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Xieling Chen; Di Zou; Gary Cheng; Haoran Xie – Education and Information Technologies, 2024
The rise of massive open online courses (MOOCs) brings rich opportunities for understanding learners' experiences based on analyzing learner-generated content such as course reviews. Traditionally, the unstructured textual data is analyzed qualitatively via manual coding, thus failing to offer a timely understanding of the learner's experiences.…
Descriptors: Artificial Intelligence, Semantics, Course Evaluation, MOOCs
Rogers Kaliisa; Ryan Shaun Baker; Barbara Wasson; Paul Prinsloo – Journal of Learning Analytics, 2025
This article investigates the state of AI regulations from diverse geopolitical contexts including the European Union, the United States, China, and several African nations, and their implications for learning analytics (LA) and AI research. We used a comparative analysis approach of 11 AI regulatory documents and applied the OECD framework to…
Descriptors: Artificial Intelligence, Learning Analytics, Foreign Countries, Federal Regulation
Tianjiao Wang; Xiaona Xia – SAGE Open, 2023
The study of learning behaviors with multi features is of great significance for interactive cooperation. The data prediction and decision are to realize the comprehensive analysis and value mining. In this study, hierarchical learning behavior based on feature cluster is proposed. Based on the massive data in interactive learning environment, the…
Descriptors: Cluster Grouping, Mathematical Models, Artificial Intelligence, Learning Analytics
Teo Susnjak – International Journal of Artificial Intelligence in Education, 2024
A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies has been on the science of…
Descriptors: Prediction, Learning Analytics, Artificial Intelligence, At Risk Students
Zhennan Sun; Mingyong Pang; Yi Zhang – Education and Information Technologies, 2025
The evolution of individual and global learning preferences is influenced by correlation factors. This study introduces a novel evolutionary modeling approach to observe and analyze factors that affect the evolution of learning preferences. The influencing factors considered in this study are closely interwoven with the underlying personality of…
Descriptors: Learning Analytics, Learning Processes, Preferences, Student Characteristics
Wannapon Suraworachet; Qi Zhou; Mutlu Cukurova – Journal of Computer Assisted Learning, 2025
Background: Many researchers work on the design and development of multimodal collaboration support systems with AI, yet very few of these systems are mature enough to provide actionable feedback to students in real-world settings. Therefore, a notable gap exists in the literature regarding students' perceptions of such systems and the feedback…
Descriptors: Graduate Students, Student Attitudes, Artificial Intelligence, Cooperative Learning
Egle Gedrimiene; Ismail Celik; Antti Kaasila; Kati Mäkitalo; Hanni Muukkonen – Education and Information Technologies, 2024
Artificial intelligence (AI) and learning analytics (LA) tools are increasingly implemented as decision support for learners and professionals. However, their affordances for guidance purposes have yet to be examined. In this paper, we investigated advantages and challenges of AI-enhanced LA tool for supporting career decisions from the user…
Descriptors: Artificial Intelligence, Learning Analytics, Career Choice, Decision Making
Ouyang, Fan; Xu, Weiqi; Cukurova, Mutlu – International Journal of Computer-Supported Collaborative Learning, 2023
Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued the importance of examining the complexity of CPS, including its multimodality, dynamics, and synergy from the complex adaptive systems perspective. However, there is limited empirical…
Descriptors: Artificial Intelligence, Learning Analytics, Cooperative Learning, Problem Solving
Buckingham Shum, Simon; Lim, Lisa-Angelique; Boud, David; Bearman, Margaret; Dawson, Phillip – International Journal of Educational Technology in Higher Education, 2023
Effective learning depends on effective feedback, which in turn requires a set of skills, dispositions and practices on the part of both students and teachers which have been termed "feedback literacy." A previously published teacher "feedback literacy competency framework" has identified what is needed by teachers to implement…
Descriptors: Automation, Feedback (Response), Learning Analytics, Artificial Intelligence
Reet Kasepalu; Pankaj Chejara; Luis P. Prieto; Tobias Ley – International Journal of Computer-Supported Collaborative Learning, 2023
Teachers in a collaborative learning (CL) environment have the demanding task of monitoring several groups of students at the same time and intervening when needed. This withitness (both the situational awareness and interventions taken in class) of the teacher might be increased with the help of a guiding dashboard alerting the teacher of…
Descriptors: Cooperative Learning, Teacher Behavior, Observation, Educational Technology
Flora Ji-Yoon Jin; Bhagya Maheshi; Wenhua Lai; Yuheng Li; Danijela Gasevic; Guanliang Chen; Nicola Charwat; Philip Wing Keung Chan; Roberto Martinez-Maldonado; Dragan Gaševic; Yi-Shan Tsai – Journal of Learning Analytics, 2025
This paper explores the integration of generative AI (GenAI) in the feedback process in higher education through a learning analytics (LA) tool, examined from a feedback literacy perspective. Feedback literacy refers to students' ability to understand, evaluate, and apply feedback effectively to improve their learning, which is crucial for…
Descriptors: College Students, Student Attitudes, Artificial Intelligence, Learning Analytics
Senay Kocakoyun Aydogan; Turgut Pura; Fatih Bingül – Malaysian Online Journal of Educational Technology, 2024
In every culture and era, education is considered the most fundamental reality and rule that societies prioritize and deem essential. Throughout the process spanning thousands of years, from the emergence of writing to the present day, education has undergone various forms and formats of change. Education has been a continuous guide for shaping,…
Descriptors: Prediction, Academic Achievement, Artificial Intelligence, Algorithms
Jyoti Prakash Meher; Rajib Mall – IEEE Transactions on Education, 2025
Contribution: This article suggests a novel method for diagnosing a learner's cognitive proficiency using deep neural networks (DNNs) based on her answers to a series of questions. The outcome of the forecast can be used for adaptive assistance. Background: Often a learner spends considerable amounts of time in attempting questions on the concepts…
Descriptors: Cognitive Ability, Assistive Technology, Adaptive Testing, Computer Assisted Testing
Halim Acosta; Seung Lee; Daeun Hong; Wookhee Min; Bradford Mott; Cindy Hmelo-Silver; James Lester – International Educational Data Mining Society, 2025
Understanding the relationship between student behaviors and learning outcomes is crucial for designing effective collaborative learning environments. However, collaborative learning analytics poses significant challenges, not only due to the complex interplay between collaborative problem-solving and collaborative dialogue but also due to the…
Descriptors: Learning Analytics, Cooperative Learning, Student Behavior, Prediction
Anca Muresan; Mihaela Cardei; Ionut Cardei – International Educational Data Mining Society, 2025
Early identification of student success is crucial for enabling timely interventions, reducing dropout rates, and promoting on-time graduation. In educational settings, AI-powered systems have become essential for predicting student performance due to their advanced analytical capabilities. However, effectively leveraging diverse student data to…
Descriptors: Artificial Intelligence, At Risk Students, Learning Analytics, Technology Uses in Education

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