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Mitrovic, Antonija, Ed.; Bosch, Nigel, Ed. – International Educational Data Mining Society, 2022
For this 15th iteration of the International Conference on Educational Data Mining (EDM 2022), the conference was held in Durham, England, with an online hybrid format for virtual participation as well. EDM is organized under the auspices of the International Educational Data Mining Society. The theme of this year's conference is Inclusion,…
Descriptors: Information Retrieval, Data Analysis, Feedback (Response), Inclusion
Cunningham, Jim – International Educational Data Mining Society, 2015
In this paper, I describe preliminary work on a new research project in learning analytics at Arizona State University. In conjunction with an innovative remedial mathematics course using Khan Academy and student coaches, this study seeks to measure the effectiveness of visualized data in assisting student coaches as they help remedial math…
Descriptors: Research Projects, Remedial Mathematics, Coaching (Performance), Visual Aids
Popescu, Paul Stefan – International Educational Data Mining Society, 2015
In this digital era, learning from data gathered from different software systems may have a great impact on the quality of the interaction experience. There are two main directions that come to enhance this emerging research domain, Intelligent Data Analysis (IDA) and Human Computer Interaction (HCI). HCI specific research methodologies can be…
Descriptors: Data Analysis, Electronic Learning, Interaction, Design
Liu, Zhongxiu – International Educational Data Mining Society, 2015
Data-driven methods have been a successful approach to generating hints for programming problems. However, the majority of previous studies are focused on procedural hints that aim at moving students to the next closest state to the solution. In this paper, I propose a data-driven method to generate remedy hints for BOTS, a game that teaches…
Descriptors: Programming, Educational Games, Puzzles, Problem Solving
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Jiang, Yuheng; Golab, Lukasz – International Educational Data Mining Society, 2016
We propose a graph mining methodology to analyze the relationships among academic programs from the point of view of cooperative education. The input consists of student - job interview pairs, with each student labelled with his or her academic program. From this input, we build a weighted directed graph, which we refer to as a program graph, in…
Descriptors: Undergraduate Students, Student Placement, Cooperative Education, Research Methodology
Bergner, Yoav; Kerr, Deirdre; Pritchard, David E. – International Educational Data Mining Society, 2015
Determining how learners use MOOCs effectively is critical to providing feedback to instructors, schools, and policy-makers on this highly scalable technology. However, drawing inferences about student learning outcomes in MOOCs has proven to be quite difficult due to large amounts of missing data (of various kinds) and to the diverse population…
Descriptors: Online Courses, Data Analysis, Discussion Groups, Outcomes of Education
Stamper, John C.; Lomas, Derek; Ching, Dixie; Ritter, Steve; Koedinger, Kenneth R.; Steinhart, Jonathan – International Educational Data Mining Society, 2012
Traditional experimental paradigms have focused on executing experiments in a lab setting and eventually moving successful findings to larger experiments in the field. However, data from field experiments can also be used to inform new lab experiments. Now, with the advent of large student populations using internet-based learning software, online…
Descriptors: Internet, Feedback (Response), Computer Software, Data Collection
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection