Predicting Student Performance on Post-Requisite Skills Using Prerequisite Skill Data: An Alternative Method for Refining Prerequisite Skill Structures [Konferenzbericht] Paper presented at the International Conference on Learning Analytics & Knowledge Conference (LAK) (6th, Edinburgh, United Kingdom, Apr 25-29, 2016).

Autor/inn/en	Adjei, Seth A.; Botelho, Anthony F.; Heffernan, Neil T.
Titel	Predicting Student Performance on Post-Requisite Skills Using Prerequisite Skill Data: An Alternative Method for Refining Prerequisite Skill Structures [Konferenzbericht] Paper presented at the International Conference on Learning Analytics & Knowledge Conference (LAK) (6th, Edinburgh, United Kingdom, Apr 25-29, 2016).
Quelle	(2016), (6 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Prediction; Learning Analytics; Attribution Theory; Prerequisites; Teacher Surveys; Teacher Attitudes; Comparative Analysis; Evaluation Methods; Difficulty Level; Task Analysis; Correlation; Mathematics Tests; Student Placement; Computer Assisted Testing; Remedial Instruction; Mathematics Achievement; Rating Scales; Accuracy; Computer Software + Suchen Sie Ihr Suchwort? Vorhersage; Voraussetzung; Lehrerverhalten; Schwierigkeitsgrad; Aufgabenanalyse; Korrelation; Schülerpraktikum; Förderkurs; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Rating-Skala
Abstract	Prerequisite skill structures have been closely studied in past years leading to many data-intensive methods aimed at refining such structures. While many of these proposed methods have yielded success, defining and refining hierarchies of skill relationships are often difficult tasks. The relationship between skills in a graph could either be causal, therefore, a prerequisite relationship (skill A must be learned before skill B). The relationship may be noncausal, in which case the ordering of skills does not matter and may indicate that both skills are prerequisites of another skill. In this study, we propose a simple, effective method of determining the strength of pre-to-post-requisite skill relationships. We then compare our results with a teacher-level survey about the strength of the relationships of the observed skills and find that the survey results largely confirm our findings in the data-driven approach. [This paper was published in: "LAK '16: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge," (pp. 469-473).] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01