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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
Susanne de Mooij; Joni Lämsä; Lyn Lim; Olli Aksela; Shruti Athavale; Inti Bistolfi; Flora Jin; Tongguang Li; Roger Azevedo; Maria Bannert; Dragan Gaševic; Sanna Järvelä; Inge Molenaar – Educational Psychology Review, 2025
While behavioral, contextual, and physiological data streams have long been used to investigate self-regulated learning (SRL), a systematic understanding of the current state how different data streams and modalities contribute to measuring regulation processes across diverse learning contexts remains limited. This systematic literature review…
Descriptors: Independent Study, Artificial Intelligence, Metacognition, Measures (Individuals)
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
Kamila Misiejuk; Sonsoles López-Pernas; Rogers Kaliisa; Mohammed Saqr – Journal of Learning Analytics, 2025
Generative artificial intelligence (GenAI) has opened new possibilities for designing learning analytics (LA) tools, gaining new insights about student learning processes and their environment, and supporting teachers in assessing and monitoring students. This systematic literature review maps the empirical research of 41 papers utilizing GenAI…
Descriptors: Literature Reviews, Artificial Intelligence, Learning Analytics, Data Collection
Giora Alexandron; Aviram Berg; Jose A. Ruiperez-Valiente – IEEE Transactions on Learning Technologies, 2024
This article presents a general-purpose method for detecting cheating in online courses, which combines anomaly detection and supervised machine learning. Using features that are rooted in psychometrics and learning analytics literature, and capture anomalies in learner behavior and response patterns, we demonstrate that a classifier that is…
Descriptors: Cheating, Identification, Online Courses, Artificial Intelligence
Henrique S. Mamede, Editor; Arnaldo Santos, Editor – IGI Global, 2025
The ever-changing landscape of distance learning AI and learning analytics transforms engagement and efficiency in education. AI systems analyze behavior and performance data to provide real-time feedback for improved outcomes. Learning analytics further help educators to identify at-risk students while fostering better teaching strategies. By…
Descriptors: Artificial Intelligence, Technology Uses in Education, Learning Analytics, Distance Education
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
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
Secil Caskurlu; Ceren Ocak; Chih-Pu Dai – Journal of Learning Analytics, 2025
This scoping review aims to provide an overview of how multimodal learning analytics has been applied in K-8 research and offers methodological insights and recommendations to bridge the gap between theory and practice. We identified 14 peer-reviewed empirical studies published between 2011 and 2023 through searches in relevant databases and…
Descriptors: Literature Reviews, Elementary Secondary Education, Learning Modalities, 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

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