Deep Learning or Deep Ignorance? Comparing Untrained Recurrent Models in Educational Contexts.
Deep Learning or Deep Ignorance? Comparing Untrained Recurrent Models in Educational Contexts.
复制标题
深度学习还是深度无知？
DOI：
10.1007/978-3-031-11644-5_23
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Heffernan, Neil
中科院分区：
文献类型：
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作者：
Botelho, Anthony;Prihar, Ethan;Heffernan, Neil
近年来，深度学习方法的开发和应用在教育背景下得到了发展。也许部分原因是，通过在课堂和更大规模的MOOC平台上采用基于计算机的学习系统，可以获得大量数据，许多教育研究人员正在利用各种新兴的深度学习方法来研究各种能力的学习和学生行为。例如，递归神经网络的变体不仅被用来预测学习结果，还被用来研究学生数据中的顺序和时间趋势；人们普遍认为，它们能够学习学习和行为结构随着时间的推移的高维表示，例如在处理指定内容时学生的知识状态的演变。然而，最近的研究已经开始对这一观点提出异议，相反，他们发现可能是模型的复杂性导致了许多预测任务中性能的提高，并且这些方法可能不会通过模型训练固有地学习这些时间表示。在这项工作中，我们在学生情绪检测器的背景下进一步探索这些主张，以及扩展现有的探索知识追踪基准的工作。具体地说，我们观察了与深度学习网络相比，训练模型的表现如何，深度学习网络的训练只应用于输出层。虽然使用训练的递归模型的先前工作的最高结果被发现是更好的，但我们的未训练版本的应用表现得相当好，甚至超过了以前的非深度学习方法。
The development and application of deep learning methodologies has grown within educational contexts in recent years. Perhaps attributable, in part, to the large amount of data that is made available through the adoption of computer-based learning systems in classrooms and larger-scale MOOC platforms, many educational researchers are leveraging a wide range of emerging deep learning approaches to study learning and student behavior in various capacities. Variations of recurrent neural networks, for example, have been used to not only predict learning outcomes but also to study sequential and temporal trends in student data; it is commonly believed that they are able to learn high-dimensional representations of learning and behavioral constructs over time, such as the evolution of a students’ knowledge state while working through assigned content. Recent works, however, have started to dispute this belief, instead finding that it may be the model’s complexity that leads to improved performance in many prediction tasks and that these methods may not inherently learn these temporal representations through model training. In this work, we explore these claims further in the context of detectors of student affect as well as expanding on existing work that explored benchmarks in knowledge tracing. Specifically, we observe how well trained models perform compared to deep learning networks where training is applied only to the output layer. While the highest results of prior works utilizing trained recurrent models are found to be superior, the application of our untrained-versions perform comparably well, outperforming even previous non-deep learning approaches.