Accurately forecasting course enrollment rates in higher education is of great concern in order to minimize unnecessary administrative costs as well as burden to both students and faculty. This research aimed to first recreate course enrollment predictions based on a conditional probability analysis using student data from San Diego State…

Purpose: The purpose of this paper is to reveal the smart collaborative learning service. This concept aims to build teams of learners based on the complementarity of their skills, allowing flexible participation and offering interdisciplinary collaboration opportunities for all the learners. The success of this environment is related to predict…

In this study, we built a personalized hybrid course recommendation system (PHCRS) that considers students' interests, abilities and career development. To meet students' individual needs, we adopted the five most widely used algorithms, including content-based filtering, popularity-based methods, item-based collaborative filtering, user-based…

Educational data mining has become an effective tool for exploring the hidden relationships in educational data and predicting students' academic achievements. This study proposes a new model based on machine learning algorithms to predict the final exam grades of undergraduate students, taking their midterm exam grades as the source data. The…

Graphical representations of data are common in many disciplines. Previous research has found that physics students appear to have better graph comprehension skills than students from social science disciplines, regardless of the task context. However, the graph comprehension skills of physics students have not yet been compared with (veterinary)…

In recent years, there has been an increase in the demand for higher education in Turkey, where the demand, as in most other countries, exceeds what is available. The main purpose of this research is to develop machine learning algorithms for predicting the percentage of student placement based on the data related to the university's academic…

This study aims at finding out students' course success in vocational courses of computer and instructional technologies department by means of machine learning algorithms. In the scope of the study, a dataset was formed with demographic information and exam scores obtained from the students studying in the Department of Computer Education and…

Traditionally, clustering algorithms focus on partitioning the data into groups of similar instances. The similarity objective, however, is not sufficient in applications where a "fair-representation" of the groups in terms of protected attributes like gender or race, is required for each cluster. Moreover, in many applications, to make…

Recently, machine learning (ML) has evolved and finds its application in higher education (HE) for various data analysis. Studies have shown that such an emerging field in educational technology provides meaningful insights into several dimensions of educational quality. An in-depth analysis of the application of ML could have a positive impact on…

Learning data analytics improves the learning field in higher education using educational data for extracting useful patterns and making better decisions. Identifying potential at-risk students may help instructors and academic guidance to improve the students' performance and the achievement of learning outcomes. The aim of this research study is…

Contribution: This study applies supervised and unsupervised machine learning (ML) techniques to discover which significant attributes that a successful learner often demonstrated in a computer course. Background: Students often experienced difficulties in learning an introduction to computers course. This research attempts to investigate how…

Some of the educational organizations have multi-education paths such as engineering and medicine collages. In such colleges, the behavior of the student in the preparatory year determines which education path the student will join in the future. In this paper, an adaptive recommendation system is proposed for predicting a suitable education…

We encounter variables with little variation often in educational data mining (EDM) due to the demographics of higher education and the questions we ask. Yet, little work has examined how to analyze such data. Therefore, we conducted a simulation study using logistic regression, penalized regression, and random forest. We systematically varied the…

In contrast to simple feedback, which provides students with the correct answer, elaborated feedback provides an explanation of the correct answer with respect to the student's error. Elaborated feedback is thus a challenge for AI in education systems because it requires dynamic explanations, which traditionally require logical reasoning and…

Like many other service organizations, drop-in peer tutoring centers often struggle to determine the required number of qualified tutors necessary to meet learner expectations. Service work is largely a response to probabilistic calls for staff action and therefore difficult to forecast with precision. Moreover, forecasting models under long…