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The conventional statistical evaluations are limited in providing good predictions of the university educational quality. This paper presents an approach with both conventional statistical analysis and neural network modelling/prediction of students' performance. Conventional statistical evaluations are used to identify the factors that likely affect the students' performance. The neural network is modelled with 11 input variables, two layers of hidden neurons, and one output layer. Levenberg-Marquardt algorithm is employed as the backpropagation training rule. The performance of neural network model is evaluated through the error performance, regression, error histogram, confusion matrix and area under the receiver operating characteristics curve. Overall, the neural network model has achieved a good prediction accuracy of 84.8%, along with limitations.

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Experimentation is a key component of any engineering education, and it can be either hardware or simulation (software) based experiments or mixed. While the teaching and learning are provided in an alternative manner (mostly online) for ensuring the continuity of education and the student learning experiences due to the COVID-19 pandemic, both hardware and software based laboratory assessments must also be conducted in an alternative manner. In this paper, we share and discuss a systematic approach as an alternative laboratory assessment (ALA) for Multimedia Engineering modules in the Transnational Education (TNE) programme between Queen Mary University of London (QMUL) and Beijing University of Post and Telecommunications (BUPT). We have conducted this study to verify how the ALA using the systematic approach for two multimedia modules have achieved the intended learning outcomes. Based on the quantitative analysis, we have suggested how we can improve the laboratory assessment in the alternative approach.

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