A neurological disorder, along with several behavioral issues, may be to blame for a child's subpar performance in the academic journey (such as anxiety, depression, learning disorders, and irritability). These symptoms can be used to diagnose children with ASD, and supervised machine learning models can help differentiate between ASD traits and…

Background: Behavioural intention (BI) has been predicted using other variables by adopting the technology acceptance model (TAM). However, few studies have examined whether BI can predict learning performance. Objectives: The present study used an extended TAM to investigate whether students' BI is a predictor of their listening learning…

This study examines essential factors that affect children' quality of response towards a non-traditional learning platform specifically, self-learning modules (SLMs) as Philippine public school's mode of service-learning delivery. Our objective is to determine the predictive power of access to internet, household food security, and parental…

This work proposes a dropout prediction approach that is able to self-adjust their outcomes at any moment of a degree program timeline. To that end, a rule-based classification technique was used to identify courses, grade thresholds and other attributes that have a high influence on the dropout behavior. This approach, which is generic so that it…

The proliferation of mobile computing technologies is playing major role in the growth of mobile learning (M-learning) market around the globe. The purpose of this paper is to develop a research model in the lines of commonly used models the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Acceptance Model (TAM) by…

The purpose of this research was to measure the acceptance of Google Classroom use by junior high school students using variables such as habits, satisfaction, knowledge, and skills. To achieve the set objective, it adopted the TAM, which measures technology usability and ease of use. TAM also provides a theoretical basis for measuring students'…

The purpose of the study is to analyse and identify the stages of adoption of the blended learning (BL or b-learning) methodology in higher education contexts, and to assess the relationship of these stages with a set of variables related to personal and professional characteristics, attributes perceived on BL and contextual variables. About 980…

Over the last two decades, educational technology (ET) integration has become an increasingly important aspect of higher education, particularly with the growth of online, distance and hybrid courses and degree programs. Furthermore, accrediting agencies such as the Higher Learning Commission (HLC) are paying close attention to online and hybrid…

Early-warning intervention for students at risk of failing their online courses is increasingly important for higher education institutions. Students who show high levels of engagement appear less likely to be at risk of failing, and how engaged a student is in their online experience can be characterized as factors contributing to their social…

Performance prediction is a leading topic in learning analytics research due to its potential to impact all tiers of education. This study proposes a novel predictive modeling method to address the research gaps in existing performance prediction research. The gaps addressed include: the lack of existing research focus on performance prediction…

In the 21st century, attainment of a college degree is more important than ever to achieve economic self-sufficiency, employment, and an adequate standard of living. Projections suggest that by 2020, 65% of jobs available in the U.S. will require postsecondary education. This reality creates an unprecedented demand for higher education, and…

Logistic regression is a popular tool used to build and evaluate binary choice models. It has been applied in a variety of situations and contexts involving dichotomous choice. In the current paper, we apply it to explain and predict the individual choice of adopting online learning through a Massive Online Open Course (MOOC), a specific artefact…

The past few years has seen the rapid growth of data mining approaches for the analysis of data obtained from Massive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a student may achieve on a given grade-related assessment based on information, considered as prior performance or prior…

The purpose of this study was to determine what variables predict the use of instructional technology among community college instructors. Legislators, community college administrators, and students expect innovative lessons from instructors that use technology. This study addresses the problem of not knowing what predicts instructional technology…

The Digital Agenda for Europe (2015) states that there will be 825,000 unfilled vacancies for Information and Communications Technology by 2020. This lack of IT professionals stems from the small number of students graduating in computer science. To retain more students in the field, teachers can use remote robotic experiments to explain difficult…