eNEUROANAT-CF: a Conceptual Instructional Design Framework for Neuroanatomy e-Learning Tools

Abstract

In an era which is witnessing a significant rise in incidence of neurological diseases, there is also a rise in neurophobia: the diminished confidence of clinical practitioners and students to manage patients with neurological conditions. In this context, the perceived nexus between neuroanatomy-phobia (the challenges and fear associated with learning neuroanatomy) and neurophobia highlighted the need to revisit the neuroanatomy pedagogies and their instructional designs. e-Learning can be effectively employed to enhance students' learning of neuroanatomy. This perspective describes a conceptual framework for online neuroanatomy learning (e-neuroanatomy learning conceptual framework (eNEUROANAT-CF)), which provides a theoretical grounding to newly developed neuroanatomy e-learning resources, by offering a set of instructional design principles. The framework is rooted in the theories of adult learning, cognitive load, and Mayer's theory of multimedia learning. eNEUROANAT-CF was validated by imparting user opinion regarding the best perceived instructional design features for learning neuroanatomy. Furthermore, it was effectively employed to inform the pedagogical construct of an e-tool to help students learn the spinal pathways. The perspective highlights the theoretical underpinnings of the eNEUROANAT-CF under seven categories, namely "avoidance of cognitive overload," "learning style preferences," "contextualization," "motivation," "social learning," "feedback/reflection," and "active learning." In addition, elaborative examples are provided, which explains how eNEUROANAT-CF informed the instructional design features of the abovementioned e-tool. The authors propose that any novel, interactive neuroanatomy e-learning resource rooted in the instructional design principles outlined by the eNEUROANAT-CF will improve users' learning and understanding of neuroanatomy. The research shows promise to help break the perceived nexus between neuroanatomy-phobia and neurophobia.

Keywords: Medical education; Neuroanatomy; Neurophobia; e-Learning.

Fig. 1

Contextual significance of eNEUROANAT-CF. The neuroanatomy-phobia–neurophobia nexus has been linked with impaired neurology patient care. In the context of the current limitations of conventional dissection-based pedagogies (limited lab time, high costs, health and safety concerns), e-learning offers promise to enhance neuroanatomy understanding and disrupt the nexus. eNEUROANAT-CF (e-neuroanatomy learning conceptual framework) offers a set of instructional design principles for formulating quality e-learning resources for learning neuroanatomy

Fig. 2

Link between the theoretical basis of eNEUROANAT-CF and e-tool instructional design features. Instructional design components of the eNEUROANAT-CF and underlying theoretical principles have been enlisted on the left side. The e-tool instructional design features linked with the eNEUROANAT-CF components have been highlighted on the right side. The arrows show the directional link between the e-design features and the eNEUROANAT-CF components. eNEUROANAT-CF, e-neuroanatomy learning conceptual framework; SD, self-determination; EVS, expectancy valence (value) success; MCQs, multiple choice questions

Fig. 3

Implementation of the eNEUROANAT conceptual framework in the design of a learning tool for the spinal pathways. Each panel illustrates a number of key principles embedded in the design of the tool. Panel A demonstrates the effective use of segmentation of the learning space while preserving the spatial contiguity of the learning elements (learning and sketch pads). It also illustrates that juxtaposition of textual explanations and illustrations allows the delivery of multimodal content and shows the potential use of color-coding elements to highlight important concepts. Panel B illustrates the use of formative feedback in the form of quiz answer and associated explanation as well as the use of command buttons to give user control over the learning sequence and timing. Panel C further demonstrates how implementation of segmentation, signaling, redundancy, spatial, and temporal contiguity helps in delivering a clear message without leading to cognitive overload