Student smartphones: tools or barriers? - attitudes amongst students in higher education in Chile and the UK

Smartphones are now more affordable than ever before, making them ubiquitous amongst some groups, such as students in Higher Education. Their sensing, processing, and interconnection features offer many opportunities for learning and leisure. But do they help or hinder student success?Publisher PDFPeer reviewe

Understanding persuasive technologies to improve completion rates in MOOCs

Advances in computing technologies are revolutionising education. Specifically, advances in Human-Computer Interaction impact the media and methods of delivery, facilitating a conceptual shift from traditional face-to-face instruction towards a paradigm with delivery increasingly tailored to student needs. Massive Open Online Course(MOOC) providers have now the possibility to both predict and facilitate student success by applying learning analytics techniques on the large amount of data they hold about their learners. More than ever before, key information about successful student behaviour and context can be discovered and used in digital interventions on, for example, students at risk. This is a complex issue which is receiving increased attention in Higher Education and specifically amongst MOOCs providers. This position paper discusses the relevant challenges in the use of learning analytics in MOOCs in conjunction with persuasive technologies in order to improve completion rates.PostprintPeer reviewe

Understanding persuasive technologies to improve completion rates in MOOCs

Advances in computing technologies are revolutionising education. Specifically, advances in Human-Computer Interaction impact the media and methods of delivery, facilitating a conceptual shift from traditional face-to-face instruction towards a paradigm with delivery increasingly tailored to student needs. Massive Open Online Course(MOOC) providers have now the possibility to both predict and facilitate student success by applying learning analytics techniques on the large amount of data they hold about their learners. More than ever before, key information about successful student behaviour and context can be discovered and used in digital interventions on, for example, students at risk. This is a complex issue which is receiving increased attention in Higher Education and specifically amongst MOOCs providers. This position paper discusses the relevant challenges in the use of learning analytics in MOOCs in conjunction with persuasive technologies in order to improve completion rates

Adapting to class sizes : what feedback fits best?

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Student success on face-to-face instruction and MOOCs : what can learning analytics uncover?

There are fundamental differences between the face-to-faceinstruction model and that of Massive Open Online Courses.This paper hypothesises that despite these fundamental dif-ferences, the success factors in these learning contexts arecomparable. The factors contributing to student successmight be related to the same basic principles, even if mani-festing themselves differently in each context - especially asthe very definition of success is closely dependent on whatcan be measured. Learning analytics can help to uncover theindicators which have the potential for identifying studentsat-risk and for institutions to exercise a timely intervention.Publisher PDFPeer reviewe

Reprogramming embedded systems at run-time

The dynamic re-programming of embedded systems is a long-standing problem in the field. With the advent of wireless sensor networks and the 'Internet of Things' it has now become necessary to be able to reprogram at run-time due to the difficulty of gaining access to such systems once deployed. The issues of power consumption, flexibility, and operating system protections are examined for a range of approaches, and a critical comparison is given. A combination of approaches is recommended for the implementation of real-world systems and areas where further work is required are highlighted.Postprin

A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection

Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors’ sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection