Response to De Jong et al.’s (2023) paper “Let's talk evidence – The case for combining inquiry-based and direct instruction”

  • John Sweller
  • , Lin Zhang
  • , Greg Ashman
  • , William Cobern
  • , Paul A. Kirschner

Research output: Contribution to journalReview articlepeer-review

Abstract

De Jong et al. (2023) objected to the evidence presented by Zhang et al. (2022) to support their concerns about the unreserved acceptance and promotion of inquiry-based learning and problem solving in current policy documents related to the teaching of science. In their response, De Jong et al. (2023) reiterated their advocacy for inquiry approaches, arguing that an emphasis on a mixture of inquiry learning and explicit instruction is needed. The present article rebuts De Jong et al. (2023), in which we: 1) challenge their view of and approach to scientific methods in establishing the efficacy of different instructional approaches; 2) indicate that an underpinning theory to explain the cognitive machinery that drives inquiry-based instructional approaches is missing from their argument; and 3) address the empirical issues arising in their argument. We also highlight potential agreement with De Jong et al. (2023) on the essential role of explicit instruction and thus raise a call to the field to revise current science educational policies and standards to reflect such a role. Our agreements and disagreements advance the debate to a new focus concerning when and how inquiry-based learning and explicit instruction should be used and combined. While De Jong et al. (2023), in their theory-free paper, provided no answer to how explicit instruction and inquiry learning should be combined, we offer our suggestions based on evolutionary psychology and the expertise reversal effect from cognitive load theory.
Original languageEnglish
Article number100584
JournalEducational Research Review
Volume42
DOIs
StatePublished - Feb 1 2024

Keywords

  • Cognitive load theory
  • Controlled trials
  • Explicit instruction
  • Inquiry learning
  • Randomised

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