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Lazrig, Ibrahim; Humpherys, Sean L. – Information Systems Education Journal, 2022
Can sentiment analysis be used in an educational context to help teachers and researchers evaluate students' learning experiences? Are sentiment analyzing algorithms accurate enough to replace multiple human raters in educational research? A dataset of 333 students evaluating a learning experience was acquired with positive, negative, and neutral…
Descriptors: College Students, Learning Analytics, Educational Research, Learning Experience
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Abdelhafez, Hoda Ahmed; Elmannai, Hela – International Journal of Information and Communication Technology Education, 2022
Learning data analytics improves the learning field in higher education using educational data for extracting useful patterns and making better decisions. Identifying potential at-risk students may help instructors and academic guidance to improve the students' performance and the achievement of learning outcomes. The aim of this research study is…
Descriptors: Learning Analytics, Mathematics, Prediction, Academic Achievement
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Salas-Rueda, Ricardo-Adan; Salas-Rueda, Erika-Patricia; Salas-Rueda, Rodrigo-David – Turkish Online Journal of Distance Education, 2021
This mixed research aims to design and implement the Web Application on Bayes' Theorem (WABT) in the Statistical Instrumentation for Business subject. WABT presents the procedure to calculate the probability of Bayes' Theorem through the simulation of data about the supply of products. Technology Acceptance Model (TAM), machine learning and data…
Descriptors: Bayesian Statistics, Probability, College Students, Business Administration Education
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Frick, Theodore W.; Myers, Rodney D.; Dagli, Cesur – Educational Technology Research and Development, 2022
In this naturalistic design-research study, we tracked 172,417 learning journeys of students who were interacting with an online resource, the Indiana University Plagiarism Tutorials and Tests (IPTAT) at https://plagiarism.iu.edu. IPTAT was designed using First Principles of Instruction (FPI; Merrill in Educ Technol Res Dev 50:43-59, 2002,…
Descriptors: Time, Educational Principles, Instructional Design, Instructional Effectiveness
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Nahar, Khaledun; Shova, Boishakhe Islam; Ria, Tahmina; Rashid, Humayara Binte; Islam, A. H. M. Saiful – Education and Information Technologies, 2021
Information is everywhere in a hidden and scattered way. It becomes useful when we apply Data mining to extracts the hidden, meaningful, and potentially useful patterns from these vast data resources. Educational data mining ensures a quality education by analyzing educational data based on various aspects. In this paper, we have analyzed the…
Descriptors: Learning Analytics, College Students, Engineering Education, Data Collection
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Mao, Ye; Marwan, Samiha; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2020
Modeling student learning processes is highly complex since it is influenced by many factors such as motivation and learning habits. The high volume of features and tools provided by computer-based learning environments confounds the task of tracking student knowledge even further. Deep Learning models such as Long-Short Term Memory (LSTMs) and…
Descriptors: Time, Models, Artificial Intelligence, Bayesian Statistics