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Megan N. Imundo; Siyuan Li; Jiachen Gong; Andrew Potter; Tracy Arner; Danielle S. McNamara – Grantee Submission, 2025
Personalized learning (PL) is a student-centered instructional approach in which learning goals, pacing, content, and environments are customized to address individual student needs (Bernacki et al., 2021; Ellis, 2009; Lee, 2014; Miliband, 2006; Office of Educational Technology, 2010; Sota, 2016; Zhang et al., 2020). In grades K-12, PL has been…
Descriptors: Self Determination, Individualized Instruction, Electronic Learning, Higher Education
Peer reviewed Peer reviewed
Eduardo Davalos; Namrata Srivastava; Yike Zhang; Amanda Goodwin; Gautam Biswas – Grantee Submission, 2024
As online learning tools become more widespread, understanding student behaviors through learning analytics is increasingly important. Traditional methods relying on system log data fall short of capturing the full range of cognitive strategies students use. To address this, we developed an in-depth post-assignment reflection dashboard that…
Descriptors: Visualization, Eye Movements, Electronic Learning, Online Courses
Li, Chenglu; Xing, Wanli; Leite, Walter – Grantee Submission, 2021
To support online learners at a large scale, extensive studies have adopted machine learning (ML) techniques to analyze students' artifacts and predict their learning outcomes automatically. However, limited attention has been paid to the fairness of prediction with ML in educational settings. This study intends to fill the gap by introducing a…
Descriptors: Learning Analytics, Prediction, Models, Electronic Learning
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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
RenĂ© F. Kizilcec; Maximilian Chen; Kaja K. Jasinska; Michael Madaio; Amy Ogan – Grantee Submission, 2021
School closures due to teacher strikes or political unrest in low-resource contexts can adversely affect children's educational outcomes and career opportunities. Phone-based educational technologies could help bridge these gaps in formal schooling, but it is unclear whether or how children and their families will use such systems during periods…
Descriptors: Electronic Learning, Handheld Devices, Telecommunications, Educational Technology
Danielle S. McNamara; Laura K. Allen; Scott A. Crossley; Mihai Dascalu; Cecile A. Perret – Grantee Submission, 2017
Language is of central importance to the field of education because it is a conduit for communicating and understanding information. Therefore, researchers in the field of learning analytics can benefit from methods developed to analyze language both accurately and efficiently. Natural language processing (NLP) techniques can provide such an…
Descriptors: Natural Language Processing, Learning Analytics, Educational Technology, Automation
Ostrow, Korinn S.; Selent, Doug; Wang, Yan; Van Inwegen, Eric G.; Heffernan, Neil T.; Williams, Joseph Jay – Grantee Submission, 2016
Researchers invested in K-12 education struggle not just to enhance pedagogy, curriculum, and student engagement, but also to harness the power of technology in ways that will optimize learning. Online learning platforms offer a powerful environment for educational research at scale. The present work details the creation of an automated system…
Descriptors: Learning Analytics, Technology Uses in Education, Randomized Controlled Trials, Automation