Framing systems thinking from the perspective of self-regulated learning and metacognition – the process model of systems thinking

Systems thinking is central to understanding complex biological phenomena, yet existing frameworks primarily specify the skills learners should demonstrate and provide less detail on how they recognise, select, regulate, and transfer a systems perspective. We introduce the Process Model of Systems Thinking (PMST), which conceptualises systems thinking as a domain-specific learning strategy grounded in self-regulated learning and metacognition. The PMST distinguishes two recursively connected phases: Forethinking and Selecting Systems Thinking, and Applying and Adapting Systems Thinking as a Learning Strategy. Across both phases, conceptual systems knowledge, conceptual knowledge about the phenomenon, metaconceptual knowledge, and conditional and procedural metastrategic knowledge support task interpretation, strategy selection, application, monitoring, and adaptation. Theory-guided vignettes illustrate the model’s proposed processes, while selected studies on self-regulated learning, systems modelling, system language use, and metacognitive prompting provide empirical reference points for assessing its integrative potential. The PMST distinguishes supported systems-thinking performance from autonomous strategy selection and offers a framework for comparing instructional approaches according to the phases and knowledge resources they address. As a provisional and revisable model, it generates testable propositions for research on systems-thinking development, instruction, transfer, and increasingly autonomous use across biological contexts.

Authors

Institutions

Publication Details

Journal
Journal of Biological Education
Published
2026-08-25
DOI
https://doi.org/10.1080/00219266.2026.2719536
Primary Topic
Complex Systems and Decision Making
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Framing systems thinking from the perspective of self-regulated learning and metacognition – the process model of systems thinking

Marie‐Christine P. J. Knippels, Alexander Bergmann
Journal of Biological Education
Complex Systems and Decision Making
article

Framing systems thinking from the perspective of self-regulated learning and metacognition – the process model of systems thinking

Marie‐Christine P. J. Knippels, Alexander Bergmann
article en

Abstract

Systems thinking is central to understanding complex biological phenomena, yet existing frameworks primarily specify the skills learners should demonstrate and provide less detail on how they recognise, select, regulate, and transfer a systems perspective. We introduce the Process Model of Systems Thinking (PMST), which conceptualises systems thinking as a domain-specific learning strategy grounded in self-regulated learning and metacognition. The PMST distinguishes two recursively connected phases: Forethinking and Selecting Systems Thinking, and Applying and Adapting Systems Thinking as a Learning Strategy. Across both phases, conceptual systems knowledge, conceptual knowledge about the phenomenon, metaconceptual knowledge, and conditional and procedural metastrategic knowledge support task interpretation, strategy selection, application, monitoring, and adaptation. Theory-guided vignettes illustrate the model’s proposed processes, while selected studies on self-regulated learning, systems modelling, system language use, and metacognitive prompting provide empirical reference points for assessing its integrative potential. The PMST distinguishes supported systems-thinking performance from autonomous strategy selection and offers a framework for comparing instructional approaches according to the phases and knowledge resources they address. As a provisional and revisable model, it generates testable propositions for research on systems-thinking development, instruction, transfer, and increasingly autonomous use across biological contexts.

Journal of Biological Education
Utrecht University (NL), University of Bamberg (DE)
Technische Universität Kaiserslautern, Universität Leipzig
Reduced inequalities
Openalex Percentile: Top 6%
Complex Systems and Decision Making
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.