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

Please always quote using this URN: urn:nbn:de:bvb:20-opus-152375
  • 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-calledIn 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.show moreshow less

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Metadaten
Author: Christoph Sonnenberg, Maria Bannert
URN:urn:nbn:de:bvb:20-opus-152375
Document Type:Journal article
Faculties:Fakultät für Humanwissenschaften (Philos., Psycho., Erziehungs- u. Gesell.-Wissensch.) / Institut Mensch - Computer - Medien
Language:English
Parent Title (English):Journal of Educational Data Mining
Year of Completion:2016
Volume:8
Issue:2
Pagenumber:51-83
Source:Journal of Educational Data Mining, Volume 8, No 2, 2016
URL:http://www.educationaldatamining.org/JEDM/index.php/JEDM/article/view/JEDM2016-8-2-3
Dewey Decimal Classification:3 Sozialwissenschaften / 37 Bildung und Erziehung / 370 Bildung und Erziehung
Tag:instructional support; metacognitive prompting; micro-level analysis; process mining; self-regulated learning; think-aloud data
Release Date:2017/08/04
Note:
Dieser Artikel ist auch Bestandteil der Dissertation:
Sonnenberg, Christoph: Analyzing Technology-Enhanced Learning Processes: What Can Process Mining Techniques Contribute to the Evaluation of Instructional Support?. - Würzburg, Univ., Diss., 2017. - [online].
URN: urn:nbn:de:bvb:20-opus-152354
Licence (German):License LogoCC BY-NC: Creative-Commons-Lizenz: Namensnennung, Nicht kommerziell 4.0 International