Sentiment Analysis of Student Surveys -- A Case Study on Assessing the Impact of the COVID-19 Pandemic on Higher Education Teaching [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021).

Autor/inn/en	Jiménez, Haydée G.; Casanova, Marco A.; Finamore, Anna Carolina; Simões, Gonçalo
Titel	Sentiment Analysis of Student Surveys -- A Case Study on Assessing the Impact of the COVID-19 Pandemic on Higher Education Teaching [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021).
Quelle	(2021), (7 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Natural Language Processing; Data Analysis; Classification; Student Surveys; College Students; Student Evaluation of Teacher Performance; Conventional Instruction; Online Courses; COVID-19; Pandemics; Models; Foreign Countries; Brazil + Suchen Sie Ihr Suchwort? Natürliche Sprache; Auswertung; Classification system; Klassifikation; Klassifikationssystem; Schülerbefragung; Collegestudent; Online course; Online-Kurs; Analogiemodell; Ausland; Brasilien
Abstract	Sentiment Analysis is a field of Natural Language Processing which aims at classifying the author's sentiment in text. This paper first describes a sentiment analysis model for students' comments about professor performance. The model achieved impressive results for comments collected from student surveys conducted at a private university in 2019/20. Then, it applies the model to different scenarios: (1) in-person classes taught in 2019 (pre-COVID); (2) the emergency shift to online, synchronous classes taught in the first semester of 2020 (early-COVID); and (3) the planned online classes taught in the second semester of 2020 (late-COVID). The results show that students acknowledged the effort professors did to keep classes running during the first semester of 2020, and that the enthusiasm continued throughout the second semester. Furthermore, the results show that students evaluated professors' performance for online courses better than for in-person courses. [For the full proceedings, see ED615472.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01