The interplay between self-regulation in learning and cognitive load

By Tina Seufert. Educational Research and Reviews, 2018

193 citations recorded by the source corpus.

Abstract Research on self-regulated learning and on cognitive load has been two of the most prominent and influential research lines in educational research during the last decades. However, both lines developed quite independently from one another. This paper aims to bridge both concepts in order to better understand self-regulation as well as cognitive affordances of complex and dynamic learning processes. In fact, most learning environments require learners to self-regulate their learning process. They have to set their goals and plan, use strategies and monitor their learning progress and if necessary they have to regulate, i.e. to adapt their learning behavior. However, in all these phases of self-regulation, learners need to invest cognitive and metacognitive resources in addition to dealing with the original learning task. The affordances of self-regulation thus will cause cognitive load. This paper analyzes the affordances in the different phases of self-regulated learning in terms of intrinsic, extraneous and germane load. As a conclusion, the interplay between different affordances of self-regulation and learners’ resources and aptitudes is described in an integrated model of self-regulation and cognitive load.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/4621da34e335079910c9427c7ed9a68d6865f479

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Melina Klepsch
2. Tina Seufert

In Developmental and Educational Psychology

1. Melina Klepsch
2. Lev Tankelevitch
3. Sean Rintel
4. Tina Seufert
5. Shaaron E. Ainsworth

In Experimental and Cognitive Psychology

1. Melina Klepsch
2. Tina Seufert
3. Shaaron E. Ainsworth

Citations in the corpus

• The Metacognitive Demands and Opportunities of Generative AI (2023, background): “Monitor metacognitive abilities during GenAI interactions and explore whether metacognitive interventions improve mental model updating.”
• Understanding instructional design effects by differentiated measurement of intrinsic, extraneous, and germane cognitive load (2020, background): “As described by Seufert (2018), it is not only learning the content that causes load on learners’ working memory.”