A Machine Learning-Based Recommender System for Improving Students Learning Experiences

Nacim Yanes,Ayman Mohamed Mostafa,Saleh Naif Almuayqil,Mohamed Ezz

doi:10.1109/access.2020.3036336

Nacim Yanes, Ayman Mohamed Mostafa + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.3036336

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Abstract

Outcome-based education (OBE) is a well-proven teaching strategy based upon a predefined set of expected outcomes. The components of OBE are Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs). These latter are assessed at the end of each course and several recommended actions can be proposed by faculty members' to enhance the quality of courses and therefore the overall educational program. Considering a large number of courses and the faculty members' devotion, bad actions could be recommended and therefore undesirable and inappropriate decisions may occur. In this paper, a recommender system, using different machine learning algorithms, is proposed for predicting suitable actions based on course specifications, academic records, and course learning outcomes' assessments. We formulated the problem as a multi-label multi-class binary classification problem and the dataset was translated into different problem transformation and adaptive methods such as one-vs.-all, binary relevance, label powerset, classifier chain, and ML-KNN adaptive classifier. As a case study, the proposed recommender system is applied to the college of Computer and Information Sciences, Jouf University, Kingdom of Saudi Arabia (KSA) for helping academic staff improving the quality of teaching strategies. The obtained results showed that the proposed recommender system presents more recommended actions for improving students' learning experiences.

Highlights

Over the last century, a significant concern on the ability of education systems to equip the students with the adequate professional and career preparation needed for the 21st century has evolved
The proposed machine learning-based recommender system is considered a simulation to the hybrid recommender approach presented in [28] where the student’s or learner’s average score in specific or all modules can affect the quality content that can lead to poor enhancements in student learning outcomes
The results were conducted by testing different problem transformation and adaptive techniques such as OvA, BR, LP, CC, and adaptive ML – KNN with different classification algorithms such as SVC [46, and 47], LR [48], RF [49], Gaussian NB [50], and DT [51]

Summary

Introduction

A significant concern on the ability of education systems to equip the students with the adequate professional and career preparation needed for the 21st century has evolved. Outcome-based education means clearly focusing and organizing everything in educational system which is important for all students to be able to do successfully at the end /program/graduation as stated by many works in the literature including [3, 4, 5, 6, and 7] and many others. The course CS 230 has a teaching mechanism (3, 1, and 0) This means 3 credit hours of theoretical lectures, 1 credit hour for exercises with no practical hours. The course CS 350 which is “Introduction to Database Systems” is considered a theoretical and practical course It has a teaching mechanism (3, 0, and 2) while the course CS 360 basically depends on teaching with labs more than the theoretical lectures. These teaching mechanisms will be a main concept in determining the best recommended actions based on the distribution method of credit hours

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