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Haim, Aaron; Gyurcsan, Robert; Baxter, Chris; Shaw, Stacy T.; Heffernan, Neil T. – International Educational Data Mining Society, 2023
Despite increased efforts to assess the adoption rates of open science and robustness of reproducibility in sub-disciplines of education technology, there is a lack of understanding of why some research is not reproducible. Prior work has taken the first step toward assessing reproducibility of research, but has assumed certain constraints which…
Descriptors: Conferences (Gatherings), Educational Research, Replication (Evaluation), Access to Information
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Cohausz, Lea; Tschalzev, Andrej; Bartelt, Christian; Stuckenschmidt, Heiner – International Educational Data Mining Society, 2023
Demographic features are commonly used in Educational Data Mining (EDM) research to predict at-risk students. Yet, the practice of using demographic features has to be considered extremely problematic due to the data's sensitive nature, but also because (historic and representation) biases likely exist in the training data, which leads to strong…
Descriptors: Information Retrieval, Data Processing, Pattern Recognition, Information Technology
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Chopra, Shivangi; Gautreau, Hannah; Khan, Abeer; Mirsafian, Melicaalsadat; Golab, Lukasz – International Educational Data Mining Society, 2018
It is well known that post-secondary science and engineering programs attract fewer female students. In this paper, we analyze gender differences through text mining of over 30,000 applications to the engineering faculty of a large North American university. We use syntactic and semantic analysis methods to highlight differences in motivation,…
Descriptors: Gender Differences, Undergraduate Students, Engineering Education, STEM Education
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Klingler, Severin; Wampfler, Rafael; Käser, Tanja; Solenthaler, Barbara; Gross, Markus – International Educational Data Mining Society, 2017
Gathering labeled data in educational data mining (EDM) is a time and cost intensive task. However, the amount of available training data directly influences the quality of predictive models. Unlabeled data, on the other hand, is readily available in high volumes from intelligent tutoring systems and massive open online courses. In this paper, we…
Descriptors: Classification, Artificial Intelligence, Networks, Learning Disabilities
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Sarwar, Sohail; García-Castro, Raul; Qayyum, Zia Ul; Safyan, Muhammad; Munir, Rana Faisal – International Association for Development of the Information Society, 2017
Learner categorization has a pivotal role in making e-learning systems a success. However, learner characteristics exploited at abstract level of granularity by contemporary techniques cannot categorize the learners effectively. In this paper, an architecture of e-learning framework has been presented that exploits the machine learning based…
Descriptors: Student Characteristics, Profiles, Courseware, Electronic Learning
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Hildebrandt, Mireille – Journal of Learning Analytics, 2017
This article is a revised version of the keynote presented at LAK '16 in Edinburgh. The article investigates some of the assumptions of learning analytics, notably those related to behaviourism. Building on the work of Ivan Pavlov, Herbert Simon, and James Gibson as ways of "learning as a machine," the article then develops two levels of…
Descriptors: Behaviorism, Data Processing, Profiles, Learning Processes
Selent, Douglas; Patikorn, Thanaporn; Heffernan, Neil – Grantee Submission, 2016
In this paper, we present a dataset consisting of data generated from 22 previously and currently running randomized controlled experiments inside the ASSISTments online learning platform. This dataset provides data mining opportunities for researchers to analyze ASSISTments data in a convenient format across multiple experiments at the same time.…
Descriptors: Intelligent Tutoring Systems, Data, Randomized Controlled Trials, Electronic Learning
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Martinez-Maldonado, Roberto; Shum, Simon Buckingham; Schneider, Bertrand; Charleer, Sven; Klerkx, Joris; Duval, Erik – Journal of Learning Analytics, 2017
The continuous advancement of natural user interfaces (NUIs) allows for the development of novel and creative ways to support collocated collaborative work in a wide range of areas, including teaching and learning. The use of NUIs, such as those based on interactive multi-touch surfaces and tangible user interfaces (TUIs), can offer unique…
Descriptors: Computer Interfaces, Computer System Design, Visualization, Technology Uses in Education
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Stapel, Martin; Zheng, Zhilin; Pinkwart, Niels – International Educational Data Mining Society, 2016
The number of e-learning platforms and blended learning environments is continuously increasing and has sparked a lot of research around improvements of educational processes. Here, the ability to accurately predict student performance plays a vital role. Previous studies commonly focused on the construction of predictors tailored to a formal…
Descriptors: Teaching Methods, Academic Achievement, Electronic Learning, Mathematics Instruction