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This scoping review aims to explore the various swarm technologies and how they have been used in the object classification domain with the desire to motivate the design of a generic swarm intelligence ontology based on the components of various swarm technologies. We used the PRISMA-ScR as a guide to our scoping review protocol. We conducted a search across thirteen databases and a random search as well on the internet for articles. We performed screening of all the articles by title to remove duplicates, we further on did a screening by the year of publication to ensure that all articles to be considered were published between 2012 and 2022 and we then did abstract or text synthesis. Our search query retrieved 3224 potential articles from the thirteen databases and 10 articles from a random search on the internet making a total of 3234 articles identified. Deduplication and screening were done on the identified articles and 287 articles which satisfied our inclusion criteria remained. We grouped the articles into three categories namely year of publication, swarm technology and swarm application. The year of publication showed a linear trend line which is an indication of growth in the swarm intelligence domain. Of the six categories of aims we identified we voluntarily chose to ignore articles where the aim was not specified. We noticed that 64.9% of articles were aimed at either modifying or improving.  The swarm technology category indicated that 58.54% of the included articles were based on the Particle Swarm Optimization either independently or as part of a hybrid algorithm. 83.97% of the articles used classification as their swarm application. Interesting to note was the appearance of feature selection and optimization in this category. This scoping review gave an overview of how swarm technologies have been used in the object classification domain. Further research can be done by bringing and using existing algorithms in the development of generic swarm intelligence inspired ontologies.

The increased use of the internet raises concerns about the security of data and other resources shared in cyberspace. Although efforts to improve data security are visible, the need to continuously explore other avenues for preventing and mitigating cyberattacks is apparent. Swarm intelligence models have, in the past, been considered in cybersecurity though there was no formal representation of the swarm intelligence knowledge domain that defines how these models fit into the cybersecurity body of knowledge. This article reviews the aspects of three swarm intelligence models that may inspire the design of the desired swarm intelligence ontology. The algorithms are particle swarm optimization, ant colony optimization, and the artificial bee colony model. In each case, we investigate the main driving features of the model, the causal aspects, and the effects of those causal aspects on the resolution of the cybersecurity problem. We also investigate how these features can be recommended as the building blocks of the desired swarm intelligence ontology. Investigations indicate that the artificial bee colony model has three outstanding aspects considered for the design of the swarm intelligence ontology and that is the quality, popularity, and communication. Foraging through pheromone deposits is an outstanding component of ant colony optimization that aids in locating threats sources more quickly by using the shortest route or tracks with high pheromone deposits. The particle swarm optimization model, on the other hand, adds alignment, cohesion, and collision avoidance aspects to the ontology to augment the ant colony and artificial bee colony algorithms.  In our view, although intrusion detection is a complex problem in cybersecurity, the power of integrated swarm intelligence models is more than the sum of the individual capabilities of each swarm intelligence model individually. The article, therefore, proposes a swarm intelligence ontology that will potentially bring us closer to resolving the general cybersecurity problem.

This study examined feedback in South African university first-year accounting classes. Teachers' complex approaches are based on their subject matter and pedagogical competence and their views about learning. Among the many approaches is feedback teaching. Feedback is providing performance-related information during or after class sessions or tasks. Feedback improves teachers and students and restructures knowledge. Accounting education found cognitively determined feedback strategies that promote information retention. Cognitivism underpins this paper. Cognitivism and accounting education share intellectualism, critical thinking, deep learning and information retention, long-term memory, and complicated problem-solving. Accounting feedback improves fundamentals. Cognitivism dominates accounting education. This work used exploratory qualitative research. This study selected three first-year accounting professors from different campuses. Participants' feedback knowledge, practices, and challenges were collected through semi-structured interviews. Participant reactions validated the findings. Thematically evaluated data. The study examined feedback comprehension, implementation techniques, and challenges. Accounting professors use feedback differently, which affects teaching and learning. Participants also listed many challenges to their practice.

Forming university exam schedules in Southern Africa has always been difficult. It is a scheduling problem with strict rules (hard constraints) and flexible ones (soft constraints). Students must take a final exam for most courses they are enrolled in each semester, held in a supervised setting. Therefore, finding a suitable time and location for each student is a common challenge. This paper proposes a solution to this scheduling problem based on the behaviour of bees, using a model called BSet, which uses simulated robotic devices to find and schedule courses. The results show that this approach is efficient and effective in creating exam schedules.