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Weidlich, Joshua; Gaševic, Dragan; Drachsler, Hendrik – Journal of Learning Analytics, 2022
As a research field geared toward understanding and improving learning, Learning Analytics (LA) must be able to provide empirical support for causal claims. However, as a highly applied field, tightly controlled randomized experiments are not always feasible nor desirable. Instead, researchers often rely on observational data, based on which they…
Descriptors: Causal Models, Inferences, Learning Analytics, Comparative Analysis
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Johanes, Petr; Thille, Candace – British Journal of Educational Technology, 2019
Education and education research are experiencing increased digitization and datafication, partly thanks to the rise in popularity of massively open online courses (MOOCs). The infrastructures that collect, store and analyse the resulting big data have received critical scrutiny from sociological, epistemological, ethical and analytical…
Descriptors: Data Collection, Learning Analytics, Online Courses, Higher Education
Benjamin A. Motz; Öykü Üner; Harmony E. Jankowski; Marcus A. Christie; Kim Burgas; Diego del Blanco Orobitg; Mark A. McDaniel – Grantee Submission, 2023
For researchers seeking to improve education, a common goal is to identify teaching practices that have causal benefits in classroom settings. To test whether an instructional practice exerts a causal influence on an outcome measure, the most straightforward and compelling method is to conduct an experiment. While experimentation is common in…
Descriptors: Learning Analytics, Experiments, Learning Processes, Learning Management Systems
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Danielle S. McNamara; Tracy Arner; Elizabeth Reilley; Paul Alvarado; Chani Clark; Thomas Fikes; Annie Hale; Betheny Weigele – Grantee Submission, 2022
Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…
Descriptors: Electronic Learning, Educational Technology, Networks, Learning Analytics
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Matcha, Wannisa; Uzir, Nora'ayu Ahmad; Gasevic, Dragan; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2020
This paper presents a systematic literature review of learning analytics dashboards (LADs) research that reports empirical findings to assess the impact on learning and teaching. Several previous literature reviews identified self-regulated learning as a primary focus of LADs. However, there has been much less understanding how learning analytics…
Descriptors: Learning Analytics, Computer Interfaces, Educational Research, Learning Strategies
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Osler, James Edward, II – Journal of Educational Technology, 2021
This paper provides a novel instructional methodology that is designed to conceptually address the four main challenges faced by 21st century students, who must learn in a multitude of educational settings (face to face, hybrid and online). The online learning neuroscience supported instructional methodology detailed in this article also provides…
Descriptors: Electronic Learning, Engineering, Instructional Innovation, Blended Learning
Doran, Elizabeth; Li, Ann; Aikens, Nikki; Dang, Myley; Kopack Klein, Ashley; Reid, Natalie; Scott, Myah; Rakibullah, Sharika; Cannon, Judy; Harrington, Jeff; Larson, Addison; Bernstein, Sara; Tarullo, Louisa; Malone, Lizabeth – Office of Planning, Research and Evaluation, 2022
Head Start is a national program that helps young children from families with low incomes get ready to succeed in school. It does this by working to promote their early learning and health and their families' well-being. The Head Start Family and Child Experiences Survey (FACES) provides national information about Head Start programs and…
Descriptors: Social Services, Low Income Students, Federal Programs, Family Relationship
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Rehrey, George; Shepard, Linda; Hostetter, Carol; Reynolds, Amberly; Groth, Dennis – Journal of Learning Analytics, 2019
To successfully implement Learning Analytics (LA) systems within higher education, we need to engage administrators, faculty, and staff alike. This paper is by and primarily for practitioners. We suggest implementation strategies that consider the human factor in adopting new technologies by analyzing the viability of our Learning Analytics…
Descriptors: Learning Analytics, Change Agents, School Culture, Technology Integration