Building Student’s Study Path using Markov Chain Process with Apriori Cross Join Pearson Correlation

Student’s study path could be advised by using bestpossible path from Markov Chain rule based on student’sacademic performance records with several assumption on thecurrent curriculum. Finding the Markov’s rule is crucial processbecause it will determine study path’s scenarios which rely onstudent current performance to choose the next best possiblepath. The rule would be built using the whole student’s academicperformance on the same curriculum by implementing AprioriCross Join Pearson Correlation Test on two consecutivesemesters. It will then create path consist of paired courses A->B with Pearson value that would be implemented as rule in Markov Proces

A Computational Model of Accelerated Future Learning through Feature Recognition

Abstract. Accelerated future learning, in which learning proceeds more effectively and more rapidly because of prior learning, is considered to be one of the most interesting measures of robust learning. A growing body of studies have demonstrated that some instructional treatments lead to accelerated future learning. However, little study has focused on under- standing the learning mechanisms that yield accelerated future learning. In this paper, we present a computational model that demonstrates accelerated future learning through the use of machine learning techniques for feature recognition. In order to understand the behavior of the proposed model, we conducted a controlled simulation study with four alternative versions of the model to investigate how both better prior knowledge learning and better learning strategies might independently yield accelerated future learning. We measured the learning outcomes of the models by rate of learning and the fit to the pattern of errors made by real students. We found out that both stronger prior knowledge and a better learning strategy can speed up the learning process. Some model variations generate human-like error patterns, but others learn to avoid errors more quickly than students.