Students' academic performance prediction is one of the most important applications of Educational Data Mining (EDM) that helps to improve the quality of the education process. The attainment of student outcomes in an Outcome-based Education (OBE) system adds invaluable rewards to facilitate corrective measures to the learning processes.…

The EdSurvey package helps users download, explore variables in, extract data from, and run analyses on large-scale assessment data. The analysis functions in EdSurvey account for the use of plausible values for test scores, survey sampling weights, and their associated variance estimator. We describe the capabilities of the package in the context…

Studies have shown that test-takers tend to use keyword-matching strategies when taking listening tests. Keyword-matching involves matching content words in the written modality (test items) against those heard in the audio text. However, no research has investigated the effect of such keywords in listening tests, or the impact of gazing upon…

The large rates of students' failure is a very frequent problem in undergraduate courses, being even more evident in exact sciences. Pointing out the reasons of such problem is a paramount research topic, though not an easy task. An alternative is to use Educational Data Mining techniques (EDM), which enables one to convert data from educational…

Synthesis writing is widely taught across domains and serves as an important means of assessing writing ability, text comprehension, and content learning. Synthesis writing differs from other types of writing in terms of both cognitive and task demands because it requires writers to integrate information across source materials. However, little is…

This paper introduces a novel approach to identify at-risk students with a focus on output interpretability through analyzing learning activities at a finer granularity on a weekly basis. Specifically, this approach converts the predicted output from the former weeks into meaningful probabilities to infer the predictions in the current week for…

This study attempts to predict secondary school students' performance in English and Mathematics subjects using data mining (DM) techniques. It aims to provide insights into predictors of students' performance in English and Mathematics, characteristics of students with different levels of performance, the most effective DM technique for students'…

The need for a knowledge-based society has perpetuated an increasing demand for higher education around the globe. Recently, there has been an increase in the demand for Computer Science professionals due to the rise in the use of ICT in the business, health and education sector. The enrollment numbers in Computer Science undergraduate programmes…

Recent trends in "educational technology" have led to emergence of methods such as teaching analytics (TA) in understanding and management of the teaching-learning processes. Didactically, "teaching analytics" is one of the promising and emerging methods within the Education domain that have proved to be useful, towards…

Introduction: The concept and study of relevance has been a central subject in information science. Although research in information retrieval has been focused on topical relevance, other kinds of relevance are also important and justify further study. Motivational relevance is typically inferred by criteria such as user satisfaction and success.…

Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting…

Committing to a major is a fateful step in an undergraduate education, yet the relationship between courses taken early in an academic career and ultimate major issuance remains little studied at scale. Using transcript data capturing the academic careers of 26,892 undergraduates enrolled at a private university between 2000 and 2020, we describe…

Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on…

Purpose: In this study, Logistic Regression (LR), CHAID (Chi-squared Automatic Interaction Detection) analysis and data mining methods are used to investigate the variables that predict the mathematics success of the students. Research Methods: In this study, a quantitative research design was employed during the data collection and the analysis…

Digit comparison is strongly related to individual differences in children's arithmetic ability. Why this is the case, however, remains unclear to date. Therefore, we investigated the relative contribution of three possible cognitive mechanisms in first and second graders' digit comparison performance: digit identification, digit--number word…