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Hui Shi; Yihang Zhou; Vanessa P. Dennen; Jaesung Hur – Education and Information Technologies, 2024
The imbalance in student-teacher ratio and the diversity of student population pose challenges to MOOC's quality of instructor support. An understanding of student profiles, such as who they are and how they behave, is critical to improving personalized support of MOOC learning environments. While past studies have explored different types of…
Descriptors: MOOCs, Behavior Patterns, Student Behavior, Cluster Grouping
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Kasakowskij, Regina; Haake, Joerg M.; Seidel, Niels – International Educational Data Mining Society, 2023
Improving competence requires practicing, e.g. by solving tasks. The Self-Assessment task type is a new form of scalable online task providing immediate feedback, sample solution and iterative improvement within the newly developed SAFRAN plugin. Effective learning not only requires suitable tasks but also their meaningful usage within the…
Descriptors: Self Evaluation (Individuals), Student Behavior, College Students, Learning Processes
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Jelena Andelkovic Labrovic; Nikola Petrovic; Jelena Andelkovic; Marija Meršnik – Journal of Computing in Higher Education, 2025
The focus of this study was on identifying patterns of student behavior to support data-informed decision-making which would then improve the learning experience and learning outcomes of online English language courses. Learning analytics approach (or more specifically cluster analysis) was used to identify engagement patterns in online learning.…
Descriptors: Electronic Learning, Online Courses, Behavior Patterns, Student Behavior
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Gökhan Akçapinar; Erkan Er; Alper Bayazit – International Review of Research in Open and Distributed Learning, 2024
Lecture capture videos, a popular type of instructional content used by instructors to share course recordings online, play a significant role in educational settings. Compared to other educational videos, these recordings require minimal time and effort to produce, making them a preferred choice for disseminating course materials. Despite their…
Descriptors: Learner Engagement, Video Technology, Lecture Method, Student Behavior
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Shuai He; Yu Lu – Interactive Learning Environments, 2024
Currently, generative AI has undergone rapid development. Numerous studies have attested to the benefits of Gen AI in programming, mathematics and other disciplines. However, since Gen AI mostly uses English as the intrinsic training parameter, it is more effective in facilitating the teaching of courses that use international common notation, but…
Descriptors: Instructional Effectiveness, Technology Uses in Education, Artificial Intelligence, Humanities Instruction
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Zhan, Zehui; Wu, Qianyi; Lin, Zhihua; Cai, Jiayi – Australasian Journal of Educational Technology, 2021
This study investigated the effect of classroom settings on teacher-student interaction in higher education by comparing the behavioural sequences in smart classrooms (SCs) and traditional multimedia classrooms (TMCs). Twenty in-classroom teaching sessions were randomly selected from six universities in South China, involving 1,043 students and 23…
Descriptors: Technology Uses in Education, Classroom Environment, Teacher Student Relationship, Behavior Patterns
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Kuan-Fu Chen; Gwo-Jen Hwang; Mei-Rong Alice Chen – Educational Technology Research and Development, 2024
Laboratory courses can help students learn in a meaningful way. In the past, students encountered difficulties in chemistry laboratory courses due to limited access to equipment and space for practicing experimental operations. In recent years, virtual laboratories have allowed students to repeatedly practice in order to achieve their experimental…
Descriptors: Concept Mapping, Virtual Classrooms, Laboratories, Student Behavior
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Fatahi, Somayeh; Shabanali-Fami, Faezeh; Moradi, Hadi – Education and Information Technologies, 2018
The learning style of a learner is an important parameter in his learning process. Therefore, learning styles should be considered in the design, development, and implementation of e-learning environments to increase learners' performance. Thus, it is important to be able to automatically determine learning styles of learners in an e-learning…
Descriptors: Cognitive Style, Educational Technology, Technology Uses in Education, Student Behavior
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Pearson, Russell J. – Journal of Chemical Education, 2019
During problem-based learning sessions, undergraduate students were tasked with answering chemistry-related questions using clicker-handset technology in which the last response made by each handset would override any previous vote. The benefits, if any, of showing cohort responses from clicker questions during versus after polling were explored.…
Descriptors: Peer Teaching, Audience Response Systems, Problem Based Learning, Undergraduate Students
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Du, Xin; Duivesteijn, Wouter; Klabbers, Martijn; Pechenizkiy, Mykola – International Educational Data Mining Society, 2018
Behavioral records collected through course assessments, peer assignments, and programming assignments in Massive Open Online Courses (MOOCs) provide multiple views about a student's study style. Study behavior is correlated with whether or not the student can get a certificate or drop out from a course. It is of predominant importance to identify…
Descriptors: Student Behavior, Assignments, Large Group Instruction, Online Courses