Identifying Competency Gaps Among Engineering Students in a Post K-12 Setting Through the Use of Clustering Algorithms

This paper leverages on the power of clustering algorithms to determine potential deficiencies in mathematics competencies of incoming first year engineering students who are graduates of the recently implemented K to 12 program. To achieve this objective, a total of 23 prerequisite mathematics competencies in Algebra, Advance Algebra, Plane and Spherical Trigonometry, Analytical Geometry, and Solid Mensuration common to engineering programs offered in the country were identified from the approved policies, standards, and guidelines published by the Commission on Higher Education. A survey instrument was developed to assess the self-rated proficiencies of participating respondents in these competencies using a 5-point liker scale (5 being the highest). Results of the clustering analysis showed the formation of four distinct groups of students based on their self-rated proficiencies: (1) above average, (2) average, (3) below average, and (4) poor. It was found that participants generally gave low self-ratings to Advanced Algebra and Analytic Geometry prerequisites. Although it is expected for non-STEM graduates of the K to 12 program to struggle in engineering programs, further analysis of the clusters revealed that about half of the STEM respondents self-rated their proficiencies in the below average and poor clusters. Though it may be argued that this finding may need to be explored further in the absence of ground truth labels, diagnostic examination scores garnered by the participants validate this observation. In conclusion, this study highlights the need for universities to implement targeted intervention programs to address the identified deficiencies and ensure the success of their enrollees in engineering programs.