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International Journal of Artificial Intelligence in Education最新文献

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Three Algorithms for Grouping Students: A Bridge Between Personalized Tutoring System Data and Classroom Pedagogy 学生分组的三种算法:个性化教学系统数据与课堂教学之间的桥梁
IF 4.9 Q1 Social Sciences Pub Date : 2022-09-12 DOI: 10.1007/s40593-022-00309-y
Christopher G. Lechuga, Shayan Doroudi
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引用次数: 1
Can Multi-Label Classifiers Help Identify Subjectivity? A Deep Learning Approach to Classifying Cognitive Presence in MOOCs. 多标签分类器能否帮助识别主观性?对 MOOC 中的认知存在进行分类的深度学习方法。
IF 4.9 Q1 Social Sciences Pub Date : 2022-09-02 DOI: 10.1007/s40593-022-00310-5
Yuanyuan Hu, Claire Donald, Nasser Giacaman

This paper investigates using multi-label deep learning approach to extending the understanding of cognitive presence in MOOC discussions. Previous studies demonstrate the challenges of subjectivity in manual categorisation methods. Training automatic single-label classifiers may preserve this subjectivity. Using a triangulation approach, we developed a multi-label, fine-tuning BERT classifier to analyse cognitive presence to enrich results with state-of-the-art, single-label classifiers. We trained the multi-label classifiers on the MOOC discussion messages that were categorised into the same phase of cognitive presence by the expert coders, and tested the best-performing classifiers on the messages that the coders categorised into different phases. The results suggest that multi-label classifiers slightly outperformed the single-label classifiers, and the multi-label classifiers predicted the discussion messages as either one category or two adjacent categories of cognitive presence. No messages were tagged as non-adjacent categories by the multi-label classifier. This is an improvement compared to manual categorisation by our expert coders, who obtained non-adjacent categories and even three categories of cognitive presence in one message. In addition to the fully correct prediction, parts of messages were partially correctly predicted by the multi-label classifier. We report an in-depth quantitative and qualitative analysis of these messages in the paper. The automatic categorisation results suggest that the multi-label classifiers have the potential to help educators and researchers identify research subjectivity and tolerate the multiplicity in cognitive presence categorisation. This study contributes to extending the literature on understanding cognitive presence in MOOC discussions.

本文研究使用多标签深度学习方法来扩展对 MOOC 讨论中认知存在的理解。以往的研究表明,人工分类方法存在主观性的挑战。训练自动单标签分类器可能会保留这种主观性。利用三角测量法,我们开发了一种多标签、微调 BERT 分类器来分析认知存在,以丰富最先进的单标签分类器的结果。我们在被专家编码员归类为同一认知存在阶段的 MOOC 讨论信息上训练了多标签分类器,并在被编码员归类为不同阶段的信息上测试了表现最佳的分类器。结果表明,多标签分类器的表现略优于单标签分类器,多标签分类器将讨论信息预测为认知存在的一个类别或两个相邻类别。没有一条信息被多标签分类器标记为非相邻类别。与我们的专家编码员进行的人工分类相比,这是一个进步,因为专家编码员在一条信息中获得了不相邻的类别,甚至三个认知存在类别。除了完全正确的预测外,多标签分类器还对部分信息进行了部分正确预测。我们在论文中对这些信息进行了深入的定量和定性分析。自动分类结果表明,多标签分类器有可能帮助教育工作者和研究人员识别研究的主观性,并容忍认知存在分类的多重性。本研究有助于扩展有关理解 MOOC 讨论中认知存在的文献。
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引用次数: 0
Making FAQ Chatbots More Inclusive: An Examination of Non-Native English Users’ Interactions with New Technology in Massive Open Online Courses 让常见问题聊天机器人更具包容性:大规模在线开放课程中非英语母语用户与新技术互动的研究
IF 4.9 Q1 Social Sciences Pub Date : 2022-09-01 DOI: 10.1007/s40593-022-00311-4
Song-Ae Han, Min Liu, Zilong Pan, Ying Cai, Peixia Shao
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引用次数: 3
Teaching How to Teach Promotes Learning by Teaching 教以教促学
IF 4.9 Q1 Social Sciences Pub Date : 2022-08-31 DOI: 10.1007/s40593-022-00306-1
Noboru Matsuda, Dan Lv, Guoguo Zheng
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引用次数: 0
Supporting College Choice Among International Students through Collaborative Filtering. 通过协同过滤支持留学生选择大学。
IF 4.9 Q1 Social Sciences Pub Date : 2022-08-19 DOI: 10.1007/s40593-022-00307-0
Caitlin Tenison, Guangming Ling, Laura McCulla

