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Supporting skill integration in an intelligent tutoring system for code tracing

Supporting skill integration in an intelligent tutoring system for code tracing INTRODUCTIONSkill integration is important and challenging for students' mastery development. Based on research evidence from the science of learning and the science of instruction, Ambrose et al. (2010) proposed a general framework of mastery development: students must acquire component skills, practice integrating skills, and know when to apply skills. In their view, the second phase, skill integration, is a difficult phase with high cognitive load demands where students need to combine component skills with fluency and autonomy. The Knowledge‐Learning‐Instruction (KLI) framework that aims at bridging instructional decision making and the science of learning (Koedinger, Corbett, & Perfetti, 2012), also stated that integrative knowledge has significance for learning and instruction. In particular, they defined an ‘integrative knowledge component’ as a knowledge component (KC) that ‘integrates or must be integrated (or connected) with other KCs to produce behavior’ (p. 771). The notion of integrative skills1In this work, we use the terms skill and integrative skill as synonyms for ‘knowledge component (KC)’ and ‘integrative KC’. A KC in the KLI framework (Koedinger, Corbett, & Perfetti, 2012; Koedinger, McLaughlin, & Stamper, 2012) is defined as an acquired unit of cognitive function or structure that can be inferred from performance on a set of related tasks, such http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Computer Assisted Learning Wiley

Supporting skill integration in an intelligent tutoring system for code tracing

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References (123)

Publisher
Wiley
Copyright
© 2023 John Wiley & Sons Ltd.
ISSN
0266-4909
eISSN
1365-2729
DOI
10.1111/jcal.12757
Publisher site
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Abstract

INTRODUCTIONSkill integration is important and challenging for students' mastery development. Based on research evidence from the science of learning and the science of instruction, Ambrose et al. (2010) proposed a general framework of mastery development: students must acquire component skills, practice integrating skills, and know when to apply skills. In their view, the second phase, skill integration, is a difficult phase with high cognitive load demands where students need to combine component skills with fluency and autonomy. The Knowledge‐Learning‐Instruction (KLI) framework that aims at bridging instructional decision making and the science of learning (Koedinger, Corbett, & Perfetti, 2012), also stated that integrative knowledge has significance for learning and instruction. In particular, they defined an ‘integrative knowledge component’ as a knowledge component (KC) that ‘integrates or must be integrated (or connected) with other KCs to produce behavior’ (p. 771). The notion of integrative skills1In this work, we use the terms skill and integrative skill as synonyms for ‘knowledge component (KC)’ and ‘integrative KC’. A KC in the KLI framework (Koedinger, Corbett, & Perfetti, 2012; Koedinger, McLaughlin, & Stamper, 2012) is defined as an acquired unit of cognitive function or structure that can be inferred from performance on a set of related tasks, such

Journal

Journal of Computer Assisted Learning – Wiley

Published: Apr 1, 2023

Keywords: adaptive educational systems; computer science education; domain modelling; instructional design; intelligent tutoring systems; programming education

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