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Yang, Christopher C. Y.; Ogata, Hiroaki – Education and Information Technologies, 2023
The application of student interaction data is a promising field for blended learning (BL), which combines conventional face-to-face and online learning activities. However, the application of online learning technologies in BL settings is particularly challenging for students with lower self-regulatory abilities. In this study, a personalized…
Descriptors: Individualized Instruction, Learning Analytics, Intervention, Academic Achievement
Christopher C. Y. Yang; Jiun-Yu Wu; Hiroaki Ogata – Education and Information Technologies, 2025
Blended learning (BL) combines traditional classroom activities with online learning resources, enabling students to obtain higher academic performance through well-defined interactive learning strategies. However, lacking the capacity to self-regulate their learning, many students might fail to comprehensively study the learning materials after…
Descriptors: Blended Learning, Educational Technology, Learning Analytics, Self Management
Eady, Michelle J.; Green, Corinne A.; Fulcher, David; Boniface, Tim – Journal of Further and Higher Education, 2022
Research has demonstrated a correlation between lecture attendance and students' academic achievement. However, students may not attend lectures for a variety of reasons. The provision of lecture recordings online can also negatively impact lecture attendance and student achievement. These facts implore us to be courageous and explore new…
Descriptors: Learning Analytics, Undergraduate Study, Curriculum Design, Teacher Education Curriculum
Du, Xiaoming; Ge, Shilun; Wang, Nianxin – International Journal of Information and Communication Technology Education, 2022
In the context of education big data, it uses data mining and learning analysis technology to accurately predict and effectively intervene in learning. It is helpful to realize individualized teaching and individualized teaching. This research analyzes student life behavior data and learning behavior data. A model of student behavior…
Descriptors: Prediction, Data, Student Behavior, Academic Achievement
Brott, Pamelia E. – Open Learning, 2023
This practical, practice-based article sets out to define and describe vlogging based on the author's experiences while teaching a blended learning course. Vlogging is a short duration video recording that engages the learner in critical self-reflection. It is a scaffolding strategy for moving students from a descriptive diary to situated…
Descriptors: Video Technology, Reflection, Educational Technology, Learning Analytics
Jennifer Scianna; Rogers Kaliisa – Educational Technology Research and Development, 2024
Educational researchers have pointed to socioemotional dimensions of learning as important in gaining a more nuanced description of student engagement and learning. However, to date, research focused on the analysis of emotions has been narrow in its focus, centering on affect and sentiment analysis in isolation while neglecting how emotions…
Descriptors: Computer Mediated Communication, Discussion, Discourse Analysis, Asynchronous Communication
Yangyang Luo; Xibin Han; Chaoyang Zhang – Asia Pacific Education Review, 2024
Learning outcomes can be predicted with machine learning algorithms that assess students' online behavior data. However, there have been few generalized predictive models for a large number of blended courses in different disciplines and in different cohorts. In this study, we examined learning outcomes in terms of learning data in all of the…
Descriptors: Prediction, Learning Management Systems, Blended Learning, Classification
Sudeshna Pal; Patsy Moskal; Anchalee Ngampornchai – International Journal on E-Learning, 2024
This study investigated the effectiveness of blended instruction in enhancing student success in an advanced undergraduate engineering course. The research used learning analytics captured from pre-recorded lecture videos, course grade data, and student surveys. Results revealed positive correlations between lecture video viewership and course…
Descriptors: Blended Learning, Advanced Courses, Engineering Education, Undergraduate Students
Yang, Christopher C. Y.; Ogata, Hiroaki – Educational Technology & Society, 2023
Blended learning (BL) is regarded as an effective strategy for combining traditional face-to-face classroom activities with various types of online learning tools (e.g., e-books). An effective feature of e-books is the ability to use digital notes. When e-books are used in BL, the strategic adoption of note-taking provides benefits that influence…
Descriptors: Blended Learning, Sequential Approach, Notetaking, Electronic Publishing
Esnaashari, Shadi; Gardner, Lesley A.; Arthanari, Tiru S.; Rehm, Michael – Journal of Computer Assisted Learning, 2023
Background: It is vital to understand students' Self-Regulatory Learning (SRL) processes, especially in Blended Learning (BL), when students need to be more autonomous in their learning process. In studying SRL, most researchers have followed a variable-oriented approach. Moreover, little has been known about the unfolding process of students' SRL…
Descriptors: Metacognition, Student Attitudes, Learning Strategies, Questionnaires
Oscar Yecid Aparicio-Gómez; Olga Lucia Ostos-Ortiz; Constanza Abadía-García – Journal of Technology and Science Education, 2024
In today's educational environment, the convergence of emerging technologies and active methodologies has become a fundamental driver of change in university education. Emerging technologies, such as artificial intelligence, virtual reality, machine learning, and data analytics, are redefining the dynamics of higher education. Active…
Descriptors: Technological Advancement, Technology Uses in Education, Higher Education, Problem Based Learning
Amy Goodman; Youngjin Lee; Willard Elieson; Gerald Knezek – Journal of Computers in Mathematics and Science Teaching, 2023
Virtual learning environments give students more autonomy over their learning than traditional face-to-face classes and require that students adapt the ways they consume and assimilate new information. One theory of this process is self-regulated learning, which is illustrated in Efklides' Metacognitive and Affective model of Self-Regulated…
Descriptors: Self Management, Learning Theories, Learning Analytics, Undergraduate Students
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
Han, Feifei; Pardo, Abelardo; Ellis, Robert A. – Journal of Computer Assisted Learning, 2020
This study examines the extent to which the learning orientations identified by student self-reports and the observation of their online learning events were related to each other and to their academic performance. The participants were 322 first-year engineering undergraduates, who were enrolled in a blended course. Using students' self-report on…
Descriptors: College Students, Electronic Learning, Blended Learning, Curriculum Design
Ayyanathan, N. – Shanlax International Journal of Education, 2022
Effective engagement and monitoring of students' online self-learning capacity and application of their acquired knowledge in the final year project is a challenging task for the educators worldwide. The author builds an evaluation framework to assess the stage-wise performance of students in this undertaken project. The primary objective of this…
Descriptors: Learning Analytics, Taxonomy, Student Projects, Active Learning