Scheduling Synchronous Tutoring Sessions in Learning Activities

Gueye, Amadou Dahirou; Faye, Pape Mamadou Djidiack; Gueye, Bounama; Lishou, Claude

doi:10.1007/978-3-030-11932-4_33

Cited by 2 publications

(2 citation statements)

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“…This is obviously a problem for the early prevention of the at-risk student. A further step in the analysis of logs has been taken with the incorporation of plugins that are inserted in LMSs [2,[23][24][25][26][27][28][29][30][31] and facilitate the analysis and interpretation of the logs. The use of these tools provides a series of advances in the organisation and interpretation of the data and in the detection of the student at risk in the different learning objects (activities and resources), because it allows the teacher to choose the standard deviation (SD) that they consider to be an indicator of the detection of at-risk students (1, 2, 3, etc., SD) in their subject.…”

Section: Learning Analytics Proceduresmentioning

confidence: 99%

Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques

et al. 2021

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In this study, we used a module for monitoring and detecting students at risk of dropping out. We worked with a sample of 49 third-year students in a Health Science degree during a lockdown caused by COVID-19. Three follow-ups were carried out over a semester: an initial one, an intermediate one and a final one with the UBUMonitor tool. This tool is a desktop application executed on the client, implemented with Java, and with a graphic interface developed in JavaFX. The application connects to the selected Moodle server, through the web services and the REST API provided by the server. UBUMonitor includes, among others, modules for log visualisation, risk of dropping out, and clustering. The visualisation techniques of boxplots and heat maps and the cluster analysis module (k-means ++, fuzzy k-means and Density-based spatial clustering of applications with noise (DBSCAN) were used to monitor the students. A teaching methodology based on project-based learning (PBL), self-regulated learning (SRL) and continuous assessment was also used. The results indicate that the use of this methodology together with early detection and personalised intervention in the initial follow-up of students achieved a drop-out rate of less than 7% and an overall level of student satisfaction with the teaching and learning process of 4.56 out of 5.

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Section: Learning Analytics Proceduresmentioning

confidence: 99%

Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques

et al. 2021

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“…In another, the use of hypermedia smart tutoring systems explained 60.4% of the variance in learning outcomes [34]. Similarly, in various other studies the use of PBL methodology predicted learning outcomes on quizzes by 42.3%, and the use of effective learning patterns by 74.2% (these data refer to the multiple correlation coefficient or determination coefficient (R 2 ), which represents the proportion of the variability explained by the independent variable with respect to the dependent ones), in addition to increasing student satisfaction with the teaching-learning process [3,[30][31][32][33][34].…”

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confidence: 99%

Monitoring Students at the University: Design and Application of a Moodle Plugin

Manzanares

García‐Osorio

2020

Applied Sciences

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Early detection of at-risk students is essential, especially in the university environment. Moreover, personalized learning has been shown to increase motivation and lower student dropout rates. At present, the average dropout rates among students following courses leading to the award of Spanish university degrees are around 18% and 42.8% for presential teaching and online courses, respectively. The objectives of this study are: (1) to design and to implement a Modular Object-Oriented Dynamic Learning Environment (Moodle) plugin, “eOrientation”, for the early detection of at-risk students; (2) to test the effectiveness of the “eOrientation” plugin on university students. We worked with 279 third-year students following health sciences degrees. A process for extracting information records was also implemented. In addition, a learning analytics module was developed, through which both supervised and unsupervised Machine Learning techniques can be applied. All these measures facilitated the personalized monitoring of the students and the easier detection of students at academic risk. The use of this tool could be of great importance to teachers and university governing teams, as it can assist the early detection of students at academic risk. Future studies will be aimed at testing the plugin using the Moodle environment on degree courses at other universities.

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Scheduling Synchronous Tutoring Sessions in Learning Activities

Cited by 2 publications

References 3 publications

Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques

Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques

Monitoring Students at the University: Design and Application of a Moodle Plugin

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