Search

Your search keyword '"Polyzou, Agoritsa"' showing total 20 results
Start Over Author "Polyzou, Agoritsa"
20 results on '"Polyzou, Agoritsa"'

1. Session-Based Course Recommendation Frameworks Using Deep Learning

Author

Khan, Md Akib Zab and Polyzou, Agoritsa

Abstract

Academic advising plays an important role in students' decision-making in higher education. Data-driven methods provide useful recommendations to students to help them with degree completion. Several course recommendation models have been proposed in the literature to recommend courses for the next semester. One aspect of the data that has yet to be explored is the suitability of the recommended courses taken together in a semester. Students may face more difficulty coping with the workload of courses if there is no relationship among courses taken within a semester. To address this problem, we propose to employ session-based approaches to recommend a set of courses for the next semester. In particular, we test two session-based recommendation models, CourseBEACON and CourseDREAM. Our experimental evaluation shows that session-based methods outperform existing popularity-based, sequential, and non-sequential recommendation approaches. Accurate course recommendation can lead to better student advising, which, in turn, can lead to better student performance, lower dropout rates, and better overall student experience and satisfaction. [For the complete proceedings, see ED630829.]

Published
2023

2. FaiREO: User Group Fairness for Equality of Opportunity in Course Recommendation

Author

Polyzou, Agoritsa, Kalantzi, Maria, and Karypis, George

Subjects
Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

Course selection is challenging for students in higher educational institutions. Existing course recommendation systems make relevant suggestions to the students and help them in exploring the available courses. The recommended courses can influence students' choice of degree program, future employment, and even their socioeconomic status. This paper focuses on identifying and alleviating biases that might be present in a course recommender system. We strive to promote balanced opportunities with our suggestions to all groups of students. At the same time, we need to make recommendations of good quality to all protected groups. We formulate our approach as a multi-objective optimization problem and study the trade-offs between equal opportunity and quality. We evaluate our methods using both real-world and synthetic datasets. The results indicate that we can considerably improve fairness regarding equality of opportunity, but we will introduce some quality loss. Out of the four methods we tested, GHC-Inc and GHC-Tabu are the best performing ones with different advantageous characteristics., Comment: 30 pages

Published
2021

3. Scholars Walk: A Markov Chain Framework for Course Recommendation

Author

Polyzou, Agoritsa, Nikolakopoulos, Athanasios N., and Karypis, George

Abstract

Course selection is a crucial and challenging problem that students have to face while navigating through an undergraduate degree program. The decisions they make shape their future in ways that they cannot conceive in advance. Available departmental sample degree plans are not personalized for each student, and personal discussion time with an academic advisor is usually limited. Data-driven methods supporting decision making have gained importance to empower student choices and scale advice to large cohorts. We propose "Scholars Walk," a random-walk-based approach that captures the sequential relationships between the different courses. Based on the "wisdom of the crowd" and the students' prior courses, we recommend a short list of courses for next semester. Our experimental evaluation illustrates that Scholars Walk outperforms other collaborative filtering and popularity-based approaches. At the same time, our framework is very efficient, easily interpretable, while also being able to take into consideration important aspects of the educational domain. [For the full proceedings, see ED599096.]

Published
2019

4. FERN: Fair Team Formation for Mutually Beneficial Collaborative Learning

Author

Kalantzi, Maria, Polyzou, Agoritsa, and Karypis, George

Subjects
Computer Science - Computers and Society
Abstract

Automated Team Formation is becoming increasingly important for a plethora of applications in open source community projects, remote working platforms, as well as online educational systems. The latter case, in particular, poses significant challenges that are specific to the educational domain. Indeed, teaming students aims to accomplish far more than the successful completion of a specific task. It needs to ensure that all members in the team benefit from the collaborative work, while also ensuring that the participants are not discriminated with respect to their protected attributes, such as race and gender. Towards achieving these goals, this work introduces FERN, a fair team formation approach that promotes mutually beneficial peer learning, dictated by protected group fairness as equality of opportunity in collaborative learning. We formulate the problem as a multi-objective discrete optimization problem. We show this problem to be NP-hard and propose a heuristic hill-climbing algorithm. Extensive experiments on both synthetic and real-world datasets against well-known team formation techniques show the effectiveness of the proposed method., Comment: 11 pages

