Despite the widespread availability and increasing use of cyberlearning environments, there remains a need for more research about their usefulness in undergraduate education, particularly in STEM education. The process of evaluating the usefulness of a cyberlearning environment is an essential measure of its success and is useful in assisting the design process and ensuring user satisfaction. Unfortunately, there are relatively few empirical studies that provide a comprehensive test of the usefulness of cyberlearning in education. Additionally, there is a lack of standards upon whose usefulness evaluators agree.In this research, we present multiple user studies that can be used to assess the usefulness of a cyberlearning environment used in Computer Science and Software Engineering courses through testing its usability and measuring its utility using user interface and user experience evaluations. Based on these assessments, we propose an evaluation framework to evaluate cyberlearning environments. To help illustrate the framework utility and usability evaluations, we explain them through an example SEP-CyLE (Software Engineering and Programming Cyberlearning Environment). The evaluation techniques used are cognitive walkthroughs with a think-aloud protocol and a heuristic evaluation survey. We further use a network-based analysis to find the statistically significant correlated responses in the heuristic evaluation survey with regard to the students' perceptions of using SEP-CyLE.Our goal is to improve cyberlearning practice and to emphasize the need for evaluating cyberlearning environments with respect to its designated tasks and its users using UI/UX evaluations. Our experiments demonstrated participants were able to utilize SEP-CyLE efficiently to accomplish the tasks we posed to them and to enhance their software development concepts, specifically, software testing. We discovered areas of improvement in the visibility and navigation of SEP-CyLE's current design. We provide our recommendations for improving SEP-CyLE and provide guidance and possible directions for future research on designing cyberlearning environments for computer education.
A highly efficient lightweight forward static slicing approach is presented and evaluated. The approach does not compute the program/system dependence graph but instead dependence and control information is computed as needed while computing the slice on a variable. The result is a list of line numbers, dependent variables, aliases, and function calls that are part of the slice for all variables (both local and global) for the entire system. The method is implemented as a tool, called srcSlice, on top of srcML, an XML representation of source code. The approach is highly scalable and can generate the slices for all variables of the Linux kernel in approximately 20 min on a typical desktop. Benchmark results are compared with the CodeSurfer slicing tool from GrammaTech Inc., and the approach compares well with regard to accuracy of slices.
Abstract-A highly efficient lightweight forward static slicing method is introduced. The method is implemented as a tool on top of srcML, an XML representation of source code. The approach does not compute the program dependence graph but instead dependency information is computed as needed while computing the slice on a variable. The result is a list of line numbers, dependent variables, aliases, and function calls that are part of the slice for a given variable. The tool produces the slice in this manner for all variables in a given system. The approach is highly scalable and can generate the slices for all variables of the Linux kernel in less than 13 minutes. Benchmark results are compared with the CodeSurfer slicing tool and the approach compares well with regards to accuracy of slices.
The design of existing machine-learning-based DoS detection systems in software-defined networking (SDN) suffers from two major problems. First, the proper time window for conducting network traffic analysis is unknown and has proven challenging to determine. Second, it is unable to detect unknown types of DoS saturation attacks. An unknown saturation attack is an attack that is not represented in the training data. In this paper, we evaluate three supervised classifiers for detecting a family of DDoS flooding attacks (UDP, TCP-SYN, IP-Spoofing, TCP-SARFU, and ICMP) and their combinations using different time windows. This work represents an extension of the runner-up best-paper award entitled ‘Detecting Saturation Attacks in SDN via Machine Learning’ published in the 2019 4th International Conference on Computing, Communications and Security (ICCCS). The results in this paper show that the trained supervised models fail in detecting unknown saturation attacks, and their overall detection performance decreases when the time window of the network traffic increases. Moreover, we investigate the performance of four semi-supervised classifiers in detecting unknown flooding attacks. The results indicate that semi-supervised classifiers outperform the supervised classifiers in the detection of unknown flooding attacks. Furthermore, to further increase the possibility of detecting the known and unknown flooding attacks, we propose an enhanced hybrid approach that combines two supervised and semi-supervised classifiers. The results demonstrate that the hybrid approach has outperformed individually supervised or semi-supervised classifiers in detecting the known and unknown flooding DoS attacks in SDN.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.