Feature Extraction for Classifying Students Based on Their Academic Performance [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (11th, Raleigh, NC, Jul 16-20, 2018).

Autor/inn/en	Polyzou, Agoritsa; Karypis, George
Titel	Feature Extraction for Classifying Students Based on Their Academic Performance [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (11th, Raleigh, NC, Jul 16-20, 2018).
Quelle	(2018), (7 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Low Achievement; Predictor Variables; Classification; Student Characteristics; Undergraduate Students; Grades (Scholastic); Student Interests; Technology Uses in Education; Educational Technology; Artificial Intelligence; At Risk Students + Suchen Sie Ihr Suchwort? Unterdurchschnittliche Leistung; Prädiktor; Classification system; Klassifikation; Klassifikationssystem; Notenspiegel; Studieninteresse; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Unterrichtsmedien; Künstliche Intelligenz
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.] (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	2020/1/01