Wearable technology, or the so-called wearable computing, is increasingly becoming popular in learning and teaching activities. Higher education is particularly benefitting from the advanced applications of this emerging technology, owing to the mature utilisation of the features and facilities presented by wearable devices. Essential factors like students' interactivity and engagement in learning activities have found to be easily achieved when effectively utilising this evolving technology by academic organisations. This paper surveys the information and experiments published recently on wearable technology in higher education and its acceptance factors by consumers according to the Technology Acceptance Model (TAM). Alongside the academic advantages of this technology, the paper demonstrates the limitations and negative aftereffects accompanying its applications. The outcomes of this research revealed that there are a considerable number of restrictions limiting the wider application of this technology and its acceptance in higher education, which should be analysed with appropriate solutions proposed.

Anomalies detection is concerned with the problem of finding non-conforming patterns in datasets. Il-agure (2016) described a novel approach to measure the amount of information shared between any random anomaly variables. The CRISP data mining methodology was updated to be applicable for link mining study. The proposed mutual information approach to provide a semantic investigation of the anomalies and the updated methodology can be used in other link mining studies. The purpose of this paper is to evaluate how mutual information interprets semantic anomalies, using density-based cluster technique, via trial 2, which is different than the clustering technique used in trial 1 (hierarchical based cluster). A cluster method allows for many options regarding the algorithm for combining groups, with each choice resulting in a different grouping structure. Therefore, cluster analysis can be an appropriate statistical tool for discovering underlying structures in various kinds of datasets.