Deep Learning on Curriculum Study Pattern by Selective Cross Join in Advising Students’ Study Path

Advising engineering students in their study path need to understand the curriculum structure, student capabilities and challenge that commonly appear in courses. This paper offered the simple method to help student advisor in analyzing student performance in their study path based on academic progress record of the student it-self and pattern that been built from other students that have taken the courses. Using selective cross join for each  possible permutation of pair courses with respect to courses’ grade to create knowledge base. This knowledge base will be used to construct complex tree of any possible study path that might be taken by student to reach the end of study including course that must be retaken. Finding the best suggestion for study path using Monte Carlo tree search styl

The Impact of Information Quantity and Quality on Parameter Estimation for a Selection of Dynamic Bayesian Network Models with Latent Variables

abstract: Dynamic Bayesian networks (DBNs; Reye, 2004) are a promising tool for modeling student proficiency under rich measurement scenarios (Reichenberg, in press). These scenarios often present assessment conditions far more complex than what is seen with more traditional assessments and require assessment arguments and psychometric models capable of integrating those complexities. Unfortunately, DBNs remain understudied and their psychometric properties relatively unknown. If the apparent strengths of DBNs are to be leveraged, then the body of literature surrounding their properties and use needs to be expanded upon. To this end, the current work aimed at exploring the properties of DBNs under a variety of realistic psychometric conditions. A two-phase Monte Carlo simulation study was conducted in order to evaluate parameter recovery for DBNs using maximum likelihood estimation with the Netica software package. Phase 1 included a limited number of conditions and was exploratory in nature while Phase 2 included a larger and more targeted complement of conditions. Manipulated factors included sample size, measurement quality, test length, the number of measurement occasions. Results suggested that measurement quality has the most prominent impact on estimation quality with more distinct performance categories yielding better estimation. While increasing sample size tended to improve estimation, there were a limited number of conditions under which greater samples size led to more estimation bias. An exploration of this phenomenon is included. From a practical perspective, parameter recovery appeared to be sufficient with samples as low as N = 400 as long as measurement quality was not poor and at least three items were present at each measurement occasion. Tests consisting of only a single item required exceptional measurement quality in order to adequately recover model parameters. The study was somewhat limited due to potentially software-specific issues as well as a non-comprehensive collection of experimental conditions. Further research should replicate and, potentially expand the current work using other software packages including exploring alternate estimation methods (e.g., Markov chain Monte Carlo).Dissertation/ThesisDoctoral Dissertation Family and Human Development 201