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Showing 1 to 15 of 54 results Save | Export
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Geigle, Chase; Zhai, ChengXiang – Journal of Educational Data Mining, 2017
Massive open online courses (MOOCs) provide educators with an abundance of data describing how students interact with the platform, but this data is highly underutilized today. This is in part due to the lack of sophisticated tools to provide interpretable and actionable summaries of huge amounts of MOOC activity present in log data. To address…
Descriptors: Large Group Instruction, Online Courses, Educational Technology, Technology Uses in Education
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Liu, Ran; Koedinger, Kenneth R. – Journal of Educational Data Mining, 2017
As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it…
Descriptors: Educational Technology, Technology Uses in Education, Data Collection, Data Analysis
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Khosravi, Hassan; Kitto, Kirsty; Cooper, Kendra – Journal of Educational Data Mining, 2017
Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests…
Descriptors: Teaching Methods, College Students, Educational Technology, Technology Uses in Education
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Kim, Kerry J.; Meir, Eli; Pope, Denise S.; Wendel, Daniel – Journal of Educational Data Mining, 2017
Computerized classification of student answers offers the possibility of instant feedback and improved learning. Open response (OR) questions provide greater insight into student thinking and understanding than more constrained multiple choice (MC) questions, but development of automated classifiers is more difficult, often requiring training a…
Descriptors: Classification, Computer Assisted Testing, Multiple Choice Tests, Test Format
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Mühling, Andreas – Journal of Educational Data Mining, 2017
This article presents "concept landscapes"--a novel way of investigating the state and development of knowledge structures in groups of persons using concept maps. Instead of focusing on the assessment and evaluation of single maps, the data of many persons is aggregated and data mining approaches are used in analysis. New insights into…
Descriptors: Concept Mapping, Data Collection, Electronic Publishing, Educational Theories
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Dan, Alex; Reiner, Miriam – Journal of Educational Data Mining, 2017
One of the recommended approaches in instructional design methods is to optimize the value of working memory capacity and avoid cognitive overload. Educational neuroscience offers novel processes and methodologies to analyze cognitive load based on physiological measures. Observing psychophysiological changes when they occur in response to the…
Descriptors: Brain Hemisphere Functions, Diagnostic Tests, Cognitive Ability, Psychophysiology
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Crossley, Scott A.; Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S. – Journal of Educational Data Mining, 2016
This study investigates a novel approach to automatically assessing essay quality that combines natural language processing approaches that assess text features with approaches that assess individual differences in writers such as demographic information, standardized test scores, and survey results. The results demonstrate that combining text…
Descriptors: Essays, Scoring, Writing Evaluation, Natural Language Processing
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Spoon, Kelly; Beemer, Joshua; Whitmer, John C.; Fan, Juanjuan; Frazee, James P.; Stronach, Jeanne; Bohonak, Andrew J.; Levine, Richard A. – Journal of Educational Data Mining, 2016
Random forests are presented as an analytics foundation for educational data mining tasks. The focus is on course- and program-level analytics including evaluating pedagogical approaches and interventions and identifying and characterizing at-risk students. As part of this development, the concept of individualized treatment effects (ITE) is…
Descriptors: Data Analysis, Individualized Instruction, Teaching Methods, Intervention
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Sonnenberg, Christoph; Bannert, Maria – Journal of Educational Data Mining, 2016
In computer-supported learning environments, the deployment of self-regulatory skills represents an essential prerequisite for successful learning. Metacognitive prompts are a promising type of instructional support to activate students' strategic learning activities. However, despite positive effects in previous studies, there are still a large…
Descriptors: Data Analysis, Metacognition, Prompting, Cues
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Agrawal, Rakesh; Golshan, Behzad; Papalexakis, Evangelos – Journal of Educational Data Mining, 2016
A study plan is the choice of concepts and the organization and sequencing of the concepts to be covered in an educational course. While a good study plan is essential for the success of any course offering, the design of study plans currently remains largely a manual task. We present a novel data-driven method, which given a list of concepts can…
Descriptors: Lesson Plans, Educational Planning, Curriculum Design, Evidence Based Practice
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Sweeney, Mack; Rangwala, Huzefa; Lester, Jaime; Johri, Aditya – Journal of Educational Data Mining, 2016
An enduring issue in higher education is student retention to successful graduation. National statistics indicate that most higher education institutions have four-year degree completion rates around 50%, or just half of their student populations. While there are prediction models which illuminate what factors assist with college student success,…
Descriptors: Systems Approach, Data Analysis, Prediction, Academic Achievement
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Yang, Diyi; Kraut, Robert E.; Rose, Carolyn P. – Journal of Educational Data Mining, 2016
Although thousands of students enroll in Massive Open Online Courses (MOOCs) for learning and self-improvement, many get confused, harming learning and increasing dropout rates. In this paper, we quantify these effects in two large MOOCs. We first describe how we automatically estimate students' confusion by looking at their clicking behavior on…
Descriptors: Online Courses, Dropouts, Dropout Rate, Correlation
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Schneider, Bertrand; Blikstein, Paulo – Journal of Educational Data Mining, 2015
In this paper, we describe multimodal learning analytics (MMLA) techniques to analyze data collected around an interactive learning environment. In a previous study (Schneider & Blikstein, submitted), we designed and evaluated a Tangible User Interface (TUI) where dyads of students were asked to learn about the human hearing system by…
Descriptors: Educational Research, Data Collection, Data Analysis, Educational Environment
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Pelanek, Radek – Journal of Educational Data Mining, 2015
Researchers use many different metrics for evaluation of performance of student models. The aim of this paper is to provide an overview of commonly used metrics, to discuss properties, advantages, and disadvantages of different metrics, to summarize current practice in educational data mining, and to provide guidance for evaluation of student…
Descriptors: Models, Data Analysis, Data Processing, Evaluation Criteria
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Clement, Benjamin; Roy, Didier; Oudeyer, Pierre-Yves; Lopes, Manuel – Journal of Educational Data Mining, 2015
We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two…
Descriptors: Learning Activities, Intelligent Tutoring Systems, Models, Teaching Methods
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