Modelling multiple problem-solving strategies and strategy shift in cognitive diagnosis for growth

IF 1.5 3区 心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS British Journal of Mathematical & Statistical Psychology Pub Date : 2022-07-10 DOI:10.1111/bmsp.12280
Manqian Liao, Hong Jiao
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Abstract

Problem-solving strategies, defined as actions people select intentionally to achieve desired objectives, are distinguished from skills that are implemented unintentionally. In education, strategy-oriented instructions that guide students to form problem-solving strategies are found to be more effective for low-achieving students than the skill-oriented instructions designed for enhancing their skill implementation ability. Although the existing longitudinal cognitive diagnosis models (CDMs) can model the change in students' dynamic skill mastery status over time, they are not designed to model the shift in students' problem-solving strategies. This study proposes a longitudinal CDM that considers both between-person multiple strategies and within-person strategy shift. The model, separating the strategy choice process from the skill implementation process, is intended to provide diagnostic information on strategy choice as well as skill mastery status. A simulation study is conducted to evaluate the parameter recovery of the proposed model and investigate the consequences of ignoring the presence of multiple strategies or strategy shift. Further, an empirical data analysis is conducted to illustrate the use of the proposed model to measure strategy shift, growth in skill implementation ability and skill mastery status.

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多问题解决策略建模与成长认知诊断中的策略转换
问题解决策略被定义为人们为实现预期目标而有意选择的行动,与无意中实施的技能不同。在教育方面,指导学生形成问题解决策略的策略导向指导比旨在提高学生技能实施能力的技能导向指导更有效。虽然现有的纵向认知诊断模型(CDMs)可以模拟学生动态技能掌握状态随时间的变化,但它们并不能模拟学生问题解决策略的变化。本研究提出了一种纵向清洁发展机制,考虑了人与人之间的多重策略和人与人之间的策略转移。该模型将策略选择过程与技能实施过程分离,旨在提供关于策略选择和技能掌握状态的诊断信息。通过仿真研究来评估所提出模型的参数恢复,并研究忽略多种策略或策略转移存在的后果。此外,实证数据分析表明,该模型可用于衡量战略转移、技能实施能力增长和技能掌握状况。
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来源期刊
CiteScore
5.00
自引率
3.80%
发文量
34
审稿时长
>12 weeks
期刊介绍: The British Journal of Mathematical and Statistical Psychology publishes articles relating to areas of psychology which have a greater mathematical or statistical aspect of their argument than is usually acceptable to other journals including: • mathematical psychology • statistics • psychometrics • decision making • psychophysics • classification • relevant areas of mathematics, computing and computer software These include articles that address substantitive psychological issues or that develop and extend techniques useful to psychologists. New models for psychological processes, new approaches to existing data, critiques of existing models and improved algorithms for estimating the parameters of a model are examples of articles which may be favoured.
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