Abstract
Education is one of the drivers of economy and the role of higher education institutions (HEIs) as knowledge contributors to the nation's economy is significant. Educational organisations being service organizations quality of service depends directly on the capability, commitment, and motivation of faculty who provide it and ensuring quality is a challenge for education managers. One method of ensuring quality is by assessing the performance of faculty and ranking them based on their performance against set standards-Academic Performance Indicators. Teachers of modern education system have to carry out multiple tasks- administrative, teaching, research, societal engagement, mentoring, extra-curricular activities and so on. Hence, setting standards for each of these activities and measuring them on the same yardstick may not yield desired results. This is especially true in multidisciplinary institutions wherein faculty have different tasks and roles as per their specialization and discipline. Therefore, conventional assessment criteria may not suffice the decision makers of educational institutions. Principal Component analysis (PCA) is a standard statistical technique that can be used to reduce the dimensionality of a data set by assessing the dimensional structure of a dataset (Dunteman, 1989) and reducing a large number of variables into a smaller set of linear combinations (components). PCA is a variable reduction method that can be used to reduce the multiple variables in the performance appraisal criteria and result in smaller dataset for further analysis. In this study, the faculty performance scores (API) as per UGC format were analysed using PCA and the most important components were identified contributing to the performance of faculty.
Highlights
In higher education institutions assessing faculty performance across various disciplines is a challenge as the nature of work differs from faculty to faculty
The descriptive statistics of the faculty performance scores with 22 parameters is shown in the table with the mean and standard deviation and the number of sample (n) in each indicator are presented in table 1
The results showed that there was a correlation between the multiple variables (22 indicators) of academic performance indicators (API) for faculty performance evaluation and only seven components could successfully explain the statistic variations in the data and model the output instead of using all the variables
Summary
In higher education institutions assessing faculty performance across various disciplines is a challenge as the nature of work differs from faculty to faculty. Evaluation of their performance has to encompass all the parameters which reflect their nature of work and the multiple roles carried out by them to yield acceptable results. The traditional methods for evaluation may not capture all the contributions by them and may not yield acceptable results thereby contributing to dissatisfaction and demotivation among faculty. In modern education, teaching is a complex activity with operational area and it relies on clearly defined set of competencies possessed by professionals working in this field [2]
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