LMS acceptance: The instructor role

Learning management systems (LMS) have become the norm in recent years in higher education to further engage students and lecturers. The e-learning tools within LMS provide knowledge sharing and community building opportunities that can support both critical thinking and higher order learning skills through conversation and collaboration. However, the mere existence of tools does not guarantee users’ adoption and acceptance. Several effective arrangements are required to engage users. This paper focuses on different aspects of lecturers’ attitude that impact user engagement with LMS tools reporting on findings from 74 interviews with students and lecturers from different disciplines within a major Australian university. Results indicate that lecturers’ teaching style and habits, active participation in online activities as well as designing appropriate tasks and assessment procedure are important determinants of lecturers’ attitude in engaging students with LMS tools.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.