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Hansen, Christian; Hansen, Casper; Hjuler, Niklas; Alstrup, Stephen; Lioma, Christina – International Educational Data Mining Society, 2017
The analysis of log data generated by online educational systems is an important task for improving the systems, and furthering our knowledge of how students learn. This paper uses previously unseen log data from Edulab, the largest provider of digital learning for mathematics in Denmark, to analyse the sessions of its users, where 1.08 million…
Descriptors: Foreign Countries, Markov Processes, Mathematical Models, Student Behavior
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Cvetkovski, Stefan; Jorm, Anthony F.; Mackinnon, Andrew J. – Higher Education Research and Development, 2018
Studies of psychological distress (PD) in university students have shown that they have high prevalence rates. These findings have raised concerns that PD may be leading to poorer student outcomes, such as elevated dropout rates. The aim of this study was to examine the association of PD in undergraduate university students with the competing…
Descriptors: Stress Variables, Foreign Countries, Undergraduate Students, National Surveys
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Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio – Journal of Educational and Behavioral Statistics, 2016
We extend to the longitudinal setting a latent class approach that was recently introduced by Lanza, Coffman, and Xu to estimate the causal effect of a treatment. The proposed approach enables an evaluation of multiple treatment effects on subpopulations of individuals from a dynamic perspective, as it relies on a latent Markov (LM) model that is…
Descriptors: Causal Models, Markov Processes, Longitudinal Studies, Probability
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Stewart, Wayne; Stewart, Sepideh – PRIMUS, 2014
For many scientists, researchers and students Markov chain Monte Carlo (MCMC) simulation is an important and necessary tool to perform Bayesian analyses. The simulation is often presented as a mathematical algorithm and then translated into an appropriate computer program. However, this can result in overlooking the fundamental and deeper…
Descriptors: Markov Processes, Monte Carlo Methods, College Mathematics, Mathematics Instruction
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Hlavatý, Robert; Dömeová, Ludmila – International Education Studies, 2014
The paper is focused on students of Mathematical methods in economics at the Czech university of life sciences (CULS) in Prague. The idea is to create a model of students' progress throughout the whole course using the Markov chain approach. Each student has to go through various stages of the course requirements where his success depends on the…
Descriptors: Foreign Countries, Markov Processes, Mathematical Models, Progress Monitoring
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Rusconi, Patrice; Marelli, Marco; D'Addario, Marco; Russo, Selena; Cherubini, Paolo – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2014
Evidence evaluation is a crucial process in many human activities, spanning from medical diagnosis to impression formation. The present experiments investigated which, if any, normative model best conforms to people's intuition about the value of the obtained evidence. Psychologists, epistemologists, and philosophers of science have proposed…
Descriptors: Experimental Psychology, Models, Intuition, Evidence
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Blazquez, Maite; Budria, Santiago – Education Economics, 2012
In this paper, we use the 2000-2008 waves of the German Socioeconomic Panel to examine overeducation transitions. The results are based on a first-order Markov model that allows us to account for both the initial conditions problem and potential endogeneity in attrition. We found that overeducation dynamics, especially the probability of entering…
Descriptors: Educational Attainment, Overachievement, Education Work Relationship, Personality Traits
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Farmer, Jim – Australian Senior Mathematics Journal, 2010
In Volume 23(2) of the "Australian Senior Mathematics Journal," Boncek and Harden present an exercise in fitting a Markov chain model to rainfall data for Darwin Airport (Boncek & Harden, 2009). Days are subdivided into those with precipitation and precipitation-free days. The author abbreviates these labels to wet days and dry days.…
Descriptors: Markov Processes, Probability, Secondary School Curriculum, Mathematics Curriculum
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Li, Guo-Dong; Yamaguchi, Daisuke; Nagai, Masatake; Masuda, Shiro – International Journal of Learning and Change, 2008
In this paper, we propose a new prediction analysis model which combines the first order one variable Grey differential equation Model (abbreviated as GM(1,1) model) from grey system theory and time series Autoregressive Integrated Moving Average (ARIMA) model from statistics theory. We abbreviate the combined GM(1,1) ARIMA model as ARGM(1,1)…
Descriptors: Markov Processes, Prediction, Statistical Data, Foreign Countries
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Worts, Diana; Sacker, Amanda; McDonough, Peggy – Social Indicators Research, 2010
This paper addresses a key methodological challenge in the modeling of individual poverty dynamics--the influence of measurement error. Taking the US and Britain as case studies and building on recent research that uses latent Markov models to reduce bias, we examine how measurement error can affect a range of important poverty estimates. Our data…
Descriptors: Poverty, Measurement, Error of Measurement, Probability
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Huang, Yueh-Min; Huang, Tien-Chi; Wang, Kun-Te; Hwang, Wu-Yuin – Educational Technology & Society, 2009
The ability to apply existing knowledge in new situations and settings is clearly a vital skill that all students need to develop. Nowhere is this truer than in the rapidly developing world of Web-based learning, which is characterized by non-sequential courses and the absence of an effective cross-subject guidance system. As a result, questions…
Descriptors: Markov Processes, Transfer of Training, Probability, Internet
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Fox, Jean-Paul – Journal of Educational and Behavioral Statistics, 2005
The randomized response (RR) technique is often used to obtain answers on sensitive questions. A new method is developed to measure latent variables using the RR technique because direct questioning leads to biased results. Within the RR technique is the probability of the true response modeled by an item response theory (IRT) model. The RR…
Descriptors: Item Response Theory, Models, Probability, Markov Processes
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Nicholls, Miles G. – Higher Education: The International Journal of Higher Education and Educational Planning, 2007
In this paper, absorbing markov chains are used to analyse the flows of higher degree by research candidates (doctoral and master) within an Australian faculty of business. The candidates are analysed according to whether they are full time or part time. The need for such analysis stemmed from what appeared to be a rather poor completion rate (as…
Descriptors: Probability, Databases, Markov Processes, Student Characteristics