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MOLFAS: a Multi-stage Online Learning hffectiveness Assessment Scheme in MOOC
Proceedings of 2020 Ieee International Conference on Teaching, Assessment, and Learning for Engineering ; : 31-38, 2020.
Article in English | Web of Science | ID: covidwho-1312298
ABSTRACT
Many universities around the world arc now providing online courses on platforms of MOOC (Massive Open Online Course) related to the COVID-19 outbreak. Therefore, it is important for universities to quantify student-learning effectiveness based on MOOC. However, its operation faces many challenges to educational administrators and teachers. The most important thing is that the completion rate of learners is usually low, and therefore it is not comprehensive and objective to directly use online data to assess and predict the student-learning effectiveness. In order to assess multi-stage learning effectiveness of students in MOOC comprehensively, we proposed MOLEAS, a multi-stage online learning effectiveness assessment scheme. First, MOLEAS uses matrix completion to predict missing learning data of students. We take two open courses offered in the icourse MOOC platform as examples to analyze online data, and then we study the student-learning effectiveness using the matrix completion. The prediction results prove the effectiveness and reliability of our model. Then, combined with the prediction, MOLEAS utilizes the influencing factors of student-learning effectiveness, and a series of measures to improve the entire learning effectiveness of students in MOOC. What is more, we design a simulation practice platform which presents strong support to practice online teaching.
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Collection: Databases of international organizations Database: Web of Science Document Type: Article Type of study: Prognostic study Language: English Journal: Proceedings of 2020 Ieee International Conference on Teaching, Assessment, and Learning for Engineering Clinical aspect: Prediction Year: 2020

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Search on Google
Collection: Databases of international organizations Database: Web of Science Document Type: Article Type of study: Prognostic study Language: English Journal: Proceedings of 2020 Ieee International Conference on Teaching, Assessment, and Learning for Engineering Clinical aspect: Prediction Year: 2020
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