Developing expertise is essential to improving pupil outcomes. Teachers’ engagement with education research has seen a significant increase over recent years – which is long overdue and very much needed.

Each week, I will post a research paper on a particular theme linked to developing Expert Teaching, covering several sub-categories of the main theme.

The first #ExpertTeaching research paper comes from Agarwal et al (2017) and focuses on the benefits of retrieval practice.


We examined the effects of retrieval practice for students who varied in working memory capacity as a function of the lag between study of material and its initial test, whether or not feedback was given after the test, and the retention interval of the final test. We sought to determine whether a blend of these conditions exists that maximises benefits from retrieval practice for lower and higher working memory capacity students. College students learned general knowledge facts and then restudied the facts or were tested on them (with or without feedback) at lags of 0–9 intervening items. Final cued recall performance was better for tested items than for restudied items after both 10 minutes and 2 days, particularly for longer study–test lags. Furthermore, on the 2-day delayed test the benefits from retrieval practice with feedback were significantly greater for students with lower working memory capacity than for students with higher working memory capacity (r = −.42). Retrieval practice may be an especially effective learning strategy for lower ability students.

This week’s #ExpertTeaching research paper is from Benjamin and Tullis (2010) and poses the question ‘What makes distributed practice effective?”


The advantages provided to memory by the distribution of multiple practice or study opportunities are among the most powerful effects in memory research. In this paper, we critically review the class of theories that presume contextual or encoding variability as the sole basis for the advantages of distributed practice, and recommend an alternative approach based on the idea that some study events remind learners of other study events. Encoding variability theory encounters serious challenges in two important phenomena that we review here: superadditivity and nonmonotonicity. The bottleneck in such theories lies in the assumption that mnemonic benefits arise from the increasing independence, rather than interdependence, of study opportunities. The reminding model accounts for many basic results in the literature on distributed practice, readily handles data that are problematic for encoding variability theories, including superadditivity and nonmonotonicity, and provides a unified theoretical framework for understanding the effects of repetition and the effects of associative relationships on memory.

This week’s #ExpertTeaching research paper is a hefty one from Bjork & Bjork (1992) and focuses on memory. This is a signficant development from Ebinhaus’ research on The Forgetting Curve.


Speakers at the William K. Estes Symposium at Harvard University were asked to pick, if possible, a research topic where they could trace the influence of W. K. Estes in the work to be reported at the symposium. In the first author’s case, that did not narrow down the possible topics in any substantial way. The work that seemed most timely to report at the symposium, however-the collaborative effort we refer to herein as a “new theory of disuse”-seemed not to be a particularly good example of the various significant influences William K. Estes has had on the two of us. Upon reflection, however, certain formal aspects of our theory correspond to a version of Estes’ stimulus sampling theory, a version that incorporates what we consider to be one of the great insights in the history of research on learning and memory. That insight, initially reported in two short papers in the 1955 volume of the Psychological Review (Estes, 1955a, 1955b), is implemented in the so-called stimulus fluctuation version of Estes’ statistical theory of learning.

However delayed and unconscious the influences may have been, we feel that our new theory of disuse owes some of its features to Estes’ theory of stimulus fluctuation. One goal of this chapter is to sketch the similarities and differences between our theory-of-disuse framework and Estes’ fluctuation model. In the sections that follow, we first summarize the characteristics of human memory that we feel suggest the storage and retrieval properties we postulate in our theory of disuse. We then present that framework along with some of its predictions and some arguments why such a pattern of storage and retrieval characteristics might be, overall, adaptive. We conclude with a section in which we first describe and pay homage to Estes’ stimulus fluctuation insight, and we then compare and contrast the fluctuation model and our theory of disuse.

This week’s #ExpertTeaching research paper is from Dunlosky et. al. (2013) and focuses on improving students’ learning with effective learning techniques: promising directions from cognitive science and education psychology.


Many students are being left behind by an educational system that some people believe is in crisis. Improving educational outcomes will require efforts on many fronts, but a central premise of this monograph is that one part of a solution involves helping students to better regulate their learning through the use of effective learning techniques. Fortunately, cognitive and educational psychologists have been developing and evaluating easy-to-use learning techniques that could help students achieve their learning goals. In this monograph, we discuss 10 learning techniques in detail and offer recommendations about their relative utility.We selected techniques that were expected to be relatively easy to use and hence could be adopted by many students. Also, some techniques (e.g., highlighting and rereading) were selected because students report relying heavily on them, which makes it especially important to examine how well they work.The techniques include elaborative interrogation, self-explanation, summarization, highlighting (or underlining), the keyword mnemonic, imagery use for text learning, rereading, practice testing, distributed practice, and interleaved practice.

To offer recommendations about the relative utility of these techniques, we evaluated whether their benefits generalize across four categories of variables: learning conditions, student characteristics, materials, and criterion tasks. Learning conditions include aspects of the learning environment in which the technique is implemented, such as whether a student studies alone or with a group. Student characteristics include variables such as age, ability, and level of prior knowledge. Materials vary from simple concepts to mathematical problems to complicated science texts. Criterion tasks include different outcome measures that are relevant to student achievement, such as those tapping memory, problem solving, and comprehension.

We attempted to provide thorough reviews for each technique, so this monograph is rather lengthy. However, we also wrote the monograph in a modular fashion, so it is easy to use. In particular, each review is divided into the following sections:

1. General description of the technique and why it should work

2. How general are the effects of this technique?

2a. Learning conditions 2b. Student characteristics 2c. Materials
2d. Criterion tasks

3. Effects in representative educational contexts

4. Issues for implementation

5. Overall assessment

The review for each technique can be read independently of the others, and particular variables of interest can be easily compared across techniques.

To foreshadow our final recommendations, the techniques vary widely with respect to their generalizability and promise for improving student learning. Practice testing and distributed practice received high utility assessments because they benefit learners of different ages and abilities and have been shown to boost students’ performance across many criterion tasks and even in educational contexts. Elaborative interrogation, self-explanation, and interleaved practice received moderate utility assessments. The benefits of these techniques do generalize across some variables, yet despite their promise, they fell short of a high utility assessment because the evidence for their efficacy is limited. For instance, elaborative interrogation and self- explanation have not been adequately evaluated in educational contexts, and the benefits of interleaving have just begun to be systematically explored, so the ultimate effectiveness of these techniques is currently unknown. Nevertheless, the techniques that received moderate-utility ratings show enough promise for us to recommend their use in appropriate situations, which we describe in detail within the review of each technique.

Five techniques received a low utility assessment: summarization, highlighting, the keyword mnemonic, imagery use for text learning, and rereading.These techniques were rated as low utility for numerous reasons. Summarization and imagery use for text learning have been shown to help some students on some criterion tasks, yet the conditions under which these techniques produce benefits are limited, and much research is still needed to fully explore their overall effectiveness.The keyword mnemonic is difficult to implement in some contexts, and it appears to benefit students for a limited number of materials and for short retention intervals. Most students report rereading and highlighting, yet these techniques do not consistently boost students’ performance, so other techniques should be used in their place (e.g., practice testing instead of rereading).

Our hope is that this monograph will foster improvements in student learning, not only by showcasing which learning techniques are likely to have the most generalizable effects but also by encouraging researchers to continue investigating the most promising techniques. Accordingly, in our closing remarks, we discuss some issues for how these techniques could be implemented by teachers and students, and we highlight directions for future research.