Design and Implementation of an Intelligent System to Predict the Student Graduation AGPA
AbstractSince Accumulated Grad-Point Average (AGPA) is crucial in the professional life of students, it is an interesting and challenging problem to create profiles for those students who are likely to graduate with low AGPA. Identifying this kind of students accurately will enable the university staff to help them improve their ability by providing them with special academic guidance and tutoring. In this paper, using a large and feature rich dataset of marks of high secondary school subjects, we developed a data mining model to classify the newly-enrolled students into two groups; “weak students” (i.e. students who are likely to graduate with low AGPA) and “normal students” (i.e. students who are likely to graduate with high AGPA). We investigated the suitability of evolving fuzzy clustering methods to predict the ability of students graduating in five disciplines at Sultan Qaboos University in the Sultanate of Oman. A solid test has been conducted to determine the model quality and validity. The experimental results showed a high level of accuracy, ranging from 71%-84%. This accuracy revealed the suitability of evolving fuzzy clustering methods for predicting the students’ AGPA.
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).