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

Sonnenberg, Christoph; Bannert, Maria

doi:10.18608/jla.2015.21.5

Cited by 66 publications

(44 citation statements)

References 58 publications

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“…As such, it appears that the provided structure affected the reading and writing patterns of the participants. This finding is coherent with research on SRL, which showed that interventions that aided regulation by means of prompts, changed the learning process of the learners (Sonnenberg & Bannert, 2015). Furthermore, prior research has shown that advance organizers, which provide structure to the learning content, can aid learners navigation and comprehension on the Internet (Urakami & Krems, 2012).…”

Section: Discussionsupporting

confidence: 82%

“…At the macro-level, several studies have shown that different learning processes can be observed in (groups of) learners who learn under different task conditions (Bannert, Reimann, & Sonnenberg, 2014;Sobocinski, Malmberg, & Järvelä, 2017;Sonnenberg & Bannert, 2015), or who have different learning outcomes (Schoor & Bannert, 2012). As learners gain experience in a task, they may approach the task differently (see e.g., expertise reversal effect; Kalyuga et al, 2003).…”

Section: Macro-level Navigation Patternsmentioning

confidence: 99%

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Children’s Macro-Level Navigation Patterns in Hypermedia and their Relation with Task Structure and Learning Outcomes

Paans

Molenaar

Segers

2020

FLR

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This study investigated macro-level navigation patterns in children's hypermedia learning, and how they related to task structure and learning outcomes. For this purpose, 5th and 6th grade learners performed a hypermedia assignment in which a high (n=57) versus a low (n=54) level of structure was provided. By means of qualitative analyses of their navigation activities, 6 macro-level navigation patterns were distinguished: linear reading, selective reading, video viewing, massed writing, late onset writing, and unpredictable reading. Results showed that the linear reading pattern was more frequent in the high structure environment, and that both the high structure environment and the linear reading pattern were associated with the highest quality of the children's written assignments. Navigation patterns and task structure did not clearly predict children's declarative knowledge gains or knowledge transfer. These findings show that there are multiple ways to navigate through a hypermedia environment, but that these are not all equally successful for learning. Moreover, the provided task structure in the environment may affect the occurrence of successful navigation patterns.

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Section: Discussionsupporting

confidence: 82%

Section: Macro-level Navigation Patternsmentioning

confidence: 99%

Children’s Macro-Level Navigation Patterns in Hypermedia and their Relation with Task Structure and Learning Outcomes

Paans

Molenaar

Segers

2020

FLR

View full text Add to dashboard Cite

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“…This is the primary rationale for fostering teachers’ self‐regulation in technology integration contexts. Theories of self‐regulation affirm that self‐regulated learners are more adaptive in identifying and deploying effective strategies for problems and evaluating their effectiveness to guarantee outstanding performances (eg, Bannert et al ., 2015; Sonnenberg & Bannert, 2015; Taub & Azevedo, 2018; Zhou & Winne, 2012; Zimmerman, 1986). Integrating different types of technologies into teaching is trendy for future education.…”

Section: Discussionmentioning

confidence: 99%

Latent profiles of self‐regulated learning and their impacts on teachers’ technology integration

Huang¹,

Li²,

Poitras³

et al. 2020

Brit J Educational Tech

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Past research shows that both teachers’ technological pedagogical content knowledge (TPACK) and their engagement in metacognitive activities are essential to technology integration in the classroom. However, the interplay between teachers’ TPACK ability and their metacognitive skills is still underexplored, especially in the context of developing technology‐infused lesson plans. This study examined how the interrelations among metacognitive activities and TPACK constructs affected preservice teachers’ technology integration in instructional design. Sixty‐four preservice teachers designed a lesson with nBrowser, a computer‐based learning environment (CBLE) that helps teachers incorporate technology into instruction by promoting self‐regulated learning (SRL). Drawing on the lesson plans, we extracted six types of metacognitive processes preservice teachers exhibited while solving the task and generated two distinct SRL profiles according to the identified latent profile of metacognitive patterns. The competent self‐regulated learners demonstrated more efforts in metacognitive monitoring activities than the less competent self‐regulated learners in regulating their task solving processes. When comparing TPACK comprehension and design performance between the two profiles, the competent self‐regulated learners outperformed the less competent self‐regulated learners on comprehension and design outcomes. This study provides deep insights into teachers’ self‐regulation in CBLEs and emphasizes the pivotal role of metacognition and SRL in teachers’ TPACK development. What is already known about this topic Success in technology integration calls for teachers’ conceptual understanding of technological pedagogical content knowledge (TPACK). Teachers’ self‐regulated learning (SRL) ability mediates their TPACK development since metacognitive activities in the SRL process enable teachers to monitor and evaluate TPACK learning towards the sophisticated levels of TPACK. What this paper adds nBrowser, the computer‐based learning environment fosters teachers’ engagement in regulated TPACK development. Analysis of teachers’ lesson plans affords opportunities to identify teachers’ specific metacognitive processes in self‐regulated TPACK development. Latent profile analysis helps to understand the heterogeneity of teachers’ metacognitive processes and establish distinctive profiles regarding teachers’ self‐regulation. Teachers’ TPACK development differed significantly across distinctive SRL profiles. Implications for practice and/or policy Teacher educators should find ways to support teachers’ SRL ability in technology education. The identification of SRL profiles could contribute to designing metacognitive scaffolds in the specific domain of teachers’ technology education.

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“…To check the behaviour proportion on event logs in the model, the fitness significance is calculated (defined in Kurniati, Kusuma, & Wisudawan, 2016;Sonnenberg & Bannert, 2015) using the following formula:…”

Section: Process Miningmentioning

confidence: 99%

Examining Student Regulation of Collaborative, Computational, Problem-Solving Processes in Open-Ended Learning Environments

Emara

Hutchins

Grover

et al. 2021

JLA

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The integration of computational modelling in science classrooms provides a unique opportunity to promote key 21st century skills including computational thinking (CT) and collaboration. The open-ended, problem-solving nature of the task requires groups to grapple with the combination of two domains (science and computing) as they collaboratively construct computational models. While this approach has produced significant learning gains for students in both science and CT in K–12 settings, the collaborative learning processes students use, including learner regulation, are not well understood. In this paper, we present a systematic analysis framework that combines natural language processing (NLP) of collaborative dialogue, log file analyses of students’ model-building actions, and final model scores. This analysis is used to better understand students’ regulation of collaborative problem solving (CPS) processes over a series of computational modelling tasks of varying complexity. The results suggest that the computational modelling challenges afford opportunities for students to a) explore resource-intensive processes, such as trial and error, to more systematic processes, such as debugging model errors by leveraging data tools, and b) learn from each other using socially shared regulation (SSR) and productive collaboration. The use of such SSR processes correlated positively with their model-building scores. Our paper aims to advance our understanding of collaborative, computational modelling in K–12 science to better inform classroom applications.

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

Cited by 66 publications

References 58 publications

Children’s Macro-Level Navigation Patterns in Hypermedia and their Relation with Task Structure and Learning Outcomes

Children’s Macro-Level Navigation Patterns in Hypermedia and their Relation with Task Structure and Learning Outcomes

Latent profiles of self‐regulated learning and their impacts on teachers’ technology integration

Examining Student Regulation of Collaborative, Computational, Problem-Solving Processes in Open-Ended Learning Environments

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