Seeding Course Forums using the Teacher-in-the-Loop
Seeding Course Forums using the Teacher-in-the-Loop
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使用教师在环中播种课程论坛
DOI：
10.1145/3448139.3448142
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Gal, Kobi
中科院分区：
文献类型：
--
作者：
Shusterman, Einat;Kim, Hyunsoo Gloria;Facciotti, Marc;Igo, Michele;Sripathi, Kamali;Karger, David;Segal, Avi;Gal, Kobi
在线论坛是现代课程不可或缺的一部分，但激励学生参与有益于教育的讨论可能具有挑战性。我们提出的解决方案是初始化（或“种子”）一个新的课程论坛，其中包含来自同一课程过去实例的评论，旨在引发有益于学习的讨论。在这项工作中，我们开发了选择高质量种子的方法，并评估了它们对186名学生生物课的一门课程的影响。我们设计了一个量表来测量给定线程的“播种适宜性”得分（一个开场白和随后的讨论）。然后，我们构建了一个有监督的机器学习（ML）模型，用于预测给定线程的播种适合性得分。该模型通过两种方式进行评估：首先，将其性能与课程讲师对测试/保留数据的专家意见进行比较;其次，将其嵌入实时课程中，课程讲师积极使用它来促进播种。对于课程中的每一项阅读作业，我们都向课程讲师展示了一份种子推荐的排名列表，他们可以查看该列表并过滤掉内容不一致或格式错误的种子。然后，我们进行了一项随机对照研究，其中一组学生被展示了ML模型推荐的种子，另一组学生被展示了另一种模型推荐的种子，该模型纯粹根据之前课程实例中生成的讨论长度对种子进行排名。我们发现，从任何一种播种模型中收到帖子的学生组比没有收到种子帖子的对照组产生了更多的讨论。此外，接受基于ML的模型选择的种子的学生表现出更高的参与度，以及更大的学习收益，而不是那些接受种子的讨论长度排名。
Online forums are an integral part of modern day courses, but motivating students to participate in educationally beneficial discussions can be challenging. Our proposed solution is to initialize (or “seed”) a new course forum with comments from past instances of the same course that are intended to trigger discussion that is beneficial to learning. In this work, we develop methods for selecting high-quality seeds and evaluate their impact over one course instance of a 186-student biology class. We designed a scale for measuring the “seeding suitability” score of a given thread (an opening comment and its ensuing discussion). We then constructed a supervised machine learning (ML) model for predicting the seeding suitability score of a given thread. This model was evaluated in two ways: first, by comparing its performance to the expert opinion of the course instructors on test/holdout data; and second, by embedding it in a live course, where it was actively used to facilitate seeding by the course instructors. For each reading assignment in the course, we presented a ranked list of seeding recommendations to the course instructors, who could review the list and filter out seeds with inconsistent or malformed content. We then ran a randomized controlled study, in which one group of students was shown seeds that were recommended by the ML model, and another group was shown seeds that were recommended by an alternative model that ranked seeds purely by the length of discussion that was generated in previous course instances. We found that the group of students that received posts from either seeding model generated more discussion than a control group in the course that did not get seeded posts. Furthermore, students who received seeds selected by the ML-based model showed higher levels of engagement, as well as greater learning gains, than those who received seeds ranked by length of discussion.
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DOI：
10.1080/08886504.2000.10782307
发表时间：
2000-12
期刊：
Journal of Research on Computing in Education
影响因子：
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作者：
Dawn M. Poole
通讯作者：
Dawn M. Poole
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DOI：
--
发表时间：
2006
期刊：
影响因子：
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作者：
S. Plaut
通讯作者：
S. Plaut
开放课程：通过敞开的门——作为研究、学习和参与的开放课程
DOI：
--
发表时间：
2010
期刊：
影响因子：
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作者：
D. Cormier;George Siemens
通讯作者：
George Siemens
困惑与超越：使用学生的主题标签检测课程论坛中的困惑
DOI：
10.1145/3375462.3375485
发表时间：
2020
期刊：
LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge
影响因子：
--
作者：
Geller, Shay A.;Hoernle, Nicholas;Gal, Kobi;Segal, Avi;Zhang, Amy X.;Karger, David;Facciotti, Marc T.;Igo, Michele
通讯作者：
Igo, Michele