Placement Model for Students into Appropriate Academic Class using Machine Learning

Anadi Stella Uju, Khalid Haruna, Ibrahim A. Lawal , Raliya Abubakar , Aminu Usman Jibril and Rabi Mustapha

KeywordsPlacement Model; Appropriate Academic Class; Machine Learning; Correlation coefficient


Choosing the right academic major for junior secondary students into senior secondary school will assist both students and their teachers toward achieving the academic goal. Traditionally, students seeking admission into senior classes (Gambia, Sierra Leone, Ghana, Liberia and Nigeria) must have passed stipulated examinations like Basic Education Certificate Examination (BECE) and/or West Africa Junior Certificate Examination, which are done at the end of year three (at a sitting). They must pass the exam(s) satisfactorily with no emphasis on any of Science, Art or Commercial related subjects. Some schools use “Mock exam” or “Placement exam” as the basis for their placement of students but all are done at a sitting (end of year three). Though this method is to an extent valid but associated with some challenges (bias) as it does not carry along the student’s academic history in making decision for placement into appropriate class. In this research, we proposed a model that predicts appropriate academic class of Science, Art or Commercial for Junior students based on the progressive academic performances (history) of their predecessors on related subjects using ten supervised machine learning techniques. Two evaluation techniques were applied. The validity of the proposed model was tested by comparing the proposed model against a baseline, in which the proposed model showed a better performance in predicting appropriate academic class. The correlation coefficient between the proposed model and the baseline was 0.3. This research will benefit all stakeholders in education and students in particular because their academic performances over time stands a better chance for appropriate placement.