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Yang, Tzu-Chi; Chen, Sherry Y. – Interactive Learning Environments, 2023
Individual differences exist among learners. Among various individual differences, cognitive styles can strongly predict learners' learning behavior. Therefore, cognitive styles are essential for the design of online learning. There are a variety of cognitive style dimensions and overlaps exist among these dimensions. In particular, Witkin's field…
Descriptors: Student Behavior, Educational Technology, Electronic Learning, Cognitive Style
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Sun, Fu-Rong; Hu, Hong-Zhen; Wan, Rong-Gen; Fu, Xiao; Wu, Shu-Jing – Interactive Learning Environments, 2022
To determine the impact of cognitive style on change of concept of engagement in the flipped classroom, a sequential analysis from the perspective of Bloom's Taxonomy was conducted to establish if significant differences existed between the learning achievements and engagement of students with different cognitive styles. The participants were…
Descriptors: Learning Analytics, Preservice Teachers, Educational Change, Learner Engagement
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Joseph, Lumy; Abraham, Sajimon; Mani, Biju P.; N., Rajesh – Journal of Educational Computing Research, 2022
A fixed learning path for all learners is a major drawback of virtual learning systems. An online learning path recommendation system has the advantage of offering flexibility to select appropriate learning content. Learning Analytics Intervention (LAI) provides several educational benefits, particularly for low-performing students. Researchers…
Descriptors: Cognitive Style, Learning Analytics, Educational Benefits, Integrated Learning Systems
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Aleksandra Maslennikova; Daniela Rotelli; Anna Monreale – Journal of Learning Analytics, 2023
Students organize and manage their own learning time, choosing when, what, and how to study due to the flexibility of online learning. Each person has unique learning habits that define their behaviours and distinguish them from others. To investigate the temporal behaviour of students in online learning environments, we seek to identify suitable…
Descriptors: Learning Analytics, Online Courses, Time Management, Self Management
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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
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Tempelaar, Dirk T.; Rienties, Bart; Nguyen, Quan – Applied Cognitive Psychology, 2020
Worked-examples have been established as an effective instructional format in problem-solving practices. However, less is known about variations in the use of worked examples across individuals at different stages in their learning process in student-centred learning contexts. This study investigates different profiles of students' learning…
Descriptors: Individual Differences, Preferences, Demonstrations (Educational), Learning Analytics
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Chuang, Tsung-Yen; Yeh, Martin K.-C.; Lin, Yu-Lun – Educational Technology & Society, 2021
Students with different cognitive styles benefit from different instructional strategies, including learning through playing video games. Although playing video games can be an effective learning method, we do not know its impact on the reasoning ability of students with different cognitive styles. The purposes of this study are to investigate…
Descriptors: Game Based Learning, Video Games, Computer Games, Puzzles
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Priyanka, Priyanka Gupta; Mehrotra, Deepti – Journal of Information Technology Education: Innovations in Practice, 2022
Aim/Purpose: This paper focuses on designing and implementing the rubric for objective JAVA programming assessments. An unsupervised learning approach was used to group learners based on their performance in the results obtained from the rubric, reflecting their learning ability. Background: Students' learning outcomes have been evaluated…
Descriptors: Objective Tests, Outcomes of Education, Scoring Rubrics, Programming Languages
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Maaliw, Renato R., III – Online Submission, 2020
Most virtual learning environment fails to recognize that students have different needs when it comes to learning. With the evolving characteristics and tendencies of students, these learning environments must provide adaptation and personalization features for adaptive learning materials, course content and navigational designs to support…
Descriptors: Virtual Classrooms, Electronic Learning, Integrated Learning Systems, Individualized Instruction
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Rozo, Hugo; Real, Miguel – Journal of Technology and Science Education, 2019
The present article constitutes a systematic review of the literature with the objective of identifying the appropriate elements that must be considered when designing and creating adaptive digital educational resources. The methodological process was rigorous and systematic, employing an article search in which the texts related to the object of…
Descriptors: Instructional Design, Intelligent Tutoring Systems, Instructional Materials, Educational Technology
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Shahzad, Muhammad; Nadeem, Muhammad Asif; U-Nisa, Zaib – Pakistan Journal of Distance and Online Learning, 2021
The study was conducted in three phases that is testing, using multimedia and observation of the controlled group. Major objective of the research were to assess the implications of software development design on learners and to identify effects of interactive multimedia environment on learning patterns of learners' at graduate level. The…
Descriptors: Educational Environment, Multimedia Instruction, Instructional Effectiveness, Hypermedia
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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