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Gardner, Josh; Brooks, Christopher; Li, Warren – Journal of Learning Analytics, 2018
In this paper, we evaluate the complete undergraduate co-enrollment network over a decade of education at a large American public university. We provide descriptive and exploratory analyses of the network, demonstrating that the co-enrollment networks evaluated follow power-law degree distributions similar to many other large-scale networks; that…
Descriptors: Markov Processes, Classification, Undergraduate Students, Grade Point Average
Andrade, Alejandro; Danish, Joshua A.; Maltese, Adam V. – Journal of Learning Analytics, 2017
Interactive learning environments with body-centric technologies lie at the intersection of the design of embodied learning activities and multimodal learning analytics. Sensing technologies can generate large amounts of fine-grained data automatically captured from student movements. Researchers can use these fine-grained data to create a…
Descriptors: Measurement, Interaction, Models, Educational Environment
Oskarsson, An T.; Van Boven, Leaf; McClelland, Gary H.; Hastie, Reid – Psychological Bulletin, 2009
The authors review research on judgments of random and nonrandom sequences involving binary events with a focus on studies documenting gambler's fallacy and hot hand beliefs. The domains of judgment include random devices, births, lotteries, sports performances, stock prices, and others. After discussing existing theories of sequence judgments,…
Descriptors: Markov Processes, Inferences, Predictor Variables, Statistical Analysis
Peer reviewedGay, Dennis A.; Wong, Daniel W. – International Journal of Rehabilitation Research, 1988
Counselor ratings and statistical data from client files were gathered for 71 rehabilitation clients. A Markov Chain model was developed which provided viable probabilities for predicting rehabilitation outcome at three time periods, from 1 to 13 weeks after initial interview. Predictions generally followed a time progression toward stronger and…
Descriptors: Adults, Disabilities, Markov Processes, Models
Borden, Victor M. H.; Dalphin, John F. – 1998
This study used Markov chain matrices to simulate the effect of varying degrees of change in student characteristics on retention and graduation rates. Data were applied to a 1-year enrollment transition matrix that tracks how students of each class level progress into the same or higher class levels, to a completed degree, or to non-returning…
Descriptors: Academic Persistence, College Students, Credits, Enrollment
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
Peer reviewedShah, Chandra; Burke, Gerald – Higher Education, 1999
A Markov chain is used to model the movement of undergraduates through the higher education system in Australia. Given the student's age on commencing a course of study, the model provides estimates of the probability of course completion, mean time for completion, and mean time spent in the higher education system. (Author/MSE)
Descriptors: Academic Persistence, Age, College Students, Enrollment Management

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