Motivational interference in multitask learning environments: A quantum cognition perspective on engagement, procrastination, and task commitment

IF 4.4 4区 心理学 Q3 PSYCHOLOGY, BIOLOGICAL
Learning and Motivation Pub Date : 2026-08-01 Epub Date: 2026-08-04 DOI:10.1016/j.lmot.2026.102321
Yeping Liu
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引用次数: 0

Abstract

The present study examined whether structured multitask learning (ML) might produce stronger motivational and behavioral outcomes than single-task learning (STL) and conventional teacher-led instruction among intermediate Chinese English as a Foreign Language (EFL) learners. Drawing upon a quantum cognition perspective, engagement, procrastination, and task commitment were treated as context-sensitive learner outcomes that might be shaped by different arrangements of classroom task demands. Using a quasi-experimental pretest-posttest design, 95 learners from two English institutes in China participated in the study and were assigned to three instructional conditions: ML (n = 30), STL (n = 31), and control group (CG, n = 34). The 12-week intervention was implemented through Select Reading 2. Learners in the ML group completed integrated activities in which reading, vocabulary processing, oral or written production, peer interaction, and teacher feedback were connected within the same instructional cycle. Learners in the STL group completed the same task components sequentially, while learners in the CG received regular teacher-led instruction without structured task integration. Engagement, procrastination, and task commitment were measured before and after the intervention through validated self-report scales. The quantitative findings indicated no significant pretest differences among the three groups, suggesting baseline comparability. Posttest ANOVA and Tukey HSD comparisons further showed that the ML group reported significantly higher engagement and task commitment and significantly lower procrastination than both the STL and CG groups. The STL group also outperformed the CG, though its gains were smaller than those of the ML group. The findings suggest that carefully structured ML can assist EFL teachers in designing integrated, feedback-rich classroom tasks that foster sustained engagement, strengthen learner commitment, and reduce delay-oriented learning behavior.
多任务学习环境中的动机干扰:参与、拖延和任务承诺的量子认知视角
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来源期刊
CiteScore
2.90
自引率
0.00%
发文量
53
期刊介绍: Learning and Motivation features original experimental research devoted to the analysis of basic phenomena and mechanisms of learning, memory, and motivation. These studies, involving either animal or human subjects, examine behavioral, biological, and evolutionary influences on the learning and motivation processes, and often report on an integrated series of experiments that advance knowledge in this field. Theoretical papers and shorter reports are also considered.
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