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Coughlan, Tim – Educational Technology Research and Development, 2020
Open data has potential value as a material for use in learning activities. However, approaches to harnessing this are not well understood or in mainstream use in education. In this research, early adopters from a diverse range of educational projects and teaching settings were interviewed to explore their rationale for using open data in…
Descriptors: Data, Inquiry, Active Learning, Authentic Learning
Xue, Xiaorui; Xie, Shiwei; Mishra, Shitanshu; Wright, Anna M.; Biswas, Gautam; Levin, Daniel T. – Educational Technology Research and Development, 2022
Recent advances in eye-tracking technology afford the possibility to collect rich data on attentional focus in a wide variety of settings outside the lab. However, apart from anecdotal reports, it is not clear how to maximize the validity of these data and prevent data loss from tracking failures. Particularly helpful in developing these…
Descriptors: Case Studies, Eye Movements, Comparative Analysis, Human Posture
Ifenthaler, Dirk; Gibson, David; Prasse, Doreen; Shimada, Atsushi; Yamada, Masanori – Educational Technology Research and Development, 2021
This paper is based on: (1) a literature review focussing on the impact of learning analytics on supporting learning and teaching; (2) a Delphi study involving international expert discussion on current opportunities and challenges of learning analytics; as well as (3) outlining a research agenda for closing identified research gaps. Issues and…
Descriptors: Learning Analytics, Policy Formation, Educational Policy, Educational Practices
Alzahrani, Asma Shannan; Tsai, Yi-Shan; Aljohani, Naif; Whitelock-wainwright, Emma; Gasevic, Dragan – Educational Technology Research and Development, 2023
Learning analytics (LA) has gained increasing attention for its potential to improve different educational aspects (e.g., students' performance and teaching practice). The existing literature identified some factors that are associated with the adoption of LA in higher education, such as stakeholder engagement and transparency in data use. The…
Descriptors: Teacher Attitudes, Trust (Psychology), Learning Analytics, Higher Education
Xing, Wanli; Lee, Hee-Sun; Shibani, Antonette – Educational Technology Research and Development, 2020
Constructing scientific arguments is an important practice for students because it helps them to make sense of data using scientific knowledge and within the conceptual and experimental boundaries of an investigation. In this study, we used a text mining method called Latent Dirichlet Allocation (LDA) to identify underlying patterns in students…
Descriptors: Persuasive Discourse, Science Instruction, Scientific Concepts, Logical Thinking
Han, Jeongyun; Huh, Sun Young; Cho, Young Hoan; Park, SoHyun; Choi, Jinhan; Suh, Bongwon; Rhee, Wonjong – Educational Technology Research and Development, 2020
This study investigates the possibility of utilizing online learning data to design face-to-face activities in a flipped classroom. We focus on heterogeneous group formation for effective collaborative learning. Fifty-three undergraduate students (18 males, 35 females) participated in this study, and 8 students (3 males, 5 females) among them…
Descriptors: Electronic Learning, Learning Analytics, Data Use, Synchronous Communication
Zotou, Maria; Tambouris, Efthimios; Tarabanis, Konstantinos – Educational Technology Research and Development, 2020
Problem based learning (PBL) supports the development of transversal skills and could underpin the training of a workforce competent to withstand the constant generation of new information. However, the application of PBL is still facing challenges, as educators are usually unsure how to structure student-centred courses, how to monitor students'…
Descriptors: Problem Based Learning, Data Use, Learning Analytics, Skill Development
Herodotou, Christothea; Rienties, Bart; Boroowa, Avinash; Zdrahal, Zdenek; Hlosta, Martin – Educational Technology Research and Development, 2019
By collecting longitudinal learner and learning data from a range of resources, predictive learning analytics (PLA) are used to identify learners who may not complete a course, typically described as being at risk. Mixed effects are observed as to how teachers perceive, use, and interpret PLA data, necessitating further research in this direction.…
Descriptors: Prediction, Learning Analytics, Teacher Role, Teacher Attitudes

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