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Discovering the Effects of Metacognitive Prompts on the Sequential Structure of SRL-Processes Using Process Mining Techniques

Please always quote using this URN: urn:nbn:de:bvb:20-opus-152362
  • According to research examining self‐regulated learning (SRL), we regard individual regulation as a specific sequence of regulatory activities. Ideally, students perform various learning activities, such as analyzing, monitoring, and evaluating cognitive and motivational aspects during learning. Metacognitive prompts can foster SRL by inducing regulatory activities, which, in turn, improve the learning outcome. However, the specific effects of metacognitive support on the dynamic characteristics of SRL are not understood. Therefore, the aim ofAccording to research examining self‐regulated learning (SRL), we regard individual regulation as a specific sequence of regulatory activities. Ideally, students perform various learning activities, such as analyzing, monitoring, and evaluating cognitive and motivational aspects during learning. Metacognitive prompts can foster SRL by inducing regulatory activities, which, in turn, improve the learning outcome. However, the specific effects of metacognitive support on the dynamic characteristics of SRL are not understood. Therefore, the aim of our study was to analyze the effects of metacognitive prompts on learning processes and outcomes during a computer‐based learning task. Participants of the experimental group (EG, n=35) were supported by metacognitive prompts, whereas participants of the control group (CG, n=35) received no support. Data regarding learning processes were obtained by concurrent think‐aloud protocols. The EG exhibited significantly more metacognitive learning events than did the CG. Furthermore, these regulatory activities correspond positively with learning outcomes. Process mining techniques were used to analyze sequential patterns. Our findings indicate differences in the process models of the EG and CG and demonstrate the added value of taking the order of learning activities into account by discovering regulatory patterns.show moreshow less

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Metadaten
Author: Christoph Sonnenberg, Maria Bannert
URN:urn:nbn:de:bvb:20-opus-152362
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 Learning Analystics
ISSN:1929‐7750
Year of Completion:2015
Volume:2
Issue:1
Pagenumber:72-100
Source:Journal of Learning Analytics, 2(1), 72–100. (2015)
URL:http://learning-analytics.info/journals/index.php/JLA/article/view/4090
Dewey Decimal Classification:3 Sozialwissenschaften / 37 Bildung und Erziehung / 370 Bildung und Erziehung
Tag:HeuristicsMiner algorithm; metacognitive prompting; process 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-ND: Creative-Commons-Lizenz: Namensnennung, Nicht kommerziell, Keine Bearbeitung