Exploration of Skillification and Its Use in Awarding Credit for Prior Learning

Abstract

In applications for prior learning credit, learners with professional experience describe competencies gained during their employment history or past study, explaining how these competencies correspond to what is taught in a course sufficiently well to award them credit for prior learning. Comparing skills that are extracted algorithmically from artifacts describing a learner’s prior experience to skills similarly extracted from syllabi offers a potential solution. Current research indicates that algorithmically extracted skills can be used to identify relevance between artifacts and groups of courses but that connecting an artifact to a specific course requires guidance to learners on the artifact creation, careful use of analytical techniques, and faculty involvement. The guidance includes setting expectations for language volume and creating focused text, like an essay, rather than more general descriptive artifacts, like résumés. Effective analytical techniques accommodate observed variation in skill volume between different artifacts, optimizing the potential identification of appropriate relationships. Basing decisions on the degree of overlap in the total artifact-course skill set appears more effective than considering the number of matches to program skills. Deterministic matching is insufficient for identifying course-level correspondence, while approaches that capture information about the covariation of skills among courses offer promise.