Ahmad Slim

Network analysis of university courses

Crucial courses have a high impact on students progress at universities and ultimately on graduation rates. Detecting such courses should therefore be a major focus of decision makers at universities. Based on complex network analysis and graph theory, this paper proposes a new framework to not only detect such courses, but also quantify their cruciality. The experimental results conducted using data from the University of New Mexico (UNM) show that the distribution of course cruciality follows a power law distribution. The results also show that the ten most crucial courses at UNM are all in mathematics. Applications of the proposed framework are extended to study the complexity of curricula within colleges, which leads to a consideration of the creation of optimal curricula. Optimal curricula along with the earned letter grades of the courses are further exploited to analyze the student progress. This work is important as it presents a robust framework to ensure the ease of flow of students through curricula with the goal of improving a universitys graduation rate.

Employing Markov Networks on Curriculum Graphs to Predict Student Performance

Colleges and universities are increasingly interested in tracking student progress as they monitor and work to improve their retention and graduation rates. Ideally, early indicators of student progress, or lack thereof, can be used to provide appropriate interventions that increase the likelihood of student success. In this paper we present a framework that uses data mining and machine learning techniques, and in particular, linear regression and a Markov network (MN), to predict the performance of students early in their academic careers. The results obtained show that the proposed framework can predict student progress, specifically student grade point average (GPA) within the intended major, with minimal error after observing a single semester of performance. Furthermore, as additional performance is observed, the predicted GPA in subsequent semesters becomes increasingly accurate, providing the ability to advise students regarding likely success outcomes early in their academic careers.

Predicting student success based on prior performance

Colleges and universities are increasingly interested in tracking student progress as they monitor and work to improve their retention and graduation rates. Ideally, early indicators of student progress, or lack thereof, can be used to provide appropriate interventions that increase the likelihood of student success. In this paper we present a framework that uses machine learning, and in particular, a Bayesian Belief Network (BBN), to predict the performance of students early in their academic careers. The results obtained show that the proposed framework can predict student progress, specifically student grade point average (GPA) within the intended major, with minimal error after observing a single semester of performance. Furthermore, as additional performance is observed, the predicted GPA in subsequent semesters becomes increasingly accurate, providing the ability to advise students regarding likely success outcomes early in their academic careers.

The Complexity of University Curricula According to Course Cruciality

Many universities have recently focused significant efforts on enhancing their graduation rates. Numerous factors may impact a students ability to succeed and ultimately graduate, including pre-university preparation, as well as the student support services provided by a university. However, even the best efforts to improve in these areas may fail if other institutional factors overwhelm their ability to facilitate student progress. Specifically, in this paper we consider degree to which the underlying curriculum that a student must traverse in order to earn a degree impacts progress. Using complex network analysis and graph theory, this paper proposes a framework for analyzing university course networks at the university, college and departmental levels. The analyses we provide are based on quantifying the importance of a course based on its delay and blocking factors, as well as the number of curricula that incorporate the course, leading to a metric we refer to as the course cruciality. Experimental results, using data from the University of New Mexico, show that the distribution of course cruciality follows a power law distribution. Applications of the proposed framework are extended to study the complexity of curricula within colleges as well as the tendency of a universitys disciplines to associate with others that are unlike them. This work may be useful to both students and decision makers at universities as it presents a robust framework for analyzing the ease of flow of students through curricula, which may lead to improvements that facilitate improved student success.

Prediction of graduation delay based on student performance

Numerous factors may impact a students ability to succeed and ultimately graduate, including pre-university preparation, as well as the student support services provided by a university. In this work we study and analyze the impact of such factors on the graduation rates of a university using three predictive models: Support Vector Machines (SVMs), Gaussian Processes (GPs) and Deep Boltzmann Machines (DBMs). We train those models using actual student data. In particular, we used high school GPA, ACT score, gender and ethnicity as the main feature set for training those models. The results show that the DBMs edges out SVMs and GPs in some regards, which has been discussed in detail in the paper, although the difference in performance among the models is negligible with respect to overall accuracies obtained.

Crucial based curriculum balancing: A new model for curriculum balancing

The Balanced Academic Curriculum Problem (BACP) aims at scheduling the courses of a curriculum to their respective terms while meeting the prerequisite conditions and balancing the workloads of terms. Different variants of the BACP have been proposed in literature in an attempt to improve the performance and solution quality. In this work, we extend the BACP model by adding a new criterion related to course cruciality. We argue that this work has a direct impact on student success and graduation rates. The proposed framework tends to design a curriculum that will better fit to real life situations by moving the courses with relatively higher crucial values to closest possible terms while meeting all the constraints of BACP. To achieve this goal, curriculum balancing is formulated as a multi-objective optimization problem using linear objective functions which is another advantage over other proposed models implemented using quadratic non-linear functions.