Effects of a Digital Math Training Intervention on Self-Efficacy: Can Clipart Explainers Support Learners?

Markus H. Hefter, Rudolf vom Hofe, Kirsten Berthold
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引用次数: 2

Abstract

In the mathematics domain, learning from worked examples is a best practice method for initial skill acquisition. However, open questions refer to effective digital training interventions in the field. Subject to these questions are the potential effects of self-explanations on self-efficacy, and the role of clip art explainers currently in vogue (cartoon teachers plus explanations in speech bubbles). We thus developed and field-tested a short-term (approximately 45 minutes) digital training intervention on mathematical proportionality with 113 German secondary school students (Mage: 14.12 years). We applied a quantitative experimental research design to analyse learning processes and outcomes with tests and questionaires. To investigate the potential supportive effects of the clipart explainers, we compared two versions of our intervention: with clip art explainers (clipart condition) and without them (control condition). Our training intervention revealed a significant positive within-subjects effect on the learners’ mathematical self-efficacy related to proportionality tasks. The clipart explainers had a significant negative between-subject effect on the subjective difficulty of the instructional material—with no indications of being detrimental to learning. Finally, we detected self-explanation quality and task engagement to be significant predictors for learning outcomes. Our findings underscore the importance of having learners deeply process the given materials.
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数字数学训练干预对自我效能感的影响:剪贴画解释器能支持学习者吗?
在数学领域,从工作实例中学习是获得初始技能的最佳实践方法。然而,开放式问题涉及到该领域有效的数字培训干预措施。这些问题的主题是自我解释对自我效能感的潜在影响,以及目前流行的剪贴画解释者的作用(卡通教师加上语音泡泡中的解释)。因此,我们针对113名德国中学生(年龄14.12岁)开发并实地测试了一种短期(约45分钟)的数学比例数字培训干预。我们采用定量实验研究设计,通过测试和问卷来分析学习过程和结果。为了调查剪贴画解释器的潜在支持作用,我们比较了两种版本的干预:有剪贴画解释器(剪贴画条件)和没有剪贴画解释器(对照条件)。我们的训练干预对学习者在比例任务上的数学自我效能感有显著的正向影响。剪纸解释对教学材料的主观难度有显著的负向影响,没有迹象表明对学习有害。最后,我们发现自我解释质量和任务投入是学习结果的重要预测因素。我们的研究结果强调了让学习者深入处理给定材料的重要性。
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来源期刊
CiteScore
1.10
自引率
0.00%
发文量
19
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