Published
2020

5. Feature Extraction for Classifying Students Based on Their Academic Performance

Author

Polyzou, Agoritsa and Karypis, George

Abstract

Developing tools to support students and learning in a traditional or online setting is a significant task in today's educational environment. The initial steps towards enabling such technologies using machine learning techniques focused on predicting the student's performance in terms of the achieved grades. The disadvantage of these approaches is that they do not perform as well in predicting poor-performing students. The objective of our work is two-fold. First, in order to overcome this limitation, we explore if poorly performing students can be more accurately predicted by formulating the problem as binary classification. Second, in order to gain insights as to which are the factors that can lead to poor performance, we engineered a number of human-interpretable features that quantify these factors. These features were derived from the students' grades from the University of Minnesota, an undergraduate public institution. Based on these features, we perform a study to identify different student groups of interest, while at the same time, identify their importance. [For the full proceedings, see ED593090.]

Published
2018

6. Causal Inference in Higher Education: Building Better Curriculums

Author

Kaur, Prableen, Polyzou, Agoritsa, and Karypis, George

Subjects
Statistics - Applications, I.6, I.6.5
Abstract

Higher educational institutions constantly look for ways to meet students' needs and support them through graduation. Recent work in the field of learning analytics have developed methods for grade prediction and course recommendations. Although these methods work well, they often fail to discover causal relationships between courses, which may not be evident through correlation-based methods. In this work, we aim at understanding the causal relationships between courses to aid universities in designing better academic pathways for students and to help them make better choices. Our methodology employs methods of causal inference to study these relationships using historical student performance data. We make use of a doubly-robust method of matching and regression in order to obtain the casual relationship between a pair of courses. The results were validated by the existing prerequisite structure and by cross-validation of the regression model. Further, our approach was also tested for robustness and sensitivity to certain hyperparameters. This methodology shows promising results and is a step forward towards building better academic pathways for students., Comment: Learning at Scale 2019 June 24-25, 2019, Chicago, IL, USA

Published
2019
Full Text
View/download PDF

7. Grade prediction with course and student specific models

Author

Polyzou, Agoritsa and Karypis, George

Subjects
Computer Science - Computers and Society, J.1, H.2.8
Abstract

The accurate estimation of students' grades in future courses is important as it can inform the selection of next term's courses and create personalized degree pathways to facilitate successful and timely graduation. This paper presents future-course grade predictions methods based on sparse linear models and low-rank matrix factorizations that are specific to each course or student-course tuple. These methods identify the predictive subsets of prior courses on a course-by-course basis and better address problems associated with the not-missing-at-random nature of the student-course historical grade data. The methods were evaluated on a dataset obtained from the University of Minnesota. This evaluation showed that the course-specific models outperformed various competing schemes with the best performing scheme achieving an RMSE across the different courses of 0.632 vs 0.661 for the best competing method., Comment: 12 pages, 3 figures, 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2016

Published
2019
Full Text
View/download PDF

8. Feature Extraction for Next-Term Prediction of Poor Student Performance

Author

Polyzou, Agoritsa and Karypis, George

Abstract

Developing tools to support students and learning in a traditional or online setting is a significant task in today's educational environment. The initial steps toward enabling such technologies using machine learning techniques focused on predicting the student's performance in terms of the achieved grades. However, these approaches do not perform as well in predicting poor-performing students. The objective of our work is twofold. First, in order to overcome this limitation, we explore if poorly performing students can be more accurately predicted by formulating the problem as binary classification, based on data provided before the start of the semester. Second, in order to gain insights as to which are the factors that can lead to poor performance, we engineered a number of human-interpretable features that quantify these factors. These features were derived from the students' grades from the University of Minnesota, an undergraduate public institution. Based on these features, we perform a study to identify different student groups of interest, while at the same time, identify their importance. As the resulting models provide us with different subsets of correct predictions, their combination can boost the overall performance.

Published
2019
Full Text
View/download PDF

9. Learning Behavioral Pattern Analysis Based on Digital Textbook Reading Logs

Author

Yin, Chengjiu, Ren, Zhuo, Polyzou, Agoritsa, Wang, Yong, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Streitz, Norbert, editor, and Konomi, Shin’ichi, editor

Published
2019
Full Text
View/download PDF

10. Grade Prediction with Course and Student Specific Models

Author

Polyzou, Agoritsa, Karypis, George, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bailey, James, editor, Khan, Latifur, editor, Washio, Takashi, editor, Dobbie, Gill, editor, Huang, Joshua Zhexue, editor, and Wang, Ruili, editor

