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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
Orchard, Ryan K. – Journal of Educational Technology Systems, 2019
Learning management systems (LMS) allow for a variety of ways in which online multiple-choice assessments ("tests") can be configured, including the ability to allow for multiple attempts and options for which of and how the attempts will count. These options are usually chosen according to the instinct of the instructor; however, LMS…
Descriptors: Integrated Learning Systems, Data Use, Electronic Learning, Assignments
Judy Shih, Hui-chia – Innovation in Language Learning and Teaching, 2021
This paper attempts to investigate and compare two forms of learning logs using Google Sheets -- individual and collaborative learning logs -- and their effect on EFL university students' development of learner autonomy over the course of a semester. Subjects were 62 EFL learners from an intact English elective course at a private university in…
Descriptors: Cooperative Learning, Learning Analytics, Data Use, Personal Autonomy
Raudonyte, Ieva – UNESCO International Institute for Educational Planning, 2021
Although the number of countries conducting large-scale assessments has increased significantly over the past two decades, this has not necessarily led to the effective use of learning assessment data in policy-making and planning. To better understand the reasons for this, the UNESCO International Institute for Educational Planning (IIEP)…
Descriptors: Learning Analytics, Policy Formation, Educational Planning, Educational Policy
Gonzalez, Naihobe; Alberty, Elizabeth; Brockman, Stacey; Nguyen, Tutrang; Johnson, Matthew; Bond, Sheldon; O'Connell, Krista; Corriveau, Adrianna; Shoji, Megan; Streeter, Megan; Engle, Jennifer; Goodly, Chelsea; Neely, Adrian N.; White, Mary Aleta; Anderson, Mindelyn; Matthews, Channing; Mason, Leana; Means, Sheryl Felecia – Mathematica, 2022
The Education-to-Workforce Indicator Framework (E-W Framework), commissioned by the Bill & Melinda Gates Foundation and developed in partnership with leading experts representing more than 15 national and community organizations, is designed to encourage greater cross-sector collaboration and alignment across local, state, and national data…
Descriptors: Education Work Relationship, Evidence Based Practice, Student Characteristics, Partnerships in Education
Mozahem, Najib Ali – International Journal of Mobile and Blended Learning, 2020
Higher education institutes are increasingly turning their attention to web-based learning management systems. The purpose of this study is to investigate whether data collected from LMS can be used to predict student performance in classrooms that use LMS to supplement face-to-face teaching. Data was collected from eight courses spread across two…
Descriptors: Integrated Learning Systems, Data Use, Prediction, Academic Achievement
Admiraal, Wilfried; Vermeulen, Jordi; Bulterman-Bos, Jacquelien – Technology, Pedagogy and Education, 2020
Computer-based assessments can provide students with feedback to guide their learning as well as inform teachers who extract information to prepare their teaching. Five secondary school teachers were monitored during one school year to answer the following research questions: (1) What kind of student data do teachers use for their teaching…
Descriptors: Learning Analytics, Computer Assisted Testing, Data Use, Formative Evaluation
West, Deborah; Luzeckyj, Ann; Searle, Bill; Toohey, Danny; Vanderlelie, Jessica; Bell, Kevin R. – Australasian Journal of Educational Technology, 2020
This article reports on a study exploring student perspectives on the collection and use of student data for learning analytics. With data collected via a mixed methods approach from 2,051 students across six Australian universities, it provides critical insights from students as a key stakeholder group. Findings indicate that while students are…
Descriptors: Stakeholders, Undergraduate Students, Graduate Students, Student Attitudes
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
Maldonado, Monica; Mugglestone, Konrad; Roberson, Amanda Janice – Institute for Higher Education Policy, 2021
Data-informed decision-making has always been -- and always will be -- a smart approach to policy, including at institutions of higher education. Just over one year since the COVID-19 pandemic radically and abruptly shifted every aspect of higher education, states and institutions are tackling the same student success goals as before, but with…
Descriptors: Data Analysis, Learning Analytics, Decision Making, Higher Education
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
Miller, Gary E., Ed.; Ives, Kathleen S., Ed. – Stylus Publishing LLC, 2020
eLearning has entered the mainstream of higher education as an agent of strategic change. This transformation requires eLearning leaders to develop the skills to innovate successfully at a time of heightened competition and rapid technological change. In this environment eLearning leaders must act within their institutions as much more than…
Descriptors: Electronic Learning, Higher Education, Change Strategies, Leadership Effectiveness
Lasater, Kara; Albiladi, Waheeb S.; Bengtson, Ed – Journal of Cases in Educational Leadership, 2021
Data use is considered a key lever in school improvement processes, but the punitive pressure of high-stakes accountability can influence whether or not data use is enacted in ways which facilitate improvement. School leaders must learn to respond to high-stakes accountability in ways which lead teachers to feel safe, efficacious, and agentic with…
Descriptors: Leadership Role, High Stakes Tests, Data Use, Educational Improvement
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
Spector, Michael, Ed.; Kumar, Vivekanandan, Ed.; Essa, Alfred, Ed.; Huang, Yueh-Min, Ed.; Koper, Rob, Ed.; Tortorella, Richard A. W., Ed.; Chang, Ting-Wen, Ed.; Li, Yanyan, Ed.; Zhang, Zhizhen, Ed. – Lecture Notes in Educational Technology, 2018
This book demonstrates teachers' and learners' experiences with big data in education; education and cloud computing; and new technologies for teacher support. It also discusses the advantages of using these frontier technologies in teaching and learning and predicts the future challenges. As such, it enables readers to better understand how…
Descriptors: Educational Technology, Technological Advancement, Data Use, Technology Uses in Education

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