Elsevier

Educational Research Review

Volume 11, January 2014, Pages 1-26
Educational Research Review

Review
Effectiveness of learning strategy instruction on academic performance: A meta-analysis

https://doi.org/10.1016/j.edurev.2013.11.002Get rights and content

Highlights

  • We investigate effectiveness of instructed learning strategies through meta-analysis.

  • The most effective strategies are planning and the motivational aspect task value.

  • Instructing metacognitive knowledge enhances effectiveness of strategies.

  • Highest effects are found in writing, lowest effects in reading.

  • Effect sizes are moderated by type of instrument measuring academic performance.

Abstract

In this meta-analysis the results of studies on learning strategy instruction focused on improving self-regulated learning were brought together to determine which specific strategies were the most effective in increasing academic performance. The meta-analysis included 58 studies in primary and secondary education on interventions aimed at improving cognitive, metacognitive, and management strategy skills, as well as motivational aspects and metacognitive knowledge. A total of 95 interventions and 180 effect sizes demonstrated substantial effects in the domains of writing (Hedges’ g = 1.25), science (.73), mathematics (.66) and comprehensive reading (.36). These domains differed in terms of which strategies were the most effective in improving academic performance. However, metacognitive knowledge instruction appeared to be valuable in all of them. Furthermore, it was found that the effects were higher when self-developed tests were used than in the case of intervention-independent tests. Finally, no differential effects were observed for students with different ability levels. To conclude, the authors have listed some implications of their analysis for the educational practice and made some suggestions for further research.

Introduction

Self-regulated learners are students who are capable of supporting their own learning processes by applying domain-appropriate learning strategies (e.g., Boekaerts, 1997, Zimmerman, 1990, Zimmerman, 1994). Self-regulated learning can be described as: “an active, constructive process whereby learners set goals for their learning and then attempt to monitor, regulate and control their cognition, motivation and behavior, guided and constrained by their goals and the contextual features in the environment” (Pintrich, 2000, p. 453). In short: students who are able to self-regulate their learning are active, responsible learners who act purposefully (i.e. use learning strategies) to achieve their academic goals. To this end, they need metacognitive knowledge; knowledge and awareness about their own cognition (Flavell, 1976, Flavell, 1979).

The term ‘metacognition’ is not new in this field, and it is sometimes used interchangeably with self-regulation (Dinsmore, Alexander, & Loughlin, 2008). This is because self-regulation includes the regulation of cognition, which relates to (cognitive) strategies and metacognition. Whereas metacognition is more narrowly defined and refers only to knowledge regarding cognition (Dinsmore et al., 2008), self-regulated learning is broader in a sense, as it comprises both the knowledge and control of not only cognition, but also of motivation.

Students have to acquire knowledge, as it is required to apply learning strategies. Furthermore, in order to become effective self-regulated learners, they have to practice the actual application of this knowledge. However, becoming a self-regulated learner is not an end in itself; it is a means to another end, namely to improve academic performance, as it is demonstrated that self-regulated learners usually do well in education (e.g., Zimmerman, 1990). Research (Dignath et al., 2008, Hattie et al., 1996) has suggested a causal relationship between strategy use and performance: using the proper learning strategies improves academic performance. As not all students spontaneously master the use of learning strategies and certainly not in the most effective way, students require additional instruction of learning strategies.

Learning strategies are defined as “processes (or sequences of processes) that, when matched to the requirements of tasks, facilitate performance” (Pressley, Goodchild, Fleet, & Zajchowski, 1989, p.303). Learning strategies have been repeatedly demonstrated to be positively correlated with academic performance (Alexander et al., 1998, Hattie et al., 1996, Weinstein et al., 2000). They structure the processing of information by facilitating particular activities, such as the planning of learning tasks, goal setting, monitoring the progress toward these goals, making adjustments if needed, and evaluating the learning process and the outcomes (Boekaerts, 1997). The literature has provided a large number of strategies, ranging from very basic re-reading approaches to more complex methods of synthesizing knowledge or drawing conceptual frameworks. These strategies can be categorized in many ways according to various taxonomies and classifications (e.g. Mayer, 2008, Pressley, 2002, Weinstein and Mayer, 1986). In this study the following categories have been defined: cognitive, metacognitive and management strategies.

