The increasing use of learning management systems (LMSs) generates vast amounts of clickstream data, opening new avenues for predicting learner performance. Traditionally, LMS predictive analytics have relied on either supervised machine learning or Markov models to classify learners based on predicted learning outcomes. Machine learning excels at…

Poor academic performance remains among the most concerning educational issues, especially in higher education and online learning. To address the concern, institutions like the University of Southeastern Philippines (USeP) leverage educational data mining (EDM) techniques to generate relevant information from learning management systems (LMS)…

E-learning readiness (ELR) is critical for implementing digital education strategies, particularly in developing countries where online learning faces unique challenges. This study aims to provide a concise and actionable framework for assessing and predicting ELR in Algerian universities by combining the ADKAR model with advanced machine learning…

Learning interaction activities are the key part of tracking and evaluating learning behaviors, that plays an important role in data-driven autonomous learning and optimized learning in interactive learning environments. In this study, a big data set of learning behaviors with multiple learning periods is selected. According to the instance…

It is very important to identify talented students as soon as they are admitted to college so that appropriate resources are provided and allocated to them to optimize and excel in their education. Currently, this process is labor-intensive and time-consuming, as it involves manual reviews of each student's academic record. This raises the…

"Enhancing Smart Universities with Emotional Intelligence" investigates the successful blending of technology innovations and human beings' emotional intelligence within higher education institutions in the midst of digital transformation. Today's "smart" universities improve student experiences, expedite administrative…

This study introduces a modular teaching framework for business data analytics (BDA) curricula and programs. The framework integrates gamification features of the SAP business processes, ERPsim Games, and SAP data warehousing into the experiential learning of BDA curricula. The pedagogical practices of deploying the framework in an undergraduate…

Purpose: This paper aims to investigate the relationship between the students' digital activities and their academic performance through two stages. In the first stage, students' digital activities were studied and clustered based on the attributes of their activity log of learning management system (LMS) data set. In the second stage, the…

Due to the precautionary measures during the COVID-19 pandemic many universities offered unproctored take-home exams. We propose methods to detect potential collusion between students and apply our approach on event log data from take-home exams during the pandemic. We find groups of students with suspiciously similar exams. In addition, we…

This study explores the extent to which college context (two- vs. four-year), gender, and race/ethnicity correlated with worsening course outcomes during emergency remote teaching during the COVID-19 pandemic, by comparing outcomes within students between the fall 2019 pre-pandemic and spring 2020 pandemic terms. In particular, it explores the…

Typical data science instruction uses generic datasets like survival rates on the Titanic, which may not be motivating for students. Will introducing real-life data science problems fill this motivational deficit? To analyze this question, we contrasted learning with generic datasets and artificial problems (Phase 1) with a community-sourced…

This study sought to understand factors influencing student satisfaction with online learning technologies in U.S. higher education institutions after the spring semester of 2020 when the COVID-19 pandemic forced all higher education to an online format. Understanding these factors can help universities acknowledge student preferences and improve…

The current study provides an integrated comprehensive analysis of mediation-moderation models to understand 567 tourism and hospitality students' viewpoints by exploring multidisciplinary contributions relevant to the big data and new technology application bodies of literature. The results show that self-efficacy was the primary motivation…

Educational Data Mining (EDM) is an emerging field that aims to better understand students' behavior patterns and learning environments by employing statistical and machine learning methods to analyze large repositories of educational data. Analysis of variable data in the early stages of a course might be used to develop a comprehensive…

A comparative longitudinal data analysis between two online non-thesis master's programs--natural resource management and environmental science--in a college of natural resources to determine the relationship between student characteristics and disenrollment risks. Risks varied between the two programs, with significance found to increase the risk…