A Data Mining Approach On Occupational Competency Assessment: The Case Of Addis Ababa

Abstract

There are many factors affect the process of occupational competency assessment such as language, regulations prepared by the government, and economic matter. The primary objective of this study was to identify patterns in the available data that could be useful for analyzing factors affecting a candidate?�?s performance using data mining techniques. The dataset for this study has been obtained from Addis Ababa Occupational Competency Assessment and Certification Center (OCACC) and it contains 324, 236 records. From the available dataset, 3000 records have not been either assessed or not scheduled for the assessment so that they have been removed. Hence, a dataset of 321,236 records has been used for the final experiment. WEKA software for association and classification has been used as major tools. On top of that by computing the efficiency of experimented algorithms, it has been shown that the Apriori algorithm has better accuracy. As it has been indicated in the ARM analysis result if a candidate is young, single, unemployed with no work experience, he/she likely are not competent. Additionally, if the candidates also young, single, mode of training regular and applied for qualification based assessment, he/she also be incompetent. Likewise, if the candidates those who are young, mode of training regularly and have a year gap to assess their competency after accomplish their study, she or he will not also be incompetent. However, the study selects the feasible rules which have a possible role to realize a candidate?��?s performance. Based on the analysis result, occupational standards (OS) have a higher influence on candidate?��?s performance and also the majority of candidates have no awareness about occupational competency?��?s (OC) which is derived from OS. On the other hand, from the resulted analysis, ca