Successful computer-based assessments for learning greatly rely on an effective learner modeling approach to analyze learner data and evaluate learner behaviors. In addition to explicit learning performance (i.e., product data), the process data logged by computer-based assessments provide a treasure trove of information about how learners solve…

Background: This study aims to assess how well several machine learning (ML) algorithms predict the physical, social, and mental health condition of university students. Objectives: The physical health measurements used in the study include BMI (Body Mass Index), %BF (percentage of Body Fat), BSC (Blood Serum Cholesterol), SBP (Systolic Blood…

Adaptive online learning can be realized through the evaluation of the learning process. Monitoring and supervising learners' cognitive levels and adjusting learning strategies can increasingly improve the quality of online learning. This analysis is made possible by real-time measurement of learners' cognitive levels during the online learning…

The present study compared the diagnostic accuracy of a single computer adaptive test (CAT), Star Reading or Star Math, and a combination of the two in a gated screening framework to predict end-of-year proficiency in reading and math. Participants included 13,009 students in Grades 3-8 who had at least one fall screening score and end-of-year…

Predicting academic performance for students majoring in computer science has long been a significant field of research in computing education. Previous studies described that accurate prediction of students' early-stage performance could identify low-performing students and take corrective action to improve performance. Besides, adopting machine…

Many instructors use a syllabus quiz to ensure that students learn and understand the content of the syllabus. In this project, I move beyond this exercise's primary function and examine students' syllabus quiz scores to see if they can predict how well students perform in the course overall. Using data from 495 students enrolled in 18 sections of…

This study highlights the role and importance of advanced, machine learning-driven predictive models in enhancing the accuracy and timeliness of identifying students at-risk of negative academic outcomes in data-driven Early Warning Systems (EWS). K-12 school districts have, at best, 13 years to prepare students for adulthood and success. They…

Online formative quizzes have been shown to be an effective tool for improving students' academic achievement. This quasi-experimental study investigated the effects of students' engagement in online formative quizzes on academic achievement in an undergraduate engineering course, employing a one-group post-test research design. Participants (n =…

Online quizzes and learning platforms provided by textbook publishers have become common components of undergraduate education. Here, I examine how participation in these formative assessments related to student course performance. Over multiple semesters, students completed either free online unlimited attempt quizzes or assignments from a…

This study evaluated the forecasting accuracy of trend estimation methods applied to time-series data from computer adaptive tests (CATs). Data were collected roughly once a month over the course of a school year. We evaluated the forecasting accuracy of two regression-based growth estimation methods (ordinary least squares and Theil-Sen). The…

Purpose: This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability. Design/methodology/approach: The real-world…