A Stochastic Modelling Approach to Student Performance Prediction on an Internet-Mediated Environment

Student performance prediction presents institutions and learners with results that assist them to gauge their academic abilities within their context of learning. Performance prediction has been done using different approaches over the years. In this case, stochastic modelling is used and it takes into consideration the use of random variables in the prediction process. The random variables are generated from different scenarios in order to generate a possible output. As a result, the generated output is used to indicate the likelihood of very rare occurrence scenarios which may or may not take place at a future date. With the vast availability of educational data that is available within the learning sector, this data forms the basis of input data that is required for the prediction of student performance within internet-worked environments. This paper develops the prediction model using Stochastic Differential Equations (SDEs). This then gives way to the analysis of data collected from varied respondents within universities leading to the generation of a student performance trajectory

Algorithmic prediction of internet technology utilization in learning

The conference aimed at supporting and stimulating active productive research set to strengthen the technical foundations of engineers and scientists in the continent, through developing strong technical foundations and skills, leading to new small to medium enterprises within the African sub-continent. It also seeked to encourage the emergence of functionally skilled technocrats within the continent.Internet technology has been revolutionary over the years especially in the educational sector. However, the utility of internet technology in the learning process of a student in a higher learning institution has not been determined over the years. This has been due to the evolution that has taken place in education. This paper aims at helping in the development of an algorithmic model that will be used for the prediction of internet technology utilization in learning. Specifically, the research will focus on modelling the Cobb- Douglas production theorem to predict the learning output of a given student considering the utility of the internet technology, the infrastructural investment made by the institution of higher learning and the effort of the student. The results of this ongoing research will eventually be of great importance in helping institutions of higher learning determine their returns after investing in internet technology. The students will also be informed on how to use the internet technology in a better way in order to get the best out of the resource.Strathmore University; Institute of Electrical and Electronics Engineers (IEEE

Post-harvest evaluation of selected hybrids to maize weevil Sitophilus zeamais resistance

Sitophilus zeamais has been identified as one of the most destructive pests of maize stored in tropical regions. While most maize hybrids are being developed, it is necessary to evaluate their resistance to this pest. This study determined the resistance of selected maize hybrids to infestation by S. zeamais. Twenty two hybrids with varying resistance to weevil infestation and two checks DUMA 41-suceptible and MTP0701-resistant were used in a randomized complete block design experiment. Assessment was done at 10, 60 and 120 days of maize storage. Data was collected on percent weevil damage, grain weight loss and number of live and dead weevils. Heritability and correlation of factors were also estimated. Analysis of variance showed significant differences (P ≤ 0.05) on weight loss. The selection of the resistant genotypes was based on percent weight loss after 60 days. KH631Q and PH4 were selected as the most resistant and moderately resistant hybrids, respectively. The resistant check MTPO701 was also found to maintain resistance to weevil attack. There was a strong positive correlation between weight loss, number of live weevils and percent damage. Moderate heritability estimates of hybrids at 60 days of storage indicated the possibility of their parents to transfer the desirable traits to subsequent generations. Therefore, parents of the resistant hybrids could be utilized in breeding programs for maize weevil resistance and be deployed to farmers for use, respectively