Cognitive strategies are used to increase the understanding of a certain domain. They refer directly to the use of the information learned and are therefore domain- or even task-specific. Three main subcategories of cognitive strategies can be distinguished: rehearsal, elaboration and organization strategies (Pintrich, Smith, Garcia, & McKeachie, 1991). Rehearsal strategies are used to select and encode information in a verbatim manner. Here the focus is on repeating material in order to facilitate learning or remembering, for example when learning vocabulary or idiom. Elaboration strategies help students store information into their long-term memory by building internal connections between the items to be learned and already existing knowledge. Summarizing and paraphrasing are examples of this type of strategy, which is mostly used in reading. In mathematics an example of the rehearsal strategy is finding similarities between new problems and the ones solved earlier, using comparable calculations. Lastly, organization strategies help students select appropriate information by drawing graphs or pictures and establishing connections among the different elements to create meaningful units of information (Weinstein et al., 2000).

Metacognitive strategies regulate students’ cognition by activating relevant cognitive approaches. As metacognitive strategies are linked to cognitive domains, they always involve a particular degree of learning content and can be considered as higher order strategies. Three subcategories related to the three phases of the learning process can be distinguished: planning, monitoring and evaluation (Schraw & Dennison, 1994). Planning strategies are deployed at the start of a learning episode and include subprocesses such as goal setting and allocating resources. Examples of these strategies are making a plan, deciding upon the amount of time to spend on an activity, and choosing what to do first. Monitoring strategies are used for checking one’s comprehension. These strategies can be considered as continuous assessments of one’s learning and/or strategy use. Examples include self-questioning and changing the approach to a specific learning task if necessary, for instance, re-reading a passage if its’ meaning is not properly understood. After the learning process, evaluation strategies can be used in the analysis of one’s performance and the effectiveness of the learning methods. In writing, for example, reviewing a text is a strategy that might help improve the written text, while in mathematics it is important to check whether the answers found make sense in the context of the original problem.

Management strategies are strategies to manage the aspects in the context which directly influence the learning process. This type of strategy is related to the theoretical framework proposed by Pintrich (2000), which explicitly refers to the contextual features that influence learning. Management strategies can be classified into three main subcategories: management of effort, management of peers and others (e.g., teachers) and management of the environment. Effort-management refers to strategies which reflect the commitment to completing one’s study goals, in spite of difficulties or distractions (Pintrich et al., 1991). It is a form of actively motivating oneself to persist in studying. The second subcategory, management of peers (or others), includes strategies deducted from theories that reflect a socio-constructive view of learning, in which peers work together to construct knowledge. Asking fellow students to assist in learning, working together on tasks, as well as forms of reciprocal teaching can be very effective in enhancing one’s learning and understanding (e.g., Palincsar & Brown, 1984). Finally, management of the environment relates to strategies which help in using the environment to optimize the possibilities for learning, e.g., by using the library or dictionaries and finding a quiet place to study.

Apart from focusing on the strategies mentioned above, learning strategy trainings frequently address two related topics, namely motivation and knowledge. These elements could be considered as a crucial condition for learning strategies if the aim is specifically to enhance one’s self-regulated learning. As learning strategies are controllable and have to be implemented consciously by an individual (Bjorklund, Dukes, & Brown, 2009), students need to have sufficient knowledge regarding these strategies as well as the motivation to apply them. Therefore, in completing the overview of learning strategies for self-regulated learning, motivational aspects and metacognition are considered as well.

Motivation is a multi-facetted construct which can help students engage in learning in various ways. With respect to academic performance, there are several aspects that might influence a student’s approach to a task, for instance self-efficacy beliefs, which refer to one’s perception of one’s ability to accomplish a task and one’s confidence in one’s skills to perform this task (Pintrich et al., 1991). Furthermore, task-value beliefs concern the extent to which students perceive academic tasks as interesting and important. Finally, goal orientation relates to the reasons why students perform a task, which can be either intrinsic (e.g., curiosity) or extrinsic (e.g., rewards) (Pintrich et al., 1991). All these motivational aspects play a role in students’ decisions whether to engage in or refrain from strategy use (e.g., Garner, 1990, Hadwin and Winne, 1996).

