Exploring fonts as retrieval cues in text-based learning

A growing body of research focuses on optimizing retrieval processes to help learners recall information from memory. The context-dependent memory effect suggests that information retrieval is more effective when the retrieval context matches the learning context. This phenomenon has been investigated in a variety of study materials and samples. However, there is a research gap in which this effect has not yet been investigated with font. Therefore, the font may act as a contextual retrieval cue, making it easier for learners to recall the presented information. In this context, the present experiment assumes that a match between the font used during learning (encoding phase) and that used during testing (decoding phase) improves retrieval processes and thus learning performance, while a mismatch has a negative effect. To test this assumption, 130 university students were randomly assigned to one condition of a two (font of learning texts: Haettenschweiler vs. Times New Roman) × two (font of learning questions: Haettenschweiler vs. Times New Roman) between-subjects design. Contrary to expectations, using the same font for encoding and decoding did not improve learning performance. Furthermore, learners did not report higher levels of mental effort when they had to work on learning questions presented in the same font as the learning texts. Although the font of the learning text is the most salient feature, it does not seem to serve as an effective retrieval cue in text-based learning.

Authors

Institutions

Publication Details

Journal
Acta Psychologica
Published
2024-11-01
DOI
https://doi.org/10.1016/j.actpsy.2024.104606
Citations
1
Primary Topic
Second Language Acquisition and Learning
Type
article
Field-Weighted Citation Impact
0.79
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring fonts as retrieval cues in text-based learning

Günter Daniel Rey, Sebastian Victor Jansen, Felix Krieglstein, Lukas Wesenberg et al.
1 citations
Acta Psychologica
Second Language Acquisition and Learning
0.79
article

Exploring fonts as retrieval cues in text-based learning

Günter Daniel Rey, Sebastian Victor Jansen, Felix Krieglstein, Lukas Wesenberg, Manuel Schmitz, Felicia Meusel, Nadine Scheller
article en
1 citations

Abstract

A growing body of research focuses on optimizing retrieval processes to help learners recall information from memory. The context-dependent memory effect suggests that information retrieval is more effective when the retrieval context matches the learning context. This phenomenon has been investigated in a variety of study materials and samples. However, there is a research gap in which this effect has not yet been investigated with font. Therefore, the font may act as a contextual retrieval cue, making it easier for learners to recall the presented information. In this context, the present experiment assumes that a match between the font used during learning (encoding phase) and that used during testing (decoding phase) improves retrieval processes and thus learning performance, while a mismatch has a negative effect. To test this assumption, 130 university students were randomly assigned to one condition of a two (font of learning texts: Haettenschweiler vs. Times New Roman) × two (font of learning questions: Haettenschweiler vs. Times New Roman) between-subjects design. Contrary to expectations, using the same font for encoding and decoding did not improve learning performance. Furthermore, learners did not report higher levels of mental effort when they had to work on learning questions presented in the same font as the learning texts. Although the font of the learning text is the most salient feature, it does not seem to serve as an effective retrieval cue in text-based learning.

Acta PsychologicaVol. 251
University of Zurich (CH), Chemnitz University of Technology (DE), Leipzig University (DE)
Quality Education
Openalex Percentile: Top 23%
Second Language Acquisition and Learning
0.79
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.