University front-facing webpages, such as homepages and course entry details, provide essential information to prospective students and external stakeholders, necessitating accessibility, usability, and security. This study evaluates the web accessibility, usability, and security of 242 front-facing webpages from the top 10 higher education institutions in the UK, Africa, and Israel, based on Webometrics rankings. Automated tools were employed for assessment. Findings indicate that African universities require more work on web accessibility than those in the UK and Israel. All three regions violated accessibility success criteria, with most issues occurring at level A, the minimum standard for accessibility conformance. Over 80% of webpages scored at or above average in usability. The security analysis revealed numerous vulnerabilities in African and Israeli institutions' webpages, including outdated architecture, missing certificates, and lack of encryption. This study suggests that strict web accessibility legislation does not necessarily guarantee improved webpage accessibility.
In the era of digital transformation, the need for more efficient self-service technologies has increased, particularly after the COVID-19 pandemic, which highlighted the importance of reducing physical interactions. Although there is research on commercial self-service technologies, there is a shortage in studies focusing on self-service portals designed exclusively for internal use within an academic organizational environment. This study aims to address this gap by examining the usability of a self-service portal in a university in Sweden. The study adopts a mixed-methods approach, incorporating data collection techniques such as cognitive walkthrough, usability testing and semi-structured interviews. In addition, a system usability scale and interaction log file data were used to understand user behavior and satisfaction. Findings reveal important factors influencing the user experience, which are further materialized into design guidelines aiming to enhance the usability of the self-service portals.
The proposed SquiRide Rank algorithm is designed by integrating the Squirrel Search Algorithm (SSA) with the Rider Optimization Algorithm (ROA), respectively. The concept of fictional computing and the foraging behavior realize the re-ranking process more effectively in the web environment. However, the features extracted from the web pages makes the process more effective and achieve global optimal solution through the fitness measure. The proposed SquiRide Rank algorithm effectively captures and analyzes the ranking scores of different search engines in order to generate the re-ranked score result. However, the proposed SquiRide Rank algorithm provides satisfactory results using the metrics, like precision, recall, and F-measure, which acquired with the values of 0.964, 0.996, and 0.980, respectively.
Web page ranking is the one of the key components in search engine. Ranking aims to offer ranked list of web documents that are relevant to the user query. Even though, various re-ranking algorithms are developed, providing prompt responses to the user query result a major challenge in the dynamic web environment. Therefore, an efficient and effective ranking algorithm named Squirrel Ride Rank (SquiRide Rank) algorithm is developed to re-rank the web documents in the search engine. The proposed SquiRide Rank algorithm is designed by integrating the Squirrel Search Algorithm (SSA) with the Rider Optimization Algorithm (ROA), respectively. The concept of fictional computing and the foraging behavior realize the re-ranking process more effectively in the web environment. The features extracted from the web pages makes the process more effective and achieve global optimal solution through the fitness measure. The proposed SquiRide Rank algorithm effectively captures and analyzes the ranking scores of different search engines in order to generate the re-ranked score result. However, the proposed SquiRide Rank algorithm provides satisfactory results using the metrics, like precision, recall, and F-measure, which acquired with the values of 0.964, 0.996, and 0.980, respectively.
