互动性
模式(遗传算法)
认知负荷
认知心理学
认知
感知
计算机科学
学习理论
认知资源理论
心理学
认知科学
机器学习
多媒体
神经科学
作者
Wesley Pyke,Johan Lunau,Amir‐Homayoun Javadi
标识
DOI:10.1177/17470218241308143
摘要
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.
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