Abstract
Lecturer performance analysis has enormous influence on the educational life of lecturers in universities. The existing system in universities in Kurdistan-Iraq is conducted conventionally, what is more, the evaluation process of performance analysis of lecturers is assessed by the managers at various branches at the university andin view of that, in some cases the outcomes of this process cause a low level of endorsement among staffs who believe that most of these cases are opinionated. This paper suggests a smart and an activesystem in which both unique and multiple soft computing classifier techniques are used to examine performance analysis of lecturers of college of engineering at Salahaddin University-Erbil (SUE). The dataset collected from the quality assurancedepartment at SUE. The dataset composes of three sub-datasets namely: Student Feedback (FB), Continuous Academic Development (CAD) and lecturer’s portfolio (PRF). Each of the mentioned sub-datasets is classified with a different classifier technique. FB uses Back-Propagation Neural Network (BPNN), CAD uses Naïve Bayes Classifier (NBC) and the third sub-dataset uses Support Vector Machine (SVM) as a classifier technique. After implementing the system, the results of the above sub-datasets are collected and then fed as input data to BPNN technique to obtain the final result and accordingly, the lectures are awarded, warned or punished.
Highlights
The development of lecturer performance analysis has excessive impact on universities, as it monitors the scholastic value through inspiring the awareness of researchers and academics and encouraging the value of education and training at universities
After applying the described techniques using both phases of training and testing inall subsystems, henceforward the outcomescan be shown in the following tables
It is mentioned that FB subsystem uses BackPropagation Neural Network (BPNN) as a classifier
Summary
The development of lecturer performance analysis has excessive impact on universities, as it monitors the scholastic value through inspiring the awareness of researchers and academics and encouraging the value of education and training at universities. The same thing applied for the PRF sub dataset It is partitioned into training and testing sets, the PRF sub system is using SVM technique as a classifier. The final sub dataset is CAD which uses NBC as a classifier technique. Do you think that the lecturer prepared suitable course books for the classes that he teaches and did he explained the course book’s aims and goals for the learners for the new academic year?
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
