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Showing 1 to 15 of 24 results Save | Export
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Fancsali, Stephen E.; Murphy, April; Ritter, Steve – International Educational Data Mining Society, 2022
Ten years after the announcement of the "rise of the super experiment" at Educational Data Mining 2012, challenges to implementing "internet scale" educational experiments often persist for educational technology providers, especially when they seek to test substantive instructional interventions. Studies that deploy and test…
Descriptors: Learning Analytics, Educational Technology, Barriers, Data Analysis
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Langenhagen, Julian – International Educational Data Mining Society, 2022
Although badges are among the most-used game elements in gamified education, studies about their optimal features to motivate learning are scarce. How should a badge be designed to represent an incentive for a specific goal like optimal exam preparation? This study examines usage data of a higher education learning app to determine whether the…
Descriptors: Data Analysis, Goal Orientation, Computer Software, Game Based Learning
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Karimov, Ayaz; Saarela, Mirka; Kärkkäinen, Tommi – International Educational Data Mining Society, 2023
Within the last decade, different educational data mining techniques, particularly quantitative methods such as clustering, and regression analysis are widely used to analyze the data from educational games. In this research, we implemented a quantitative data mining technique (clustering) to further investigate students' feedback. Students played…
Descriptors: Student Attitudes, Feedback (Response), Educational Games, Information Retrieval
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Hsiao, I-Han, Ed.; Sahebi, Shaghayegh, Ed.; Bouchet, Francois, Ed.; Vie, Jill-Jenn, Ed. – International Educational Data Mining Society, 2021
For this 14th iteration of the International Conference on Educational Data Mining (EDM 2021), the conference was held completely online. EDM is organized under the auspices of the International Educational Data Mining Society and was meant to happen in Paris, France. The official theme of this year's conference was Shifting Landscape of…
Descriptors: Blended Learning, Distance Education, Learning Analytics, Educational Technology
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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
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Zhu, Jile; Li, Xiang; Wang, Zhuo; Zhang, Ming – International Educational Data Mining Society, 2017
Although millions of students have access to varieties of learning resources on Massive Open Online Courses (MOOCs), they are usually limited to receiving rapid feedback. Providing guidance for students, which enhances the interaction with students, is a promising way to improve learning experience. In this paper, we consider to show students the…
Descriptors: Large Group Instruction, Online Courses, Educational Technology, Technology Uses in Education
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Zheng, Longwei; Shi, Rui; Wu, Bingcong; Gu, Xiaoqing; Feng, Yuanyuan – International Educational Data Mining Society, 2017
The adoption of educational technologies such as e-textbook has offered a new opportunity to gain insight into teachers' usage of ICT (Information and Communication Technologies). In the e-textbook platform, customized digital products and the learning activities organized in digital environment require teachers to make greater efforts in planning…
Descriptors: Educational Technology, Technology Uses in Education, Visualization, Data Collection
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Majid al-Rifaie, Mohammad; Yee-King, Matthew; d'Inverno, Mark – International Educational Data Mining Society, 2016
This paper proposes a new technique for analysing the behaviour of students on an online course. This work considers a range of social learning behaviours supported in our recently designed and implemented collaborative learning system which supports students giving and receiving feedback on each other's developing work and practice. The course…
Descriptors: Student Behavior, Online Courses, Social Behavior, Data Analysis
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Atapattu, Thushari; Falkner, Katrina; Tarmazdi, Hamid – International Educational Data Mining Society, 2016
With a goal of better understanding the online discourse within the Massive Open Online Course (MOOC) context, this paper presents an open source visualisation dashboard developed to identify and classify emergent discussion topics (or themes). As an extension to the authors' previous work in identifying key topics from MOOC discussion contents,…
Descriptors: Online Courses, Large Group Instruction, Educational Technology, Technology Uses in Education
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Zeng, Ziheng; Chaturvedi, Snigdha; Bhat, Suma – International Educational Data Mining Society, 2017
Characterizing the nature of students' affective and emotional states and detecting them is of fundamental importance in online course platforms. In this paper, we study this problem by using discussion forum posts derived from large open online courses. We find that posts identified as encoding confusion are actually manifestations of different…
Descriptors: Online Courses, Large Group Instruction, Educational Technology, Technology Uses in Education
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Liu, Ran; Davenport, Jodi; Stamper, John – International Educational Data Mining Society, 2016
The increasing use of educational technologies in classrooms is producing vast amounts of process data that capture rich information about learning as it unfolds. The field of educational data mining has made great progress in using log data to build models that improve instruction and advance the science of learning. Thus far, however, the…
Descriptors: Educational Technology, Data Analysis, Automation, Data
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Crossley, Scott; Barnes, Tiffany; Lynch, Collin; McNamara, Danielle S. – International Educational Data Mining Society, 2017
This study takes a novel approach toward understanding success in a math course by examining the linguistic features and affect of students' language production within a blended (with both on-line and traditional face to face instruction) undergraduate course (n=158) on discrete mathematics. Three linear effects models were compared: (a) a…
Descriptors: Success, Mathematics Instruction, Language Usage, Blended Learning
Santos, Olga Cristina, Ed.; Boticario, Jesus Gonzalez, Ed.; Romero, Cristobal, Ed.; Pechenizkiy, Mykola, Ed.; Merceron, Agathe, Ed.; Mitros, Piotr, Ed.; Luna, Jose Maria, Ed.; Mihaescu, Cristian, Ed.; Moreno, Pablo, Ed.; Hershkovitz, Arnon, Ed.; Ventura, Sebastian, Ed.; Desmarais, Michel, Ed. – International Educational Data Mining Society, 2015
The 8th International Conference on Educational Data Mining (EDM 2015) is held under auspices of the International Educational Data Mining Society at UNED, the National University for Distance Education in Spain. The conference held in Madrid, Spain, July 26-29, 2015, follows the seven previous editions (London 2014, Memphis 2013, Chania 2012,…
Descriptors: Data Analysis, Educational Research, Computer Uses in Education, Integrated Learning Systems
Géryk, Jan – International Educational Data Mining Society, 2015
The efficacy of animated data visualizations in comparison with static data visualizations is still inconclusive. Some researches resulted that the failure to find out the benefits of animations may relate to the way how they are constructed and perceived. In this paper, we present visual analytics (VA) tool which makes use of enhanced animated…
Descriptors: Animation, Visualization, Visual Stimuli, Program Effectiveness
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Liu, Ran; Koedinger, Kenneth R. K – International Educational Data Mining Society, 2017
Research in Educational Data Mining could benefit from greater efforts to ensure that models yield reliable, valid, and interpretable parameter estimates. These efforts have especially been lacking for individualized student-parameter models. We collected two datasets from a sizable student population with excellent "depth" -- that is,…
Descriptors: Data Analysis, Intelligent Tutoring Systems, Bayesian Statistics, Pretests Posttests
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