Enhancing Comprehension through Knowledge Segmentation and Dual-Coding

Dual-Coding:

A Comprehensive Learning Approach: Introduced by Allan Paivio, dual-coding theory proposes that humans process information through two interconnected systems: verbal and non-verbal. This theory suggests that individuals encode and retrieve information using language (verbal code) and non-linguistic forms like images or sensory experiences (non-verbal code). The use of both codes enhances learning and memory by providing multiple pathways for encoding and retrieval. For example, learning new concepts involves reading about them (verbal) and viewing relevant images (non-verbal). Educational practices often incorporate dual coding by combining textual information with visual aids, multimedia, or non-verbal elements to enhance comprehension and retention.

Knowledge Segmentation:

In the early 1970s, Chase and Simon introduced the concept of “chunking,” suggesting that breaking information into “chunks” enhances short-term memory capacity, facilitates schema construction, and promotes more efficient retention of learning content. Content chunking is beneficial for reducing intrinsic load by simplifying complex topics. Instead of presenting a topic in its entirety, it is broken into smaller, understandable chunks to provide learners with a manageable amount of information for processing. Knowledge segmentation involves breaking information into bite-size segments, allowing learners to intellectually digest one segment before progressing to the next. This approach provides time and cognitive capacity for organizing and integrating information, leading to effective knowledge construction. Mayer and Chandler’s (2001, Experiment 2) study on narrated animations demonstrated the effectiveness of segmentation, with students who received segmented presentations outperforming those with continuous presentations, known as the segmentation effect.

edaptAI’s Approach:

edaptAI’s foundational learning principles are centered around knowledge segmentation and dual-coding. The core structure of each course is based on its knowledge map, where course learning objectives are methodically broken down into distinct, bite-sized concepts. These concepts are then organized and sequenced within content units, forming the basis for customizable learning paths. On the edaptAI platform, each concept guides students through a structured learning workflow. To enrich a learner’s cognitive processing capacity, fostering deeper understanding and retention, the learning workflow integrates 1) dual-coding, employing interactive digital content with support from diverse technology-enhanced item templates, 2) bite-sized instructional lessons designed for maximum efficiency through each learning channel, and 3) problem-solving exercises following instructional lessons to encourage active processing and seamless recall of cognitive processes through both visual and auditory channels.

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