In summarizing conditions for effective strategy trainings, Hattie et al. (1996) pointed to the relevance of contextual as well as strategic knowledge about learning tasks. Furthermore, Dignath et al. (2008) found metacognitive knowledge, or reflection, to be an important element in strategy trainings. It is therefore a component commonly used in trainings, and considered as a relevant factor in the interpretation of effects. Furthermore, it comprises declarative, procedural and conditional elements: knowledge of how, when and why to use which learning strategies (Schraw & Dennison, 1994).

Over the years, the number of learning strategy interventions conducted has become considerable. All of them have made a relevant contribution to the growing insight into the effectiveness of learning strategy instruction and strategy application. From the nineties on, important meta-analyses have been conducted in order to synthesize the findings available so far. The first meta-analysis was conducted by Hattie et al. (1996). This analysis included 51 studies, resulting in an overall effect size on student performance of Cohen’s d = .57 (SE = .04). The authors reported the highest effects for the direct teaching of cognitive skills. These effects were mostly produced by interventions aimed at the near transfer of a specific task-related skill. Multiple-component interventions, in which various strategies were addressed, revealed lower impacts. Regarding student characteristics, it was reported that in general low ability students seemed unable to profit from these interventions. However, in this and other studies several issues remained unaddressed. For example, in the analysis of Hattie et al. (1996) the trainings took place outside the regular school context and were focused on multiple independent variables aimed at increasing various kinds of performance stretching beyond achievements in the learning of content. Furthermore, in many analyses the mean effects were computed based on student performance, study skills and affect as a whole. And if a separate effect for performance was specified, it was not reported whether it was statistically significant or not.

Dignath and colleagues (Dignath and Büttner, 2008, Dignath et al., 2008) conducted two follow-up meta-analyses. They synthesized information from studies from 1992 to 2006 and simultaneously tested the effects of a number of study characteristics on academic performance via stepwise backwards metaregression (Dignath & Büttner, 2008). They reported overall effect sizes on performance of Cohen’s d = 0.61 (0.05) for primary schools and 0.54 (0.11) for secondary schools. For both school types, the effect sizes were found to be higher when metacognitive reflection was included in the trainings. With respect to the effects of other strategies on academic achievement, the results were mixed. Taking a closer look at which learning strategies were the most effective in primary education, Dignath et al. (2008) – using ANOVA – reported the highest effects for interventions which combined the instruction of different types of strategies. The authors argued that the trainings should include both metacognitive and motivational strategies. In secondary education the highest effects were indicated for interventions focused on motivation and/or metacognitive reflection. Furthermore, it was observed that group work had a negative effect in primary education but a positive impact in secondary education. Regarding school subject, contradictory results were found. In primary school, interventions in mathematics were more effective than those in reading or writing, whereas in secondary school the opposite was true. The authors did not address students’ ability, as only studies conducted in the regular education segment were included in the literature review.

Again, additional questions arose. For example, the effects largely varied between interventions in mathematics and those in reading and writing (which were combined in these analyses). Furthermore, instructing all types of strategies would be overwhelming for the students, so where to focus on as a teacher? And finally, earlier meta-analyses did not clarify the nature of the tests used in the studies analyzed, for example whether they were independent assessments or tests specifically developed for the study, a difference which might have influenced the effect sizes reported. In sum, although the previous meta-analyses provided insight into the potential effectiveness of strategy instruction, their results also gave rise to new questions, especially with respect to the application of the research findings in practice. Three issues are of interest here: which specific strategies are effective, which students profit from strategy instruction and what influence do the types of test instruments have on the effect sizes found?

Probably the most relevant question is which specific strategies are the most effective in improving student learning. Earlier meta-analyses have investigated the effectiveness of a broad spectrum of strategy trainings, including cognitive, metacognitive, and motivational learning strategies (the latter we call motivational aspects). However, in planning and implementing interventions in the curriculum, it is useful to know quite specifically which concrete strategies (included in the various broad categories) should be taught to make students’ learning more effective. And as earlier meta-analyses have found differences among the various subjects in this respect, another question arises, namely how the effectiveness of strategies is influenced by the subject-domains in which these strategies are implemented.

