Application of Predictive Analytics in Intelligent Course Recommendation

AbstractStudents who pursue admission to colleges usually experience a difficulty to select a course. In this paper, we propose a course recommendation system to find out the courses which are apt for a student pursuing admission to the college. Typically, the prediction is based on the career goal or the present job trend. In this system proposed, the prediction is formulated based on the grades acquired by the student in twelfth standard; which is taken as a sign of the previous academic performance and cognitive ability of the student. A model is generated from the legacy data or data from the students who have completed the course successfully. This model is used for predicting the courses for new students. The idea behind this approach is that when a student with specific set of skills is successful in a course then another student with similar set of skills will have a higher success probability in the said course

On Integrating Student Empirical Software Engineering Studies with Research and Teaching Goals

Background: Many empirical software engineering studies use students as subjects and are conducted as part of university courses. Aim: We aim at reporting our experiences with using guidelines for integrating empirical studies with our research and teaching goals. Method: We document our experience from conducting three studies with graduate students in two software architecture courses. Results: Our results show some problems that we faced when following the guidelines and deviations we made from the original guidelines. Conclusions: Based on our results we propose recommendations for empirical software engineering studies that are integrated in university courses.

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

Fun Anatomy: A Supplemental Website for the Upper Extremity

In accordance with the Accreditation Council for Occupational Therapy Education (ACOTE) standards, the occupational therapy program at the University of Puget Sound requires the completion of an anatomy course as part of the master’s degree curriculum. Currently, the program’s functional anatomy course does not provide any online resources to support students’ mastery of course content. Due to advances in technology, an increasing number of professional degree programs are either web-based or are adding online elements (Donovan, 2008; Friedman, Watts, Croston, & Durkin, 2002; Fallon, 2011). Course-specific online resources have been shown to enhance student learning and performance (Thompson, Ford, & Webster, 2011). A customized supplemental website was created to accompany the functional anatomy course as a learning enhancement. The website includes diagrams, flash cards, quiz questions, case studies, and additional resources. The various elements of the website were developed to address different learning styles and to cycle students through the stages of Kolb’s model of experiential learning (Kolb, 1984; Friedman, Watts, Croston, & Durkin, 2002). The goal of the website is for the user to achieve a 90% standard on the quizzes and case study questions, which is higher than the university graduate school’s 83% (3.0 GPA) requirement to maintain good standing. Use of the website in a pilot study by a sample group of the program’s students resulted in 100% positive recommendation for its use as a resource in the functional anatomy course

The design and study of pedagogical paper recommendation

For learners engaging in senior-level courses, tutors in many cases would like to pick some articles as supplementary reading materials for them each week. Unlike researchers ‘Googling’ papers from the Internet, tutors, when making recommendations, should consider course syllabus and their assessment of learners along many dimensions. As such, simply ‘Googling’ articles from the Internet is far from enough. That is, learner models of each individual, including their learning interest, knowledge, goals, etc. should be considered when making paper recommendations, since the recommendation should be carried out so as to ensure that the suitability of a paper for a learner is calculated as the summation of the fitness of the appropriateness of it to help the learner in general. This type of the recommendation is called a Pedagogical Paper Recommender.In this thesis, we propose a set of recommendation methods for a Pedagogical Paper Recommender and study the various important issues surrounding it. Experimental studies confirm that making recommendations to learners in social learning environments is not the same as making recommendation to users in commercial environments such as Amazon.com. In such learning environments, learners are willing to accept items that are not interesting, yet meet their learning goals in some way or another; learners’ overall impression towards each paper is not solely dependent on the interestingness of the paper, but also other factors, such as the degree to which the paper can help to meet their ‘cognitive’ goals.It is also observed that most of the recommendation methods are scalable. Although the degree of this scalability is still unclear, we conjecture that those methods are consistent to up to 50 papers in terms of recommendation accuracy. The experiments conducted so far and suggestions made on the adoption of recommendation methods are based on the data we have collected during one semester of a course. Therefore, the generality of results needs to undergo further validation before more certain conclusion can be drawn. These follow up studies should be performed (ideally) in more semesters on the same course or related courses with more newly added papers. Then, some open issues can be further investigated. Despite these weaknesses, this study has been able to reach the research goals set out in the proposed pedagogical paper recommender which, although sounding intuitive, unfortunately has been largely ignored in the research community. Finding a ‘good’ paper is not trivial: it is not about the simple fact that the user will either accept the recommended items, or not; rather, it is a multiple step process that typically entails the users navigating the paper collections, understanding the recommended items, seeing what others like/dislike, and making decisions. Therefore, a future research goal to proceed from the study here is to design for different kinds of social navigation in order to study their respective impacts on user behavior, and how over time, user behavior feeds back to influence the system performance

Lessons Learned from Development of a Software Tool to Support Academic Advising

We detail some lessons learned while designing and testing a decision-theoretic advising support tool for undergraduates at a large state university. Between 2009 and 2011 we conducted two surveys of over 500 students in multiple majors and colleges. These surveys asked students detailed questions about their preferences concerning course selection, advising, and career paths. We present data from this study which may be helpful for faculty and staff who advise undergraduate students. We find that advising support software tools can augment the student-advisor relationship, particularly in terms of course planning, but cannot and should not replace in-person advising.Comment: 5 Figures, revised version including more figures and cross-referencin