Wearable sensors have traditionally been used to measure and monitor vital human signs for well-being and healthcare applications. However, there is a growing interest in using and deploying these technologies to facilitate teaching and learning, particularly in a higher education environment. The aim of this paper is therefore to systematically review the range of wearable devices that have been used for enhancing the teaching and delivery of engineering curricula in higher education. Moreover, we compare the advantages and disadvantages of these devices according to the location in which they are worn on the human body. According to our survey, wearable devices for enhanced learning have mainly been worn on the head (e.g., eyeglasses), wrist (e.g., watches) and chest (e.g., electrocardiogram patch). In fact, among those locations, head-worn devices enable better student engagement with the learning materials, improved student attention as well as higher spatial and visual awareness. We identify the research questions and discuss the research inclusion and exclusion criteria to present the challenges faced by researchers in implementing learning technologies for enhanced engineering education. Furthermore, we provide recommendations on using wearable devices to improve the teaching and learning of engineering courses in higher education.
Neurological diseases (NDs) such as epilepsy, dementia, Alzheimer's and Parkinson's disease currently affect almost two thirds of Europe's population. Furthermore, enormous financial commitments are required to deal with these diseases. Therefore, there is growing concern that countries with transitional economies may struggle to handle this financial burden, which warrants the urgent development of new technologies for early disease identification and treatment. Consequently, the aim of our article is to survey the range of postgraduate programmes that strive to nurture neuroengineering graduates who will excel in designing and developing implantable and wearable technologies for ND applications. Based on the basic building blocks of these technologies, we have identified four key areas that programmes need to cover, which include Neuroscience, Integrated Circuits, Communications and Signal Processing as well as Electronic Devices. According to our systematic review, a total of fifteen institutes satisfied our search criteria and provided the necessary neuroengineering training. The majority of these programmes are located in Europe and North America, which means that cross border and interdisciplinary efforts are required to develop educational programmes in countries most vulnerable to these diseases. We also provide recommendations for how these programmes can be delivered using non-traditional teaching approaches to ensure that graduates develop the necessary soft skills required by the constantly shifting job market. INDEX TERMS Neuroengineering, Wearables, Implantables, Engineering Education.
As a natural brain process, mind-wandering happens spontaneously and is usually linked with outcomes that affect students' performance in educational settings. In addition to capturing pupil and gaze coordinates in high resolution and with accuracy, wearable eye-trackers were used in this pilot study to infer student attention during an online lecture. While gaze is a good indicator for measuring visual attention, the need for more substantial proof of internal and covert attention is essential. In this work, we used advanced eye-tracking glasses to measure the mind-wandering level in students. The data collection was carried out with 15 students in two different settings for self-caught (8 students) and probe-caught (7 students) conditions. The data was later analysed to confirm the relationship between variables. Correlating the mindwandering with engagement level (self-caught r = .37, probecaught r = -.59 ) shows a significant relationship in both conditions (P-value < .001) . Our results show that using eye-trackers for mind-wandering measurements moves us toward the greater goal of building a customised teaching environment by detecting the actual data from students and teachers in the learning environment.
The Covid-19 outbreak has caused disruptions in the education sector, making remote education the dominant mode for lecture delivery. The lack of visual feedback and physical interaction makes it very hard for teachers to measure the engagement level of students during lectures. This paper proposes a time-bounded window operation to extract statistical features from raw gaze data, captured in a remote teaching experiment and link them with the student's attention level. Feature selection or dimensionality reduction is performed to reduce the convergence time and overcome the problem of over-fitting. Recursive feature elimination (RFE) and SelectFromModel (SFM) are used with different machine learning (ML) algorithms, and a subset of optimal feature space is obtained based on the feature scores. The model trained using the optimal feature subset showed significant improvement in accuracy and computational complexity. For instance, a support vector classifier (SVC) led 2.39% improvement in accuracy along with approximately 66% reduction in convergence time.
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