Published
2016
Full Text
View/download PDF

11. Session-based Methods for Course Recommendation.

Author

Zabed Khan, Md Akib and Polyzou, Agoritsa

Subjects
MACHINE learning, POPULARITY, DATA mining, COUNSELING in higher education, SATISFACTION, DEEP learning
Abstract

In higher education, academic advising is crucial to students' decision-making. Data-driven models can benefit students in making informed decisions by providing insightful recommendations for completing their degrees. To suggest courses for the upcoming semester, various course recommendation models have been proposed in the literature using different data mining techniques and machine learning algorithms utilizing different data types. One important aspect of the data is that usually, courses taken together in a semester fit well with each other. If there is no correlation between the co-taken courses, students may find it more difficult to handle the workload. Based on this insight, we propose using session-based approaches to recommend a set of well-suited courses for the upcoming semester. We test three session-based course recommendation models, two based on neural networks (CourseBEACON and CourseDREAM) and one on tensor factorization (TF-CoC). Additionally, we propose a postprocessing approach to adjust the recommendation scores of any base course recommender to promote related courses. Using metrics capturing different aspects of the recommendation quality, our experimental evaluation shows that session-based methods outperform existing popularity-based, association-based, similarity-based, factorization-based, neural networks-based, and Markov chain-based recommendation approaches. Effective course recommendations can result in improved student advising, which, in turn, can improve student performance, decrease dropout rates, and a more positive overall student experience and satisfaction. [ABSTRACT FROM AUTHOR]

Published
2024

19. Models and Algorithms for Performance Prediction and Course Recommendation in Higher Education

Author

Polyzou, Agoritsa

Abstract

Educational institutions need to use supporting tools in order to reduce high student drop-out rates and ensure their students' timely graduation. Educational data mining involves the development of such methods that leverage student data. Their purpose is to generate warnings about students that are at risk of failing, identify the enrollment practices that are associated with academic success, and allow for suitable interventions. The application of these models in a higher education institution can improve advising and degree planning. The objective of this thesis is to develop methods that will support students to make informed decisions regarding their course registration in the context of higher education. We also explore fairness concerns that might arise in a course recommendation system. We want to create models that can be used before the semester starts in order to allow the students to make any necessary adjustments in their plans. Instructors can also benefit from them, as it will allow them to shape their expectations about the enrolled students in their class, and adjust the syllabus and course material accordingly. For the purpose of this work, we will focus on traditional higher education datasets and develop models that do not need any information for the structure and the syllabus of a particular course. All we use is the students' transcript data. In this setting, we first introduce next-term grade prediction methods to estimate the grades that a student is expected to receive for the courses they plan on taking the following semester. These algorithms are based on sparse linear and low-rank matrix factorization models that are specific to each course or student-course tuple. These methods identify the predictive subsets of prior courses on a course-by-course basis. Second, we delve into the factors that influence students' performance. We extract informative features from the students' transcript data and form the problem as a binary classification one, to identify the students that have poor performance. We consider a student's performance both in the absolute sense (in terms of grades achieved), but also in the relative sense (compared to the student's past performance). We present a comprehensive study to answer the following questions: which features are good indicators of a student’s performance? which features are the most important? The findings are interesting, as different features are the most important for different classification tasks. Next, we addressed the problem of course recommendation. Recommender systems have been extensively used in various domains to support decision making and empower user choices. Within the educational domain, recommenders can generate a personalized list of courses for each student to consider taking in the next semester. Towards that direction, we propose Scholars Walk, a random-walk-based approach that captures the sequential relationships between the different courses. Based on the ''wisdom of the crowd" and the students' prior courses, we recommend a shortlist of courses for next semester. Our framework is very efficient, easily interpretable, while also being able to take into consideration important aspects of the educational domain. Finally, we explore fairness in the context of course recommendation. It is important to ensure that the models that we develop and apply in an educational institution do not discriminate against particular groups of students. All students should be treated fairly and offered similar opportunities and quality of services based on the notion of group fairness. Based on this idea, we examine the fairness of the opportunities offered to the students by the system's recommendations. We formulate our approach as a multi-objective optimization problem, and we study the trade-offs between equal opportunity and quality. The algorithm first makes an initial assignment of courses to recommend to the students, which we assume is of optimal quality. The model then refines the initial solution to improve overall fairness. The experimental evaluation with synthetic and real datasets showcase that we can promote equality of opportunity in the recommendations without significantly weakening their quality.

Published
2020

Catalog

Books, media, physical & digital resources
See catalog results