In this paper we use historic score-reporting records and test-taker metadata to inform data-driven recommendations that support international students in their choice of undergraduate institutions for study in the United States. We investigate the use of Structural Topic Modeling (STM) as a context-aware, probabilistic recommendation method that uses test-takers' selections and metadata to model the latent space of college preferences. We present the model results from two perspectives: 1) to understand the impact of TOEFL score and test year on test-takers' preferences and choices and 2) to recommend to the test-taker additional undergraduate institutions for application consideration. We find that TOEFL scores can explain variance in the probability that test-takers belong to certain preference-groups and, by accounting for this, our system adjusts recommendations based on student score. We also find that the inclusion of year, while not significantly altering recommendations, does enable us to capture minor changes in the relative popularity of similar institutions. The performance of this model demonstrates the utility of this approach for providing students with personalized college recommendations and offers a useful baseline approach that can be extended with additional data sources.

在本文中,我们利用历史分数报告记录和考生元数据为数据驱动的推荐提供信息,帮助留学生选择在美国学习的本科院校。我们研究了结构主题建模(STM)作为一种情境感知的概率推荐方法的使用情况,该方法使用考生的选择和元数据对大学偏好的潜在空间进行建模。我们从两个角度介绍了模型结果:1)了解托福分数和考试年份对考生偏好和选择的影响;2)向考生推荐更多本科院校供其申请考虑。我们发现,托福分数可以解释考生属于某些偏好群体的概率差异,通过考虑这一点,我们的系统可以根据学生分数调整推荐。我们还发现,加入年份虽然不会显著改变推荐结果,但却能使我们捕捉到类似院校相对受欢迎程度的细微变化。该模型的表现证明了这种方法在为学生提供个性化大学推荐方面的实用性,并提供了一种有用的基准方法,可以通过其他数据源进行扩展。
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引用次数: 0
Towards a Tutoring System to Support Robotics Activities in Classrooms – Two Wizard-of-Oz Studies 建立一个支持教室机器人活动的辅导系统——两个绿野仙踪研究
IF 4.9 Q1 Social Sciences Pub Date : 2022-08-19 DOI: 10.1007/s40593-022-00305-2
S. Schulz, B. McLaren, Niels Pinkwart
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引用次数: 0
Evaluating the Construct Validity of an Automated Writing Evaluation System with a Randomization Algorithm 基于随机化算法的自动写作评价系统结构有效性评价
IF 4.9 Q1 Social Sciences Pub Date : 2022-08-09 DOI: 10.1007/s40593-022-00301-6
Matthew C. Myers, Joshua Wilson
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引用次数: 0
An AI toolkit to support teacher reflection 支持教师反思的人工智能工具包
IF 4.9 Q1 Social Sciences Pub Date : 2022-08-09 DOI: 10.1007/s40593-022-00295-1
Tanner M. Phillips, Asmalina Saleh, Gamze Ozogul
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引用次数: 0
First-Year Engineering Students’ Strategies for Taking Exams 工科一年级学生应试策略
IF 4.9 Q1 Social Sciences Pub Date : 2022-08-02 DOI: 10.1007/s40593-022-00303-4
Jakub Kuzilek, Z. Zdráhal, Jonas Vaclavek, Viktor Fuglík, J. Skočilas, A. Wolff
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引用次数: 1
Automated Essay Writing: An AIED Opinion 自动化论文写作:AIED观点
IF 4.9 Q1 Social Sciences Pub Date : 2022-08-02 DOI: 10.1007/s40593-022-00300-7
M. Sharples
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引用次数: 28
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International Journal of Artificial Intelligence in Education
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