"Your Model Is Predictive-- but Is It Useful?" Theoretical and Empirical Considerations of a New Paradigm for Adaptive Tutoring Evaluation [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	González-Brenes, José P.; Huang, Yun
Institution	International Educational Data Mining Society
Titel	"Your Model Is Predictive-- but Is It Useful?" Theoretical and Empirical Considerations of a New Paradigm for Adaptive Tutoring Evaluation [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).
Quelle	(2015), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Intelligent Tutoring Systems; Evaluation Methods; Program Evaluation; Student Behavior; Mathematics; Student Participation; Responses; Drills (Practice); Skill Development; Statistical Analysis; Middle School Students; Mathematics Instruction; Mathematics Skills; Prediction; Models; Classification; Simulation + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Programme evaluation; Programmevaluation; Student behaviour; Schülerverhalten; Mathematik; Schülermitarbeit; Schülermitwirkung; Studentische Mitbestimmung; Kompetenzentwicklung; Qualifikationsentwicklung; Statistische Analyse; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Mathematics lessons; Mathematikunterricht; Mathmatics achievement; Mathematics ability; Mathematische Kompetenz; Vorhersage; Analogiemodell; Classification system; Klassifikation; Klassifikationssystem; Simulation program; Simulationsprogramm
Abstract	Classification evaluation metrics are often used to evaluate adaptive tutoring systems-- programs that teach and adapt to humans. Unfortunately, it is not clear how intuitive these metrics are for practitioners with little machine learning background. Moreover, our experiments suggest that existing convention for evaluating tutoring systems may lead to suboptimal decisions. We propose the Learner Effort-Outcomes Paradigm (Leopard), a new framework to evaluate adaptive tutoring. We introduce Teal and White, novel automatic metrics that apply Leopard and quantify the amount of effort required to achieve a learning outcome. Our experiments suggest that our metrics are a better alternative for evaluating adaptive tutoring. [For complete proceedings, see ED560503.] (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