Browsing by Author "Morsy, Sara"
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Item Accounting for Language Changes over Time in Document Similarity Search(2015-07-07) Morsy, Sara; Karypis, GeorgeGiven 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 in order 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 above information while estimating the representation of both documents and terms within the context of non-probabilistic 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.Item Cumulative Knowledge-based Regression Models for Next-term Grade Prediction(2017-01-25) Morsy, Sara; Karypis, GeorgeGrade 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.Item Data-driven Methods for Course Selection and Sequencing(2019-05) Morsy, SaraLearning analytics in higher education is an emerging research field that combines data mining, machine learning, statistics, and education on learning-related data, in order to develop methods that can improve the learning environment for learners and allow educators and administrators to be more effective. The vast amount of data available about students' interactions and their performance in classrooms has motivated researchers to analyze this data in order to gain insights about the learning environment for the ultimate goal of improving undergraduate education and student retention rates. In this thesis, we focus on the problem of course selection and sequencing, where we would like to help students make informed decisions about which courses to register for in their following terms. By analyzing the historical enrollment and grades data, this thesis studies the two main problems of course selection and sequencing, namely grade prediction and course recommendation. In addition, it analyzes the relationship between degree planning in terms of course timing and ordering and the students' GPA and time to degree. First, we focus on predicting the grades that students will obtain on future courses so that they can make informed decisions about which courses to register for in their following terms. We model the grade prediction problem as cumulative knowledge-based linear regression models that learn the courses' required and provided knowledge components and use them to estimate a student's knowledge state at each term and predict the grades that he/she can obtain on future courses. Second, we focus on improving the knowledge-based regression models we previously developed by modeling the complex interactions among prior courses using non-linear and neural attentive models, in order to have more accurate estimation of a student's knowledge state. In addition, we model the interactions between a target course, which we would like to predict its grade, and the other courses taken concurrently with it. We hypothesize that concurrently-taken courses can affect a student's performance in a target course, and thus modeling their interactions with that course should lead to better predictions. Third, we focus on analyzing the degree plans of students to gain more insights about how course timing and sequencing relate to their GPAs and time to degree. Toward this end, we define several course timing and course sequencing metrics and compare different sub-groups of students who have achieved high vs low GPA as well as sub-groups of students who have graduated on time vs over time. Fourth, we focus on improving course recommendation by recommending to each student a set of courses which he/she is prepared for and expected to perform well in. We model this problem as a grade-aware course recommendation problem, where we propose two different approaches. The first approach ranks the courses by using an objective function that differentiates between courses that are expected to increase or decrease a student's GPA. The second approach combines the grades predicted by grade prediction methods with the rankings produced by course recommendation methods to improve the final course rankings. To obtain the course rankings in both approaches, we adapted two widely-used representation learning techniques to learn the optimal temporal ordering between courses. In summary, this thesis addresses two closely related problems by: (1) developing cumulative knowledge-based regression models for grade prediction; % (2) developing context-aware non-linear and neural attentive knowledge-based models for grade prediction; % (3) analyzing degree planning and how the time when students take courses and how they sequence them relate to their GPAs and time to degree; and % (4) developing novel approaches for grade-aware course recommendation. %Item Learning Course Sequencing for Course Recommendation(2018-05-22) Morsy, Sara; Karypis, GeorgeThe alarming graduation statistics in higher education institutions have resulted in an increased demand on finding ways to improve the learning environment for students to help them graduate in a successful and timely manner. With the rise of data available about past students, machine learning researchers have been able to learn predictive models that solve different problems in the education domain. In this paper, we focus on the problem of course recommendation that aims to recommend to each student a set of courses from which they can register for in their following term, for the ultimate goal of improving the student's GPA and graduation time. We first propose a different definition for the course recommendation problem statement, by focusing on recommending courses on which the students' expected grades will help maintain or improve their GPAs. We then leverage two widely-known representation learning techniques, in order to learn the sequence by which students take courses and create better personalized rankings for students. Our experiments on a large dataset obtained from the University of Minnesota that includes students from 23 different majors show that the methods based on the proposed problem statement can better recommend courses on which the students are expected to perform well and that align with their degree programs. Moreover, the results show that the proposed methods achieve statistically significant results for course recommendation over the current methods.