Evaluating the Impact of Instructional Support Using Data Mining and Process Mining: A Micro-Level Analysis of the Effectiveness of Metacognitive Prompts

Autor/inn/en	Sonnenberg, Christoph; Bannert, Maria
Titel	Evaluating the Impact of Instructional Support Using Data Mining and Process Mining: A Micro-Level Analysis of the Effectiveness of Metacognitive Prompts
Quelle	In: Journal of Educational Data Mining, 8 (2016) 2, S.51-83 (33 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Data Analysis; Metacognition; Prompting; Cues; Computer Assisted Instruction; Classification; Learning Strategies; Student Behavior; Protocol Analysis; Learning Activities; Scaffolding (Teaching Technique); Hypermedia; Undergraduate Students; Foreign Countries; Germany + Suchen Sie Ihr Suchwort? Auswertung; Meta cognitive ability; Meta-cognition; Metakognitive Fähigkeit; Metakognition; Benutzerführung; Stichwort; Computer based training; Computerunterstützter Unterricht; Classification system; Klassifikation; Klassifikationssystem; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Student behaviour; Schülerverhalten; Lernaktivität; Ausland; Deutschland
Abstract	In computer-supported learning environments, the deployment of self-regulatory skills represents an essential prerequisite for successful learning. Metacognitive prompts are a promising type of instructional support to activate students' strategic learning activities. However, despite positive effects in previous studies, there are still a large number of students who do not benefit from provided support. Therefore, it may be necessary to consider explicitly the conditions under which a prompt is beneficial for a student, i.e., so-called adaptive scaffolding. The current study aims to (i) classify the effectiveness of prompts on regulatory behavior, (ii) investigate the correspondence of the classification with learning outcome, and (iii) discover the conditions under which prompts induce regulatory activities (i.e., the proper temporal positioning of prompts). The think-aloud data of an experiment in which metacognitive prompts supported the experimental group (n = 35) was used to distinguish between effective and non-effective prompts. Students' activities preceding the prompt presentation were analyzed using data mining and process mining techniques. The results indicate that approximately half of the presented prompts induced metacognitive learning activities as expected. Moreover, the number of induced monitoring activities correlates positively with transfer performance. Finally, the occurrence of orientation and monitoring activities, which are not well-embedded in the course of learning, increases the effectiveness of a presented prompt. In general, our findings demonstrate the benefits of investigating metacognitive support using process data, which can provide implications for the design of effective instructional support. (As Provided).
Anmerkungen	International Working Group on Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://www.educationaldatamining.org/JEDM/index.php/JEDM/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01