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Xiaomeng Huang; Xavier Ochoa – Journal of Learning Analytics, 2025
Collaboration skills are fundamental to effective collaborative learning, career success, and responsible citizenship. Collaborative learning analytics (CLA) systems hold significant potential in helping students develop these skills by automatically collecting group interaction data, analyzing skill levels, and providing actionable feedback so…
Descriptors: Learning Analytics, Cooperative Learning, Cooperation, Skill Development
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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
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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
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Onur Karademir; Daniele Di Mitri; Jan Schneider; Ioana Jivet; Jörn Allmang; Sebastian Gombert; Marcus Kubsch; Knut Neumann; Hendrik Drachsler – Journal of Computer Assisted Learning, 2024
Background: Teacher dashboards can help secondary school teachers manage online learning activities and inform instructional decisions by visualising information about class learning. However, when designing teacher dashboards, it is not trivial to choose which information to display, because not all of the vast amount of information retrieved…
Descriptors: Learning Analytics, Secondary School Teachers, Educational Technology, Design
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Pankaj Chejara; Reet Kasepalu; Luis P. Prieto; María Jesús Rodríguez-Triana; Adolfo Ruiz Calleja; Bertrand Schneider – British Journal of Educational Technology, 2024
Multimodal learning analytics (MMLA) research has made significant progress in modelling collaboration quality for the purpose of understanding collaboration behaviour and building automated collaboration estimation models. Deploying these automated models in authentic classroom scenarios, however, remains a challenge. This paper presents findings…
Descriptors: Cooperation, Learning Activities, Models, Learning Modalities
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Pankaj Chejara; Luis P. Prieto; Yannis Dimitriadis; Maria Jesus Rodriguez-Triana; Adolfo Ruiz-Calleja; Reet Kasepalu; Shashi Kant Shankar – Journal of Learning Analytics, 2024
Multimodal learning analytics (MMLA) research has shown the feasibility of building automated models of collaboration quality using artificial intelligence (AI) techniques (e.g., supervised machine learning (ML)), thus enabling the development of monitoring and guiding tools for computer-supported collaborative learning (CSCL). However, the…
Descriptors: Learning Analytics, Attribution Theory, Acoustics, Artificial Intelligence
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Jonathan K. Foster; Peter Youngs; Rachel van Aswegen; Samarth Singh; Ginger S. Watson; Scott T. Acton – Journal of Learning Analytics, 2024
Despite a tremendous increase in the use of video for conducting research in classrooms as well as preparing and evaluating teachers, there remain notable challenges to using classroom videos at scale, including time and financial costs. Recent advances in artificial intelligence could make the process of analyzing, scoring, and cataloguing videos…
Descriptors: Learning Analytics, Automation, Classification, Artificial Intelligence
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Ariely, Moriah; Nazaretsky, Tanya; Alexandron, Giora – International Journal of Artificial Intelligence in Education, 2023
Machine learning algorithms that automatically score scientific explanations can be used to measure students' conceptual understanding, identify gaps in their reasoning, and provide them with timely and individualized feedback. This paper presents the results of a study that uses Hebrew NLP to automatically score student explanations in Biology…
Descriptors: Artificial Intelligence, Algorithms, Natural Language Processing, Hebrew
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Baran, Evrim; AlZoubi, Dana; Morales, Anasilvia Salazar – TechTrends: Linking Research and Practice to Improve Learning, 2023
Computational analysis methods and machine learning techniques introduce innovative ways to capture classroom interactions and display data on analytics dashboards. Automated classroom analytics employ advanced data analysis, providing educators with comprehensive insights into student participation, engagement, and behavioral trends within…
Descriptors: Automation, Learning Analytics, Stakeholders, Computation
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Dominguez, Federico; Ochoa, Xavier; Zambrano, Dick; Camacho, Katherine; Castells, Jaime – IEEE Transactions on Learning Technologies, 2021
Multimodal learning analytics, which is collection, analysis, and report of diverse learning traces to better understand and improve the learning process, has been producing a series of interesting prototypes to analyze learning activities that were previously hard to objectively evaluate. However, none of these prototypes have been taken out of…
Descriptors: Learning Modalities, Learning Analytics, Automation, Oral Language
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Witzenberger, Kevin; Gulson, Kalervo N. – Learning, Media and Technology, 2021
Pre-emption describes a system of automated knowledge creation and intervention that steers the present towards a desirable future, by building on knowledge derived from the past. Folding together temporalities makes it impossible to disprove pre-emption. It is increasingly featured within EdTech, introducing new forms of automated governance into…
Descriptors: Educational Technology, Technology Uses in Education, Governance, Learning Analytics
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Linxuan Zhao; Dragan Gaševic; Zachari Swiecki; Yuheng Li; Jionghao Lin; Lele Sha; Lixiang Yan; Riordan Alfredo; Xinyu Li; Roberto Martinez-Maldonado – British Journal of Educational Technology, 2024
Effective collaboration and teamwork skills are critical in high-risk sectors, as deficiencies in these areas can result in injuries and risk of death. To foster the growth of these vital skills, immersive learning spaces have been created to simulate real-world scenarios, enabling students to safely improve their teamwork abilities. In such…
Descriptors: Automation, Transcripts (Written Records), Coding, Teamwork
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Ling Wang; Shen Zhan – Education Research and Perspectives, 2024
Generative Artificial Intelligence (GenAI) is transforming education, with assessment design emerging as a crucial area of innovation, particularly in computer science (CS) education. Effective assessment is critical for evaluating student competencies and guiding learning processes, yet traditional practices face significant challenges in CS…
Descriptors: Artificial Intelligence, Computer Science Education, Technology Uses in Education, Student Evaluation
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Tsiakmaki, Maria; Kostopoulos, Georgios; Kotsiantis, Sotiris; Ragos, Omiros – Journal of Computing in Higher Education, 2021
Predicting students' learning outcomes is one of the main topics of interest in the area of Educational Data Mining and Learning Analytics. To this end, a plethora of machine learning methods has been successfully applied for solving a variety of predictive problems. However, it is of utmost importance for both educators and data scientists to…
Descriptors: Active Learning, Predictor Variables, Academic Achievement, Learning Analytics
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Tong Li; Sarah D. Creer; Tracy Arner; Rod D. Roscoe; Laura K. Allen; Danielle S. McNamara – Grantee Submission, 2022
Automated writing evaluation (AWE) tools can facilitate teachers' analysis of and feedback on students' writing. However, increasing evidence indicates that writing instructors experience challenges in implementing AWE tools successfully. For this reason, our development of the Writing Analytics Tool (WAT) has employed a participatory approach…
Descriptors: Automation, Writing Evaluation, Learning Analytics, Participatory Research
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