Effects of incorporating retrieval into learning tasks: The complexity of the tasks matters

Roelle, Julian; Berthold, Kirsten

doi:10.1016/j.learninstruc.2017.01.008

Cited by 59 publications

(52 citation statements)

References 44 publications

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“…For example, for the testing effect, the degree of information given to students (e.g., allowing open book study, cf. Roelle and Berthold, 2017 ) could influence the differences between generating a solution and studying examples. Given that the main dependent variable in testing-effect studies is text comprehension and memory for text information, it would be interesting to further investigate whether the degree of element interactivity in the text materials moderates the testing effect.…”

Section: Future Directions For Researchmentioning

confidence: 99%

Undesirable Difficulty Effects in the Learning of High-Element Interactivity Materials

et al. 2018

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According to the concept of desirable difficulties, introducing difficulties in learning may sacrifice short-term performance in order to benefit long-term retention of learning. We describe three types of desirable difficulty effects: testing, generation, and varied conditions of practice. The empirical literature indicates that desirable difficulty effects are not always obtained and we suggest that cognitive load theory may be used to explain many of these contradictory results. Many failures to obtain desirable difficulty effects may occur under conditions where working memory is already stressed due to the use of high element interactivity information. Under such conditions, the introduction of additional difficulties may be undesirable rather than desirable. Empirical evidence from diverse experiments is used to support this hypothesis.

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Section: Future Directions For Researchmentioning

confidence: 99%

Undesirable Difficulty Effects in the Learning of High-Element Interactivity Materials

et al. 2018

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“…A debate is currently ongoing as to the effectiveness of retrieval practice for more complex materials (Karpicke & Aue, 2015 ; Roelle & Berthold, 2017 ; Van Gog & Sweller, 2015 ). Practicing retrieval has been shown to improve the application of knowledge to new situations (e.g., Butler, 2010 ; Dirkx, Kester, & Kirschner, 2014 ); McDaniel et al, 2013 ; Smith, Blunt, Whiffen, & Karpicke, 2016 ); but see Tran, Rohrer, and Pashler ( 2015 ) and Wooldridge, Bugg, McDaniel, and Liu ( 2014 ), for retrieval practice studies that showed limited or no increased transfer compared to restudy.…”

Section: Retrieval Practicementioning

confidence: 99%

Teaching the science of learning

2018

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The science of learning has made a considerable contribution to our understanding of effective teaching and learning strategies. However, few instructors outside of the field are privy to this research. In this tutorial review, we focus on six specific cognitive strategies that have received robust support from decades of research: spaced practice, interleaving, retrieval practice, elaboration, concrete examples, and dual coding. We describe the basic research behind each strategy and relevant applied research, present examples of existing and suggested implementation, and make recommendations for further research that would broaden the reach of these strategies.

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“…The key finding from comparing the performance of both groups of students on a final test session is that retrieving knowledge (in a test) actually strengthens that knowledge more than simply restudying it. However, one critique of this body of research is that the way that knowledge is typically operationalized and measured is rather simplistic-relying on counts of correctly answered "informational units" as a proxy for measuring the amount of "content" that students have retained (Roelle & Berthold, 2017). Such an experimental design is limited in its ability to examine how learners represent, acquire, and ultimately retrieve a hierarchical, complex, network-like organization of concepts.…”

Section: Learning and The Development Of Conceptual Knowledgementioning

confidence: 99%

Contributions of Modern Network Science to the Cognitive Sciences: Revisiting research spirals of representation and process

Castro¹,

Siew²

2019

Preprint

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Modelling the structure of cognitive systems is a central goal of the cognitive sciences—a goal that has greatly benefitted from the application of network science approaches. This paper provides an overview of how network science has been applied to the cognitive sciences, with a specific focus on the two research “spirals” of cognitive sciences related to the representation and processes of the human mind. For each spiral, we first review classic papers in the psychological sciences that have drawn on graph-theoretic ideas or frameworks before the advent of modern network science approaches. We then discuss how current research in these areas have been shaped by modern network science, which provide the mathematical framework and methodological tools for psychologists to (i) represent cognitive network structure, and (ii) investigate and model the psychological processes that occur in these cognitive networks. Finally, we briefly comment on the future of, and the challenges facing, cognitive network science.

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Effects of incorporating retrieval into learning tasks: The complexity of the tasks matters

Cited by 59 publications

References 44 publications

Undesirable Difficulty Effects in the Learning of High-Element Interactivity Materials

Undesirable Difficulty Effects in the Learning of High-Element Interactivity Materials

Teaching the science of learning

Contributions of Modern Network Science to the Cognitive Sciences: Revisiting research spirals of representation and process

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