Predicting students' knowledge after playing a serious game based on learning analytics data: A case study

Cristina Alonso-Fernández, I. Martínez-Ortiz, R. Caballero, Manuel Freire-Morán, Baltasar Fernandez-Manjon
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引用次数: 52

Abstract

Peer Review The peer review history for this article is available at https://publons.com/publon/10. 1111/jcal.12405. Abstract Serious games have proven to be a powerful tool in education to engage, motivate, and help students learn. However, the change in student knowledge after playing games is usually measured with traditional (paper) prequestionnaires– postquestionnaires. We propose a combination of game learning analytics and data mining techniques to predict knowledge change based on in-game student interactions. We have tested this approach in a case study for which we have conducted preexperiments–postexperiments with 227 students playing a previously validated serious game on first aid techniques. We collected student interaction data while students played, using a game learning analytics infrastructure and the standard data format Experience API for Serious Games. After data collection, we developed and tested prediction models to determine whether knowledge, given as posttest results, can be accurately predicted. Additionally, we compared models both with and without pretest information to determine the importance of previous knowledge when predicting postgame knowledge. The high accuracy of the obtained prediction models suggests that serious games can be used not only to teach but also to measure knowledge acquisition after playing. This will simplify serious games application for educational settings and especially in the classroom easing teachers' evaluation tasks.
基于学习分析数据预测学生玩严肃游戏后的知识:一个案例研究
本文的同行评议历史可在https://publons.com/publon/10上获得。1111 / jcal.12405。严肃游戏已被证明是教育中吸引、激励和帮助学生学习的强大工具。然而,学生玩游戏后的知识变化通常是通过传统的(纸质)问卷前-问卷后测量的。我们建议结合游戏学习分析和数据挖掘技术来预测基于游戏内学生互动的知识变化。我们在一个案例研究中测试了这种方法,我们对227名学生进行了实验前和实验后的实验,这些学生都在玩一个之前经过验证的关于急救技术的严肃游戏。我们在学生玩游戏时收集他们的互动数据,使用游戏学习分析基础设施和标准数据格式Experience API for Serious Games。在数据收集之后,我们开发并测试了预测模型,以确定作为后测结果给出的知识是否可以准确预测。此外,我们比较了有和没有前测信息的模型,以确定先前知识在预测赛后知识时的重要性。所获得的预测模型的高准确性表明,严肃游戏不仅可以用于教学,还可以用于衡量游戏后的知识获取。这将简化严肃游戏在教育环境中的应用,特别是在课堂上,减轻教师的评估任务。
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