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Showing 1 to 15 of 21 results Save | Export
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Abdullahi Yusuf; Norah Md Noor; Shamsudeen Bello – Education and Information Technologies, 2024
Studies examining students' learning behavior predominantly employed rich video data as their main source of information due to the limited knowledge of computer vision and deep learning algorithms. However, one of the challenges faced during such observation is the strenuous task of coding large amounts of video data through repeated viewings. In…
Descriptors: Learning Analytics, Student Behavior, Video Technology, Classification
Singelmann, Lauren Nichole – ProQuest LLC, 2022
To meet the national and international call for creative and innovative engineers, many engineering departments and classrooms are striving to create more authentic learning spaces where students are actively engaging with design and innovation activities. For example, one model for teaching innovation is Innovation-Based Learning (IBL) where…
Descriptors: Engineering Education, Design, Educational Innovation, Models
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Mohd Fazil; Angelica Rísquez; Claire Halpin – Journal of Learning Analytics, 2024
Technology-enhanced learning supported by virtual learning environments (VLEs) facilitates tutors and students. VLE platforms contain a wealth of information that can be used to mine insight regarding students' learning behaviour and relationships between behaviour and academic performance, as well as to model data-driven decision-making. This…
Descriptors: Learning Analytics, Learning Management Systems, Learning Processes, Decision Making
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Zhou, Yiqiu; Kang, Jina – International Educational Data Mining Society, 2022
The complex and dynamic nature of collaboration makes it challenging to find indicators of productive learning and quality collaboration. This exploratory study developed a collaboration metric to capture temporal patterns of joint attention (JA) based on log files generated as students interacted with an immersive astronomy simulation using…
Descriptors: Astronomy, Problem Solving, Science Instruction, Cooperative Learning
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El Aissaoui, Ouafae; El Alami El Madani, Yasser; Oughdir, Lahcen; El Allioui, Youssouf – Education and Information Technologies, 2019
Adaptive E-learning platforms provide personalized learning process relying mainly on learning styles. The traditional approach to find learning styles depends on asking learners to self-evaluate their own attitudes and behaviors through surveys and questionnaires. This approach presents several weaknesses including the lack of self-awareness of…
Descriptors: Classification, Cognitive Style, Models, Electronic Learning
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Wampfler, Rafael; Emch, Andreas; Solenthaler, Barbara; Gross, Markus – International Educational Data Mining Society, 2020
Front camera data from tablets used in educational settings offer valuable clues to student behavior, attention, and affective state. Due to the camera's angle of view, the face of the student is partially occluded and skewed. This hinders the ability of experts to adequately capture the learning process and student states. In this paper, we…
Descriptors: Photography, Handheld Devices, Student Behavior, Affective Behavior
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Lai, Song; Sun, Bo; Wu, Fati; Xiao, Rong – IEEE Transactions on Learning Technologies, 2020
Adaptive e-learning can be used to personalize learning environment for students to meet their individual demands. Individual differences depend on the students' personality traits. Numerous studies have indicated that understanding the role of personality in the learning process can facilitate learning. Hence, personality identification in…
Descriptors: Personality Traits, Electronic Learning, Individual Differences, Learning Processes
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Rodrigues, Rodrigo Lins; Ramos, Jorge Luis Cavalcanti; Silva, João Carlos Sedraz; Dourado, Raphael A.; Gomes, Alex Sandro – International Journal of Distance Education Technologies, 2019
The increasing use of the Learning Management Systems (LMSs) is making available an ever-growing, volume of data from interactions between teachers and students. This study aimed to develop a model capable of predicting students' academic performance based on indicators of their self-regulated behavior in LMSs. To accomplish this goal, the authors…
Descriptors: Management Systems, Teacher Student Relationship, Distance Education, College Students
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Khusniati, Miranita; Parmin; Sudarmin – Journal of Turkish Science Education, 2017
The implementation of local wisdom-based science learning model through reconstruction of indigenous science is hoped to improve student's conservationist character. Through this model, student will be familiarized with science and local wisdom to improve their existing conservationist characters. Characters measured in this study were…
Descriptors: Teaching Methods, Indigenous Knowledge, Science Instruction, Conservation (Environment)
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Araújo, Isabel; Faria, Pedro Miguel – International Association for Development of the Information Society, 2020
The evolution of ICT and its adoption in higher education is driving greater interactivity in teaching and learning processes. The teaching/learning paradigm has been changing. Both educational actors, teacher and student, are increasingly adapting to use technologies. This article presents a study that enhances how technologies can be used in the…
Descriptors: Student Attitudes, Integrated Learning Systems, Higher Education, Technology Integration
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Hwang, Gwo-Haur; Chen, Beyin; Chen, Ru-Shan; Wu, Ting-Ting; Lai, Yu-Ling – Interactive Learning Environments, 2019
Competitive game-based learning has been widely discussed in terms of its positive and negative impacts on learners' learning effectiveness and learning behavior. Although different types of games require different kinds of knowledge to accomplish the task via competition, few studies have considered that knowledge types, such as procedural…
Descriptors: Student Behavior, Adoption (Ideas), Competition, Game Based Learning
Emond, Bruno; Buffett, Scott – International Educational Data Mining Society, 2015
This paper reports on results of applying process discovery mining and sequence classification mining techniques to a data set of semi-structured learning activities. The main research objective is to advance educational data mining to model and support self-regulated learning in heterogeneous environments of learning content, activities, and…
Descriptors: Data Analysis, Classification, Learning Activities, Inquiry
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Hargreaves, Eleanore – Educational Research, 2014
Background: Some authors consider the ultimate purpose of Assessment for Learning to be the promotion of pupils' autonomy. But the concept of autonomy is problematic and teachers' attempts to promote autonomy in the classroom can seem both vague and impractical. Purpose: In this paper, following Ecclestone (2002), I suggest that a full definition…
Descriptors: Personal Autonomy, Feedback (Response), Classroom Techniques, Elementary School Students
Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam – International Educational Data Mining Society, 2015
This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…
Descriptors: Learning Activities, Learning Processes, Data Collection, Student Behavior
Jung, Charles; Luke, Robert – 1968
Fifteen recognized leaders in the interpersonal relations area were asked to identify the interpersonal competencies that are related to the facilitations of learning processes for pupils, teachers, and teachers of teachers. Their reports were analyzed and a category system developed which would encompass four major dimensions: interpersonal…
Descriptors: Behavior, Classification, Interpersonal Competence, Interpersonal Relationship
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