In the traditional teaching mode, it is difficult for teachers to have a comprehensive understanding of each student's study, and it is also hard for them to provide targeted guidance and assistance. With the development of data collection and analysis technology, schools and educational institutions can make better use of big data technology to…

This study examines a level and model for technology acceptability and use in online learning inside universities. The unified theory of UTAUT is used as an analysis tool. An associative quantitative method is used with a sample of 392 students. Data were collected by distributing questionnaires through a specially designed Google Form. The data…

Students' attitudes towards distance education can be shaped by the compatibility of their learning styles with this new educational environment. The study aimed to investigate whether various variables and e-learning styles predict student's attitudes towards distance education. The present research was conducted on 387 students enrolled in the…

The COVID-19 pandemic has brought the challenge of online teaching to graduate courses, without prior preparation for students and teachers. In this context, the aim of the study was to identify sociodemographic predictors of difficulty in carrying out online academic activities by graduate students, by region of Brazil, during the COVID-19…

Background: Research shows that how formative assessments are operationalized plays a crucial role in shaping their engagement with formative assessments, thereby impacting their effectiveness in predicting academic achievement. Mandatory assessments can ensure consistent student participation, leading to better tracking of learning progress.…

The goal of this narrative review was to summarize the literature findings regarding the occurrence and predictors of burnout syndrome in medical students in the online learning period during the COVID-19 pandemic. This review of identified relevant studies showed that their results were highly heterogeneous, primarily due to the important…

This empirical study uses a random forest algorithm to examine the factors that influence learners' persistence in online learning at a prominent Korean institution. The data were collected from students who began their studies in Spring 2021, and encompassed a range of variables including individual attributes, academic engagement, academic…

This study aims to produce a more personalized recommendation system for online learning using multicriteria in collaborative filtering and data from the Binus Online Learning repository as a knowledge base. The study uses forecasting (regression) and consists of three stages: (1) collecting data on the results of the learning process; (2) adding…