Title: Assessment of students' academic performance in clothing and textile in tertiary institutions using ANN and ANOVA techniques

Authors: Juliana Ego Azonuche; Juliet Obiageli Okoruwa; Comfort Ukrajit Sonye; Gbenga Samuel Oladosu

Addresses: Department of Vocational Education (Home Economics Unit), Delta State University, Abraka, Nigeria ' Department of Vocational Education (Home Economics Unit), Delta State University, Abraka, Nigeria ' Department of Home Economics, Hospitality and Tourism, Faculty of Vocational and Technical Education, Ignatius Ajuru University of Education, Rumuolumi, Port Harcourt, Rivers State, Nigeria ' Department of Vocational Education (Home Economics Unit), Delta State University, Abraka, Nigeria

Abstract: The performance history of 277 students in clothing and textile from two tertiary institutions in southern Nigeria was studied by artificial neural networks (ANN) and analysis of variance (ANOVA) in terms of institution, gender, ordinary level (O-level) qualification, marital status, and age. The study was guided by five research questions and five hypotheses tested at the 0.05 level of significance. ANOVA is utilised to identify significant differences in academic performance among groups formed by the aforementioned factors. The most significant factors identified through ANOVA are used as input features for the ANN model. The dataset for the ANN model development was randomly distributed into three groups training (80%), validation (10%), and testing (10%). Hypothesis testing indicates significant differences in students' academic performance between institutions and based on O-level qualifications. Further research can build upon these findings to enhance the quality of education in the field of clothing and textiles.

Keywords: assessment; performance; clothing; textile; artificial neural network; ANN; analysis of variance; ANOVA.

DOI: 10.1504/IJLC.2024.140888

International Journal of Learning and Change, 2024 Vol.16 No.5, pp.486 - 507

Received: 09 Oct 2023
Accepted: 07 Dec 2023
Published online: 03 Sep 2024 *

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