Research studies on educational data mining are on the increase due to the benefits obtained from the knowledge acquired from machine learning processes which help to improve decision making processes in higher institutions of learning. In this study, predictive analysis was carried out to determine the extent to which the fifth year and final Cumulative Grade Point Average (CGPA) of engineering students in a Nigerian University can be determined using the program of study, the year of entry and the Grade Point Average (GPA) for the first three years of study as inputs into a Konstanz Information Miner (KNIME) based data mining model. Six data mining algorithms were considered, and a maximum accuracy of 89.15% was achieved. The result was verified using both linear and pure quadratic regression models, and R
2
values of 0.955 and 0.957 were recorded for both cases. This creates an opportunity for identifying students that may graduate with poor results or may not graduate at all, so that early intervention may be deployed.
In this data era where data is the new oil, internet data traffic is growing significantly each year [1, 2]. With the advent of state of the art technologies on data transmission and processing in the last decade, the internet has witnessed an increase in the intensity and the volume of internet activities globally [3]. User-generated dataset contains useful statistics and information that can be harnessed for learning but this may be challenged by privacy issues [4, 5]. Internet activities generate data traffic of various kinds; during both data download and upload. Monitoring and analysis of internet traffic is becoming more challenging daily due to sheer increase in the volume of the internet data traffic and the large capacity of connection trunks [2]. Internet traffic measurement and management is vital to the operations of Internet Service Providers for predicting future demands [6], and traffic monitoring can be achieved using flow statistics tools. Internet traffic measurement is typically deployed
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.