School of Computer Science
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Item Open Access Real-time flight delay analysis and prediction based on the Internet of Things Data(2016) Aljubairy, Abdulwahab Mohammed; School of Computer ScienceFlight delay is a significant problem resulting in the wasting of billions of dollars each year. Although this problem has been investigated in previous studies, all these previous studies rely on the historical records of flights provided by other agencies. Our work utilizes the emerging Internet of things (IoT) paradigm. It is now possible to collect and analyze sensors data in real-time. Our goal is to improve our understanding of the roots and signs of flight delays in order to be able to classify a given flight based on the features from flights and other data sources. We extend the existing works by adding new data sources and considering new factors in the analysis of flight delay. Through the use of real-time data, our goal is to establish a novel service to predict delays in real-time. In this project, we made a novel approach to collect the real time data from distributed sensors to study the flight delay. We create regression models to classify flights whether these flights are on-time or delayed as well as predicting how many minutes the delay would be. There are three main steps we conduct: first, we build a crawler to crawl the data from the pre-specified IoT data sources. Second, we implement an integration algorithm to integrate the data of all data sources using temporal and spatial criteria. Third, we conduct the analysis on the data with the aim to build a prediction model that could classify the flights and predict the delay time. This conducted analytical study provides three cases studies: Australia, China, and Europe. In addition, this project shows high correlation among the collected data. In addition, it shows that the prediction models in all case studies achieves very high accuracy. Comparing our models to others in previous studies, our model brings new factors that have impact on the flight delay as well as accomplish higher precision and recall.Item Open Access Providing Metacognitive Support Using Learning by Teaching Paradigm(2017) Alhazmi, Ahoud; School of Computer ScienceLearning by teaching technique is a powerful approach that enhances students to think deeply, orally and repeatedly. However, there are some obstacles to use this technique in school settings such as time-consuming, the anxiety of failing in front of the classmates and finding matching peers. In order to take advantage of this method for the student, there are several computer-based systems have been implemented to apply this approach where students teach the virtual agents to play the tutee role. All of these existing systems focus on various domains, and none of them have considered programming problem solving. In addition to that, the majority of the exiting systems did not provided meta-cognitive support. They only the focus on providing feedback about the content such as providing correct answers. This type of feedback called Knowledge of Correct Response: KCR). In our work, we build a computer-based learning environment that enables the novice programmers to teach problem solving to an animated agent. It combines learning by teaching technique and meta-cognitive support. That will help novice programmers to acquire deep learning on how to solve problems and prepare those programmers for future learning tasks. This project could provide a solution to novice programmers who usually tend to focus on writing the code rather than understanding the problem properly because that would lead them to be frustrated when they do not know how to deal with unfamiliar programming problems. We conducted an experiment in order to compare the e↵ect of providing guided meta-cognitive feedback and KCR feedback on the novice programmers’ skills in learning by teaching paradigm. We implemented two versions of our system. The first version which provides meta-cognitive feedback and the other version which provides KCR feedback. We analysed data from novice programmers, 18-25 years old, who at least studied and passed at least one programming course. They are from College of Computer at Al-lieth in Umm Al-Qura University. The place of the conducted experiment was in the college’s lab. We found that the meta-cognitive feedback e↵ect positively on the novice programmers’ skills comparing among the pre-test, post-test and delayed test. The performance of 82% of the participants in the experimental group (who received guided meta-cognitive feedback) has been improved after the post-test whereas the performance of only 30% of participants in the control group (who received KCR feedback) has been improved. Although the difficulty of the delayed test compared to the pre-test and the post-test, the performance of 70% of the participants in the experimental group has been improved whereas the performance of only 50% of the participants in the control group has been improved. We are not surprised about the improvement of the control group because learning by teaching technique can encourage ( but not to induce) the practice of meta-cognitive skills implicitly whereas the experimental group use learning teaching technique with meta-cognitive support in an explicit way.