Ciencias Exactas y Ciencias de la Salud

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Aspect based sentiment analysis in students’ evaluation of teaching
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05) Acosta Ugalde, Diego; Conant Pablos, Santiago Enrique; mtyahinojosa, emipsanchez; Guitérrez Rodríguez, Andrés Eduardo; Juárez Jiménez, Julio Antonio; Morales Méndez, Rubén; School of Engineering and Sciences; Campus Monterrey; Camacho Zuñiga, Claudia
Student evaluations of teachings (SETs) are essential for assessing educational quality. Natural Language Processing (NLP) techniques can produce informative insights from these evaluations. The large quantity of text data received from SETs has surpassed the capacity for manual processing. Employing NLP to analyze student feedback offers an efficient method for understanding educational experiences, enabling educational institutions to identify patterns and trends that might have been difficult, if not impossible, to notice with a manual analysis. Data mining using NLP techniques can delve into the thoughts and perspectives of students on their educational experiences, identifying sentiments and aspects that may have a level of abstraction that the human analysis cannot perceive. I use different NLP techniques to enhance the analysis of student feedback in the form of comments and provide better insights and understanding into factors that influence students’ sentiments. This study aims to provide an overview of the various approaches used in NLP and sentiment analysis, focusing on analyzing the models and text representations used to classify numerical scores obtained from the text feedback of a corpus of SETs in Spanish. I provide a series of experiments using different text classification algorithms for sentiment classification over numerical scores of educational aspects. Additionally, I explore two Aspect Based Sentiment Analysis (ABSA) models, a pipeline and a multi-task approach, to extract broad and comprehensive insights from educational feedback for each professor. The results of this research demonstrate the effectiveness of using NLP techniques for analyzing student feedback. The sentiment classification experiments showed favorable outcomes, indicating that it is possible to utilize student comments to classify certain educational scores accurately. Furthermore, the qualitative results obtained from the ABSA models, presented in a user-friendly dashboard, highlight the efficiency and utility of employing these algorithms for the analysis of student feedback. The dashboard provides valuable insights into the sentiments expressed by students regarding various aspects of their educational experience, allowing for a more comprehensive understanding of the factors influencing their opinions. These findings highlight the potential of NLP in the educational domain, offering a powerful tool for institutions to gain a deeper understanding of student perspectives and make data-driven decisions to enhance the quality of education.
Exploring data-driven selection hyper-heuristic approaches for the curriculum-based course timetabling
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-12) Hinojosa Cavada, Carlos Alfonso; CONANT PABLOS, SANTIAGO ENRIQUE; 56551; Conant Pablos, Santiago Enrique; emipsanchez; Ortiz Bayliss, José Carlos; School of Engineering and Sciences; Campus Monterrey
The curriculum-based timetabling problem (CB-CTT) represents a challenging field of study within educational timetabling, with real-world applications that stress its importance. Solving a CB-CTT problem requires allocating a set of courses using limited resources, subject to a set of hard constraints that must be satisfied. The goal then is to find a feasible assignment for every lecture that constitutes the courses to the positions in the timetable formed by a combination of day, period, and room; all while minimizing an objective function as specified by the constraints in the problem. Designing the timetable for the courses in the incoming term is a problem faced by universities each academic period. Given the complexity of manually designing timetables, automated methods have attracted the attention of many researchers for solving this problem. The design of timetables remains an open problem to this day. According to the no free lunch theorem, different heuristics are effective on different problem instances, stressing the importance of finding automated methods for designing timetables. This dissertation explores novel hyper-heuristic models that rely on various machine learning techniques, such as boosting, clustering and principal component analysis. In total, two models were designed and implemented as results of this work. The first model relies on gradient boosting algorithms to generate a selection hyper-heuristic. The general idea is that different instances of the CB-CTT are best solved by different heuristics. Hence, the aim is to create a model that learns from the features that describe problem instances and predicts which would be the most suitable heuristic to apply. While the classification model produces promising results in terms of accuracy, the quality of the generated solutions is bounded by the best-known single heuristic. The second model aims to remove the bounds set by the use of a single heuristic by exploring ways of combining heuristics during the timetable construction process. The selection hyper-heuristic approach is powered by principal component analysis and k-means. The model starts by identifying similar regions in the instance space and keeping track of the performance of each heuristic for those regions. Then, when constructing new timetables, the model determines the most suitable heuristic for a given region of the instance space. The method was able to outperform the synthetic oracle created by taking the result of the best isolated heuristic in several instances. This dissertation is submitted to the Graduate Programs in Engineering and Information Technologies in partial fulfillment of the requirements for the degree of Master of Science in Computer Sciences with a major in Intelligent Systems.

Ciencias Exactas y Ciencias de la Salud

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