Predicting Academic Performance: A Systematic Literature Review

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.Peer reviewe

Classification Models in Clinical Decision Making

In this dissertation, we present a collection of manuscripts describing the development of prognostic models designed to assist clinical decision making. This work is motivated by limitations of commonly used techniques to produce accessible prognostic models with easily interpretable and clinically credible results. Such limitations hinder prognostic model widespread utilization in medical practice. Our methodology is based on Rough Set Theory (RST) as a mathematical tool for clinical data anal- ysis. We focus on developing rule-based prognostic models for end-of life care decision making in an effort to improve the hospice referral process. The development of the prognostic models is demonstrated using a retrospective data set of 9,103 terminally ill patients containing physiological characteristics, diagnostic information and neurological function values. We develop four RST-based prognostic models and compare them with commonly used classification techniques including logistic regression, support vector machines, random forest and decision trees in terms of characteristics related to clinical credibility such as accessibility and accuracy. RST based models show comparable accuracy with other methodologies while providing accessible models with a structure that facilitates clinical interpretation. They offer both more insight into the model process and more opportunity for the model to incorporate personal information of those making and being affected by the decision

Predicting Academic Performance Using a Rough Set Theory-Based Knowledge Discovery Methodology

In an effort to predict student performance in an engineering course, Rough Set Theory (RST) is employed as the core of a knowledge discovery process. Student performance is captured in terms of successful course completion. Therefore, students are classified into two categories: those who pass a course and those who do not. The Rough Set Theory paradigm presented here analyzes each student based on a set of attributes. These attributes are collected through a series of surveys conducted in the first week of the course, allowing for early identification of potential unsuccessful students. Variations of the Rough Set approach are evaluated to determine the one most suited for the particular dataset. The results are promising since the accuracy of student performance prediction presents an Area under the Receiver Operating Characteristic Curve equal to 80%. The benefits anticipated from early identification of weak and/or potentially unsuccessful students will enable educators to engage these students at the onset of the course and enroll them in additional activities to improve their performance