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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
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
Shalini Nagaratnam; Christina Vanathas; Muhammad Naeim Mohd Aris; Jeevanithya Krishnan – International Society for Technology, Education, and Science, 2023
Learning Analytics (LA) captures the digital footprint of students' online learning activity. This study describes students' navigational behavior in an e-learning setting by processing the LA data obtained from Blackboard LMS. This is an attempt to understand the navigational behavior of students and the relationship with learning performance.…
Descriptors: Learning Analytics, Online Courses, Active Learning, Learning Management Systems
Rico Putra Pradana; Ave Adriana Pinem; Putu Wuri Handayani – Journal of Educators Online, 2024
This study uses self-determination theory to examine the effect of gamification on the students' behavioral, emotional, and cognitive engagement in online discussion forums by providing more instructor badges than automatic badges and using a quasi-experimental one-group pretest-posttest design. Behavioral engagement was measured using the number…
Descriptors: Learner Engagement, Game Based Learning, Scoring Rubrics, Comparative Analysis
Ibañez, Patricia; Villalonga, Cristina; Nuere, Leire – Technology, Knowledge and Learning, 2020
The main objective of educational institutions is to achieve the integral development of their students in their learning and knowledge construction process. One way to achieve these objectives is the accompaniment and continuous monitoring of students in this process, adapting the methods to their training needs. In online and mixed teaching…
Descriptors: Learning Analytics, Foreign Countries, Educational Environment, Electronic Learning
Loperfido, Fedela Feldia; Dipace, Anna; Scarinci, Alessia – Research on Education and Media, 2018
What emotions can students experience in digitally mediated learning processes? In this paper, we connect Learning Analytics to the Grounded Theory in order to analyse the emotional world of students of 11 courses within the EduOpen (www.eduopen.org) massive open online course (MOOC) platform. Namely, we have used NVivo 11 Plus software and have…
Descriptors: Learning Analytics, Emotional Experience, Psychological Patterns, Online Courses
Evaluating Student Engagement and Deep Learning in Interactive Online Psychology Learning Activities
Sugden, Nicole; Brunton, Robyn; MacDonald, Jasmine; Yeo, Michelle; Hicks, Ben – Australasian Journal of Educational Technology, 2021
There is growing demand for online learning activities that offer flexibility for students to study anywhere, anytime, as online students fit study around work and family commitments. We designed a series of online activities and evaluated how, where, and with what devices students used the activities, as well as their levels of engagement and…
Descriptors: Learning Activities, Learner Engagement, Online Courses, Handheld Devices
Shimada, Atsushi; Mouri, Kousuke; Taniguchi, Yuta; Ogata, Hiroaki; Taniguchi, Rin-ichiro; Konomi, Shin'ichi – International Educational Data Mining Society, 2019
In this paper, we focus on optimizing the assignment of students to courses. The target courses are conducted by different teachers using the same syllabus, course design, and lecture materials. More than 1,300 students are mechanically assigned to one of ten courses taught by different teachers. Therefore, mismatches often occur between students'…
Descriptors: Student Placement, Learning Activities, Learning Analytics, Cognitive Style