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Milos Ilic; Goran Kekovic; Vladimir Mikic; Katerina Mangaroska; Lazar Kopanja; Boban Vesin – IEEE Transactions on Learning Technologies, 2024
In recent years, there has been an increasing trend of utilizing artificial intelligence (AI) methodologies over traditional statistical methods for predicting student performance in e-learning contexts. Notably, many researchers have adopted AI techniques without conducting a comprehensive investigation into the most appropriate and accurate…
Descriptors: Artificial Intelligence, Academic Achievement, Prediction, Programming
Melina Verger; Chunyang Fan; Sébastien Lallé; François Bouchet; Vanda Luengo – Journal of Educational Data Mining, 2024
Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term…
Descriptors: Algorithms, Prediction, Bias, Classification
Tanaka, Tetsuo; Ueda, Mari – International Association for Development of the Information Society, 2023
In this study, the authors have developed a web-based programming exercise system currently implemented in classrooms. This system not only provides students with a web-based programming environment but also tracks the time spent on exercises, logging operations such as program editing, building, execution, and testing. Additionally, it records…
Descriptors: Scores, Prediction, Programming, Artificial Intelligence
Van Petegem, Charlotte; Deconinck, Louise; Mourisse, Dieter; Maertens, Rien; Strijbol, Niko; Dhoedt, Bart; De Wever, Bram; Dawyndt, Peter; Mesuere, Bart – Journal of Educational Computing Research, 2023
We present a privacy-friendly early-detection framework to identify students at risk of failing in introductory programming courses at university. The framework was validated for two different courses with annual editions taken by higher education students (N = 2 080) and was found to be highly accurate and robust against variation in course…
Descriptors: Pass Fail Grading, At Risk Students, Introductory Courses, Programming
Craig, Clay Martin; Brooks, Mary Elizabeth; Bichard, Shannon – International Journal of Listening, 2023
Despite the pervasive nature of podcasts, little research has examined college student's affinity for and motivations to listen to podcasts. This study investigated college students' motivations, attitudes and behaviors in association with podcasts utilizing the appreciative listening framework in conjunction with uses and gratification theory.…
Descriptors: College Students, Handheld Devices, Audio Equipment, Information Dissemination
Hongxin Yan; Fuhua Lin; Kinshuk – Canadian Journal of Learning and Technology, 2024
Online higher education provides exceptional flexibility in learning but demands high self-regulated learning skills. The deficiency of self-regulated learning skills in many students highlights the need for support. This study introduces a confidence-based adaptive practicing system as an intelligent assessment and tutoring solution to enhance…
Descriptors: Self Management, Online Courses, Intelligent Tutoring Systems, Technology Uses in Education
Salas-Rueda, Ricardo-Adán – International Electronic Journal of Mathematics Education, 2020
This quantitative research aims to analyze the design and implementation of the Web Application on the educational process of the Linear Function (WALF) considering the TPACK (Technological Pedagogical and Content Knowledge) model and data science. The sample consists of 45 students who studied the Basic Math course at a Mexican university during…
Descriptors: Pedagogical Content Knowledge, Technological Literacy, Mathematics Instruction, Mexicans
Emerson, Andrew; Rodríguez, Fernando J.; Mott, Bradford; Smith, Andy; Min, Wookhee; Boyer, Kristy Elizabeth; Smith, Cody; Wiebe, Eric; Lester, James – International Educational Data Mining Society, 2019
Recent years have seen a growing interest in block-based programming environments for computer science education. While these environments hold significant potential for novice programmers, they lack the adaptive support necessary to accommodate students exhibiting a wide range of initial capabilities and dispositions toward computing. A promising…
Descriptors: Programming, Computer Science Education, Feedback (Response), Prediction
Paassen, Benjamin; Hammer, Barbara; Price, Thomas William; Barnes, Tiffany; Gross, Sebastian; Pinkwart, Niels – Journal of Educational Data Mining, 2018
Intelligent tutoring systems can support students in solving multi-step tasks by providing hints regarding what to do next. However, engineering such next-step hints manually or via an expert model becomes infeasible if the space of possible states is too large. Therefore, several approaches have emerged to infer next-step hints automatically,…
Descriptors: Intelligent Tutoring Systems, Cues, Educational Technology, Technology Uses in Education
Lagus, Jarkko; Longi, Krista; Klami, Arto; Hellas, Arto – ACM Transactions on Computing Education, 2018
The computing education research literature contains a wide variety of methods that can be used to identify students who are either at risk of failing their studies or who could benefit from additional challenges. Many of these are based on machine-learning models that learn to make predictions based on previously observed data. However, in…
Descriptors: Computer Science Education, Transfer of Training, Programming, Educational Objectives
Mao, Ye; Zhi, Rui; Khoshnevisan, Farzaneh; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2019
Early prediction of student difficulty during long-duration learning activities allows a tutoring system to intervene by providing needed support, such as a hint, or by alerting an instructor. To be effective, these predictions must come early and be highly accurate, but such predictions are difficult for open-ended programming problems. In this…
Descriptors: Difficulty Level, Learning Activities, Prediction, Programming
Reilly, Joseph M.; Schneider, Bertrand – International Educational Data Mining Society, 2019
Collaborative problem solving in computer-supported environments is of critical importance to the modern workforce. Coworkers or collaborators must be able to co-create and navigate a shared problem space using discourse and non-verbal cues. Analyzing this discourse can give insights into how consensus is reached and can estimate the depth of…
Descriptors: Problem Solving, Discourse Analysis, Cooperative Learning, Computer Assisted Instruction
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
Miguel Baptista Nunes Ed.; Pedro Isaias Ed. – International Association for Development of the Information Society, 2022
These proceedings contain the papers of the 16th International Conference on e-Learning (EL 2022), which was organised by the International Association for Development of the Information Society, 19-21 July, 2022. This conference is part of the 16th Multi Conference on Computer Science and Information Systems 2022, 19-22 July, which had a total of…
Descriptors: Electronic Learning, Online Courses, Educational Technology, COVID-19
Feng, Mingyu, Ed.; Käser, Tanja, Ed.; Talukdar, Partha, Ed. – International Educational Data Mining Society, 2023
The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…
Descriptors: Information Retrieval, Data Analysis, Computer Assisted Testing, Cheating
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