-574.

Slavin, R.E. (1995). Cooperative Learning (2nd ed.). Boston: Allyn and Bacon.

Smet, Marijke De, Keer, Hilde Van, Wever, Bram De, & Valcke, Martin. (2010). Cross-age peer tutors in asynchronous discussion groups: Exploring the impact of three types of tutor training on patterns in tutor support and on tutor characteristics. Computers and Education, 54, 1167-1181.

Stoddard, I., Rieser, I., Andersson, S., & Friman, E. (2012). Igniting a Learning Revolution: Student-Run Higher Education for Sustainable Development. Solutions, 3(5), 34-39. http://www.thesolutionsjournal.com/node/1171

Topping, Keith J. (2005). Trends in Peer Learning. Educational Psychology, 25, 631-645.

Tschannen-Moran, M., & Hoy, A. (2001). Teacher efficacy: Capturing and elusive construct. Teaching and teacher education, 17, 783-805.

Wilkerson, L. & Hundert, E. M. (1997). Becoming a Problem-based Tutor: Increasing Self.awareness Through Faculty Development. In D. Boud & G. Feletti (Eds.), The Challenge of Problem-Based Learning (2nd Ed.)(pp.160-172). London: Kogan Page. (pp. 160-172).