Does difficulty moderate learning? A comparative analysis of the desirable difficulties framework and cognitive load theory
Year: 2024
Type: article
Abstract: There is evidence to suggest that variations in difficulty during learning can moderate long-term retention. However, the direction of this effect is under contention throughout the literature. According to both the Desirable Difficulties Framework (DDF) and the Retrieval Effort Hypothesis (REH), increasing difficulty (thus relative effort) during retrieval-based learning can help achieve superior long-term retention. One reason for this is due to improved schema formation following a deeper encoding strategy, allowing for more efficient retrieval techniques. A conflicting theory discussed in this review is the Cognitive Load Theory (CLT). The CLT states that conditions for learning are best when extraneous load is reduced, and intrinsic load is optimised. By doing this, germane resources can focus on schema formation. While both theories consider schema formation key to successful retention, the way in which it is best achieved is conflicting. To date, both theories have yet to be compared despite their commonalities. This review evaluates the aforementioned theories, before proposing a new model of difficulty in learning. The proposed model integrates principles from the DDF, REH, and CLT, incorporating insights from Perceptual Load Theory (PLT). It suggests that task difficulty should be adjusted based on the material’s complexity and the learner’s expertise. Increasing difficulty benefits low-element-interactivity tasks by enhancing focus and retention, while reducing difficulty in high-element-interactivity tasks prevents cognitive overload.
Authors: Wesley Pyke, Johan Lunau, Amir-Homayoun Javadi
Institutions: University of Kent, Tehran University of Medical Sciences
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
Estimated APC paid: $3,450
OpenAlex ID: W4405116276