A Meta-Analysis of Ten Learning Techniques

The purpose of the current study was twofold: to provide empirical estimates of the effectiveness of the 10 learning techniques, and second, to empirically evaluate a range of their potential moderators. The major conclusion from the meta-analysis is a confirmation of the major findings in Dunlosky et al. (2013a). They rated the effects by High, Moderate, and Low and there was much correspondence between their ratings and the actual effect-sizes: High in the meta-analysis was > 0.70, Moderate between 0.54 and 0.69, and Low < 0.53. This meta-analysis, however, shows the arbitrariness of these ratings, as some of the low effects were very close estimates to the moderate. mnemonics, re-reading and interleaved practice were all within 0.06 of the moderate category and these techniques may have similar importance to those Dunlosky et al. (2013a) classified as Moderate. Certainly they should not be dismissed as ineffective. Even the lowest learning techniques (Underlining and Summarization (both d = 0.44) are sufficiently effective to be included in a student’s toolbox of techniques.

The rating method into High, Medium, and Low was matched by the findings of the meta-analysis, but Table 2 shows the usual difficulties of such arbitrary (but not capricious) cut scores. Mnemonics (d = 0.50) is close to Self-Explanation (d = 0.54), although there is a clear separation between Moderate (Elaborative Interrogation d = 0.56) and Practice Testing (d = 0.74). All have a sufficient positive effect to consider by students choosing learning techniques, and it may be that there is a more optimal stage during the learning process to choose the high techniques related to consolidating learning, and the low techniques related to first encountering new material and ideas. It may also be that techniques are affected by whether the tasks are more relevant to memory vs. those that are relevant to comprehension. Many of the techniques in the authors’ list of 10 are more related to the former than the latter.

The technique with the lowest overall effect was Summarization. Dunlosky et al. (2013a) note that it is difficult to draw general conclusions about its efficacy, it is likely a family of techniques, and should not be confused with mere copying. They noted that it is easy to learn and use, training typically helps improve the effect (but such training may need to be extensive), but suggest other techniques might better serve the students. In their other article (Dunlosky et al., 2013b), the authors classified Summarization as among the “less useful techniques” that “have not fared so well when considered with an eye toward effectiveness” (p. 19). They also noted that a critical moderator for the effectiveness of all techniques is the student’s motivation to use them correctly. This meta-analysis shows that Summarization, while among the less effective of the 10 under review, still has a sufficiently high impact to be considered worthwhile in the student’s arsenal of learning techniques, and with training could be among the more easier to use techniques.

One of the sobering aspects of this meta-analysis is the finding that the majority of studies are based on Surface learning of factual, academic content, measure learning almost immediately after the technique has been used, and only measure Near transfer. This limits the generalisability of the Dunlosky et al. (2013a) review and this meta-analysis and there may well be different learning techniques that optimise deeper learning, non-academic learning, or more intensive learning that requires longer retention periods and Far transfer. The verdict is still out on the effectiveness and identification of the optimal techniques in these latter conditions. It should be noted, however, that this may be not only a criticism of the current research on learning techniques but could well be the same criticism of student experiences in most classrooms. Too many modern classrooms are still dominated by a preponderance of surface learning, teachers asking low level questions demanding content answers, and assessments privileging surface knowledge (Tyack & Cuban, 1995). Thus the 10 techniques may remain optimal for many current classrooms.

The implication for teachers is not that these learning techniques should be implemented as stand-alone “learning interventions” or fostered through study skills courses. They can be used, however, within a teaching process to maximise the surface and deeper outcomes of a series of lessons. For example, Practice Testing is among the top two techniques but it would be a mistake to then make claims that there should be more testing, especially high-stakes testing! Dunlosky et al. (2013a) concluded that more Practice Testing is better, should be spaced not massed, works with all ages, levels of ability, and across all levels of cognitive complexity. A major moderator is whether the practice tests are accompanied by feedback or not. “The advantage of Practice Testing with feedback over restudy is extremely robust. Practice Testing with feedback also consistently outperforms Practice Testing alone” (p. 35). If students continue to practice wrong answers, errors or misconceptions, then these will be successfully learnt and become high-confidence errors; hence the power of feedback. It is not the frequency of testing that matters, but the skill in using practice testing to learn and consolidate knowledge and ideas.

There are still many unanswered questions that need further attention. First, there is a need to develop a more overarching model of learning techniques to situate these 10 and the many other learning techniques. For example, we have developed a model that argues that various learning techniques can be optimised at certain phases of learning from Surface to Deep to Transfer, from acquiring and consolidating knowledge and understanding, and involves three inputs and outputs -knowing, dispositions, and motivations; which we call the skill, the will, and the thrill (Hattie and Donoghue, 2016). Memorisation and Practice Testing, for example, can be shown to be effective in the consolidating of surface knowing but not effective without first acquiring surface knowing. Problem based learning is relatively ineffective for promoting surface but more effective at the deeper understanding, and thus should be optimal after it has been shown students have sufficient surface knowledge to then work through problem based methods.

Second, it was noted above that the preponderance of current studies (and perhaps classrooms) favour Surface and Near learning and care should be taken to not generalise the results of either the original review or our meta-analysis to when Deep and Far learning is desired. Third, it is likely, as the original authors hint, having a toolbox of optimal learning techniques may be most effective, but we suggest that there may need to be a higher sense of self-regulation to know when to use them. Fourth, as the authors noted, it is likely that motivation and emotions are involved in the selection, persistence with, and effectiveness of using the learning techniques, so attention to these matters is imperative for many students. Fifth, given the extensive and robust evidence for the efficacy of these learning techniques, an important avenue of future research may centre on the value in teaching them to both teachers and students. Can these techniques be taught, and if so, how? Need they be taught in the context of specific content? In what ways can the emerging field of educational neuroscience inform these questions?

Third, Dunlosky and Rawson (2015) noted that more recent research may influence some of these findings. For example, he noted that while Interleaving was a “Low” technique, there have since been many studies demonstrating the benefits of Interleaving. For example, Carvalho and Goldstone (2015) found that the way information is ordered impacts learning and that this influence is modulated by the demands of the study task; in particular whether learning is active or passive. Learners in the active study condition tend to look for features that discriminate between categories, and these features are easier to detect when categories frequently alternate (i.e., using Interleaving). Learners in the passive study condition are more likely to look for features that consistently appear within one category’s examples, and these features are easier to detect when categories rarely alternate.

Limitation

A significant limitation of the current study is that no publications beyond 2014 have been meta-analysed. Notwithstanding, the authors are unaware of any more recent study that contradicts any of our findings. Accordingly, the study represents a comprehensive and valid quantitative review of research published between 1929 and 2014, one that complements and underpins Dunlosky et al. (2013a) qualitative review.

Concluding Remarks

The major contribution from Dunlosky et al. (2013a), and supported by the findings from this study is to highlight the relative importance of learning techniques, to identify and allow for the optimal moderators, and clearly more defensible models are needed that take into account the demands of the task, the timing of the intervention, and the role of learning techniques within content domains. Future research that examines the impact of these (and other) moderators, and incorporates findings into theoretical and conceptual models, is much needed.