A Student Advising System Using Association Rule Mining

Academic advising is a time-consuming activity that takes a considerable effort in guiding students to improve student performance. Traditional advising systems depend greatly on the effort of the advisor to find the best selection of courses to improve student performance in the next semester. There is a need to know the associations and patterns among course registration. Finding associations among courses can guide and direct students in selecting the appropriate courses that leads to performance improvement. In this paper, the authors propose to use association rule mining to help both students and advisors in selecting and prioritizing courses. Association rules find dependences among courses that help students in selecting courses based on their performance in previous courses. The association rule mining is conducted on thousands of student records to find associations between courses that have been registered by students in many previous semesters. The system has successfully generated a list of association rules that guide a particular student to select courses. The system was validated on the registration of 100 students, and the precision and recall showed acceptable prediction of courses

A Roadmap for Natural Language Processing Research in Information Systems

Natural Language Processing (NLP) is now widely integrated into web and mobile applications, enabling natural interactions between human and computers. Although many NLP studies have been published, none have comprehensively reviewed or synthesized tasks most commonly addressed in NLP research. We conduct a thorough review of IS literature to assess the current state of NLP research, and identify 12 prototypical tasks that are widely researched. Our analysis of 238 articles in Information Systems (IS) journals between 2004 and 2015 shows an increasing trend in NLP research, especially since 2011. Based on our analysis, we propose a roadmap for NLP research, and detail how it may be useful to guide future NLP research in IS. In addition, we employ Association Rules (AR) mining for data analysis to investigate co-occurrence of prototypical tasks and discuss insights from the findings

A Study of The Influence of Advising on Underrepresented Minority Undergraduate Student Persistence in STEM

In the United States, undergraduate underrepresented minority (URM) students tend to change out of declared majors in science, technology, engineering and math (STEM) disciplines at a rate of nearly sixty percent prior to earning a post secondary degree. This phenomenon contributes to a general concern that the United States is not producing enough STEM trained skilled workers to meet future employment needs of industry and government. Although there has been research developed to examine how to increase the numbers of URM students enrolling in STEM programs at higher education institutions, retention of these students remains critical. One area of increasing focus for researchers is to understand how multiple factors impact the college experience of URM students and how those factors may contribute to the student decision to persist in earning a STEM disciple degree. This research study is a phenomenological mixed method study that examines how students experience the phenomenon of advising and the influence of the advising experience of undergraduate URM students on their likelihood of persisting in STEM at a northeast US technology oriented post secondary institution. Persistence, from the perspective of the student, is driven by cognitive psychological attributes such as confidence, motivation and self-efficacy. Utilizing a Social Cognitive theoretical framework, this study examines how three distinct undergraduate URM student populations enrolled in; an Academic Services Program, Honors College, and the general undergraduate population at this institution experience advising and how their experiences may influence their propensity to persist in earning a STEM oriented degree.Ed.D., Educational Leadership and Management -- Drexel University, 201