This study investigated how one's problem-solving style impacts his/her problem-solving performance in technology-rich environments. Drawing upon experiential learning theory, we extracted two behavioral indicators (i.e., planning duration for problem solving and human-computer interaction frequency) to model problem-solving styles in…

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…

The objective of this study is to present and discuss how data visualization can be incorporated into teaching approaches by business faculty in introductory business statistics to strengthen business students' practical skills. Data visualization lessens difficulties in learning statistics by providing opportunities to illustrate analytical…

Online learning systems allow learners to freely access learning contents and record their interactions throughout their engagement with the content. By using data mining techniques on the student log data of those systems, it is possible to examine learning behavior and reveal navigation patterns through learning contents. This study was aimed at…

This study examined the effectiveness of guided data-driven learning (DDL) activities on helping technological university students with a lower-intermediate proficiency level to learn grammar and vocabulary topics for the TOEIC test. The question of whether inductive learners make more progress than deductive learners was also addressed. A total…