表示和预测在线大学课程中学生的导航路径

Renzhe Yu, Daokun Jiang, M. Warschauer
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引用次数: 5

摘要

学生导航路径的表示和预测,通常基于神经网络(NN)方法,已经看到了它们在人类对学习者行为缺乏了解的情况下改善教学和学习的潜力。然而,它们在mooc中得到了突出的研究,而在更制度化的高等教育场景中却很少被探讨。这项工作将这种研究扩展到在线大学课程的背景下。将学生通过课程页面的导航序列与自然语言处理中的文档进行比较,我们应用skip-gram模型来学习课程页面的向量嵌入,并将学习到的向量可视化,以了解学生的学习路径与预先设计的课程结构的一致程度。我们发现,最终获得不同字母分数的学生对设计顺序的依从程度不同。接下来,我们将嵌入的序列放入一个长短期记忆架构中,并测试其预测学生在给定先前序列的情况下访问下一页的能力。最高准确率达到50.8%,大大优于基于频率的41.3%的基线。这些结果表明,神经网络方法有可能帮助教师了解学生的学习行为,并促进自动化教学支持。
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Representing and predicting student navigational pathways in online college courses
Representation and prediction of student navigational pathways, typically based on neural network (NN) methods, have seen their potential of improving instruction and learning under insufficient human knowledge about learner behavior. However, they are prominently studied in MOOCs and less probed within more institutionalized higher education scenarios. This work extends such research to the context of online college courses. Comparing student navigational sequences through course pages to documents in natural language processing, we apply a skip-gram model to learn vector embedding of course pages, and visualize the learnt vectors to understand the extent to which students' learning pathways align with pre-designed course structure. We find that students who get different letter grades in the end exhibit different levels of adherence to designed sequence. Next, we fit the embedded sequences into a long short-term memory architecture and test its ability to predict next page that a student visits given her prior sequence. The highest accuracy reaches 50.8% and largely outperforms the frequency-based baseline of 41.3%. These results show that neural network methods have the potential to help instructors understand students' learning behaviors and facilitate automated instructional support.
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