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The current study focuses on student e-assessment by means of open-ended questions that require free-text answers (i.e., student essays), whose analysis and evaluation are resource-demanding tasks for the instructor, even when supported by modern e-learning platforms. Topic modeling through the Latent Dirichlet Allocation algorithm is employed in an experimental setup, aiming to (a) extract meaningful topics from the body of pooled student answers (interpretable in the educational context of the course), (b) align the extracted topics with the \u2018native\u2019 internal structure of the body of texts, and (c) offer recommendations for the teacher in the form of alternative (meaningful) restructurings of the e-assessment units and consequently of the course content units. Quantitative and qualitative evaluation of the extracted topic models have yielded positive results regarding the first two aims, and as far as the third aim is concerned, the extracted topic models expilicitly suggest that the teacher should proceed with relevant restructurings of the course content. These recommendations are of practical use for the teacher, especially when the teacher seeks to restructure the content of the course towards either fewer or more internal units. In conclusion, topic modeling provides a spectrum of possibilities to the teacher who is interested in exploring ways to improve the structure and organization of a course.<\/jats:p>","DOI":"10.1007\/s10791-024-09496-9","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T07:09:32Z","timestamp":1737097772000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Text mining technologies applied to free-text answers of students in e-assessment"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5877-6438","authenticated-orcid":false,"given":"Angelos","family":"Charitopoulos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7286-8632","authenticated-orcid":false,"given":"Maria","family":"Rangoussi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2480-0081","authenticated-orcid":false,"given":"Dimitris","family":"Metafas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0194-0654","authenticated-orcid":false,"given":"Dimitrios","family":"Koulouriotis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"9496_CR1","doi-asserted-by":"publisher","DOI":"10.1002\/9780470689646","volume-title":"Text mining: applications and theory","author":"MW Berry","year":"2010","unstructured":"Berry MW, Kogan JE. 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