Restructuring Curricular Patterns Using Bayesian Networks

2020

We present a general method for using a competences map, created by defining generalization/specialization and inclusion/part-of relationships between competences, in order to build an overlay student model in the form of a dynamic Bayesian network in which conditional probability distributions are defined per relationship type. We have created a competences map for a subset of the transversal competences defined as educational goals for the Mexican high school system, then we have built a dynamic Bayesian student model as said before, and we have use it to trace the development of the corresponding competences by some hypothetical students exhibiting representative performances along an online course (low to medium performance, medium to high performance but with low final score, and two terms medium to high performance). The results obtained suggest that the proposed way for constructing dynamic Bayesian student models on the basis of competences maps could be useful to monitor co...

IEEE Transactions on Learning Technologies, 2020

Curriculum prerequisite networks have a central role in shaping the course of university programs. The analysis of prerequisite networks has attracted a lot of research interest recently since designing an appropriate network is of great importance both academically and economically. It determines the learning goals of the program and also has a huge impact on completion time and dropping out. In this article, we introduce a data-driven probabilistic student flow approach to characterize prerequisite networks and study the distribution of graduation time based on the network topology and on the completion rate of the courses. We also present a method to identify courses that have a significant impact on graduation time. Our student flow approach is also capable of simulating the effects of policy changes and modifications of the network. We compare our methods to other techniques from the literature that measure structural properties of prerequisite networks using the example of the electrical engineering program of the

Expert Systems with Applications, 2015

University education is crucial for cultural and economic growth. Thus, the academic mission recognizes the achievement of both institutional and social objectives, and research provides the basis for the systematic creation of knowledge and the development of human capital. Universities attempt to manage a global system with a holistic vision based on data and facts and oriented to the continuous improvement of its effectiveness and efficiency. The goal is achieved by implementing a monitoring system based both on internal and external performances. As a consequence, it is necessary to consider both students perspective regarding needs, expectations, level of satisfaction and loyalty and internal key performance indicators. This paper proposes the use of Bayesian networks for jointly monitoring internal and external performance of a Master's programme of an Italian University in a holistic approach. A Bayesian network is estimated using a learning algorithm able to analyze the association structure among mixed ordinal and nominal variables. Various scenarios are evaluated thanks to efficient computational algorithms of Bayesian networks.

Journal of Telecommunication, Electronic and Computer Engineering, 2016

A growing number of universities and colleges established in Malaysia have provided many options for students to further their tertiary education either in public university or private institution. Therefore, this study tends to identify the factors that affect the decision made by students to pursue their Degree in a private institution based on Bayesian networks (BNs). BN provides a unique graphical structure that indicates the dependency between variables. A total of 150 valid responses are collected from a private university by the means of questionnaire as the survey instrument in this study. The study found that reputation of the university is not the main concern of the students, meanwhile, undesired course offered from the public university and family advices are the significant factors influencing their decision to pursue their degree in a private university

The main objective of educational institutions is to provide high quality of education. Providing a high quality of education depends on predicting the unmotivated students before they entering in to final examination. In this paper, we compare the Bayesian network classifiers for predicting the student's academic performance and generates a Model. This model helps earlier in identifying the drop outs and students who need special attention and allow the teacher to provide appropriate counselling / Advising. In Addition to this, Accurately predicting student performance is useful in many different contexts. For example, identifying exceptional students for scholarships is an essential part of the admissions process and identifying weak students who are likely to fail is also important for allocating limited tutoring resources.

2007

This paper proposes an alternative to the predic- tion of education accomplishment. It employs a data mining technique, the Bayesian belief network (Bayes net). The technique is used to analyze the inde- pendent variables that aect the education accom- plishment result of vocational students, undergrad- uate students and graduate students. The machine learning tool, WEKA, is used to construct the pre- diction model that is accurate for the prediction based on k-fold cross-validation. The experimental result shows that the Bayes net technique is able to determine important variables for the prediction of the result of education accom- plishment and this technique provides high prediction accuracy. From the models constructed by WEKA, we find that the important variables that aect the education accomplishment are the previous GPA., mother and or fathers career, the total income of the family and the grade point average when they enter the first year in bachelor study. The obtained res...

International Journal of Approximate Reasoning, 2009

Teachers viewing Bayesian network-based proficiency estimates from a classroom full of students face a different problem from a tutor looking at one student at a time. Fortunately, individual proficiency estimates can be aggregated into classroom and other group estimates through sums and averages. This paper explores a few graphical representations for group level inferences from a Bayesian network.

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, 2010

The ASSISTment online tutoring system was used by over 600 students during the school year [2004][2005]. Each student used the system as part of their math classes 1-2 times a month, doing on average over 100+ state-test items, and getting tutored on the ones they got incorrect. The ASSISTment system has 4 different skill models, each at different grain-size involving 1, 5, 39 or 106 skills. Our goal in the paper is to develop a model that will predict whether a student will get correct a given item. We compared the performance of these models on their ability to predict a student state test score, after the state test was "tagged" with skills for the 4 models. The best fitting model was the 39 skill model, suggesting that using finer-grained skills models is useful to a point. This result is pretty much the same as that which was achieved by Feng, Heffernan, Mani, & Heffernan (in press), who were working simultaneously, but using mized-effect models instead of Bayes networks. We discuss reasons why the finest-grained model might not have been able to predict the data the best. Implications for large scale testing are discussed.

Student Retention, 2020

Student attrition-the departure from an institution of higher learning prior to the achievement of a degree or earning due educational credentials-is an administratively important, scientifically interesting and yet practically challenging problem for decision makers and researchers. This study aims to find the prominent variables and their conditional dependencies/interrelations that affect student attrition in college settings. Specifically, using a large and feature-rich dataset, proposed methodology successfully captures the probabilistic interactions between attrition (the dependent variable) and related factors (the independent variables) to reveal the underlying, potentially complex/non-linear relationships. The proposed methodology successfully predicts the individual students' attrition risk through a Bayesian Belief Network-driven probabilistic model. The findings suggest that the proposed probabilistic graphical/network method is capable of predicting student attrition with 84% in AUC-Area Under the Receiver Operating Characteristics Curve. Using a 2-by-2 investigational design framework, this body of research also compares the impact and contribution of data balancing and feature selection to the resultant prediction models. The results show that (1) the imbalanced dataset produces similar predictive results in detecting the at-risk students, and (2) the feature selection, which is the process of identifying and eliminating unnecessary/unimportant predictors, results in simpler, more understandable, interpretable, and actionable results without compromising on the accuracy of the prediction task.

2011

In this paper we apply ideas from collaborative filtering to the problem of building dynamic Bayesian network (DBN) models for planning. We demonstrate that item-based collaborative filtering can be used to construct dynamic Bayesian networks for use in large, factored domains with sparse data. Such Bayesian networks can model the transition function for decision-theoretic planning. We demonstrate the feasibility and effectiveness of this technique on an academic advising domain, based on student grades in computer science and related courses at the University of Kentucky.