Erroneous Answers Categorization for Sketching Questions in Spatial Visualization Training [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).

Autor/inn/en	Li, Tiffany Wenting; Paquette, Luc
Titel	Erroneous Answers Categorization for Sketching Questions in Spatial Visualization Training [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).
Quelle	(2020), (11 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Error Patterns; Cluster Grouping; Visualization; Spatial Ability; Training Methods; Questioning Techniques; Engineering Education; Freehand Drawing; Computer Assisted Instruction; College Students; Feedback (Response); Evaluation Criteria; Formative Evaluation; Illinois (Champaign); Illinois (Urbana) + Suchen Sie Ihr Suchwort? Fehlertyp; Eingruppierung; Visualisation; Visualisierung; Räumliches Vorstellungsvermögen; Didaktik; Trainingsmaßnahme; Befragungstechnik; Fragetechnik; Ingenieurausbildung; Drawing; Zeichnen; Computer based training; Computerunterstützter Unterricht; Collegestudent
Abstract	Spatial visualization skills are essential and fundamental to studying STEM subjects, and sketching is an effective way to practice those skills. One significant challenge of supporting practice using sketching questions is the vast number of possible mistakes, making it time-consuming for instructors to provide customized and actionable feedback to students. The same challenge persists for computer programs as well. This paper introduces a clustering model designed to categorize sketching answers based on the severity and characteristics of their mistakes. The model is designed to be used by a computer-based training platform to provide customized, actionable formative feedback to students in real-time. The promising results also suggest a new and comprehensive set of evaluation criteria to assess a student's performance on sketching questions. As a broader contribution, our work is a proof-of-concept for a modeling approach to automatically evaluate and provide formative feedback on complex free-hand sketches using abstract features that may be generalized to a variety of disciplines that involve the creation of technical drawings. [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