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Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
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Raborn, Anthony W.; Leite, Walter L.; Marcoulides, Katerina M. – International Educational Data Mining Society, 2019
Short forms of psychometric scales have been commonly used in educational and psychological research to reduce the burden of test administration. However, it is challenging to select items for a short form that preserve the validity and reliability of the scores of the original scale. This paper presents and evaluates multiple automated methods…
Descriptors: Psychometrics, Measures (Individuals), Mathematics, Heuristics
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Coleman, Chad; Baker, Ryan S.; Stephenson, Shonte – International Educational Data Mining Society, 2019
Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning…
Descriptors: Prediction, At Risk Students, Predictor Variables, Elementary Secondary Education
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Hu, Qian; Rangwala, Huzefa – International Educational Data Mining Society, 2019
Student's academic performance prediction empowers educational technologies including academic trajectory and degree planning, course recommender systems, early warning and advising systems. Given a student's past data (such as grades in prior courses), the task of student's performance prediction is to predict a student's grades in future…
Descriptors: Academic Achievement, Attention, Prior Learning, Prediction
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Yang, Tsung-Yen; Baker, Ryan S.; Studer, Christoph; Heffernan, Neil; Lan, Andrew S. – International Educational Data Mining Society, 2019
"Sensor-free" detectors of student affect that use only student activity data and no physical or physiological sensors are cost-effective and have potential to be applied at large scale in real classrooms. These detectors are trained using student affect labels collected from human observers as they observe students learn within…
Descriptors: Active Learning, Measurement Techniques, Intelligent Tutoring Systems, Educational Technology
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Choffin, Benoît; Popineau, Fabrice; Bourda, Yolaine; Vie, Jill-Jênn – International Educational Data Mining Society, 2019
Spaced repetition is among the most studied learning strategies in the cognitive science literature. It consists in temporally distributing exposure to an information so as to improve long-term memorization. Providing students with an adaptive and personalized distributed practice schedule would benefit more than just a generic scheduler. However,…
Descriptors: Intervals, Scheduling, Repetition, Memorization
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Han, Yong; Wu, Wenjun; Ji, Suozhao; Zhang, Lijun; Zhang, Hui – International Educational Data Mining Society, 2019
Peer-grading is commonly adopted by instructors as an effective assessment method for MOOCs (Massive Open Online Courses) and SPOCs (Small Private online course). For solving the problems brought by varied skill levels and attitudes of online students, statistical models have been proposed to improve the fairness and accuracy of peer-grading.…
Descriptors: Peer Evaluation, Grading, Online Courses, Computer Assisted Testing
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Liang, Chen; Ye, Jianbo; Zhao, Han; Pursel, Bart; Giles, C. Lee – International Educational Data Mining Society, 2019
Strict partial order is a mathematical structure commonly seen in relational data. One obstacle to extracting such type of relations at scale is the lack of large scale labels for building effective data-driven solutions. We develop an active learning framework for mining such relations subject to a strict order. Our approach incorporates…
Descriptors: Active Learning, Mathematical Concepts, Serial Ordering, Logical Thinking
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Abdi, Solmaz; Khosravi, Hassan; Sadiq, Shazia; Gasevic, Dragan – International Educational Data Mining Society, 2019
The Elo rating system has been recognised as an effective method for modelling students and items within adaptive educational systems. The existing Elo-based models have the limiting assumption that items are only tagged with a single concept and are mainly studied in the context of adaptive testing systems. In this paper, we introduce a…
Descriptors: Models, Foreign Countries, College Students, Multivariate Analysis
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Boumi, Shahab; Vela, Adan – International Educational Data Mining Society, 2019
Simplified categorizations have often led to college students being labeled as full-time or part-time students. However, at many universities student enrollment patterns can be much more complicated, as it is not uncommon for students to alternate between full-time and part-time enrollment each semester based on finances, scheduling, or family…
Descriptors: Markov Processes, Enrollment, College Students, Full Time Students
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Smeets, Roger; Broaekman, Francette; Bouwers, Eric – International Educational Data Mining Society, 2019
Research on automated affect detection in educational software using play log data has shown promising results. Yet most studies use classroom-based software designed for adolescents or adults. In this paper, we aim to detect affection in an online educational platform primarily aimed at home use by young children. This presents two challenges: we…
Descriptors: Computer Software, Young Children, Evaluation Methods, Psychological Patterns
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Wampfler, Rafael; Klingler, Severin; Solenthaler, Barbara; Schinazi, Victor R.; Gross, Markus – International Educational Data Mining Society, 2019
The role of affective states in learning has recently attracted considerable attention in education research. The accurate prediction of affective states can help increase the learning gain by incorporating targeted interventions that are capable of adjusting to changes in the individual affective states of students. Until recently, most work on…
Descriptors: Affective Behavior, Prediction, Problem Solving, Mathematics
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Kang, Jina; An, Dongwook; Yan, Lili; Liu, Min – International Educational Data Mining Society, 2019
Collaborative problem-solving (CPS) as a key competency required in the 21st century. There has been an increasing need to understand CPS since it involves not only cognitive but also social processes, and thus its process is difficult to examine. Recent research has highlighted that computer-based learning environments provide an opportunity for…
Descriptors: Cooperative Learning, Problem Solving, Science Education, Educational Games
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Pandey, Shalini; Karypis, George – International Educational Data Mining Society, 2019
Knowledge tracing is the task of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities. Each student's knowledge is modeled by estimating the performance of the student on the learning activities. It is an important research area for providing a personalized learning platform to…
Descriptors: Learning Processes, Databases, Intelligent Tutoring Systems, Knowledge Level
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Khayi, Nisrine Ait; Rus, Vasile – International Educational Data Mining Society, 2019
In this paper, we applied a number of clustering algorithms on pretest data collected from 264 high-school students. Students took the pre-test at the beginning of a 5-week experiment in which they interacted with an intelligent tutoring system. The primary goal of this work is to identify clusters of students exhibiting similar knowledge…
Descriptors: High School Students, Cluster Grouping, Prior Learning, Intelligent Tutoring Systems
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