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Atezaz Ahmad; Jan Schneider; Dai Griffiths; Daniel Biedermann; Daniel Schiffner; Wolfgang Greller; Hendrik Drachsler – Journal of Computer Assisted Learning, 2024
Background: During the past decade, the increasingly heterogeneous field of learning analytics has been critiqued for an over-emphasis on data-driven approaches at the expense of paying attention to learning designs. Method and objective: In response to this critique, we investigated the role of learning design in learning analytics through a…
Descriptors: Instructional Design, Learning Analytics, Data Use, Literature Reviews
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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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Tetzlaff, Leonard; Schmiedek, Florian; Brod, Garvin – Educational Psychology Review, 2021
Personalized education--the systematic adaptation of instruction to individual learners--has been a long-striven goal. We review research on personalized education that has been conducted in the laboratory, in the classroom, and in digital learning environments. Across all learning environments, we find that personalization is most successful when…
Descriptors: Individualized Instruction, Instructional Effectiveness, Instructional Design, Student Characteristics
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Papamitsiou, Zacharoula; Filippakis, Michail E.; Poulou, Marilena; Sampson, Demetrios; Ifenthaler, Dirk; Giannakos, Michail – Smart Learning Environments, 2021
In the era of digitalization of learning and teaching processes, Educational Data Literacy (EDL) is highly valued and is becoming essential. EDL is conceptualized as the ability to collect, manage, analyse, comprehend, interpret, and act upon educational data in an ethical, meaningful, and critical manner. The professionals in the field of…
Descriptors: Multiple Literacies, Instructional Design, Tutors, Electronic Learning
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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
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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
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Gril, Albane; May, Madeth; Renault, Valérie; George, Sébastien – International Association for Development of the Information Society, 2021
In Technology Enhanced Learning field, learning analytics cover multiple research challenges, among which tracking data analysis and data indicator design and visualization. Part of our research effort is dedicated to changing their design process, in order to capitalize them. This would allow us to meet a need in cost savings of design workflow…
Descriptors: Comparative Analysis, Data Analysis, Cost Effectiveness, Data Use
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Rosé, Carolyn P.; McLaughlin, Elizabeth A.; Liu, Ran; Koedinger, Kenneth R. – British Journal of Educational Technology, 2019
Using data to understand learning and improve education has great promise. However, the promise will not be achieved simply by AI and Machine Learning researchers developing innovative models that more accurately predict labeled data. As AI advances, modeling techniques and the models they produce are getting increasingly complex, often involving…
Descriptors: Discovery Learning, Man Machine Systems, Artificial Intelligence, Models
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Zheng, Lanqin – Lecture Notes in Educational Technology, 2021
This book highlights the importance of design in computer-supported collaborative learning (CSCL) by proposing data-driven design and assessment. It addresses data-driven design, which focuses on the processing of data and on improving design quality based on analysis results, in three main sections. The first section explains how to design…
Descriptors: Data Use, Instructional Design, Computer Assisted Instruction, Cooperative Learning
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Clark, Jo-Anne; Liu, Yulin; Isaias, Pedro – Australasian Journal of Educational Technology, 2020
Critical success factors (CSFs) have been around since the late 1970s and have been used extensively in information systems implementations. CSFs provide a comprehensive understanding of the multiple layers and dimensions of implementation success. In the specific context of learning analytics (LA), identifying CSFs can maximise the possibilities…
Descriptors: Learning Analytics, Program Implementation, Data Use, Accuracy
Fladd, Laurie; Heacock, Laurie; Hill-Kelley, Jennifer; Lawton, Julia; Pechac, Sharmaine; Shamah, Devora; Woodruff, Amber – Achieving the Dream, 2021
This guidebook is designed for institutional leaders and student success teams who are ready to talk openly about the students they serve and who are eager to learn practical strategies from national experts and peer institutions. We cannot design an experience that meets our students where they are unless we holistically understand who they are.…
Descriptors: Instructional Leadership, Instructional Design, Holistic Approach, Higher Education
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Prestigiacomo, Rita; Hunter, Jane; Knight, Simon; Martinez Maldonado, Roberto; Lockyer, Lori – Australasian Journal of Educational Technology, 2020
Data about learning can support teachers in their decision-making processes as they design tasks aimed at improving student educational outcomes. However, to achieve systemic impact, a deeper understanding of teachers' perspectives on, and expectations for, data as evidence is required. It is critical to understand how teachers' actions align with…
Descriptors: Preservice Teachers, Preservice Teacher Education, Elementary Secondary Education, Undergraduate Students