Learning in an age of digital networks

The final years of the twentieth century and early years of the twenty first century have been marked by the rapid rise of digital and networked technologies. Some have even called it a paradigm shift and suggested that it will lead to a dramatic change in the way young people learn (Tapscott and Williams, 2010). As with all commentary on new technologies we should beware of being carried away with the excitement of the new. There is a recurrent innovation cycle beginning with over excitement followed by disappointment and once the reaction has set in against the new it is followed by a move away to yet another new technology, often before a proper assessment and evaluation of the previous cycle can take place. Equally we must be careful not to ignore the profound changes that are taking place and how they may affect universities and learning in society more generally. A recent description by a UK based think tank Demos characterized the kind of university that is emerging from the engagement with new digital and networked technologies as the 'edgeless university' (Bradwell, 2009). The term edgeless is borrowed from work on the city that suggests edgeless cities have the function of cities without being organized in their classic form. In the same way the Demos pamphlet suggests that the university retains an identifiable function but the functions of the university are no longer confined to a single institution nor are they confined to higher education institutions more broadly. Over a decade ago Brown and Duguid (2000) identified the core functions of universities as the capacity to grant degrees, to accredit students and to provide the warrant that guaranteed the credentials obtained by the students from the university. They also suggested that the introduction of what were then new technologies would lead to an increased focus on these core functions. The core role remains in the edgeless university but the boundaries to these may alter. This article tries to provide a way of thinking about new technologies that manages to balance these two conflicting needs. It identifies some current ways of thinking about the changes taking place in universities that are related to digital and networked technologies and to assess their impact. It then goes on to suggest the kinds of choices we may have to make in relation to new technologies at a variety of levels, the personal, the institutional and in terms of society in general. The edgeless university is associated with broad technological change but whether such change is inevitable is still an issue that needs to be discussed

Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams

The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In addition, a number of methods have been developed to detect concept drifts in these streams. Consider a scenario where we have a number of classifiers with diverse learning styles and different drift detectors. Intuitively, the current 'best' (classifier, detector) pair is application dependent and may change as a result of the stream evolution. Our research builds on this observation. We introduce the \mbox{Tornado} framework that implements a reservoir of diverse classifiers, together with a variety of drift detection algorithms. In our framework, all (classifier, detector) pairs proceed, in parallel, to construct models against the evolving data streams. At any point in time, we select the pair which currently yields the best performance. We further incorporate two novel stacking-based drift detection methods, namely the \mbox{FHDDMS} and \mbox{FHDDMS}_{add} approaches. The experimental evaluation confirms that the current 'best' (classifier, detector) pair is not only heavily dependent on the characteristics of the stream, but also that this selection evolves as the stream flows. Further, our \mbox{FHDDMS} variants detect concept drifts accurately in a timely fashion while outperforming the state-of-the-art.Comment: 42 pages, and 14 figure

Machine Learning-based Approaches for Advanced Monitoring of Smart Glasses

openWith today’s growing demand on productivity, product quality and effectiveness, the importance of Machine Learning-based functionalities and services has dramatically increased. Such paradigm shift can be mainly associated with the increasing availability of Internet of Things (IoT) sensors and devices, the increase of data collected in the IoT scenario and the increasing popularity and availability of machine learning approaches. One of the most appealing applications of ML-based solutions is for sure Predictive Maintenance, which aims at improving maintenance management by exploiting the estimation of the health status of a piece of equipment. One of the main formalizations of the PdM problem is the prediction of the Remaining Useful Life (RUL), that is defined as the time/process iterations remaining for a device component to perform its task before it loses functionality. This work investigates a possible application of predictive maintenance techniques for the monitoring of the battery of Smart Glasses. The work starts with the description of the considered devices, the modalities of data collection and the Exploratory Data Analysis for better understanding the task. The first experimental part consists in the application of an unsupervised anomaly detection technique, useful to initially deal with the partial and unlabeled data. The last part of the work contains the results of the application of both classical machine learning and deep learning approaches for the estimation of the RUL of the devices battery. A section for the interpretation of the machine-learning models is included for both the anomaly detection and RUL estimation approaches

Implementing Digital Media as a Pedagogical Tool in University Physical Activity Courses

Technological advancements have influenced the way we teach, learn, and communicate in education. Higher educational institutions must continually adapt to emerging technologies by implementing a variety of technologies such as photographs, audio, video, and an endless array of online platforms. Specifically, university physical activity programs, which have existed in higher educational institutions for over a century, are encouraged to incorporate digital media as a means to effectively and efficiently communicate a variety of content areas (Cardinal, 2017; Casey, Goodyear, & Armour, 2017; Tiernan, 2015). The purpose of this case study was to explore the implementation of digital media as a pedagogical tool within physical activity courses (PACs). Eight participants shared their lived experiences as instructors of record for PACs throughout the fall 2019 semester. Results showed the need for digital resources both for the instructor as well as students, the value of digital media as a social connection tool, and the need to use Canvas, video, and audio as pedagogical tools. Professional development opportunities are necessary for PAC instructors to effectively and efficiently implement digital media as a pedagogical tool

Phishing Detection using Base Classifier and Ensemble Technique

Phishing attacks continue to pose a significant threat in today's digital landscape, with both individuals and organizations falling victim to these attacks on a regular basis. One of the primary methods used to carry out phishing attacks is through the use of phishing websites, which are designed to look like legitimate sites in order to trick users into giving away their personal information, including sensitive data such as credit card details and passwords. This research paper proposes a model that utilizes several benchmark classifiers, including LR, Bagging, RF, K-NN, DT, SVM, and Adaboost, to accurately identify and classify phishing websites based on accuracy, precision, recall, f1-score, and confusion matrix. Additionally, a meta-learner and stacking model were combined to identify phishing websites in existing systems. The proposed ensemble learning approach using stack-based meta-learners proved to be highly effective in identifying both legitimate and phishing websites, achieving an accuracy rate of up to 97.19%, with precision, recall, and f1 scores of 97%, 98%, and 98%, respectively. Thus, it is recommended that ensemble learning, particularly with stacking and its meta-learner variations, be implemented to detect and prevent phishing attacks and other digital cyber threats

Using an Audience Response System Smartphone App to Improve Resident Education in the Pediatric Intensive Care Unit.

In the Pediatric Intensive Care Unit (PICU), most teaching occurs during bedside rounds, but technology now provides new opportunities to enhance education. Specifically, smartphone apps allow rapid communication between instructor and student. We hypothesized that using an audience response system (ARS) app can identify resident knowledge gaps, guide teaching, and enhance education in the PICU. Third-year pediatric residents rotating through the PICU participated in ARS-based education or received traditional teaching. Before rounds, experimental subjects completed an ARS quiz using the Socrative app. Concomitantly, the fellow leading rounds predicted quiz performance. Then, discussion points based on the incorrect answers were used to guide instruction. Scores on the pre-rotation test were similar between groups. On the post-rotation examination, ARS participants did not increase their scores more than controls. The fellow's prediction of performance was poor. Residents felt that the method enhanced their education whereas fellows reported that it improved their teaching efficiency. Although there was no measurable increase in knowledge using the ARS app, it may still be a useful tool to rapidly assess learners and help instructors provide learner-centered education