Decision Tree Modeling of Wheel-Spinning and Productive Persistence in Skill Builders

Autor/inn/en	Kai, Shimin; Almeda, Ma. Victoria; Baker, Ryan S.; Heffernan, Cristina; Heffernan, Neil
Titel	Decision Tree Modeling of Wheel-Spinning and Productive Persistence in Skill Builders
Quelle	In: Journal of Educational Data Mining, 10 (2018) 1, S.36-71 (36 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Decision Making; Models; Intervention; Computer Assisted Instruction; Mastery Learning; Mathematics Instruction; Online Courses; Persistence; Classification; Learning Problems; Problem Solving; Skill Development; Teaching Methods; Learning Processes; Statistical Analysis; Vignettes; Middle School Students + Suchen Sie Ihr Suchwort? Decision-making; Entscheidungsfindung; Analogiemodell; Computer based training; Computerunterstützter Unterricht; Mathematics lessons; Mathematikunterricht; Online course; Online-Kurs; Ausdauer; Classification system; Klassifikation; Klassifikationssystem; Lernproblem; Problemlösen; Kompetenzentwicklung; Qualifikationsentwicklung; Teaching method; Lehrmethode; Unterrichtsmethode; Learning process; Lernprozess; Statistische Analyse; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin
Abstract	Research on non-cognitive factors has shown that persistence in the face of challenges plays an important role in learning. However, recent work on wheel-spinning, a type of unproductive persistence where students spend too much time struggling without achieving mastery of skills, show that not all persistence is uniformly beneficial for learning. For this reason, it becomes increasingly pertinent to identify the key differences between unproductive and productive persistence toward informing interventions in computer-based learning environments. In this study, we use a classification model to distinguish between productive persistence and wheel-spinning in ASSISTments, an online math learning platform. Our results indicate that there are two types of students who wheel-spin: first, students who do not request any hints in at least one problem but request more than one bottom-out hint across any 8 problems in the problem set; second, students who never request two or more bottom out hints across any 8 problems, do not request any hints in at least one problem, but who engage in relatively short delays between solving problems of the same skill. These findings suggest that encouraging students to both engage in spaced practice and use bottom-out hints sparingly is likely helpful for reducing their wheel-spinning and improving learning. These findings also provide insight on when students are struggling and how to make students' persistence more productive. (As Provided).
Anmerkungen	International Working Group on Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://jedm.educationaldatamining.org/index.php/JEDM
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01