Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums

大型语言模型作为MOOC评分者

Large Language Models As MOOCs Graders

本研究在三门MOOC中以18种设置评估GPT-4与GPT-3.5，发现结合教师答案和评分量规的Zero-shot-CoT评分比同伴评分更贴近教师评分。

When to reply? Context Sensitive Models to Predict Instructor Interventions in MOOC Forums

Due to time constraints, course instructors often need to selectively participate in student discussion threads, due to their limited bandwidth and lopsided student--instructor ratio on online forums. We propose the first deep learning models for this binary prediction problem. We propose novel attention based models to infer the amount of latent context necessary to predict instructor intervention. Such models also allow themselves to be tuned to instructor's preference to intervene early or late. Our three proposed attentive model variants to infer the latent context improve over the state-o…

Tracing Forum Posts to MOOC Content using Topic Analysis

Massive Open Online Courses are educational programs that are open and accessible to a large number of people through the internet. To facilitate learning, MOOC discussion forums exist where students and instructors communicate questions, answers, and thoughts related to the course. The primary objective of this paper is to investigate tracing discussion forum posts back to course lecture videos and readings using topic analysis. We utilize both unsupervised and supervised variants of Latent Dirichlet Allocation (LDA) to extract topics from course material and classify forum posts. We validate…

Resource Mention Extraction for MOOC Discussion Forums

In discussions hosted on discussion forums for MOOCs, references to online learning resources are often of central importance. They contextualize the discussion, anchoring the discussion participants' presentation of the issues and their understanding. However they are usually mentioned in free text, without appropriate hyperlinking to their associated resource. Automated learning resource mention hyperlinking and categorization will facilitate discussion and searching within MOOC forums, and also benefit the contextualization of such resources across disparate views. We propose the novel prob…

使用大型语言模型为大规模开放在线课程评分

Grading Massive Open Online Courses Using Large Language Models

本研究探索用大型语言模型（LLMs）替代MOOC中的同伴评分，通过零样本思维链提示结合教师答案和评分标准，在18个场景中验证其评分与教师评分更一致，展示了自动化评分的潜力。

Using Discourse Signals for Robust Instructor Intervention Prediction

We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs). Our key finding is that using automatically obtained discourse relations improves the prediction of when instructors intervene in student discussions, when compared with a state-of-the-art, feature-rich baseline. Our supervised classifier makes use of an automatic discourse parser which outputs Penn Discourse Treebank (PDTB) tags that represent in-post discourse features. We show PDTB relation-based features increase the robustness of the classifier and complement…