There has been an ongoing debate about the age at which learners are capable of self-regulating their learning. Some researchers claim that children are not yet capable to engage in metacognitive activity because it requires a particular level of cognition, accurate knowledge of academic tasks and learning, and the ability to monitor oneself. And, as argued by some, these are elements which are not yet fully developed at such a young age (e.g., Paris & Newman, 1990). Others see metacognitive activity and self-regulated learning initiatives in children already as early as in Kindergarten (e.g., Whitebread et al., 2009). These different viewpoints are partly dependent on the researchers’ theoretical backgrounds and what they consider to be metacognition and self-regulated learning. There is general consensus, however, on the view that both concepts develop as children mature (e.g., Veenman & Spaans, 2005).

The debate about the prerequisites for students to engage in strategy use is not only limited to their age. Other variables, such as background and capability, and the way in which these elements influence the effect of learning strategies, also play a role. For example, according to Hattie et al. (1996), low achievers seem unable to benefit from most types of interventions. However, considering that new models of self-regulated learning and strategy instruction have emerged since these authors’ meta-analysis was conducted, it seems interesting to re-assess the effects of these types of student characteristics.

Another issue not addressed thoroughly in earlier meta-analyses relates to the outcome measures used in the original studies to determine the effectiveness of the interventions. Most studies only use self-developed measures to evaluate student performance. This situation could cause effect size inflation, as researchers may direct student performance toward the test used at the end of the intervention. The question then arises whether these effects would also have been found when intervention independent tests had been used. This issue was addressed in a meta-analysis of student performance performed by Haller, Child, and Walberg (1988), who reported that there was no difference between the effects found via self-developed versus intervention independent tests, although it was not clear on which analysis this conclusion was based. In a study on the effects of metacognitive instruction interventions on comprehensive reading, Chiu (1998) also examined whether the type of measurement instrument matters. He found that the effects were higher when the test was nonstandardized (Cohen’s d = .61) than when it was standardized (Cohen’s d = .24).

The results of the meta-analyses by Hattie et al. (1996) and Dignath et al. (2008) may have been influenced by the fact that they did not correct for the type of test instrument used in the primary studies. Considering the common use of self-developed tests in strategy interventions, it would therefore be interesting to research whether the training effect outcomes indicated by these instruments actually differ from those yielded by independent achievement assessments and how this difference might have influenced the interpretation of the results.

This article addresses a number of research questions, using a meta-analytical approach. The first one is: Which strategies instructed are the most effective in improving academic performance? In answering this question, we include three types of strategies: cognitive, metacognitive and management strategies (and their respective substrategies), while also considering the effects of motivational aspects and metacognitive knowledge. In doing so we elaborate on earlier findings, taking a closer look at these broad categories to investigate which concrete substrategies are the most effective. Furthermore, we distinguish among subject domains to look for differential effects. Secondly, our focus is on student characteristics, which forms the basis for our second research question: Do the effects of the strategies instructed differ for different types of students? Next, we address the possible influence of the measurement instruments used to evaluate the strategies’ effectiveness. We expect the highest effect sizes for studies based on self-developed tests. To check this hypothesis, we formulate our third research question: does the type of measurement instrument used in the interventions influence the effect sizes reported? Finally, we assess whether publication bias affects our results. Several sources of evidence have shown that studies which present relatively high effect sizes have in general more chance of being published than accounts of lower effect sizes (Ahn et al., 2012, Borenstein et al., 2009). This bias is reflected in a meta-analysis. In our research, however, we checked for publication bias using a statistical method. via statistical means insight can be gained into the extent and effects of possible publication bias. This emphasis brings us to our last research question: To what degree are results influenced by publication bias?

Section snippets

Method

In order to be able to conduct our analysis, first the relevant literature had to be located and coded. Before explaining our methods of analysis we will describe how we selected the literature and specify the content in which we are interested.

Descriptives

A total of 58 articles including 95 strategy-interventions met our eligibility criteria and were included in our analysis. The majority of the interventions took place in the context of mathematics (n = 44), followed by (comprehensive) reading, writing and science (n = 23, n = 16, and n = 9, respectively). In total, 180 effect sizes were coded, which indicates that many interventions had been evaluated using multiple tests. Table 1 presents a summary of the study characteristics. A table with the key

Conclusion and discussion

This meta-analysis addressed the question which learning strategies are the most effective in enhancing the academic performance of students in primary and secondary education. To establish the effects of these strategies, we relied on research studies which describe interventions in which learning strategies are instructed, assuming that the instruction of these strategies would result in their adoption by the students. A search for literature published in a period of more than a decade

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