Studies have shown that on-demand assistance, additional instruction given on a problem per student request, improves student learning in online learning environments. Students may have opinions on whether an assistance was effective at improving student learning. As students are the driving force behind the effectiveness of assistance, there…

This paper describes two classes of advanced data mining methods that can obtain actionable insight from cooperative education data: text mining of job descriptions and graph mining of job interview data. While these methods are not new in general, they have not been widely used in co-operative education research. A technical overview of each…

The purpose of this research was to build a classification model and to measure the correlation of self-efficacy with visual-verbal preferences using data mining methods. This research used the J48 classifier and linear projection method as an approach to see patterns of data distribution between self-efficacy and visual-verbal preferences. The…

Many researchers who are interested in studying students' online self-regulated learning (SRL) have heavily relied on self-reported surveys. Data mining is an alternative technique that can be used to discover students' SRL patterns from large data logs saved on a course management system. The purpose of this study was to identify students' online…

This paper presents a tool for creating student models in logistic regression. Creating student models has typically been done by expert selection of the appropriate terms, beginning with models as simple as IRT or AFM but more recently with highly complex models like BestLR. While alternative methods exist to select the appropriate predictors for…

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…

The purpose of this study is to predict the mathematical literacy levels of the students participating in the research through the data obtained from PISA 2015 exam organized by OECD using data mining and to determine the variables that affect mathematics literacy. For this purpose, students' mathematics literacy levels and the variables that…

The persistent difficulty of retaining college students through graduation has become a global problem. The purpose of this quantitative, descriptive, and retrospective study was to apply data mining methods, tools, and algorithms to analyze enrollment data for issues affecting STEM students' retention at an historically black college (HBCU). The…

The tremendous growth of educational institutions' electronic data provides the opportunity to extract information that can be used to predict students' overall success, predict students' dropout rate, evaluate the performance of teachers and instructors, improve the learning material according to students' needs, and much more. This paper aims to…

This article demonstrates how a digital environment offers new opportunities for transforming qualitative data into quantitative data in order to use data mining and information visualization for mixed methods research. The digital approach to mixed methods research is illustrated by a framework which combines qualitative methods of multimodal…

The rapid development of education informatization has accumulated a large amount of data for learning analytics, and adopting educational data mining to find new patterns of data, develop new algorithms and models, and apply known predictive models to the teaching system to improve learning is the challenge and vision of the education field in…

Applying Herzberg's motivation-hygiene theory, we studied the determinants of student satisfaction in using R in a Decision Support Systems course that previously used Excel to teach Data Mining and Business Analytics (DMBA). The course is a degree requirement, and prior programming experience is not a prerequisite. We hypothesized that motivators…

Educational Data Mining plays a critical role in advancing the learning environment by contributing state-of-the-art methods, techniques, and applications. The recent development provides valuable tools for understanding the student learning environment by exploring and utilizing educational data using machine learning and data mining techniques.…

The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…

To examine the similarities and differences between two closely related yet distinct fields -- Educational Data Mining (EDM) and Learning Analytics (LA) -- this study conducted a literature review of the empirical studies published in both fields. We synthesized 492 LA and 194 EDM articles published during 2015-2019. We compared the similarities…