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Hussain, Sadiq; Gaftandzhieva, Silvia; Maniruzzaman, Md.; Doneva, Rositsa; Muhsin, Zahraa Fadhil – Education and Information Technologies, 2021
Educational data mining helps the educational institutions to perform effectively and efficiently by exploiting the data related to all its stakeholders. It can help the at-risk students, develop recommendation systems and alert the students at different levels. It is beneficial to the students, educators and authorities as a whole. Deep learning…
Descriptors: Regression (Statistics), Academic Achievement, Learning Analytics, Models
Kelli A. Bird; Benjamin L. Castleman; Yifeng Song – Journal of Policy Analysis and Management, 2025
Predictive analytics are increasingly pervasive in higher education. However, algorithmic bias has the potential to reinforce racial inequities in postsecondary success. We provide a comprehensive and translational investigation of algorithmic bias in two separate prediction models--one predicting course completion, the second predicting degree…
Descriptors: Algorithms, Technology Uses in Education, Bias, Racism
Vo, Thi Ngoc Chau; Nguyen, Phung – IEEE Transactions on Learning Technologies, 2021
A course-level early final study status prediction task is to predict as soon as possible the final success of each student after studying a course. It is significant because each successful course accomplishment is required for a degree. Further, early predictions provide enough time to make necessary changes for ultimate success. This article…
Descriptors: Prediction, Academic Achievement, Data Collection, Learning Processes
Saint, John; Whitelock-Wainwright, Alexander; Gasevic, Dragan; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2020
The recent focus on learning analytics (LA) to analyze temporal dimensions of learning holds the promise of providing insights into latent constructs, such as learning strategy, self-regulated learning (SRL), and metacognition. These methods seek to provide an enriched view of learner behaviors beyond the scope of commonly used correlational or…
Descriptors: Undergraduate Students, Engineering Education, Learning Analytics, Learning Strategies
Chinsook, Kittipong; Khajonmote, Withamon; Klintawon, Sununta; Sakulthai, Chaiyan; Leamsakul, Wicha; Jantakoon, Thada – Higher Education Studies, 2022
Big data is an important part of innovation that has recently attracted a lot of interest from academics and practitioners alike. Given the importance of the education industry, there is a growing trend to investigate the role of big data in this field. Much research has been undertaken to date in order to better understand the use of big data in…
Descriptors: Student Behavior, Learning Analytics, Computer Software, Rating Scales
Foster, Carly; Francis, Peter – Assessment & Evaluation in Higher Education, 2020
This is a systematic review conducted of primary research literature published between 2007 and 2018 on the deployment and effectiveness of data analytics in higher education to improve student outcomes. We took a methodological approach to searching databases; appraising and synthesising results against predefined criteria. We reviewed research…
Descriptors: Literature Reviews, Program Implementation, Program Effectiveness, Learning Analytics
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
Crescenzi-Lanna, Lucrezia – British Journal of Educational Technology, 2020
Learning Analytics and Multimodal Learning Analytics are changing the way of analysing the learning process while students interact with an educational content. This paper presents a systematic literature review aimed at describing practices in recent Multimodal Learning Analytics and Learning Analytics research literature in order to identify…
Descriptors: Learning Modalities, Learning Analytics, Student Behavior, Progress Monitoring
Godwin-Jones, Robert – Language Learning & Technology, 2021
Data collection and analysis is nothing new in computer-assisted language learning, but with the phenomenon of massive sets of human language collected into corpora, and especially integrated into systems driven by artificial intelligence, new opportunities have arisen for language teaching and learning. We are now seeing powerful artificial…
Descriptors: Data Collection, Academic Achievement, Learning Analytics, Computer Assisted Instruction
Han, Feifei; Ellis, Robert – Australasian Journal of Educational Technology, 2020
This study combined the methods from student approaches to learning and learning analytics research by using both self-reported and observational measures to examine the student learning experience. It investigated the extent to which reported approaches and perceptions and observed online interactions are related to each other and how they…
Descriptors: Measurement Techniques, Observation, Learning Analytics, Data Collection
Santiago Berrezueta Ed. – Lecture Notes in Educational Technology, 2023
The proceedings of the 18th edition of Latin American Conference on Learning Technologies (LACLO) demonstrates the developments in the research of learning science, learning resources, challenges and solutions. This Proceedings book showcases a collection of quality articles that explores and discusses trending topics in education in the upcoming…
Descriptors: Educational Technology, Active Learning, Design, Telecommunications