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Direct linkTong, Yao; Zhan, Zehui – Interactive Technology and Smart Education, 2023
Purpose: The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners' online learning behaviors, and comparing three algorithms -- multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART).…
Descriptors: MOOCs, Online Courses, Learning Analytics, Prediction
Ean Teng Khor; Dave Darshan – International Journal of Information and Learning Technology, 2024
Purpose: This study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course. Design/methodology/approach: The exploration and visualisation of the…
Descriptors: Prediction, Academic Achievement, Electronic Learning, Artificial Intelligence
Aom Perkash; Qaisar Shaheen; Robina Saleem; Furqan Rustam; Monica Gracia Villar; Eduardo Silva Alvarado; Isabel de la Torre Diez; Imran Ashraf – Education and Information Technologies, 2024
Developing tools to support students, educators, intuitions, and government in the educational environment has become an important task to improve the quality of education and learning outcomes. Information and communication technology (ICT) is adopted by educational institutions; one such instance is video interaction in flipped teaching.…
Descriptors: Academic Achievement, Colleges, Artificial Intelligence, Predictor Variables
Moubayed, Abdallah; Injadat, Mohammadnoor; Shami, Abdallah; Lutfiyya, Hanan – American Journal of Distance Education, 2020
E-learning platforms and processes face several challenges, among which is the idea of personalizing the e-learning experience and to keep students motivated and engaged. This work is part of a larger study that aims to tackle these two challenges using a variety of machine learning techniques. To that end, this paper proposes the use of k-means…
Descriptors: Learner Engagement, Electronic Learning, Individualized Instruction, Undergraduate Students
Zhang, Jia-Hua; Zou, Liu-cong; Miao, Jia-jia; Zhang, Ye-Xing; Hwang, Gwo-Jen; Zhu, Yue – Interactive Learning Environments, 2020
Extensive studies have been conducted to diagnose and predict students' academic performance by analyzing a large amount of data related to their learning behaviors in a blended learning environment. But there is a lack of research examining how individualized learning interventions could improve students' academic performance in such a learning…
Descriptors: Individualized Instruction, Academic Achievement, Interaction, Blended Learning
Download full textNaujokaitiene, Justina; Tamoliune, Giedre; Volungeviciene, Airina; Duart, Josep M. – Journal of New Approaches in Educational Research, 2020
Student engagement is one of the most relevant topics within the academic and research community nowadays. Higher education curriculum, teaching and learning integrate new technology- supported learning solutions. New methods and tools enhance teacher and learner interactions and influence learner engagement positively. This research addresses the…
Descriptors: Learning Analytics, Learner Engagement, Instructional Improvement, Interaction
Akpinar, Nil-Jana; Ramdas, Aaditya; Acar, Umut – International Educational Data Mining Society, 2020
Educational software data promises unique insights into students' study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive open online courses, it is unclear if the same methods can be applied to blended courses and a deeper understanding of student strategies is often missing. We use pattern…
Descriptors: Learning Strategies, Blended Learning, Learning Analytics, Student Behavior
Liu, Sannyuya; Peng, Xian; Cheng, Hercy N. H.; Liu, Zhi; Sun, Jianwen; Yang, Chongyang – Journal of Educational Computing Research, 2019
Course reviews, which is designed as an interactive feedback channel in Massive Open Online Courses, has promoted the generation of large-scale text comments. These data, which contain not only learners' concerns, opinions and feelings toward courses, instructors, and platforms but also learners' interactions (e.g., post, reply), are generally…
Descriptors: Course Evaluation, Online Courses, Student Attitudes, Course Content
Ifenthaler, Dirk, Ed.; Sampson, Demetrios G., Ed.; Isaías, Pedro, Ed. – Cognition and Exploratory Learning in the Digital Age, 2022
This book is about inclusivity and open education in the digital age. It reports the latest data on this topic from the 2021 Cognition and Exploratory Learning in the Digital Age (CELDA) conference. This annual conference focuses on challenges pertaining to the evolution of the learning process, the role of pedagogical approaches and the progress…
Descriptors: Teaching Methods, Educational Innovation, Educational Technology, Technology Uses in Education
Levi-Gamlieli, Hadas; Cohen, Anat; Nachmias, Rafi – Technology, Instruction, Cognition and Learning, 2015
The aim of this study is to identify online learning behavior that is excessively intense as reflected in a student's overly frequent interaction with the instructor through various communication channels. Then, this study aims to use learning analytics methodologies to discover whether a student with the identified behavior also displays the same…
Descriptors: Student Behavior, Online Courses, Web Sites, Teacher Student Relationship
Jackowicz, Stephen, Ed.; Ozturk, Omer Tayfur, Ed. – Online Submission, 2021
"Proceedings of International Conference on Studies in Education and Social Sciences" includes full papers presented at the International Conference on Studies in Education and Social Sciences (ICSES) which took place on November 11-14, 2021 in Antalya, Turkey. The aim of the conference is to offer opportunities to share ideas, to…
Descriptors: Emotional Intelligence, Depression (Psychology), Anxiety, Stress Variables
Jackowicz, Stephen, Ed.; Ozturk, Omer Tayfur, Ed. – International Society for Technology, Education, and Science, 2021
"Proceedings of International Conference on Studies in Education and Social Sciences" includes full papers presented at the International Conference on Studies in Education and Social Sciences (ICSES) which took place on November 11-14, 2021 in Antalya, Turkey. The aim of the conference is to offer opportunities to share ideas, to…
Descriptors: Emotional Intelligence, Depression (Psychology), Anxiety, Stress Variables
Sampson, Demetrios G., Ed.; Ifenthaler, Dirk, Ed.; Isaías, Pedro, Ed. – International Association for Development of the Information Society, 2021
These proceedings contain the papers of the 18th International Conference on Cognition and Exploratory Learning in the Digital Age (CELDA 2021), held virtually, due to an exceptional situation caused by the COVID-19 pandemic, from October 13-15, 2021, and organized by the International Association for Development of the Information Society…
Descriptors: Computer Simulation, Open Educational Resources, Telecommunications, Handheld Devices
