There is a growing awareness among researchers about the apparent variations in the academic performance of students in tertiary institutions. Although, many studies have employed traditional statistical methods in identifying the factors responsible for the disparity, the statistical tool for setting a yardstick is yet to be established. Machine learning techniques have been employed as a paradigm in the modeling of students' academic performance in higher learning. However, they could be the springboard for improving prediction of students' academic performance. This work therefore aimed at designing a framework of intelligent recommender system, based on background factors, which can predict students' first year academic performance and recommend necessary actions for improvement.

Botnets have carved a niche in contemporary networking and cybersecurity due to the impact of their operations. The botnet threat continues to evolve and adapt to countermeasures as the security landscape continues to shift. As research efforts attempt to seek a deeper and robust understanding of the nature of the threat for more effective solutions, it becomes necessary to again traverse the threat landscape, and consolidate what is known so far about botnets, that future research directions could be more easily visualised. This research uses the general exploratory approach of the qualitative methodology to survey the current botnet threat landscape: Covering the typology of botnets and their owners, the structure and lifecycle of botnets, botnet attack modes and control architectures, existing countermeasure solutions and limitations, as well as the prospects of a botnet threat. The product is a consolidation of knowledge pertaining the nature of the botnet threat; which also informs future research directions into aspects of the threat landscape where work still needs to be done.

Persuasive techniques are recently being explored by computer science researchers as an effective strategy towards creating applications that are aimed at positive attitudinal changes especially in the health domain but finding effective evaluation approaches for these technologies remain an herculean task for all stakeholders involved and in order to overcome this limitation, the Persuasive System Design (PSD) model was designed but researchers claim that the model is too theoretical in nature and some of its design principles are too subjective as they cannot be measured quantitatively. Hence, the focus of this paper is to critically review the PSD model and popular models currently being used to evaluate the usability of information systems as usability has been identified as an important requirement currently used to evaluate the overall success of persuasive technologies. To achieve the stated objectives, the systematic review method of research was done to objectively analyze the PSD model, its applicability as an evaluation tool was tested on a popular mobile health application installed on the Samsung Galaxy Tablet using android Operation system. Exhaustive evaluation of the application was performed by 5 software usability researchers using the method of cognitive walkthrough. From the analysis, it was realized that the PSD model is a great tool at designing persuasive technologies but as an evaluation tool, it is too theoretical in nature, its evaluation strategies are too subjective in nature and the 28 principles described in it overlap with one another. As a result, the PSD model was extended with an integrated usability model and the fuzzy Analytic Hierarchical Technique was proposed theoretically to evaluate usability constructs so as to make evaluation of persuasive technologies more quantitative in nature and easier for researchers to analyze their design early enough to minimize developmental efforts and other resources.

Academic dishonesty has been a growing concern in e-