As mentioned in the previous section, the best accuracy of applying the ANFIS classifier in our experiments occurred when using the “Programming Skills” attribute. This indicates that our results are consistent with expert opinion.
7 Chapter 7. CONCLUSION
This chapter provides the answers of the research questions depending on the proposed framework of the research problem, discusses the conclusion from the experiments, and goes on to note the study limitations, contributions, and recommendations for further studies in the future.
This research study provides a multi-faceted domain for statistical analysis and data analytics in order to consider different attributes affecting CS and IT graduates future employability in Jordan. Neuro-fuzzy technique is used to classify CS and IT graduates in Jordan according to their employment status.
Additionally, there is a critical need to find a relation between what is supplied by the ministries of high education (graduates) and what is needed by the labour market (employees). In order to answer the research questions, this research study tried to figure out the most effective factors affecting the future of employability for graduate students from both of Computer Science and Information Technology majors in the Middle East by applying the following steps:
- To better understanding the current status of IT employment rates in Jordan, we contacted the Ministry of Digital Economy and Entrepreneurship and the Ministry of Labour in Jordan, to collect useful data. The collected data indicated that about 40% of total graduates can got job, male
students have high chance to get job against female. The data indicated that most of CS, IT graduates are employed in the private sectors with more than 90%, and less than 10% of total graduates are employed in the public sector. The computer science graduates got the highest number of jobs, and the lowest number goes to information network systems specialization with less than 1% of total graduates. Also, the collected data showed that the demand for male graduates in the IT market of Jordan is more than for female graduates. And also, the statistics indicated the superiority of some specializations in the IT field, such as computer network security engineering, computer engineering, and software engineering.
- After that, the testing process included comparing the results of building an employability prediction model of ANFIS with the results of several classifiers, such as decision tree, SVM, Naïve Bayes, and MLP. In the testing phase, we adopted an incremental approach based on increasing the number of applied attributes gradually. We started by applying the classifiers with only three attributes, and increased the number of attributes by one in each testing phase. In the last
testing phase, we applied seven attributes. The experimental studies showed that the ANFIS algorithm as a classification technique achieved the best prediction for unseen instance based on several evaluation measure such as Kappa, RMSE and accuracy measures, but unfortunately, the computational time for implemented ANFIS to build the classifier become very high when the number of attributes is greater than five. This indicates that ANFIS has a complexity problem when applying a large number of attributes.
- After applying the ANFIS algorithm to extract the classifier from the dataset by using an incremental approach, we found that certain attributes significantly affect the accuracy of the classifier during the testing phase. In this thesis, we found the most important attributes that effect the accuracy of the classifier; these factor most affect the IT student’s employability future. These attributes are:
1- Gender: during the experimental studies that were performed using ANFIS algorithm on our dataset, we found “gender” attribute to be very important for enhancing the accuracy of the classifier. The gender of the graduate in Jordan play important role in employability issue.
2- GPA: this attribute is very important affecting the graduate’s employment. The “GPA” attribute came out in the third place of the most affecting attributes on accuracy of the classifier during experiments. Student that achieved high GPA has good chance to get job.
3- English skills: our experiments indicated that the English language skills is a significant factor that helps the CS and IT graduates to get job in their fields. The English skills attribute achieved a significant effect on the accuracy of the constructed classifier. When adding this attribute to the selected attributes list, the accuracy is enhanced with a ratio of 9%, which indicates a very important influencing factor.
4- Programming skills: this attribute is considered as the most important factor that affect CS, IT student’s employability future. In this thesis this attribute has proved its superiority over the other attributes in terms of accuracy of the classifier when it takes in consideration to build the classifier. The accuracy achieving the highest enhanced ratio of 10%, therefore, the programming skills must be taken into consideration by the students and universities as well. The graduate must have a high level of programming skills to get a job.
- All in all, in this thesis the ANFIS algorithm is implemented on the training dataset that was collected from the tracer studies conducted in three Jordanian universities. The training dataset is used for training
and testing purposes. The testing process carried out based on 10-fold cross validation, where the dataset divided into 10 portions, one of them used for testing and the rest are used for training. This process is performed ten times with different portion each time. The final classifier that is built from ANFIS consist of 7 attributes as antecedents and one as consequent which is the employability status. the results indicated Gender, GPA, Programming skills and English skills are the most important factors affecting the CS and IT student’s employability future, thus the universities should be interested in providing their students with the required skills to get a job. When we compared our results with the experts’ opinions about the most important factors that affect the CS and IT student’s employability we noted compatibility between the experts’ opinions and our results. According to the results, a list of recommendations is provided to the ministry of high education as followed:
- The admission process should follow a certain standard in accepting allocated percentage from both males and females in the majors of CS and IT.
- English language skills should be taken into considerations in accepting newly registered students in both of CS and IT majors.
- Decision makers and curriculum developers should enhance the courses given to CS and IT students with more programming and soft skills.