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Showing 1 to 15 of 101 results Save | Export
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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
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Katerina Berková; Martina Chalupová; František Smrcka; Marek Musil; Dagmar Frendlovská – Education and Information Technologies, 2024
Learning analytics dashboards (LADs) are very important tools for contemporary education. Not only researchers, but also schools at different levels of education and students are evaluating in this way today. A large number of studies have addressed the issue, but there are few studies that have explored the possibilities of transferring the…
Descriptors: Learning Analytics, Formative Evaluation, Self Evaluation (Individuals), Universities
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Fu Chen; Chang Lu; Ying Cui – Education and Information Technologies, 2024
Successful computer-based assessments for learning greatly rely on an effective learner modeling approach to analyze learner data and evaluate learner behaviors. In addition to explicit learning performance (i.e., product data), the process data logged by computer-based assessments provide a treasure trove of information about how learners solve…
Descriptors: Computer Assisted Testing, Problem Solving, Learning Analytics, Learning Processes
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Hyomin Kim; Gyunam Park; Minsu Cho – Education and Information Technologies, 2024
Learning analytics, located at the intersection of learning science, data science, and computer science, aims to leverage educational data to enhance teaching and learning. However, as educational data increases, distilling meaningful insights presents challenges, particularly concerning individual learner differences. This work introduces a…
Descriptors: Learner Engagement, Academic Achievement, Learning Processes, Learning Analytics
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Hanqiang Liu; Xiao Chen; Feng Zhao – Education and Information Technologies, 2024
Massive open online courses (MOOCs) have become one of the most popular ways of learning in recent years due to their flexibility and convenience. However, high dropout rate has become a prominent problem that hinders the further development of MOOCs. Therefore, the prediction of student dropouts is the key to further enhance the MOOCs platform.…
Descriptors: MOOCs, Video Technology, Behavior Patterns, Prediction
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Yuan Liu; Yongquan Dong; Chan Yin; Cheng Chen; Rui Jia – Education and Information Technologies, 2024
The open online course (MOOC) platform has seen an increase in usage, and there are a growing number of courses accessible for people to select. An effective method is urgently needed to recommend personalized courses for users. Although the existing course recommendation models consider that users' interests change over time, they often model…
Descriptors: MOOCs, Online Courses, Models, Course Selection (Students)
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Samit Bhattacharya; Ujjwal Biswas; Shubham Damkondwar; Bhupender Yadav – Education and Information Technologies, 2024
Classroom monitoring using information communications technology (ICT) plays a significant role in enhancing teaching-learning in a blended learning environment. Learning analytics (LA) is such a popular classroom monitoring tool. LA helps teachers to the collection, interpretation, and analysis of students performance data generated during…
Descriptors: Information Technology, Learning Analytics, Blended Learning, Classroom Techniques
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Xia, Xiaona; Qi, Wanxue – Education and Information Technologies, 2023
MOOCs might be an important organization way to realize the online learning process. Online technology and sharing technology enable MOOCs to realize the adaptive scheduling of learning resources, as well as the independent construction of learning sequences. At the same time, it also generates a large number of complex learning behaviors. How to…
Descriptors: MOOCs, Learning Processes, Learning Analytics, Graphs
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Mengke Wang; Taotao Long; Na Li; Yawen Shi; Zengzhao Chen – Education and Information Technologies, 2025
Feedback plays an indispensable role in pre-service teachers' microteaching practice. It provides essential information about their microteaching performance, which is of great significance in their reflection and improvement. As AI and teaching analytics advance, feedback is no longer exclusively human-generated. AI technologies are increasingly…
Descriptors: Feedback (Response), Preservice Teachers, Microteaching, Reflection
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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
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Lucas Paulsen; Euan Lindsay – Education and Information Technologies, 2024
This systematic review explores the emerging themes in the design and implementation of student-facing learning analytics dashboards in higher education. Learning Analytics has long been criticised for focusing too much on the analytics, and not enough on the learning. The review is then guided by an interest in whether these dashboards are still…
Descriptors: Learning Analytics, Educational Technology, Learning Processes, College Students
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Irene Benedetto; Moreno La Quatra; Luca Cagliero; Lorenzo Canale; Laura Farinetti – Education and Information Technologies, 2024
Modern educational technology systems allow learners to access large amounts of learning materials such as educational videos, learning notes, and teaching books. Automated summarization techniques simplify the access and exploration of complex data collections by producing synthetic versions of the original content. This paper addresses the…
Descriptors: Learning Analytics, Documentation, Blended Learning, Video Technology
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Sang-Soog Lee; Na Li; Jinhee Kim – Education and Information Technologies, 2024
The undeniable potential benefits of learning analytics (LA) in education have led to growing investment in developing and integrating LA systems into K-12 schools. Yet, the actual integration and adoption depend on levels of teachers' acceptance and usage. This study aims to propose a conceptual model to reveal the decisive factors affecting…
Descriptors: Learning Analytics, Information Systems, Adoption (Ideas), Teacher Attitudes
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Önder, Asuman; Akçapinar, Gökhan – Education and Information Technologies, 2023
The effective use of self-regulation strategies has been considered significant in online learning environments. It is known that learners must be supported in this context. Academic help-seeking (AHS), as one of the main self-regulated learning strategies, is associated with academic success. However, learners may avoid seeking help for…
Descriptors: Students, Help Seeking, Student Behavior, Learning Analytics
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Yiming Liu; Lingyun Huang; Tenzin Doleck – Education and Information Technologies, 2024
Learning analytics dashboards (LADs) are emerging tools that convert abstract, complex information with visualizations to facilitate teachers' data-driven pedagogical decision-making. While many LADs have been designed, teachers' capacities for using such LADs are not well articulated in the literature. To fill the gap, this study provided a…
Descriptors: Learning Analytics, Teacher Attitudes, Self Management, Psychological Patterns
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