A systematic review of interleaving as a concept learning strategy : a study protocol

Firth, Jonathan and Rivers, Ian and Boyle, James (2019) A systematic review of interleaving as a concept learning strategy : a study protocol. Social Science Protocols, 2. pp. 1-7.

[img]
Preview
Text (Firth-etal-SSP-2019-A-systematic-review-of-interleaving-as-a-concept-learning-strategy)
Firth_etal_SSP_2019_A_systematic_review_of_interleaving_as_a_concept_learning_strategy.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (184kB)| Preview

    Abstract

    Background: Education Scotland’s (2018) framework for interventions for equity supporting the Scottish Attainment Challenge highlights the promotion of high quality learning and the effective use of evidence and data. This study protocol outlines the methodology of a systematic review of the literature into the use of interleaving to facilitate the effective learning and teaching of new concepts. Methods: The systematic review has been pre-registered with PROSPERO, an international database of prospectively registered systematic reviews. The review will investigate whether presenting examples of to-be-learned concepts in an interleaved order is a more effective learning strategy than presenting examples blocked by topic, in terms of learners' ability to remember examples and to transfer learning to novel examples. Discussion: Interleaving is widely recommended as an evidence-based approach to teaching with considerable potential as a strategy for learners experiencing difficulties in working memory functioning and conceptual learning, but to date there has not been a comprehensive review of the evidence base. The review will address this gap. It will synthesize primary research studies from the past decade, investigate boundary conditions and variables that interact with interleaving, and will include a meta-analysis of recent studies. This protocol provides the details of the rationale of the review, and details the inclusion criteria and approaches to data extraction.