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Nina Brandau; Sigrid Hartong – Discourse: Studies in the Cultural Politics of Education, 2025
Critical research on data visualisations in education has so far mostly focused on the representation of numeric data in dashboards or graphs. Consequently, the question of how schools are made visible and (re)enacted through visual structures on data-mediated interfaces more broadly -- what we call Graphic Data Visualisations (GDV) -- has…
Descriptors: Visual Aids, Learning Management Systems, Design, Educational Practices
Roger Sheng So – ProQuest LLC, 2024
Understanding student engagement with the institution from the first day of classes to the end of the semester would help inform the institution of the potential risk that a student will drop out of a class or of the school. Learning Management Systems (LMS) record student interactions with the system and might be able to be used to identify…
Descriptors: Learning Management Systems, Data Use, At Risk Students, Learner Engagement
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Qian Liu; Tehmina Gladman; Julia Muir; Chen Wang; Rebecca Grainger – SAGE Open, 2023
One apparent challenge associated with learning analytics (LA) has been to promote adoption by university educators. Researchers suggest that a visualization dashboard could serve to help educators use LA to improve learning design (LD) practice. We therefore used an educational design approach to develop a pedagogically useful and easy-to-use LA…
Descriptors: Learning Management Systems, Learning Analytics, Visual Aids, Instructional Design
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Rozita Tsoni; Georgia Garani; Vassilios S. Verykios – Interactive Learning Environments, 2024
New challenges in education demand effective solutions. Although Learning Analytics (LA), Educational Data Mining (EDM) and the use of Big Data are often presented as a panacea, there is a lot of ground to be covered in order for the EDM to answer the real questions of educators. An important step toward this goal is to implement holistic…
Descriptors: Data Use, Distance Education, Learning Analytics, Educational Research
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Md Akib Zabed Khan; Agoritsa Polyzou – Journal of Educational Data Mining, 2024
In higher education, academic advising is crucial to students' decision-making. Data-driven models can benefit students in making informed decisions by providing insightful recommendations for completing their degrees. To suggest courses for the upcoming semester, various course recommendation models have been proposed in the literature using…
Descriptors: Academic Advising, Courses, Data Use, Artificial Intelligence
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Khamisi Kalegele – International Journal of Education and Development using Information and Communication Technology, 2023
Pragmatically, machine learning techniques can improve educators' capacity to monitor students' learning progress when applied to quality data. For developing countries, the major obstacle has been the unavailability of quality data that fits the purpose. This is partly because the in-use information systems are either not properly managed or not…
Descriptors: Artificial Intelligence, Learning Management Systems, Progress Monitoring, Data Use
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Olga Agatova; Alexander Popov; Suad Abdalkareem Alwaely – Interactive Learning Environments, 2024
The paper examines the special aspects of using Big Data technology in education. The population was made up of 356 third-year university students. To study Big Data technology, a questionnaire was used where respondents rated: cloud technology; apps; Massive Open Online Courses (MOOCs) and digital learning platforms. The study suggested that the…
Descriptors: Data Use, Learning Processes, Technology Uses in Education, Information Storage
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Hongyu Xie; He Xiao; Yu Hao – International Journal of Web-Based Learning and Teaching Technologies, 2024
Modern e-learning system is a representative service form in innovative service industry. This paper designs a personalized service domain system, optimizes various parameters and can be applied to different education quality evaluation, and proposes a decision tree recommendation algorithm. Information gain is carried out through many existing…
Descriptors: Artificial Intelligence, Electronic Learning, Individualized Instruction, Models
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Tamra Ross; Rachel Sondergaard; Cindy Ives; Andrew Han; Sabine Graf – Technology, Knowledge and Learning, 2025
To meet student demand for responsive, adaptable, and up-to-date online courses, educators and learning designers need tools to analyse student interactions with their peers, educators and learning resources. Learning Management Systems (LMSs) store large volumes of detailed user data, but offer only limited, pre-set reports and visualizations to…
Descriptors: Access to Information, Learning Analytics, Instructional Design, Evaluation Methods
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West, Paige; Paige, Frederick; Lee, Walter; Watts, Natasha; Scales, Glenda – Journal of Civil Engineering Education, 2022
The expansion of online learning in higher education has both contributed to researchers exploring innovative ways to develop learning environments and created challenges in identifying student interactions with course material. Learning analytics is an emerging field that can identify student interactions and help make data-informed course design…
Descriptors: Learning Analytics, Student Attitudes, Electronic Learning, Construction Management
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Liu, Fang; Zhao, Liang; Zhao, Jiayi; Dai, Qin; Fan, Chunlong; Shen, Jun – IEEE Transactions on Learning Technologies, 2022
Educational process mining is now a promising method to provide decision-support information for the teaching-learning process via finding useful educational guidance from the event logs recorded in the learning management system. Existing studies mainly focus on mining students' problem-solving skills or behavior patterns and intervening in…
Descriptors: Data Use, Learning Management Systems, Problem Solving, Learning Processes
LeBlanc, Robert Jean; Aguilera, Earl; Burriss, Sarah; de Roock, Roberto; Fassbender, Will; Monea, Bethany; Nichols, T. Philip; Pandya, Jessica Zacher; Robinson, Brad; Smith, Anna; Stornaiuolo, Amy – National Council of Teachers of English, 2023
A digital platform is a networked infrastructure that allows people to engage in various kinds of interactions: social, economic, political, educational. Digital platforms can play a fundamental part of today's English language arts (ELA) classroom. Students read, write, view, connect, and interact with, on, and through a kaleidoscope of digital…
Descriptors: Learning Management Systems, Language Arts, English Instruction, Educational Technology
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Ian Hardy; Vicente Reyes; Louise G. Phillips; M. Obaidul Hamid – Journal of Education Policy, 2024
Data infrastructures exist in a variety of formats. This article draws on the insights of senior personnel involved in developing a new data dashboard in one state jurisdiction in Australia. While literature on dashboards often focuses on the teachers and learners influenced by them, there is less attention to those involved in their development…
Descriptors: Learning Analytics, Learning Processes, Learning Management Systems, Computer Software
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Pangrazio, Luci; Stornaiuolo, Amy; Nichols, T. Philip; Garcia, Antero; Philip, Thomas M. – Harvard Educational Review, 2022
In this contribution to the Platform Studies in Education symposium, Luci Pangrazio, Amy Stornaiuolo, T. Philip Nichols, Antero Garcia, and Thomas M. Philip explore how digital platforms can be used to build knowledge and understanding of datafication processes among teachers and students. The essay responds to the turn toward data-driven teaching…
Descriptors: Teaching Methods, Learning Analytics, Vignettes, Learning Processes
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Yürüm, Ozan Rasit; Taskaya-Temizel, Tugba; Yildirim, Soner – Education and Information Technologies, 2023
Video clickstream behaviors such as pause, forward, and backward offer great potential for educational data mining and learning analytics since students exhibit a significant amount of these behaviors in online courses. The purpose of this study is to investigate the predictive relationship between video clickstream behaviors and students' test…
Descriptors: Video Technology, Educational Technology, Learning Management Systems, Data Collection
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