Predicting Factors that Affect East Asian Students’ Reading Proficiency in PISA

Teachers, schools, and parents contribute to equipping students with essential knowledge and skills during their education years. When students are approaching the end of their education, they are randomly selected to participate in Program for International Student Assessment (PISA) to assess their reading proficiency. Existing work on analyzing PISA achievement results concentrates solely on identifying factors related to Parent or in combination with Student. Limited work has been proposed on how factors related to Teacher and School affect the students’ reading proficiency in PISA. This study focuses on identifying the factors related to Teacher and/or School that affect East Asian students’ reading proficiency in PISA. The PISA achievement results from East Asian students are chosen as the domain study because they are consistently the top performers in PISA in the past decade. Decision Tree (DT), Naïve Bayes (NB), K-Nearest Neighbors (KNN) and Random Forest (RF) are compared. Hamming score is used as the evaluation metric. The results indicate that RF produces the best predictive models with highest Hamming score of 0.8427. Based on the findings, School-related factors such as the number of school’s disciplinary cases, size of the school, the availability of computers with Internet facilities, the quality and educational qualifications of teachers have higher impact on the PISA achievement results. The identified factors can be used as a reference in assessing the current school’s teaching, learning environment, and organizing extra activities as part of intervention programs to cultivate reading habits and enhance reading abilities among students

Adaptive web service selection based on data type matching for dynamic web service composition

Although there are many web services provided for access in World Wide Web (WWW), some services are not available at all times.It is very important to ensure all services are available when a service composition takes place.A web service that meets the requirements of the workflow but does not match the data type will still cause a failure in composition.To address this concern, we propose an adaptive web service selection method which is able to replace a current web service which has been used for composition but fails during execution time.The proposed algorithm will select the most appropriate web service based on web service discovery engine recommendation and match the requirement based on WSDL description. Upon matching the requirements of the workflow, the selected web service will be matched according to the input and output data type. The goal of this paper is to ensure every web service that meets the requirements of the workflow does not get rejected when the data type does not fulfill the matching criteria

Webs: A web accessibility barrier severity metric

A novel metric for quantitatively measuring the severity of websites barriers that limit the accessibility for disabled people is proposed. The metric is based on the Web Content Accessibility Guidelines (WCAG 2.0), which is the most adopted voluntary web accessibility standard internationally that can be tested automatically. The proposed metric is intended to rank the accessibility barriers based on their severity rather than the total conformance to priority levels.Our metric meets the requirements as a measurement for scientific research. An experiment is conducted to assess the results of our metric and to reveal the commonplace violations that persist in websites and affect disabled people interacting with the web

Development of Flood Monitoring System

Flood is one of the natural disasters that occurs every year in Malaysia. It destroys the infrastructure and causes fatalities. Flood monitoring system can monitor the flood level and warn people upon the danger of the flood. Existing flood monitoring techniques include hydrological modelling, image classifications and wireless sensor networks are the current measure control. Unlike the existing systems, this project intends to develop a more robust and durable system which can withstand the wet weather condition. It aims to monitor the water level and water velocity then alert the communities in the future implementation whenever there is risk of flood occurrence. In order to do this, the system needs to have the basic information such as water conditions, water level and water velocity

Project-based Learning for Software Engineering–An Implementation Framework

The ruling pedagogy for software engineering education still remains “chalk and talk” even though it has many drawbacks leading to its unproductiveness. In recent years, many researches were conducted to propose a systematic teaching and learning method to prepare students with good project management, verbal and written communication skills before facing the real working life. Particularly, teaching in this era of Internet of Things requires a good pedagogical method to ensure that the teaching and learning focus on both theory as well as experiential learning. Thus, in this paper, we propose a framework based on the project-based learning for software engineering subject that focuses on understanding common knowledge as well as the ability to develop real life product

Behavioral Intention Prediction in Driving Scenes: A Survey

In the driving scene, the road agents usually conduct frequent interactions and intention understanding of the surroundings. Ego-agent (each road agent itself) predicts what behavior will be engaged by other road users all the time and expects a shared and consistent understanding for safe movement. Behavioral Intention Prediction (BIP) simulates such a human consideration process and fulfills the early prediction of specific behaviors. Similar to other prediction tasks, such as trajectory prediction, data-driven deep learning methods have taken the primary pipeline in research. The rapid development of BIP inevitably leads to new issues and challenges. To catalyze future research, this work provides a comprehensive review of BIP from the available datasets, key factors and challenges, pedestrian-centric and vehicle-centric BIP approaches, and BIP-aware applications. Based on the investigation, data-driven deep learning approaches have become the primary pipelines. The behavioral intention types are still monotonous in most current datasets and methods (e.g., Crossing (C) and Not Crossing (NC) for pedestrians and Lane Changing (LC) for vehicles) in this field. In addition, for the safe-critical scenarios (e.g., near-crashing situations), current research is limited. Through this investigation, we identify open issues in behavioral intention prediction and suggest possible insights for future research.Comment: 254 reference

Cloud-based web service composition using action script

In this paper, we introduce the use of ActionScript 3.0 with web service composition for complex Flash Applications. ActionScript 3.0 is an object-oriented programming language which is used for arithmetic operation and presenting interactive output in Adobe Flash platform. Nevertheless, implementation of all the processes within the flash file itself consume too much of resources in the implementing device. In this paper, we have proposed to use web service composition in ActionScript 3.0 implementation to reduce the development time.Web service composition can provide a flow of system by invoking multiple web services.By using web service composition, we can produce a complex flash application without increasing the size of an independent flash file. Finally, by implementing web service composition in ActionScript 3.0, we can develop a complex web service application with smaller size and portable

Using brand knowledge to predict beer brand preference and loyalty for samples of new frequent users in Perth and Beijing

This study tests a model of Brand Knowledge and Brand Equity of brands of beer on new and frequent users in two populations that differ in their stage of the beer product life cycle and culture. Using Multiple Logistic Regression (MLR) and Binomial Logistic Regression (BLR), models based on the respondents\u27 Brand Knowledge are able to correctly identify Chinese respondents&rsquo; preferred brand of beer 56% of the time, while correctly identifying 77% of respondents in an Australian sample when three top brands are tested. The model could further identify 67% of those that stay or switch in both the Australian and the Chinese samples.<br /