Uncovering adults' problem-solving patterns from process data with hidden Markov model and network analysis

IF 5.1 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Journal of Computer Assisted Learning Pub Date : 2024-11-14 DOI:10.1111/jcal.13089

Xiaoxiao Liu, Okan Bulut, Ying Cui, Yizhu Gao

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引用次数: 0

Abstract

Background

Process data captured by computer-based assessments provide valuable insight into respondents' cognitive processes during problem-solving tasks. Although previous studies have utilized process data to analyse behavioural patterns or strategies in problem-solving tasks, the connection between latent cognitive states and their theoretical interpretation in problem solving remains unclear.

Objectives

This research aims to investigate the connections between similar hidden response states and unfold respondents' transition paths in problem-solving processes. Analysing process data from the 2012 United States Programme for the International Assessment of Adult Competencies (PIAAC), this study seeks to discern patterns in problem solving among participants.

Methods

The hidden Markov model was first used to uncover the hidden states based on a sequence of observed actions. Next, Gaussian graphical network analysis was employed to analyse the relationships between hidden response states.

Results and Conclusions

Results indicated that correct responders had simpler, clearer state relationships, while incorrect responders displayed more complex connections. Respondents who solved the tasks correctly had clearer thoughts about the problem-solving process, whereas incorrect respondents struggled to understand the problem and failed to figure out solutions. Cognitive state changes during problem solving also varied between groups. The correct groups showed cohesive, logical transitions, in contrast to the emerged isolated, erratic patterns of the incorrect groups.

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利用隐马尔可夫模型和网络分析从过程数据中发现成人解决问题的模式

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来源期刊

Journal of Computer Assisted Learning EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

9.70

自引率

6.00%

发文量

116

期刊介绍： The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope