Lecturers’ Portfolio Sub-Dataset. The second criterion of measuring lecturer performance in the SUE is lecturer portfolio for one full academic year. The criterion shows the activity and the hardworking spirit of the lecturer in helping the quality of the education and university. The portfolio Sub-Dataset is composed of 11 features and 313 instances. Each college in SUE is responsible in establishing a committee of quality assurance for each department in order to evaluate lecturers’ portfolio. The committee will consist of a number of lecturers and head of the department in the corresponding department. Continuous Academic Development Sub-Dataset. The last criterion of measuring performance of lecturers in SUE University is continuous academic development. The criterion describes the activity and participation of lecturers in workshops seminars, committees, and publications. The lecturers are given points according to their activities in the continuous learning process. The Sub-Dataset contains 3 features and 341 instances.

Combined Dataset. The combination of the above Sub-Datasets is prepared for the proposed system when all the Sub-Datasets are used as a single dataset as input to the system. The data set contains 313 instances with 26 features. The detail of the dataset is proposed in Table 1. The data labeling for the final decision is released according to a special rule which is released from the quality assurance department in the university. The detail of the labeling of the final decision is described in Table 2.

Cite as :

Tarik A. Rashid and Hawraz A. Ahmad (2016). Lecturer Performance System Using Neural Network With Particle Swarm Optimization. In Computer Applications in Engineering Education. Wiley publication, Vol. 24, Issue 4, pp. 629-638. https://doi.org/10.1002/cae.21737

Hawraz Awuny, Tarik A. Rashid (2105). Lecturer Performance Analysis using Multiple Classifiers, Journal of Computer Science. Journal of Computer Sciences, Vol. 12, No. 5, pp. 255.264, DOI: https://thescipub.com/abstract/10.3844/jcssp.2016.255.264