In recent years, several data-driven methods have been developed to help undergraduate students during course selection and sequencing. These methods tend to utilize the whole set of past course registration data, regardless of the past students' graduation GPA and time to degree (TTD). Though some previous work has shown through the results of their developed models that students of different GPA tend to take courses in different sequence, the actual analysis of the degree plans and how/if they relate to the students' graduation GPA and time-to-degree has not received much attention. This study analyzes how the student's academic level when they take different courses, as well as the pairwise degree similarity between pairs of students relate to the students' graduation GPA and TTD. Our study uses a large-scale dataset that contains 25 majors from different colleges at the University of Minnesota and spans 16 years. The analysis shows that TTD is highly correlated with both the timing and ordering of courses that students follow in their degree plans, while the correlation between graduation GPA and the course timing and ordering is not as high. We also perform a case study that uses course timing and ordering features to predict whether the student at each semester will graduate on-time or overtime. The results show that careful curriculum planning is needed to improve graduation rates in universities.

Grade prediction for courses not yet taken by students is important so as to guide them while registering for next-term courses. Moreover, it can help their advisers for designing personalized degree plans and modifying them based on the students' performance. In this paper, we present cumulative knowledge-based regression models with different course-knowledge spaces for the task of next-term grade prediction. These models utilize historical student-course grade data as well as the information available about the courses that capture the relationships between courses in terms of the knowledge components provided by them. Our experiments on a large dataset obtained from the College of Science and Engineering at University of Minnesota show that our proposed methods achieve better performance than competing methods and that these performance gains are statistically significant.

Given a query document, ranking the documents in a collection based on how similar they are to the query is an essential task with extensive applications. For collections that contain documents whose creation dates span several decades, this task is further complicated by the fact that the language changes over time. For example, many terms add or lose one or more senses to meet people’s evolving needs. To address this problem, we present methods that take advantage of two types of information to account for the language change. The first is the citation network that often exists within the collection, which can be used to link related documents with significantly different creation dates (and hence different language use). The second is the changes in the usage frequency of terms that occur over time, which can indicate changes in their senses and uses. These methods utilize the preceding information while estimating the representation of both documents and terms within the context of nonprobabilistic static and dynamic topic models. Our experiments on two real-world datasets that span more than 40 years show that our proposed methods improve the retrieval performance of existing models and that these improvements are statistically significant.