The lecture is broken: a manifesto for change

Let me start by saying that I love lecturing. I take pride in preparing high-quality slides and standing in front of the class imparting my knowledge to a captive audience who seem to appreciate it, on the whole. But if I’m honest, I’m just not convinced the lecture is fit for purpose. There is growing evidence that the traditional didactic lecture is past its use-by date. Students are now sophisticated IT-literate learners who demand a rich, multimedia experience from their studies. They have grown up on a diet of rich media (YouTube, iTunesU, podcasts, blogs, Facebook, Twitter, Wikipedia, Google, etc.) and are fully conversant in finding information quickly to satisfy their needs. Didactic lectures are often delivered in rooms that serve multiple purposes and fail to address the unique needs and desires of aural, visual and kinaesthetic learners with a single, blunt instrument (often a PowerPoint presentation). Attendance patterns in lectures exhibit some large variations and if the main tool in our arsenal is the lecture, there may be over 40 per cent of our students who may regularly missing (or avoiding) this mechanism. This paper highlights some of these problematic areas and propose some radical ideas for a future teaching environment in which the lecture takes a back seat in favour of a ‘didactic mash-up’ of engagement activities and exploitation of the full power of the Internet as a learning tool. This includes looking at how our IT facilities are used, how staff-student ratios can be better applied, how our future learning spaces should be constructed and how academic staff can guide students through the mass of online learning that is available 24 hours a day via the Internet

A Platform Independent Game Technology Model for Model Driven Serious Games Development

Game‑based learning (GBL) combines pedagogy and interactive entertainment to create a virtual learning environment in an effort to motivate and regain the interest of a new generation of ‘digital native’ learners. However, this approach is impeded by the limited availability of suitable ‘serious’ games and high‑level design tools to enable domain experts to develop or customise serious games. Model Driven Engineering (MDE) goes some way to provide the techniques required to generate a wide variety of interoperable serious games software solutions whilst encapsulating and shielding the technicality of the full software development process. In this paper, we present our Game Technology Model (GTM) which models serious game software in a manner independent of any hardware or operating platform specifications for use in our Model Driven Serious Game Development Framework

Passive Indoor Positioning System (PIPS) Using Near Field Communication (NFC) Technology

Travel can be an enjoyable experience but it can also be stressful when one is unable to get to the destination in timely manner. Satellite navigation systems (satnav) such as the ubiquitous Global Positioning System (GPS) provide an aid to locating unfamiliar places without hassle. However, the effectiveness of satnav stops at the doorstep of the building due to its requirement for line of sight with orbiting satellites. Within a large complex building, navigation typically relies on building signage, information from kiosks and getting assistance from information desks. The advancement of mobile devices and wireless technology offer an interesting proposition for the development of indoor positioning systems. In this paper, we propose a passive indoor positioning system to provide navigational aid and discuss findings from our pilot experiment using NFC technology

Multiple Density Maps Information Fusion for Effectively Assessing Intensity Pattern of Lifelogging Physical Activity

Physical activity (PA) measurement is a crucial task in healthcare technology aimed at monitoring the progression and treatment of many chronic diseases. Traditional lifelogging PA measures require relatively high cost and can only be conducted in controlled or semi-controlled environments, though they exhibit remarkable precision of PA monitoring outcomes. Recent advancement of commercial wearable devices and smartphones for recording one’s lifelogging PA has popularized data capture in uncontrolled environments. However, due to diverse life patterns and heterogeneity of connected devices as well as the PA recognition accuracy, lifelogging PA data measured by wearable devices and mobile phones contains much uncertainty thereby limiting their adoption for healthcare studies. To improve the feasibility of PA tracking datasets from commercial wearable/mobile devices, this paper proposes a lifelogging PA intensity pattern decision making approach for lifelong PA measures. The method is to firstly remove some irregular uncertainties (IU) via an Ellipse fitting model, and then construct a series of monthly based hour-day density map images for representing PA intensity patterns with regular uncertainties (RU) on each month. Finally it explores Dempster-Shafer theory of evidence fusing information from these density map images for generating a decision making model of a final personal lifelogging PA intensity pattern. The approach has significantly reduced the uncertainties and incompleteness of datasets from third party devices. Two case studies on a mobile personalized healthcare platform MHA [1] connecting the mobile app Moves are carried out. The results indicate that the proposed approach can improve effectiveness of PA tracking devices or apps for various types of people who frequently use them as a healthcare indicator

