Toward a Better Understanding of Academic Programs Educational Objectives: A Data Analytics-Based Approach release_yhcn3xwmb5enpn54tngfhrjudy

Abstract

In outcome-based academic programs, Program Education Objects (PEOs) are the key pillars on which program components are built. They are articulated linguistically as broad statements of graduates' professional and career accomplishments within a few years of graduation. Moreover, PEOs are mapped into a set of skills and attributes known as Program Learning Outcomes (PLOs). It goes without saying that a profound understanding of the PEOs is a key factor in the success of an academic program. For this sake, this paper proposes a data analytics-based approach to examine the correlations among PEOs. More specifically, it applies a data similarity-based approach to analyze the correlations among the PEOs of engineering programs. To this end, a dataset of PEOs–PLOs mapping of a set of engineering programs has been extracted from their self-study reports. The collected dataset has undergone preprocessing steps to transform it into a suitable representation. This involves data cleaning, data annotation using a developed set of PEOs labels, and removal of data instances with multiple PEO labels. Each PEO is then represented as a vector space model whose dimensions are the PLOs, and their values are the relative frequencies of PLOs computed from all data instances of that PEO. After that, three data similarity measures, namely Euclidean distance, cosine measure, and Manhattan distance, are applied to measure the similarity between PEOs vector space models. The resultant similarity matrices are then analyzed at the level of a specific measure, an agreement between measures, and average similarity across all measures. The analysis results contribute to a better understanding of the PEOs correlations and provide very useful actionable insights for empowering decision making toward systemization and optimization of academic programs processes.
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