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O. S. Adewale; O. C. Agbonifo; E. O. Ibam; A. I. Makinde; O. K. Boyinbode; B. A. Ojokoh; O. Olabode; M. S. Omirin; S. O. Olatunji – Interactive Learning Environments, 2024
With the advent of technological advancement in learning, such as context-awareness, ubiquity and personalisation, various innovations in teaching and learning have led to improved learning. This research paper aims to develop a system that supports personalised learning through adaptive content, adaptive learning path and context awareness to…
Descriptors: Cognitive Style, Individualized Instruction, Learning Processes, Preferences
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Obeng, Asare Yaw – Cogent Education, 2023
The learning processes have been significantly impacted by technology. Numerous learners have adopted technology-based learning systems as the preferred form of learning. It is then necessary to identify the learning styles of learners to deliver appropriate resources, engage them, increase their motivation, and enhance their satisfaction and…
Descriptors: Predictor Variables, Cognitive Style, Electronic Learning, College Freshmen
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Smail, Linda – Journal on Mathematics Education, 2017
Mathematics is the foundation of all sciences, but most students have problems learning math. Although students' success in life related to their success in learning, many would not take a math course unless it is their university's core requirements. Multiple reasons exist for students' poor performance in mathematics, but one prevalent variable…
Descriptors: Bayesian Statistics, Study Habits, Personality Traits, Mathematics Anxiety
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Premlatha, K. R.; Dharani, B.; Geetha, T. V. – Interactive Learning Environments, 2016
E-learning allows learners individually to learn "anywhere, anytime" and offers immediate access to specific information. However, learners have different behaviors, learning styles, attitudes, and aptitudes, which affect their learning process, and therefore learning environments need to adapt according to these differences, so as to…
Descriptors: Electronic Learning, Profiles, Automation, Classification
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Maaliw, Renato R. III; Ballera, Melvin A. – International Association for Development of the Information Society, 2017
The usage of data mining has dramatically increased over the past few years and the education sector is leveraging this field in order to analyze and gain intuitive knowledge in terms of the vast accumulated data within its confines. The primary objective of this study is to compare the results of different classification techniques such as Naïve…
Descriptors: Classification, Cognitive Style, Electronic Learning, Decision Making
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Mcdermott, Paul A.; Watkins, Marley W.; Drogalis, Anna Rhoad; Chao, Jessica L.; Worrell, Frank C.; Hall, Tracey E. – Psychology in the Schools, 2016
Contextually based assessments reveal the circumstances accompanying maladjustment (the when, where, and with whom) and supply clues to the motivations underpinning problem behaviors. The Adjustment Scales for Children and Adolescents (ASCA) is a teacher rating scale composed of indicators describing behavior in 24 classroom situational contexts.…
Descriptors: Social Adjustment, Emotional Adjustment, Bayesian Statistics, Cognitive Style
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Ruggeri, Azzurra; Lombrozo, Tania; Griffiths, Thomas L.; Xu, Fei – Developmental Psychology, 2016
Children are active learners: they learn not only from the information people offer and the evidence they happen to observe, but by actively seeking information. However, children's information search strategies are typically less efficient than those of adults. In two studies, we isolate potential sources of developmental change in how children…
Descriptors: Information Seeking, Search Strategies, Cognitive Style, Cognitive Structures
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Aslan, Burak Galip; Öztürk, Özlem; Inceoglu, Mustafa Murat – Educational Sciences: Theory and Practice, 2014
Considering the increasing importance of adaptive approaches in CALL systems, this study implemented a machine learning based student modeling middleware with Bayesian networks. The profiling approach of the student modeling system is based on Felder and Silverman's Learning Styles Model and Felder and Soloman's Index of Learning Styles…
Descriptors: Foreign Countries, Undergraduate Students, Graduate Students, Cognitive Style
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Clewley, Natalie; Chen, Sherry Y.; Liu, Xiaohui – Educational Technology & Society, 2011
Web-based instruction programs are used by learners with diverse knowledge, skills and needs. These differences determine their preferences for the design of Web-based instruction programs and ultimately influence learners' success in using them. Cognitive style has been found to significantly affect learners' preferences of web-based instruction…
Descriptors: Cognitive Style, Web Based Instruction, Internet, Bayesian Statistics
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Bruce, Christine; Stoodley, Ian; Pham, Binh – Studies in Higher Education, 2009
As part of their journey of learning to research, doctoral candidates need to become members of their research community. In part, this involves coming to be aware of their field in ways that are shared amongst longer-term members of the research community. One aspect of candidates' experience we need to understand, therefore, involves how they…
Descriptors: Information Technology, Researchers, Doctoral Programs, Student Experience