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Bárcena, M. J.; Garín, M. A.; Martín, A.; Tusell, F.; Unzueta, A. – Journal of Statistics Education, 2019
Teaching some concepts in statistics greatly benefits from individual practice with immediate feedback. In order to provide such practice to a large number of students we have written a simulator based on an historical event: the loss in May 22, 1968, and subsequent search for the nuclear submarine USS Scorpion. Students work on a simplified…
Descriptors: Computer Simulation, Computer Assisted Instruction, Teaching Methods, Bayesian Statistics
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Loftus, Mary; Madden, Michael G. – Teaching in Higher Education, 2020
How do we teach and learn with our students about data literacy, at the same time as Biesta (2015) calls for an emphasis on 'subjectification' i.e. 'the coming into presence of unique individual beings'? (Good Education in an Age of Measurement: Ethics, Politics, Democracy. Routledge) Our response to these challenges and the datafication of higher…
Descriptors: Teaching Methods, Data Analysis, Literacy, Learning Processes
Yasuda, Keiji; Kawashima, Hiroyuki; Hata, Yoko; Kimura, Hiroaki – International Association for Development of the Information Society, 2015
An adaptive learning system is proposed that incorporates a Bayesian network to efficiently gauge learners' understanding at the course-unit level. Also, learners receive content that is adapted to their measured level of understanding. The system works on an iPad via the Edmodo platform. A field experiment using the system in an elementary school…
Descriptors: Adaptive Testing, Bayesian Statistics, Networks, Computer Assisted Instruction
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Wynton, Sarah K. A.; Anglim, Jeromy – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2017
While researchers have often sought to understand the learning curve in terms of multiple component processes, few studies have measured and mathematically modeled these processes on a complex task. In particular, there remains a need to reconcile how abrupt changes in strategy use can co-occur with gradual changes in task completion time. Thus,…
Descriptors: Learning Strategies, Learning Processes, Bayesian Statistics, Computer Assisted Instruction
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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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Achumba, Ifeyinwa E.; Azzi, Djamel; Stocker, James – International Journal of Virtual and Personal Learning Environments, 2010
The laboratory component of undergraduate engineering education poses challenges in resource constrained engineering faculties. The cost, time, space and physical presence requirements of the traditional (real) laboratory approach are the contributory factors. These resource constraints may mitigate the acquisition of meaningful laboratory…
Descriptors: Cost Effectiveness, Engineering Education, Web Based Instruction, Computer Simulation
Vos, Hans J. – 1994
A method is proposed for optimizing cutting scores for a selection-placement-mastery problem simultaneously. A simultaneous approach has two advantages over separate optimization. First, test scores used in previous decisions can be used as "prior data" in later decisions, increasing the efficiency of the decisions. Then, more realistic…
Descriptors: Bayesian Statistics, Computer Assisted Instruction, Criteria, Cutting Scores