•  
  •  
 

Document Type (Journals)

Original Research

Abstract

The selection process for health sciences programs such as occupational therapy is complex, involving a combination of academic and non-academic selection criteria as entry requirements. This study aimed to investigate the predictive value of entry requirement variables on academic performance during the first and second year of an undergraduate occupational therapy program in South Africa. A quantitative, cross-sectional design was utilized, and the sample included 129 first- and second- year occupational therapy students. Data was collected retrospectively through educational data mining of university records and institutional dashboards. The variables analyzed included National Senior Certificate Grade12 (NSC12) grades (Mathematics, Life Sciences, Physical Sciences, English), Composite Index score (CIS), National Benchmark Test (NBT) scores (Academic literacy, Quantitative literacy and Mathematics), and course progression metrics (final course marks). Data analyses included descriptive statistics and logistic regression to identify patterns and relationships between the variables and academic performance. Significant associations were identified between NSC subject marks, NBT scores and academic performance across various courses in the first and second year, highlighting the potential of targeted educational interventions for student entry profiles based on pre-admission criteria. The study findings support the continued application of entry requirements for undergraduate occupational therapy programs, but further investigation is needed to ensure the most significant predictors of success are applied in student admission processes. Targeted interventions and scaffolded learning approaches are needed for students who might enter occupational therapy programs without the required level of foundational knowledge to improve student outcomes in occupational therapy education.

Biography

Eileen du Plooy*

ORCID 0000-0002-4032-2384

Centre for Academic Technologies, Academic Development and Support, University of Johannesburg, South Africa

Postal address: Centre for Academic Technologies, University of Johannesburg, PO Box 524, Auckland Park, 2006, South Africa

*Corresponding Author: Email: Eileend@uj.ac.za

Eileen du Plooy is Senior Manager: Student Advising and Digital Learning Support at the University of Johannesburg and a Senior Fellow (SFHEA) of Advance Higher Education. Her work examines how learning analytics, personalized adaptive learning, and data-informed advising strategies can enhance student success across higher education.

Daleen Casteleijn

ORCID 0000-0002-0611-8662

Department of Occupational Therapy, School of Healthcare Sciences, University of Pretoria, Pretoria, South Africa

Daleen.casteleijn@wits.ac.za

Prof. Daleen Casteleijn is an occupational therapy educator and researcher specializing in mental health, develops outcome measures to track activity participation and evaluates intervention impact. With expertise in instrument development and psychometrics, she publishes widely, chairs a foundation, serves on editorial boards, and regularly presents on a key occupational therapy model.

Denise Franzsen

ORCID 0000-0001-8295-6329

Department of Occupational Therapy, School of Therapeutic Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

Denise.Franzsen@wits.ac.za

Dr. Denise Franzsen has been a lecturer in the Department of Occupational Therapy at the University of Witwatersrand for more than 40 years. She has specialized in research the application of models and frameworks in occupational therapy, academic concessions for students and types of learning.

Gopika Ramkilawon

ORCID 0000-0002-2919-1918

Department of Statistics, Faculty of Natural and Agricultural Sciences

University of Pretoria, Pretoria, South Africa

Gopika.ramkilawon@up.ac.za

Ms. Gopika Ramkilawon is a Senior Research Consultant in the Department of Statistics at the University of Pretoria. She obtained her BSc in Mathematical Statistics, BSc (Hons) in Mathematical Statistics, and MSc in Advanced Data Analytics. Her research interests include applied statistics, distribution theory, epidemiology, machine learning, and statistical methodologies.

Declaration of Interest

The authors report no declarations of interest.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Share

COinS