Using Serious Games to Create Awareness on Visual Impairments

Visual impairments define a wide spectrum of disabilities that vary in severity, from the need to wear glasses, to permanent loss of vision or blindness. This paper discusses the process undertaken in creating two simulators, one which emulates partially-sighted visual impairment and another focused on full -blindness. In order to create the simulators, extensive research was conducted surrounding the effects of partially-sightedness and blindness, highlighting existing software and games that promote awareness for visual impairments. This paper underlines the necessity of raising awareness for visual impairments and the effectiveness of applying serious games for this very goal. After developing the simulators, experiments were conducted to evaluate the effectiveness of it. Findings from the experiments were analysed and documented

A Hybrid Hierarchical Framework for Gym Physical Activity Recognition and Measurement Using Wearable Sensors

Due to the many beneficial effects on physical and mental health and strong association with many fitness and rehabilitation programs, physical activity (PA) recognition has been considered as a key paradigm for internet of things (IoT) healthcare. Traditional PA recognition techniques focus on repeated aerobic exercises or stationary PA. As a crucial indicator in human health, it covers a range of bodily movement from aerobics to anaerobic that may all bring health benefits. However, existing PA recognition approaches are mostly designed for specific scenarios and often lack extensibility for application in other areas, thereby limiting their usefulness. In this paper, we attempt to detect more gym physical activities (GPAs) in addition to traditional PA using acceleration, A two layer recognition framework is proposed that can classify aerobic, sedentary and free weight activities, count repetitions and sets for the free weight exercises, and in the meantime, measure quantities of repetitions and sets for free weight activities. In the first layer, a one-class SVM (OC-SVM) is applied to coarsely classify free weight and non-free weight activities. In the second layer, a neural network (NN) is utilized for aerobic and sedentary activities recognition; a hidden Markov model (HMM) is to provide a further classification in free weight activities. The performance of the framework was tested on 10 healthy subjects (age: 30 ± 5; BMI: 25 ± 5.5 kg/ and compared with some typical classifiers. The results indicate the proposed framework has better performance in recognizing and measuring GPAs than other approaches. The potential of this framework can be potentially extended in supporting more types of PA recognition in complex applications

Uncertainty Investigation for Personalised Lifelogging Physical Activity Intensity Pattern Assessment with Mobile Devices

Lifelogging physical activity (PA) assessment is crucial to healthcare technologies and studies for the purpose of treatments and interventions of chronic diseases. Traditional lifelogging PA monitoring is conducted in non-naturalistic settings by means of wearable devices or mobile phones such as fixed placements, controlled durations or dedicated sensors. Although they achieved satisfactory outcomes for healthcare studies, the practicability become the key issues. Recent advance of mobile devices make lifelogging PA tracking for healthy or unhealthy individuals possible. However, owning to diverse physical characteristics, immaturity of PA recognition techniques, different settings from manufactories and a majority of uncertainties in real life, the results of PA measurement is leading to be inapplicable for PA pattern detection in a long range, especially hardly exploited in the wellbeing monitoring or behaviour changes. This paper investigates and compares uncertainties of existing mobile devices for individual’s PA tracking. Irregular uncertainties (IU) are firstly removed by exploiting Ellipse fitting model, and then monthly density maps that contain regular uncertainties (RU) are constructed based on metabolic equivalents (METs) of different activity types. Five months of four subjects PA intensity changes using the mobile app tracker Moves [1] and Google Fit app on wearable device Samsung wear S2 are carried out from a mobile personalised healthcare platform MHA [2]. The result indicates that uncertainty of PA intensity monitored by mobile phone is 90% lower than wearable device, where the datasets tend to be further explored by healthcare/fitness studies. Whilst PA activity monitoring by mobile phone is still a challenging issue by far due to much more uncertainties than wearable devices