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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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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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Azevedo, Ana, Ed.; Azevedo, José, Ed. – IGI Global, 2019
E-assessments of students profoundly influence their motivation and play a key role in the educational process. Adapting assessment techniques to current technological advancements allows for effective pedagogical practices, learning processes, and student engagement. The "Handbook of Research on E-Assessment in Higher Education"…
Descriptors: Higher Education, Computer Assisted Testing, Multiple Choice Tests, Guides
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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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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