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Hsu, Ting-Chia; Abelson, Hal; Patton, Evan; Chen, Shih-Chu; Chang, Hsuan-Ning – International Journal of Computer-Supported Collaborative Learning, 2021
In order to promote the practice of co-creation, a real-time collaboration (RTC) version of the popular block-based programming (BBP) learning environment, MIT App Inventor (MAI), was proposed and implemented. RTC overcomes challenges related to non-collocated group work, thus lowering barriers to cross-region and multi-user collaborative software…
Descriptors: Self Efficacy, Behavior Patterns, Student Behavior, Programming
Saqr, Mohammed; López-Pernas, Sonsoles – International Journal of Computer-Supported Collaborative Learning, 2021
This study empirically investigates diffusion-based centralities as depictions of student role-based behavior in information exchange, uptake and argumentation, and as consistent indicators of student success in computer-supported collaborative learning. The analysis is based on a large dataset of 69 courses (n = 3,277 students) with 97,173 total…
Descriptors: Computer Uses in Education, Cooperative Learning, Learning Analytics, Student Behavior
Er, Erkan; Dimitriadis, Yannis; Gaševic, Dragan – Assessment & Evaluation in Higher Education, 2021
Although dialogue can augment the impact of feedback on student learning, dialogic feedback is unaffordable by instructors teaching large classes. In this regard, peer feedback can offer a scalable and effective solution. However, the existing practices optimistically rely on students' discussion about feedback and lack a systematic design…
Descriptors: Cooperative Learning, Peer Evaluation, Feedback (Response), Learning Analytics
Bergman, Peter; Kopko, Elizabeth; Rodriguez, Julio E. – National Bureau of Economic Research, 2021
Tracking is widespread in U.S. education. In post-secondary education alone, at least 71% of colleges use a test to track students. However, there are concerns that the most frequently used college placement exams lack validity and reliability, and unnecessarily place students from under-represented groups into remedial courses. While recent…
Descriptors: Prediction, Learning Analytics, College Entrance Examinations, College Students
Hess, Richard M. – ProQuest LLC, 2021
The purpose of this study was to explore and analyze the utilization of learning analytics data produced by a learning management system as an indicator of learners' self-regulation. In the Spring of 2021, 258 learners at a four-year, mid-Atlantic university provided access to their learning management system data. Of those 258 learners, 86…
Descriptors: Self Control, Integrated Learning Systems, Learning Analytics, Undergraduate Students
Nasheen Nur – ProQuest LLC, 2021
The main goal of learning analytics and early detection systems is to extract knowledge from student data to understand students' trends of activities towards success and risk and design intervention methods to improve learning performance and experience. However, many factors contribute to the challenge of designing and building effective…
Descriptors: Artificial Intelligence, Undergraduate Students, Learning Analytics, Time Factors (Learning)
Ameloot, Elise; Rotsaert, Tijs; Schellens, Tammy – Journal of Computer Assisted Learning, 2022
Background: Although blended learning (BL) has multiple educational prospects, it also poses challenges such as keeping students motivated. Objectives: This study investigates students' perceptions of how learning analytics (LA) can be used to support the design of a BL environment in order to promote students' basic need for relatedness, which is…
Descriptors: Learning Analytics, Blended Learning, Student Attitudes, Need Gratification
Curran, Sue Ann Cecilia – ProQuest LLC, 2022
The purpose of learning analytics is to improve and optimize learning using student data (Siemens, 2013). An early alert warning is learning analytics designed to promote student success (Baneres et al., 2019; Foung, 2019; Lawson et al., 2016; Villano et al., 2018). An early alert has an intervention component that includes, at minimum, an email…
Descriptors: Failure, At Risk Students, Learning Analytics, Intervention
Khor, Ean Teng; Dave, Darshan – International Review of Research in Open and Distributed Learning, 2022
The COVID-19 pandemic induced a digital transformation of education and inspired both instructors and learners to adopt and leverage technology for learning. This led to online learning becoming an important component of the new normal, with home-based virtual learning an essential aspect for learners on various levels. This, in turn, has caused…
Descriptors: Learning Analytics, Social Networks, Network Analysis, Classification
Sefton-Green, Julian; Pangrazio, Luci – Educational Philosophy and Theory, 2022
Amidst ongoing technological and social change, this article explores the implications for critical education that result from a data-driven model of digital governance. The article argues that traditional notions of critique which rely upon the deconstruction and analysis of texts are increasingly redundant in the age of datafication, where the…
Descriptors: Data Analysis, Governance, Educational Philosophy, Barriers
Lewis, Armanda; Stoyanovich, Julia – International Journal of Artificial Intelligence in Education, 2022
Although an increasing number of ethical data science and AI courses is available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on algorithmic development or data analysis. In this paper we recount a recent experience in developing and…
Descriptors: Statistics Education, Ethics, Artificial Intelligence, Compliance (Legal)
Martinez-Maldonado, Roberto; Echeverria, Vanessa; Mangaroska, Katerina; Shibani, Antonette; Fernandez-Nieto, Gloria; Schulte, Jurgen; Buckingham Shum, Simon – International Journal of Artificial Intelligence in Education, 2022
Teachers' spatial behaviours in the classroom can strongly influence students' engagement, motivation and other behaviours that shape their learning. However, classroom teaching behaviour is ephemeral, and has largely remained opaque to computational analysis. Inspired by the notion of Spatial Pedagogy, this paper presents a system called 'Moodoo'…
Descriptors: Learning Analytics, Teaching Methods, Teacher Behavior, Spatial Ability
Bowers, Alex J.; Zhao, Yihan; Ho, Eric – High School Journal, 2022
Research on data use and school Early Warning Systems (EWS) notes a central practice of researchers and practitioners is to search for patterns in student data to predict outcomes so schools can support success when students experience challenges. Yet, the domain lacks a means to visualize the rich longitudinal data that schools collect. Here, we…
Descriptors: Learning Analytics, Visual Aids, Student Records, Longitudinal Studies
Amos Oyelere Sunday; Friday Joseph Agbo; Jarkko Suhonen – Technology, Knowledge and Learning, 2025
The recent popularity of computational thinking (CT) and the desire to apply CT in our daily lives have prompted the need for a successful pedagogical technique for learning CT in K-12 education. The application of co-design pedagogical techniques has the potential to improve students' CT learning through knowledge sharing and the creation of…
Descriptors: Thinking Skills, Computer Science Education, Research Reports, Teaching Methods
Jared McBrady – Teaching & Learning Inquiry, 2025
This case study presents the development of a system that integrated two strands of SoTL research--Decoding the Disciplines and Students as Partners--into a secondary history teacher preparation program. This system simultaneously refined teaching in undergraduate history courses and provided authentic learning experiences for secondary education…
Descriptors: Preservice Teachers, Student Participation, History, Departments

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