Development of an Instrument Measuring Cognitive Load During a Peer-to-Peer Dialogue in Mathematics

Sep 2, 2026

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Peer-to-peer dialogue can strengthen secondary students’ mathematics understanding by promoting active processing and knowledge articulation, yet it also intensifies demands on working memory. Cognitive load must therefore be differentiated into intrinsic load, extraneous load, and germane load. Existing self-report questionnaires do not separate cognitive demands linked to students’ listener and explainer roles. This study developed and validated a questionnaire measuring IL, EL, and GL separately for both roles.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Cognitive load in peer dialogue

Existing questionnaires and their limitations

Questionnaire development methodology

Role-dependent cognitive load constructs

| Received: 10 Nov 2025
Accepted: 31 Mar 2026 | Peer-to-peer dialogue can enhance students’ understanding of mathematics by stimulating active processing and articulation of knowledge. However, this type of interaction also places demands on working memory, which may hinder learning if cognitive load becomes excessive. To optimize classroom dialogue, it is important to distinguish between different types of cognitive load: intrinsic load (IL), extraneous load (EL), and germane load (GL). Existing self-report instruments do not account for the distinct cognitive demands associated with students’ roles as listeners or explainers. This study aimed to develop and validate a questionnaire to measure IL, EL, and GL separately for both listening and explaining roles during peer-to-peer dialogue in secondary mathematics classrooms. The development process involved a literature review, analysis of existing instruments, adaptation for adolescent learners, and integration of mathematical dialogue characteristics. The resulting instrument consists of 18 items, 9 for each role. To validate the instrument, two studies were conducted using peer instruction in Dutch secondary school classes (n = 65 and n = 32; ages 15-17) . Principal component analysis confirmed a three-factor structure aligned with the three types of cognitive load for both roles. The results suggest that the questionnaire is a promising tool for measuring differentiated cognitive load during classroom dialogue. It may inform instructional design aimed at balancing cognitive demand and supporting effective peer interaction in mathematics education. | | --- | --- | | INTRODUCTION | Keywords: cognitive load, mathematics, secondary school, peer-to-peer dialogues | | Load
type | Leppink et al. (2013) | | Klepsch et al. (2017) | | Ayres (2006) (IL), Cierniak et al.
(2009) (EL), and Salomon (1984) (GL) | | | --- | --- | --- | --- | --- | --- | --- | | | Expression that is used for making a statement | Subject on which
a statement is
requested | Expression that is used for making a statement | Subject on which
a statement is
requested | Expression that is used for making a statement | Subject on which
a statement is
requested | | IL | Complex | Topic/formulas/
concept/
definition | Complex/
keep in mind
simultaneously | Task/number of
elements | Easy/difficult | The lecture | | EL | Unclear/ ineffective | Language/lecture | Exhausting/ inconvenient/ difficult | Find information/ design of the task/important information | Easy/difficult to learn | The lecture | | GL | Enhance
understanding | The activity | Effort/
understanding/
comprehension | Details/
everything/
the task | Concentrate | The task | | Furthermore, explaining mathematical reasoning often requires switching between symbolic (e.g., formulas) and verbal representations. This translation process can be added to IL, especially for students who are less fluent in expressing abstract ideas in natural language. Also, since each step in a mathematical procedure builds upon previous steps, comprehension is often cumulatively dependent, a disruption in understanding at one point can compromise the entire explanation. Finally, the emphasis on arriving at the‘correct’ solution may reduce students’ willingness to experiment or reflect, potentially reducing GL while increasing EL due to performance pressure. These domain-specific features influence both the nature and magnitude of cognitive load during peer interactions in mathematics.
Existing Questionnaires
Several questionnaires aim to distinguish between IL, EL, and GL, for example, those developed by Leppink (2016) and Klepsch et al. (2017) . These instruments were translated into Dutch and administered in upper secondary mathematics classes. Following a peer discussion on a mathematical topic, students completedone of the translated questionnaires.
However, our analysis revealed that these instruments di

FAQs

Why is cognitive load differentiation important in peer-to-peer mathematics dialogue?

Peer dialogue can enhance understanding, but excessive working-memory demands can hinder learning. Differentiating intrinsic, extraneous, and germane load helps optimize instructional dialogue.

What gap exists in existing self-report instruments?

Existing questionnaires do not account for distinct cognitive demands related to students’ roles as listeners versus explainers during peer interaction.

How was the new questionnaire validated and what did the results show?

Two studies used peer instruction in Dutch secondary classes, and principal component analysis supported a three-factor structure corresponding to intrinsic, extraneous, and germane load for both roles.

Sep 2, 2026

Upload by Rowan