ChatGPT is an emerging tool that can be employed in many activities including in learning/teaching in universities. Like many other tools, it has its benefits and its drawbacks. If used properly, it can improve learning, and if used irresponsibly, it can have a negative impact on learning. The aim of this research is to study how ChatGPT can be used in academia to improve teaching/learning activities. In this paper, we study students’ opinions about how the tool can be used positively in learning activities. A survey is conducted among 430 students of an MSc degree in computer science at the University of Hertfordshire, UK, and their opinions about the tool are studied. The survey tries to capture different aspects in which the tool can be employed in academia and the ways in which it can harm or help students in learning activities. The findings suggest that many students are familiar with the tool but do not regularly use it for academic purposes. Moreover, students are skeptical of its positive impacts on learning and think that universities should provide more vivid guidelines and better education on how and where the tool can be used for learning activities. The students’ feedback responses are analyzed and discussed and the authors’ opinions regarding the subject are presented. This study shows that ChatGPT can be helpful in learning/teaching activities, but better guidelines should be provided for the students in using the tool.

Sentiment Analysis is a popular classification problem where the degree of emotion is analysed based on text. Convolution Neural Network (CNN) are part of various sentiment classification models, but their use has been limited to one-dimensional (1D) convolution or shallow two-dimensional (2D) convolution. The primary focus of this research is to use deep 2D-CNN architectures, inspired by the popular computer vision model ResNet, to replace a shallow 2D-CNN in an existing BLSTM (Bidirectional Long Short Term Memory)-2DCNN model. This research investigates a new method for sentiment analysis which is an amalgam of the practices popular in Natural Language Processing (NLP) as well as Computer Vision, striving to extract opinions from a text by transforming text into images. The text images are formed by transforming the word embeddings matrix into a greyscale image with the help of BLSTM cell. Intensive experiments have been conducted on the Sentiment140 dataset with our novel BLSTM-ResNet model, which contains residual blocks with skip connections. Several BLSTM-ResNet variants were tested to investigate the impact of network depth and dataset size on sentiment detection. Moreover, two sets of residual blocks are designed to form our shallow and deep BLSTM-ResNet models. Our best shallow BLSTM-ResNet models have achieved 4.06% and 3.43% increases in accuracy for dataset sizes 80,000 and 200,000 respectively, compared with the baseline BLSTM-2DCNN model. In addition, an overall improvement is observed on accuracy with every additional residual block in our shallow BLSTM-ResNet model until accuracy saturates, and the same trend has been seen on the impact of dataset size on the performance. Our deep BLSTM-ResNet models show the same positive impact of network depth impact and dataset size on sentiment analysis. Further investigation on the shallow and deep BLSTM-ResNet models shows that deep BLSTM-ResNet outperforms shallow BLSTM-ResNet, in general.

Learning algorithmic skills is fundamental for computer science students, as it develops critical thinking and problem‐solving abilities. These skills enable students to analyze real‐world problems and design efficient, practical solutions. Designing algorithms requires abstract thinking, problem solving, and solid mathematical backgrounds making algorithm design challenging. More importantly, due to the lack of conceptual understanding, students usually find it difficult to relate different algorithms to solve a similar problem. Also, it is challenging to modify an algorithm designed for one problem to solve another. Although many pedagogies have been proposed for teaching algorithms, many students still find the subject difficult. The Decoding the Disciplines Paradigm (DDP) has been successfully employed to facilitate meaningful understanding in many disciplines. However, little attention has been given to employing DDP for teaching algorithms in large cohorts with hundreds of students from diverse backgrounds. This study evaluates the DDP from a student perspective in teaching algorithm design. To teach algorithms using the DDP first, the bottlenecks in learning algorithms are identified, and the thought processes are dissected. Then, the instructor models the thought processes to students, who are required to work through the bottleneck. For meaningful understanding, the instructor provides opportunities to help students practice through the modeled steps. This study evaluates the advantages and disadvantages of the DDP and traditional teaching in teaching algorithm design from a student perspective via a questionnaire in a large cohort of international students. Our results show that majority of students preferred the DDP method for learning algorithms in all aspects of teaching and learning.

The purpose of redundancy in computer networks is to increase the robustness and resilience of the network. Deploying multiple paths to network devices or redundant devices could accomplish this purpose. It would also increase the complexity and chances of having a broadcast storm within the network due to potential loops from multiple paths. The Spanning-Tree Protocol was developed to uphold these desired network features and solve the loop problem. Layer 3 switches have facilitated the deployment of layer 3 links between specific switches which was only possible with layer 2 links. This paper explores alter-native layer 3 (Open Shortest Path First and Enhanced Interior Gateway Protocol) protocols that could efficiently replace the spanning-tree protocol in Broadcast Multi-Access Networks. An analysis of the performance of these protocols was made in com-parison to spanning-tree in their ability to uphold redundancy, resilience and robustness. Experimental tests were implemented for User Datagram Protocol and Transmission Control Protocol transmissions. The results showed that both layer 3 protocols can efficiently replace spanning-tree in Broadcast Multi-Access Networks.