Leveraging Student Goal Setting for Real-Time Plan Recognition in Game-Based Learning
Leveraging Student Goal Setting for Real-Time Plan Recognition in Game-Based Learning
复制标题
在基于游戏的学习中利用学生目标设定进行实时计划识别
DOI：
10.1007/978-3-031-11644-5_7
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Lester, J.
中科院分区：
文献类型：
--
作者：
Goslen, A.;Carpenter, D.;Rowe, J.;Henderson, N.;Azevedo, R.;Lester, J.
目标设定和计划是自我调节学习的组成部分。许多学生努力设定有意义的目标并制定相关计划。适应性学习环境显示出巨大的潜力，脚手架学生的目标设定和规划过程。这种支架的一个重要要求是执行学生计划识别的能力，这涉及到基于对学生解决问题的行动的观察来识别学生的目标和计划。我们介绍了一种新的计划识别框架，利用跟踪日志数据，从学生的互动在一个基于游戏的学习环境中，称为水晶岛，学生使用拖放规划支持工具，使他们能够具体化他们的科学解决问题的目标和计划之前，制定他们在学习环境中。我们将学生计划识别正式化为两个互补的任务：（1）对学生选择的解决问题的目标进行分类，（2）对学生表示将实现目标的行动序列进行分类。利用144名中学生与水晶岛互动的跟踪日志数据，我们评估了一系列用于学生目标和计划识别的机器学习模型。所有基于机器学习的技术都优于大多数基线，LSTM在目标识别方面优于其他模型，朴素贝叶斯在计划识别方面表现最佳。结果表明，在基于游戏的学习环境中，自动识别学生的解决问题的目标和计划，这对学生自我调节学习提供自适应支持的潜力。
Goal setting and planning are integral components of self-regulated learning. Many students struggle to set meaningful goals and build relevant plans. Adaptive learning environments show significant potential for scaffolding students’ goal setting and planning processes. An important requirement for such scaffolding is the ability to perform student plan recognition, which involves recognizing students’ goals and plans based upon the observations of their problem-solving actions. We introduce a novel plan recognition framework that leverages trace log data from student interactions within a game-based learning environment called CRYSTAL ISLAND, in which students use a drag-and-drop planning support tool that enables them to externalize their science problem-solving goals and plans prior to enacting them in the learning environment. We formalize student plan recognition in terms of two complementary tasks: (1) classifying students’ selected problem-solving goals, and (2) classifying the sequences of actions that students indicate will achieve their goals. Utilizing trace log data from 144 middle school students’ interactions with CRYSTAL ISLAND, we evaluate a range of machine learning models for student goal and plan recognition. All machine learning-based techniques outperform the majority baseline, with LSTMs outperforming other models for goal recognition and naive Bayes performing best for plan recognition. Results show the potential for automatically recognizing students’ problem-solving goals and plans in game-based learning environments, which has implications for providing adaptive support for student self-regulated learning.
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作者：
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DOI：
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期刊：
Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
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DOI：
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发表时间：
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