A Model for Mapping Graduates’ Skills to Industry Roles Using Machine Learning Techniques: A Case Study of Software Engineering -PhD

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Date and time: 
Wed, 2018-08-08 16:30
Location / Venue: 


Despite rapid development in information technologies, a practical way of mapping graduates’ skills to industry roles is a challenge. Although attempts have been made by posing this as a multi-classification problem and solving using machine learning techniques, existing approaches seem not to embrace attributes and machine learning structure relevant to the problem, and hence, their results may not be reliable. For example, although occupational industry roles in the organizations are structured hierarchically, many studies have approached this problem using flat instead of hierarchical method. Either relevant attributes or hierarchical structure that correctly reflects hierarchy of industry roles or both are unknown for an effective model for mapping graduates’ skills to industry roles.
Currently, hierarchical method has not been applied in skills mapping to industry roles despite its many benefits vis-à-vis flat method. However, in other areas where it has been used, classification approach contradicts underlying structure of the problem thus resulting in multiple label prediction problems. As a result, this study presents an investigation that posed skills mapping to industry roles as a hierarchically structured multiclass problem where a machine learning structure that correctly reflects the hierarchy of industry roles was applied. The aim being to demonstrate using a case study how to build a machine learning model for mapping graduates’ skills to hierarchically structured industry roles. This was achieved by establishing both underlying structural characteristic of industry roles, as concepts required for target classes, that correctly reflects the hierarchy of industry roles and concepts appropriate as attributes for hierarchical machine learning purpose, before building and evaluating the mapping model. The model is based on the underlying taxonomic structure whose basic approach is to correctly reflect the hierarchical structure of industry roles. Literature analysis of three theoretical frameworks provided basis for establishing appropriate attributes for machine learning investigation after which hierarchical classification strategy was designed to generate the model before its prototype was constructed. Four machine learning techniques (Logistic Regression, K-Nearest Neighbor, SVM, and Naïve Bayes), a benchmark dataset and 113 Software Engineering employees’ skills profile data collected from various software development firms in Nairobi were involved in the investigation using experimental design. Experiments to evaluate performance and validity of the model were designed using repeated 5-fold cross validation procedure. Performance reported on carefully selected benchmarks on multi-classification method was adopted for validation of results. 
Findings revealed five appropriate attributes for building a model for mapping skills to industry roles and the best model was SVM induced with an average generalization performance accuracy of 67% across three datasets. On benchmark dataset, our model registered performance accuracy of 85% better than 82% reported by a selected benchmark on similar dataset. These results seem to be fairly consistent with results achieved by similar hierarchical models as reported in other different problem domains such as proteins (53.3%) and music (61%). 
In conclusion, the research objective was fulfilled with the following contributions, namely conceptual model, ML architecture for the model, software prototype, hierarchical mapping framework, research findings, datasets and literature survey which will benefit researchers in general (students, universities and industry) and specially the government in developing an effective policy for training evaluation that ensures graduates are relevant to the industry.
Keywords: Hierarchical Classification, Industry-Academia Gap, Problem-solving, Skills Mapping
Expiry Date: 
Sat, 2019-08-03 16:30
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