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JTCLT Abstract

Volume 14 Number 2, 2023
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Ma, R., Zheng, M., & Xu, J. (2023). Visualized Evaluation and Intelligent Recommendation of International Chinese MOOCs Based on Learning Data Mining. Journal of Technology and Chinese Language Teaching, 14(2), 1-24.
[马瑞祾, 郑明鉴, & 徐娟. (2023). 基于学习数据挖掘的国际中文慕课评价可视化及智能推荐. 科技与中文教学 (Journal of Technology and Chinese Language Teaching), 14(2), 1-24.]

Full paper


In the context of digital transformation in education, artificial intelligence is steering international Chinese education toward a direction that emphasizes both scaled education and personalized training. However, the evaluation of scaled international Chinese language education, such as Massive Open Online Courses (MOOCs), from a learners’ perspective remains unclear. This paper, following data-driven approaches, collected and analyzed online comments from L2 learners participating in 51 international Chinese MOOCs on Coursera and Chinese University MOOC, resulting in a total of 10,050 valid comments. Employing a series of advanced statistical and textual analyses, including semantic network analysis, text clustering, topic modeling, and sentiment analysis, the article proposes a quantitative evaluation framework for international Chinese MOOCs from a bottom-up approach. This study also designed and developed the 'International Chinese MOOCs Smart Learning Companion System,' achieving the visual presentation of multi-dimensional evaluation results of MOOCs, as well as providing intelligent and personalized recommendations. Based on the results of data mining, the paper puts forward four suggestions for the construction of high-quality international Chinese MOOCs.


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