University education has become an integral and basic part of most people preparing for working life. However, placement of students into the appropriate university, college, or discipline is of paramount importance for university education to perform its role. In this study, various explainable machine learning approaches (Decision Tree [DT], Extra tree classifiers [ETC], Random forest [RF] classifiers, Gradient boosting classifiers [GBC], and Support Vector Machine [SVM]) were tested to predict students’ right undergraduate major (field of specialization) before admission at the undergraduate level based on the current job markets and experience. The DT classifier predicts the target class based on simple decision rules. ETC is an ensemble learning technique that builds prediction models by using unpruned decision trees. RF is also an ensemble technique that uses many individual DTs to solve complex problems. GBC classifiers and produce strong prediction models. SVM predicts the target class with a high margin, as compared to other classifiers. The imbalanced dataset includes secondary school marks, higher secondary school marks, experience, and salary to select specialization for students in undergraduate programs. The results showed that the performances of RF and GBC predict the student field of specialization (undergraduate major) before admission, as well as the fact that these measures are as good as DT and ETC. Statistical analysis (Spearman correlation) is also applied to evaluate the relationship between a student’s major and other input variables. The statistical results show that higher student marks in higher secondary (hsc_p), university degree (Degree_p), and entry test (etest_p) play an important role in the student’s area of specialization, and we can recommend study fields according to these features. Based on these results, RF and GBC can easily be integrated into intelligent recommender systems to suggest a good field of specialization to university students, according to the current job market. This study also demonstrates that marks in higher secondary and university and entry tests are useful criteria to suggest the right undergraduate major because these input features most accurately predict the student field of specialization.
Achieving sustainability is a major objective of Saudi Arabia’s Vision 2030. This study investigates one Ministry of Education initiative to achieve sustainability through the establishment of its Optimum Utilisation of Teaching Staff Programme (OUTSP). The study focuses in particular on the OUTSP’s two-term English Language Programme (ELP). First, it details the ELP’s modules; then, it analyses the overall results for 170 of the 431 candidates who joined the programme, before going on to examine the results of a sample group of 56 of those 170 candidates in more detail. Microsoft Excel data analysis tools were used to analyse the data. The results show that the candidates generally scored high marks in both terms. The analysis of the sampled group suggests that the noticeably higher marks gained in the second term could be explained by the fact that the candidates were all experienced teachers. The study concludes that the ELP section of the OUTSP is an effective way to achieve sustainability in education through upskilling teachers. It further concludes that the ELP benefits teachers who are willing to move away from their specialist areas or who simply wish to advance their knowledge. Finally, the study recommends that future iterations of the ELP should be spread over three terms rather than two. We also recommend providing more incentives to encourage more candidates to join the ELP.
This paper outlines the design and development of an intelligent tutoring technique to personalize the navigation of individual users in the course content and generate advice to students. Based on that, a framework for adaptive e-learning, the e-Learning Guide system (eLGuide), is implemented and an empirical evaluation of the prototype is conducted to assess the possibility of its integration with web-based learning systems. The system is tested in a real setting with the SQL-A Database Language course comprising postgraduate students of the computer science program, all new to database systems. The evaluation study shows a positive impact on learning outcomes. The results of the experimental study allowed us to conclude that eLGuide is a useful framework, which can be employed in a web-based learning environment to support students as well as teachers in a better way. ß 2015 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:542-555, 2015; View this article online at wileyonlinelibrary.com/journal/cae;
The widely use of data digitization leads to the necessity of hiding the content of a message when it enters an insecure channel. The message prepared by the sender is encrypted before transmission. The encryption process requires an encryption algorithm and a key. In image cryptography, recently numbers of effective chaos-based image encryption schemes have been proposed. This paper presents a novel image encryption scheme which encrypts the pixels' RGB component instead of the pixels itself, where every pixel in the image will be extracted to its RGB triple, encrypted, and prepared to be transmitted. In the encryption process we use another image as a key, this image should be larger than or of the same size of the plain image that we intend to encrypt. The image encryption scheme introduced is based on chaotic systems. Simulation results show that the proposed image encryption scheme achieves high level of security, and reduces the encryption and the decryption time of image data.
Brain Computer Interface (BCI) is a communication and control mechanism, which does not rely on any kind of muscular response to send a message to the external world. This technique is used to help the paralyzed people with spinal cord injury to have the ability to communicate with the external world. In this paper we emphasize to increase the BCI System bit rate for controlling a virtual telephone keypad. To achieve the proposed algorithm, a simulated virtual telephone keypad based on Steady State Visual Evoked Potential (SSVEP) BCI system is developed. Dynamic programming technique with specifically modified Longest Common Subsequence (LCS) algorithm is used. By comparing the paralyzed user selection with the recent, and then the rest, of the stored records in the file of the telephone, the user can save the rest of his choices for controlling the keypad and thence improving the overall performance of the BCI system. This axiomatic approach, which is used in searching the web pages for increasing the
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