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Download full textChung, Cheng-Yu; Hsiao, I-Han – International Educational Data Mining Society, 2021
The distributed practice effect suggests that students retain learning content better when they pace their practice over time. The key factors are practice dosage (intensity) and timing (when to practice and how in between). Inspired by the thriving development of image recognition, this study adopts one of the successful techniques,…
Descriptors: Models, Drills (Practice), Pacing, Computer Uses in Education
Andrews-Todd, Jessica; Forsyth, Carol; Steinberg, Jonathan; Rupp, André – International Educational Data Mining Society, 2018
In this paper, we describe a theoretically-grounded data mining approach to identify types of collaborative problem solvers based on students' interactions with an online simulation-based task about electronics concepts. In our approach, we developed an ontology to identify the theoretically-grounded features of collaborative problem solving…
Descriptors: Problem Solving, Cooperation, Student Behavior, Data Analysis
Chen, Zhongzhou; Lee, Sunbok; Garrido, Geoffrey – International Educational Data Mining Society, 2018
The amount of information contained in any educational data set is fundamentally constrained by the instructional conditions under which the data are collected. In this study, we show that by redesigning the structure of traditional online courses, we can improve the ability of educational data mining to provide useful information for instructors.…
Descriptors: Online Courses, Course Organization, Data Analysis, Instructional Design
Choi, Hongkyu; Lee, Ji Eun; Hong, Won-joon; Lee, Kyumin; Recker, Mimi; Walker, Andy – International Educational Data Mining Society, 2016
This research connects several data-driven educational data mining approaches to a framework for interaction developed in educational research. In particular, 10 million usage data points collected by a Learning Management System used by students and teachers in 450 online undergraduate courses were analyzed with this framework. A range of…
Descriptors: Integrated Learning Systems, Data Analysis, Multivariate Analysis, Multiple Regression Analysis
Klingler, Severin; Käser, Tanja; Solenthaler, Barbara; Gross, Markus – International Educational Data Mining Society, 2016
The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show low performance for data that inherently suffer from noise, resulting in temporally…
Descriptors: Student Behavior, Data Analysis, Behavior Patterns, Multivariate Analysis
Agnihotri, Lalitha; Aghababyan, Ani; Mojarad, Shirin; Riedesel, Mark; Essa, Alfred – International Educational Data Mining Society, 2015
Student login data is a key resource for gaining insight into their learning experience. However, the scale and the complexity of this data necessitate a thorough exploration to identify potential actionable insights, thus rendering it less valuable compared to student achievement data. To compensate for the underestimation of login data…
Descriptors: Data Analysis, Web Based Instruction, Student Behavior, Correlation
Crossley, Scott; McNamara, Danielle S.; Baker, Ryan; Wang, Yuan; Paquette, Luc; Barnes, Tiffany; Bergner, Yoav – International Educational Data Mining Society, 2015
Completion rates for massive open online classes (MOOCs) are notoriously low, but learner intent is an important factor. By studying students who drop out despite their intent to complete the MOOC, it may be possible to develop interventions to improve retention and learning outcomes. Previous research into predicting MOOC completion has focused…
Descriptors: Online Courses, Large Group Instruction, Information Retrieval, Data Analysis
