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Nachouki M, Mohamed EA, Mehdi R, Abou Naaj M. Student course grade prediction using the random forest algorithm: Analysis of predictors' importance. Trends Neurosci Educ 2023; 33:100214. [PMID: 38049293 DOI: 10.1016/j.tine.2023.100214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 09/11/2023] [Accepted: 09/12/2023] [Indexed: 12/06/2023]
Abstract
BACKGROUND Universities need to find strategies for improving student retention rates. Predicting student academic performance enables institutions to identify underachievers and take appropriate actions to increase student completion and lower dropout rates. METHOD In this work, we proposed a model based on random forest methodology to predict students' course performance using seven input predictors and find their relative importance in determining the course grade. Seven predictors were derived from transcripts and recorded data from 650 undergraduate computing students. RESULTS Our findings indicate that grade point average and high school score were the two most significant predictors of a course grade. The course category and class attendance percentage have equal importance. Course delivery mode does not have a significant effect. CONCLUSION Our findings show that courses students at risk find challenging can be identified, and appropriate actions, procedures, and policies can be taken.
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Affiliation(s)
- Mirna Nachouki
- Artificial Intelligence Research Centre, Department of Information Technology, Ajman University, UAE.
| | - Elfadil A Mohamed
- Artificial Intelligence Research Centre, Department of Information Technology, Ajman University, UAE
| | - Riyadh Mehdi
- Artificial Intelligence Research Centre, Department of Information Technology, Ajman University, UAE
| | - Mahmoud Abou Naaj
- Artificial Intelligence Research Centre, Department of Information Technology, Ajman University, UAE
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Hecker A, Nischwitz SP, Petritsch J, Holzer-Geissler JCJ, Draschl A, Wegscheider T, Lumenta DB. Undergraduate Skills Training in Pandemic Times: Where Is the Future of Medical Education? Eur J Investig Health Psychol Educ 2023; 13:1219-1228. [PMID: 37504481 PMCID: PMC10377890 DOI: 10.3390/ejihpe13070090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic forced medical programs to rapidly switch to remote teaching from scratch, impacting hands-on skills training. This study compared the efficacy of a hybrid online format to a regular in-person session for a mandatory surgical skills class. METHODS Third-year undergraduate medical students attending the surgical skills class in the winter semester of 2020/21 at the Medical University of Graz were randomly assigned to either the hybrid or in-person class, depending on their course schedule and government regulations. The hybrid class involved online videos, one-on-one peer tutoring, and an Objective Structured Clinical Examination (OSCE). Pre- and post-class self-assessments were conducted to evaluate their theoretical and practical knowledge of a single interrupted suture. RESULTS The study included 85 students in the regular in-person class and 50 in the hybrid class. A pre-class assessment revealed higher self-assessments in the hybrid class for theoretical and practical knowledge, but a post-class assessment showed no significant difference. The advantages and disadvantages of both modalities were identified, providing valuable insights for future curriculum development. CONCLUSIONS Both teaching modes were effective for undergraduate surgical skills training. This study recommends implementing positive aspects of both the hybrid and in-person formats while recognizing their respective limitations.
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Affiliation(s)
- Andrzej Hecker
- Division of Plastic, Aesthetic and Reconstructive Surgery, Department of Surgery, Medical University of Graz, 8036 Graz, Austria (D.B.L.)
- Research Unit for Digital Surgery, Division of Plastic, Aesthetic and Reconstructive Surgery, Department of Surgery, Medical University of Graz, 8036 Graz, Austria
| | - Sebastian P. Nischwitz
- Division of Plastic, Aesthetic and Reconstructive Surgery, Department of Surgery, Medical University of Graz, 8036 Graz, Austria (D.B.L.)
- Research Unit for Digital Surgery, Division of Plastic, Aesthetic and Reconstructive Surgery, Department of Surgery, Medical University of Graz, 8036 Graz, Austria
| | - Johanna Petritsch
- Division of Plastic, Aesthetic and Reconstructive Surgery, Department of Surgery, Medical University of Graz, 8036 Graz, Austria (D.B.L.)
| | - Judith C. J. Holzer-Geissler
- Division of Plastic, Aesthetic and Reconstructive Surgery, Department of Surgery, Medical University of Graz, 8036 Graz, Austria (D.B.L.)
| | - Alexander Draschl
- Division of Plastic, Aesthetic and Reconstructive Surgery, Department of Surgery, Medical University of Graz, 8036 Graz, Austria (D.B.L.)
| | - Thomas Wegscheider
- Clinical Skills Center (CSC), Medical University of Graz, 8036 Graz, Austria
| | - David Benjamin Lumenta
- Division of Plastic, Aesthetic and Reconstructive Surgery, Department of Surgery, Medical University of Graz, 8036 Graz, Austria (D.B.L.)
- Research Unit for Digital Surgery, Division of Plastic, Aesthetic and Reconstructive Surgery, Department of Surgery, Medical University of Graz, 8036 Graz, Austria
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