The Peak-End Rule and Retrospective Emotional Valence in Digital Learning Tasks: Evidence from a Word-Learning App

Area of Science:

• Cognitive Psychology
• Human-Computer Interaction
• Educational Technology

Background:

• The peak-end rule suggests emotional experiences are judged by their most intense point and conclusion, not overall duration.
• Understanding how to optimize brief learning episodes is crucial for user engagement and effective knowledge acquisition.
• Previous research on the peak-end rule has primarily focused on longer, more complex experiences.

Purpose of the Study:

• To investigate the applicability of the peak-end rule in short, controlled vocabulary-learning tasks.
• To determine if optimizing the peak and end moments of a learning experience enhances retrospective emotional valence.
• To assess the impact of peak-end optimization on learning accuracy.

Main Methods:

• Two experiments were conducted with participants learning vocabulary words.
• Study 1 manipulated task duration (4 vs. 8 words) to test duration neglect.
• Study 2 employed a 2x2 design to optimize peak (completion page) and end (check-in page) moments using visual enhancements, assessing retrospective emotional valence and learning accuracy.

Main Results:

• Study 1 showed no significant difference in retrospective emotional valence based on task duration, supporting duration neglect.
• Retrospective emotional valence correlated more strongly with the peak-end mean than with reconstructed page-level ratings.
• In Study 2, optimizing both peak and end moments significantly enhanced retrospective emotional valence, with a non-additive interaction observed. Combined peak-and-end optimization also improved learning accuracy compared to the control.

Conclusions:

• The findings provide preliminary evidence that the peak-end rule applies to brief, controlled learning tasks.
• Optimizing specific interactive moments (peak and end) can improve user experience and learning outcomes.
• A non-additive interaction in peak-end optimization suggests that optimizing one moment may reduce the benefit of optimizing the other if not coordinated.