Wearable Technology: Opportunities and Challenges for Teaching and Learning in Higher Education in Developing Countries

The higher education landscape in developing countries is faced with many challenges, one of which is high faculty to student ratio. An obvious implication of this is compromise on the quality of classroom engagement. The distractions caused by the not conducive learning space and instructors’ inability to elucidate correct feedbacks from students usually lead to poor learning outcomes. Feedback mechanisms that are unobtrusive and efficient in processing large data in real-time are needful to measure quality learning experience in such large classroom settings. With the latest impact of penetration and adoption of internet and mobile technologies in most developing counties, wearable technology is a feasible solution to manage and monitor classroom involvement; as real time student feedback can be integrated in the design and delivery of instruction in and out of the classroom. In this paper, we present state of the art of wearable technology and explored the opportunities of wearable technology in the higher education. Specifically, we presented scenarios in which wearable technology can be employed to understand and analyze physiological signals and emotional responses from learners in real-time; the end result of which would increase the quality of classroom engagement, inspire new pedagogy, drive new trends in peer-to-peer collaborations, and increase the learning outcomes. Moreover, we identified some challenges that may hinder this development such as: inconclusive user studies of wearable technology in developing countries and inadequate infrastructure. Finally, we make appropriate recommendations on how these challenges can be surmounte

The Internet of Things Will Thrive by 2025

This report is the latest research report in a sustained effort throughout 2014 by the Pew Research Center Internet Project to mark the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-LeeThis current report is an analysis of opinions about the likely expansion of the Internet of Things (sometimes called the Cloud of Things), a catchall phrase for the array of devices, appliances, vehicles, wearable material, and sensor-laden parts of the environment that connect to each other and feed data back and forth. It covers the over 1,600 responses that were offered specifically about our question about where the Internet of Things would stand by the year 2025. The report is the next in a series of eight Pew Research and Elon University analyses to be issued this year in which experts will share their expectations about the future of such things as privacy, cybersecurity, and net neutrality. It includes some of the best and most provocative of the predictions survey respondents made when specifically asked to share their views about the evolution of embedded and wearable computing and the Internet of Things

Attention-Block Deep Learning Based Features Fusion in Wearable Social Sensor for Mental Wellbeing Evaluations

With the progressive increase of stress, anxiety and depression in working and living environment, mental health assessment becomes an important social interaction research topic. Generally, clinicians evaluate the psychology of participants through an effective psychological evaluation and questionnaires. However, these methods suffer from subjectivity and memory effects. In this paper, a new multi- sensing wearable device has been developed and applied in self-designed psychological tests. Speech under different emotions as well as behavior signals are captured and analyzed. The mental state of the participants is objectively assessed through a group of psychological questionnaires. In particular, we propose an attention-based block deep learning architecture within the device for multi-feature classification and fusion analysis. This enables the deep learning architecture to autonomously train to obtain the optimum fusion weights of different domain features. The proposed attention-based architecture has led to improving performance compared with direct connecting fusion method. Experimental studies have been carried out in order to verify the effectiveness and robustness of the proposed architecture. The obtained results have shown that the wearable multi-sensing devices equipped with the attention-based block deep learning architecture can effectively classify mental state with better performance

Robust modeling of human contact networks across different scales and proximity-sensing techniques

The problem of mapping human close-range proximity networks has been tackled using a variety of technical approaches. Wearable electronic devices, in particular, have proven to be particularly successful in a variety of settings relevant for research in social science, complex networks and infectious diseases dynamics. Each device and technology used for proximity sensing (e.g., RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with specific biases on the close-range relations it records. Hence it is important to assess which statistical features of the empirical proximity networks are robust across different measurement techniques, and which modeling frameworks generalize well across empirical data. Here we compare time-resolved proximity networks recorded in different experimental settings and show that some important statistical features are robust across all settings considered. The observed universality calls for a simplified modeling approach. We show that one such simple model is indeed able to reproduce the main statistical distributions characterizing the empirical temporal networks

Opportunistic Uses of the Traditional School Day Through Student Examination of Fitbit Activity Tracker Data

In large part due to the highly prescribed nature of the typical school day for children, efforts to design new interactions with technology have often focused on less-structured after-school clubs and other out-of-school environments. We argue that while the school day imposes serious restrictions, school routines can and should be opportunistically leveraged by designers and by youth. Specifically, wearable activity tracking devices open some new avenues for opportunistic collection of and reflection on data from the school day. To demonstrate this, we present two cases from an elementary statistics classroom unit we designed that intentionally integrated wearable activity trackers and childcreated data visualizations. The first case involves a group of students comparing favored recess activities to determine which was more physically demanding. The second case is of a student who took advantage of her knowledge of teachers’ school day routines to test the reliability of a Fitbit activity tracker against a commercial mobile app

Mobile Learning Revolution: Implications for Language Pedagogy

Mobile technologies including cell phones and tablets are a pervasive feature of everyday life with potential impact on teaching and learning. “Mobile pedagogy” may seem like a contradiction in terms, since mobile learning often takes place physically beyond the teacher's reach, outside the walls of the classroom. While pedagogy implies careful planning, mobility exposes learners to the unexpected. A thoughtful pedagogical response to this reality involves new conceptualizations of what is to be learned and new activity designs. This approach recognizes that learners may act in more self-determined ways beyond the classroom walls, where online interactions and mobile encounters influence their target language communication needs and interests. The chapter sets out a range of opportunities for out-of-class mobile language learning that give learners an active role and promote communication. It then considers the implications of these developments for language content and curricula and the evolving roles and competences of teachers