ArticlePloS one2021
The impact of timetable on student's absences and performance.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Proactive and reactive engagement of artificial intelligence methods for education: a review.Frontiers in artificial intelligence · 2023Pooled it
- Factors affecting medical students' attendance on clinical attachments: a qualitative focus group study.BMC medical education · 2026Article
- Unveiling ChatGPT text using writing style.Heliyon · 2024Article
- Enhancing tertiary students' programming skills with an explainable Educational Data Mining approach.PloS one · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Student's academic performance is the point of interest for both the student and the academic institution in higher education. This performance can be affected by several factors and one of them is student absences. This is mainly due to the missed lectures and other class activities. Studies related to university timetabling investigate the different techniques and algorithms to design course timetables without analyzing the relationship between student attendance behavior and timetable design. This article first aimed at demonstrating the impact of absences and timetabling design on student's academic performance. Secondly, this study showed that the number of absences can be caused by three main timetable design factors: namely, (1) the number of courses per semester, (2) the average number of lectures per day and (3) the average number of free timeslots per day. This was demonstrated using Educational Data Mining on a large dataset collected from Prince Sultan University. The results showed a high prediction performance reaching 92% when predicting student's GPA based on absences and the factors related to timetabling design. High prediction performance reaching 87% was also obtained when predicting student absences based on the three timetable factors mentioned above. The results demonstrated the importance of designing course timetables in view of student absence behavior. Some suggestions were reported such as limiting the number of enrolled courses based on student's GPA, avoiding busy and almost free days and using automated timetabling to minimize the number of predicted absences. This in turn will help in generating balanced student timetables, and thus improving student academic performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.