Udoinyang G. Inyang

Work place: Department of Computer Science, University of Uyo, Nigeria

E-mail: udoinyanginyang@uniuyo.edu.ng


Research Interests: Autonomic Computing, Speech Recognition, Speech Synthesis, Data Mining, Data Structures and Algorithms


Udoinyang G. Inyang received the B.Sc. degree in Computer Science from University of Uyo, Nigeria in 1996. He received the M. Sc and PhD. degrees in Computer Science from The Federal
University of Technology Akure, in 2005 and 2012, respectively. He is a Senior lecturer in the department of Computer Science University of Uyo, Nigeria and has many publications in reputable international journals. His research interests include Soft Computing, Speech Technology, Data Mining and Knowledge Engineering. He is a member of IEEE, Computer Professionals Registration Council of Nigeria (CPN) and Nigerian Computer Society among other professional bodies.

Author Articles
Visual Association Analytics Approach to Predictive Modelling of Students’ Academic Performance

By Udoinyang G. Inyang Imo J. Eyoh Samuel A. Robinson Edward N. Udo

DOI: https://doi.org/10.5815/ijmecs.2019.12.01, Pub. Date: 8 Dec. 2019

Persistent and quality graduation rates of students are increasingly important indicators of progressive and effective educational institutions. Timely analysis of students’ data to guide instructors in the provision of academic interventions to students who are at risk of performing poorly in their courses or dropout is vital for academic achievement. In addition there is need for performance attributes relationship mining for the generation of comprehensible patterns. However, there is dearth in pieces of knowledge relating to predicting students’ performance from patterns. This therefore paper adopts hierarchical cluster analysis (HCA) to analyze students’ performance dataset for the discovery of optimal number of fail courses clusters and partitioning of the courses into groups, and association rule mining for the extraction of interesting course-status association. Agglomerative HCA with Ward’s linkage method produced the best clustering structure (five clusters) with a coefficient of 92% and silhouette width 0.57. Apriori algorithm with support (0.5%), confidence (80%) and lift (1) thresholds were used in the extraction of rules with student’s status as consequent. Out of the twenty one courses offered by students in the first year, seven courses frequently occur together as failed courses, and their impact on the respective students’ performance status were assessed in the rules. It is conjectured that early intervention by the instructors and management of educational activities on these seven courses will increase the students’ learning outcomes leading to increased graduation rate at minimum course duration, which is the overarching objective of higher educational institutions. As further work, the integration of other machine learning and nature inspired tools for the adaptive learning and optimization of rules respectively would be performed.

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