Early Detection of At-Risk Students Using Machine Learning Based on LMS Log Data

Kondo, Nobuhiko; Okubo, Midori; Hatanaka, Toshiharu

doi:10.1109/iiai-aai.2017.51

Cited by 35 publications

(13 citation statements)

References 2 publications

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“…Results show that identifying the changes in student activity during the course period could help in detecting at-risk students. In Kondo et al [43] was proposed an automatic method to detect at-risk students by using log data of the LMS. Experimental results indicated that using this log data, some characteristics of behavior about learning which affect the student outcomes can be detected.…”

Section: Only Interaction Data/log Filesmentioning

confidence: 99%

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

et al. 2019

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Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learning techniques can help in this direction by providing models able to detect at-risk students earlier. Therefore, lecturers, tutors or staff can pedagogically try to mitigate this problem. To early predict at-risk students in introductory programming courses, we present a comparative study aiming to find the best combination of datasets (set of variables) and classification algorithms. The data collected from Moodle was used to generate 13 distinct datasets based on different aspects of student interactions (cognitive presence, social presence and teaching presence) inside the virtual environment. Results show there are no statistically significant difference among models generated from the different datasets and that the counts of interactions together with derived attributes are sufficient for the task. The performances of the models varied for each semester, with the best of them able to detect students at-risk in the first week of the course with AUC ROC from 0.7 to 0.9. Moreover, the use of SMOTE to balance the datasets did not improve the performance of the models.

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Section: Only Interaction Data/log Filesmentioning

confidence: 99%

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

et al. 2019

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“…Authors of [8] (Article 3) also suggest a method for detection of students who seem to be expelled at the end of the course. Authors use machine learning methods to make the prediction of the further academic performance of students.…”

Section: Related Workmentioning

confidence: 99%

Analysis of student activity on the e-learning course based on «OpenEdu» platform logs

Barsukov

Sysoyev

Pereskokova

et al. 2020

Proceedings of ISP RAS

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A lot of people nowadays use online education platforms. Most of them run on the free «Open edX» open-source software platform. Using the logs that the platform provides us, we can get psychometrics of students, which can be used to improve the presentation of material or other things, which can increase the quality of online courses. We provide a ready-to-use tool that will help figure out how and for what purpose you can analyze the log files of platforms based on «Open edX».

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“…In fact, there are previous studies about learning analytics with Moodle data in medical education [12] [13]. They might be useful for analyzing and finding at-risk students [14].…”

Section: Learning Analyticsmentioning

confidence: 99%

The Trend of Institutional Research in Japanese Medical Education: A Case Study from Jichi Medical University

Asada

2019

International Journal of Institutional Research and Management

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The importance of IR (institutional research) has seen a recent increase in the field of medical education. The main reason for this trend is the need for accreditation of medical education based on global standards. In this paper, the current IR system, including the structure and contents of databases, and IR topics of the Jichi Medical University are described. Although there are useful previous studies on IR, medical universities and colleges have specific aspects that may not have been covered by previous studies. Further studies are therefore needed in this research field.

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Early Detection of At-Risk Students Using Machine Learning Based on LMS Log Data

Cited by 35 publications

References 2 publications

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

Analysis of student activity on the e-learning course based on «OpenEdu» platform logs

The Trend of Institutional Research in Japanese Medical Education: A Case Study from Jichi Medical University

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