Using advanced analytics to expand the effectiveness of traditional gap analysis

Session Description: Tailoring educational offerings to the specific needs of individual learners is critical in the development of an impactful educational intervention. This session delves into the essence of cluster analysis, a statistical technique for organizing data into meaning clusters. It showcases how this method can refine educational gaps and contribute to the development of effective educational strategies by identifying and grouping similar entities based on shared characteristics, facilitating targeted interventions and tailored approaches.

Cluster analysis is an analytical method that facilitates the segmentation of target learners into meaningful groups based on common attributes. Each cluster represents a subset of respondents characterized by relative homogeneity across a set of observations, yet distinct from other subsets within the dataset.

Cluster analysis describes a family of methods aimed at identifying groups of data elements sharing qualitatively similar features. Among these, k-means minimizes variances within each cluster by partitioning the vector space into cells, and iteratively updating cell boundaries until convergence is reached. These analyses are conducted using tools like R, Python libraries such as scikit-learn, and software platforms like RapidMiner and Orange, which offer a range of clustering algorithms and visualization capabilities.

Presenters will share a methodology in which quantitative data, such as numerical assessments and surveys covering knowledge levels, practice preferences, referral policies, alignment with expert opinion, practice characteristics, patient populations, and learning preferences are utilized to form the foundation for clustering participants based on objective criteria, enabling the identification of distinct learner groups.

In addition, qualitative data, derived from discussions, interviews, open-ended survey responses, and observations, offer rich contextual information about learners' experiences, motivations, sentiments, and challenges. By capturing learners' perspectives, beliefs, and attitudes, qualitative data adds depth and nuance to the learner profiles constructed through cluster analysis.

Utilizing this mixed methods approach broadens the scope of the cluster analysis, incorporating diverse data types for a comprehensive view. This enables the creation of more holistic learner profiles, which not only identify educational gaps associated with each profile but also unveil the underlying rationale behind them. Through a deeper understanding of learners' perspectives and experiences, educational interventions can be more precisely tailored to address their knowledge needs, while also catering to their interests and priorities. Furthermore, through the strategic application of cluster analysis and learner profiles, educators can more efficiently allocate resources and craft effective educational plans.