Learning analytics dashboards (LADs) provide educators and students with a comprehensive snapshot of the learning domain. Visualizations showcasing student learning behavioral patterns can help students gain greater self-awareness of their learning progression, and at the same time assist educators in identifying those students who may be facing…

Since the advent of massive open online courses (MOOC), it has been the focus of educators and learners around the world, however the high dropout rate of MOOC has had a serious negative impact on its popularity and promotion. How to effectively predict students' dropout status in MOOC for early intervention has become a hot topic in MOOC…

One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models' excessive dependence on the low-level…

The article is in the second phase of research is about "the big data architecture for pre-teacher preparation supply chain with prescriptive analytics of higher education in Thailand". The objectives of the study were (1) to study the pre-teacher preparation supply chain in Thailand, (2) to develop a model the big data system for the…

To support online learners at a large scale, extensive studies have adopted machine learning (ML) techniques to analyze students' artifacts and predict their learning outcomes automatically. However, limited attention has been paid to the fairness of prediction with ML in educational settings. This study intends to fill the gap by introducing a…

An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results…

The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice…

This article aims to study the performance of machine learning models in forecasting gender based on the students' open education competency perception. Data were collected from a convenience sample of 326 students from 26 countries using the eOpen instrument. The analysis comprises 1) a study of the students' perceptions of knowledge, skills, and…

Studies examining students' learning behavior predominantly employed rich video data as their main source of information due to the limited knowledge of computer vision and deep learning algorithms. However, one of the challenges faced during such observation is the strenuous task of coding large amounts of video data through repeated viewings. In…

Student outcomes are of great importance in higher education institutions. Accreditation bodies focus on them as an indicator to measure the performance and effectiveness of the institution. Forecasting students' academic performance is crucial for every educational establishment seeking to enhance performance and perseverance of its students and…

Choosing an undergraduate major is an important decision that impacts academic and career outcomes. In this work, we investigate augmenting personalized human advising for major selection using a large language model (LLM), GPT-4. Through a three-phase survey, we compare GPT suggestions and responses for undeclared first- and second-year students…

Educational data mining helps the educational institutions to perform effectively and efficiently by exploiting the data related to all its stakeholders. It can help the at-risk students, develop recommendation systems and alert the students at different levels. It is beneficial to the students, educators and authorities as a whole. Deep learning…

The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, especially in mathematics, is a well-established and proven approach in learning analytics. In this work, we report on our analysis examining the generalizability of BKT models across academic years attributed to "detector rot." We compare the…

This article introduces an ontology-based framework for group assessment analytics that investigates the impact of intra-group interactions on group performance within the context of project-based collaborative learning (PBCL). Additionally, it aims to predict learners' performance based on these interactions. The study involved 312 first-degree…

Learning analytics (LA) is considered a promising field of study as it's helping to improve learning and the context in which it occurs. A learner's performance can be defined as how well students are learning in terms of knowledge and skills development and can be analyzed based on students' outcomes and engagement in the course. We have…