More Data and Better Keywords Imply Better Educational Transcript Classification? [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).

Autor/inn/en	Danciulescu, Theodora Ioana; Mihaescu, Marian Cristian; Heras, Stella; Palanca, Javier; Julian, Vicente
Titel	More Data and Better Keywords Imply Better Educational Transcript Classification? [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).
Quelle	(2020), (7 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Data Analysis; Classification; Information Retrieval; Video Technology; Teaching Methods; Web Sites; Collaborative Writing; Accuracy; Comparative Analysis; Authors; Models; Foreign Countries; Universities; Information Systems; Spain + Suchen Sie Ihr Suchwort? Auswertung; Classification system; Klassifikation; Klassifikationssystem; Teaching method; Lehrmethode; Unterrichtsmethode; Web-Design; Author; Autor; Autorin; Analogiemodell; Ausland; University; Universität; Spanien
Abstract	Building and especially improving a classification kernel represents a challenging task. The works presented in this paper continue an already developed semi-supervised classification approach that aimed at labelling transcripts from educational videos. We questioned whether the size of the ground-truth data-set (Wikipedia articles) or the quality of the keywords used in the semi-supervised labelling have a significant impact on the accuracy metrics of the final obtained data model. Experimental results took into consideration three Wikipedia data-sets of "Small," "Medium" and "Large" sizes. For each data-set there were used three sets of keywords: offered by video authors, determined by "rake-nltk" on available transcripts and determined by "rake-nltk" on Wikipedia articles that serve as training and testing data for the LDA [latent Dirichlet allocation] model that determine keywords on the transcripts. Experiments show that the size of the data-set has little importance, while the quality of the keywords has a more significant impact. Therefore, an improved version of the previously developed classifier has been obtained by improving the quality of the keywords involved in semi-supervised training. This result paves the way towards further improvements that may finally be deployed as within a recommender system of educational videos at the Universitat Politècnica de València. [For the full proceedings, see ED607784.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01