Evaluating the kit-build concept mapping process using sub-map scoring

IF 3.1 Q1 EDUCATION & EDUCATIONAL RESEARCH Research and Practice in Technology Enhanced Learning Pub Date : 2023-09-14 DOI:10.58459/rptel.2024.19021
Ridwan Rismanto, Aryo Pinandito, Banni Satria Andoko, Yusuke Hayashi, Tsukasa Hirashima
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Abstract

Concept mapping allows learners to visually represent their knowledge by connecting nodes (concepts) and links (relations between concepts). The kit-build (KB) concept map framework enhances this process by enabling learners to recompose a concept map from provided nodes and links, leading to improved learning outcomes. Additionally, KB employs an automatic assessment method called “Full Map Scoring (FMS)”, which evaluates the learner’s understanding based on the recomposed concept map. However, FMS only evaluates the final product of the recomposition activity, neglecting the process itself. This is a potential limitation because different processes leading to the same result could reflect different levels of understanding among learners. Therefore, it is crucial to incorporate process analysis into learner assessment. To address this issue, our research proposes a new assessment procedure termed “Sub-Map Scoring (SMS)”. A concept map is generally composed of several sub-maps with each sub-map representing a set of meanings. We hypothesize that if a learner comprehends the meaning of a sub-map, the learner will recompose the sub-map as a continuous activity. Therefore, SMS evaluates the recomposition process of each sub-map from the viewpoint of continuity, and the overall SMS score is derived from these evaluations. To verify the effectiveness of SMS, we compared SMS and FMS scores using data from a practical use of the KB framework. A multiple linear regression analysis confirmed that the SMS score was a more precise predictor of learning gain than the FMS score.
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使用子图评分评估工具包构建概念映射过程
概念映射允许学习者通过连接节点(概念)和链接(概念之间的关系)来直观地表示他们的知识。kit-build (KB)概念图框架增强了这一过程,使学习者能够根据提供的节点和链接重新构建概念图,从而提高学习效果。此外,KB还采用了一种名为“全图评分”(Full Map Scoring, FMS)的自动评估方法,该方法基于重组的概念图来评估学习者的理解程度。然而,FMS只评估重组活动的最终产品,而忽略了过程本身。这是一个潜在的限制,因为导致相同结果的不同过程可能反映了学习者之间不同的理解水平。因此,将过程分析纳入学习者评估是至关重要的。为了解决这个问题,我们的研究提出了一种新的评估程序,称为“子地图评分(SMS)”。概念图通常由几个子图组成,每个子图代表一组含义。我们假设,如果学习者理解了子地图的意义,学习者将把子地图重新组合为一个持续的活动。因此,SMS从连续性的角度对每个子地图的重组过程进行评价,并从这些评价中得出SMS的总体得分。为了验证SMS的有效性,我们使用实际使用KB框架的数据比较了SMS和FMS分数。多元线性回归分析证实SMS分数比FMS分数更准确地预测学习增益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.10
自引率
3.10%
发文量
28
审稿时长
13 weeks
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