Towards A Massive Open Online Course for Cybersecurity in Smart Grids – A Roadmap Strategy

The major trends and transformations in energy systems have brought many challenges, and cybersecurity and operational security are among the most important issues to consider. First, due to the criticality of the energy sector. Second, due to the lack of smart girds’ cybersecurity professionals. Previous research has highlighted skill gaps and shortage in cybersecurity training and education in this sector. Accordingly, we proceeded by crafting a roadmap strategy to foster cybersecurity education in smart grids. This paper outlines the methodology of teaching cybersecurity in smart grids to a large group of students in selected European universities via implementing a Massive Open Online Course. Unlike other solutions, this one focuses on hands-on practical skills without trading-off theoretical knowledge. Thus, flipped learning methodology and gamification practices were used to maximize retention rate. Also, a remote lab that includes a real-time simulator was established for training. Here, the process, outcome, and obstacles to overcome in future deployments, are presented.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Big Data Privacy Scenarios

This paper is the first in a series on privacy in Big Data. As an outgrowth of a series of workshops on the topic, the Big Data Privacy Working Group undertook a study of a series of use scenarios to highlight the challenges to privacy that arise in the Big Data arena. This is a report on those scenarios. The deeper question explored by this exercise is what is distinctive about privacy in the context of Big Data. In addition, we discuss an initial list of issues for privacy that derive specifically from the nature of Big Data. These derive from observations across the real world scenarios and use cases explored in this project as well as wider reading and discussions:* Scale: The sheer size of the datasets leads to challenges in creating, managing and applying privacy policies.* Diversity: The increased likelihood of more and more diverse participants in Big Data collection, management, and use, leads to differing agendas and objectives. By nature, this is likely to lead to contradictory agendas and objectives.* Integration: With increased data management technologies (e.g. cloud services, data lakes, and so forth), integration across datasets, with new and often surprising opportunities for cross-product inferences, will also come new information about individuals and their behaviors.* Impact on secondary participants: Because many pieces of information are reflective of not only the targeted subject, but secondary, often unattended, participants, the inferences and resulting information will increasingly be reflective of other people, not originally considered as the subject of privacy concerns and approaches.* Need for emergent policies for emergent information: As inferences over merged data sets occur, emergent information or understanding will occur. Although each unique data set may have existing privacy policies and enforcement mechanisms, it is not clear that it is possible to develop the requisite and appropriate emerged privacy policies and appropriate enforcement of them automatically

Agile methods for agile universities

We explore a term, Agile, that is being used in various workplace settings, including the management of universities. The term may have several related but slightly different meanings. Agile is often used in the context of facilitating more creative problem-solving and advocating for the adoption, design, tailoring and continual updating of more innovative organizational processes. We consider a particular set of meanings of the term from the world of software development. Agile methods were created to address certain problems with the software development process. Many of those problems have interesting analogues in the context of universities, so a reflection on agile methods may be a useful heuristic for generating ideas for enabling universities to be more creative

Widening Access to Applied Machine Learning with TinyML

Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia (Harvard University) and industry (Google) produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for learners from a global variety of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies via hands-on programming and deployment of TinyML applications in both the cloud and their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.Comment: Understanding the underpinnings of the TinyML edX course series: https://www.edx.org/professional-certificate/harvardx-tiny-machine-learnin

Online Instruction in Higher Education: Promising, Research-based, and Evidence-based Practices

The purpose of this study was to review the research literature on online learning to identify effective instructional practices. We narrowed our scope to empirical studies published 2013-2019 given that studies earlier than 2013 had become quickly outdated because of changes in online pedagogies and technologies. We also limited our search to studies with undergraduate and graduate students, application of an empirical methodological design, and descriptions of methodology, data analysis, and results with sufficient detail to assure verifiability of data collection and analysis. Our analysis of the patterns and trends in the corpus of 104 research studies led to identification of five themes: course design factors, student support, faculty pedagogy, student engagement, and student success factors. Most of the strategies with promising effectiveness in the online environment are the same ones that are considered to be effective in face-to-face classrooms including the use of multiple pedagogies and learning resources to address different student learning needs, high instructor presence, quality of faculty-student interaction, academic support outside of class, and promotion of classroom cohesion and trust. Unique to the online environment are user-friendly technology tools, orientation to online instruction, opportunities for synchronous class sessions, and incorporation of social media. Given the few studies utilizing methodological designs from which claims of causality can be made or meta-analyses could be conducted, we identified only faculty feedback as an evidence-based practice and no specific intervention that we could identify as research-based in online instruction

Online Instruction in Higher Education: Promising, Research-based, and Evidence-based Practices

The purpose of this study was to review the research literature on online learning to identify effective instructional practices. We narrowed our scope to empirical studies published 2013-2019 given that studies earlier than 2013 had become quickly outdated because of changes in online pedagogies and technologies. We also limited our search to studies with undergraduate and graduate students, application of an empirical methodological design, and descriptions of methodology, data analysis, and results with sufficient detail to assure verifiability of data collection and analysis. Our analysis of the patterns and trends in the corpus of 104 research studies led to identification of five themes: course design factors, student support, faculty pedagogy, student engagement, and student success factors. Most of the strategies with promising effectiveness in the online environment are the same ones that are considered to be effective in face-to-face classrooms including the use of multiple pedagogies and learning resources to address different student learning needs, high instructor presence, quality of faculty-student interaction, academic support outside of class, and promotion of classroom cohesion and trust. Unique to the online environment are user-friendly technology tools, orientation to online instruction, opportunities for synchronous class sessions, and incorporation of social media. Given the few studies utilizing methodological designs from which claims of causality can be made or meta-analyses could be conducted, we identified only faculty feedback as an evidence-based practice and no specific intervention that we could identify as research-based in online instruction

The Process of Digital Transformation in Education During the COVID-19 Pandemic

Purpose: This document seeks to delve into the digital transformation of education during the COVID-19 pandemic, aiming to provide a comprehensive understanding of this evolving phenomenon's purpose and significance.   Design/Methodology/Approach: The research approach undertaken is characterized by a non-experimental, documentary, exploratory, and descriptive study methodology, which involves an extensive examination of existing literature and data to gain insights into the digital transformation in education during the pandemic.   Findings: The study's key findings revolve around the consensus in existing literature regarding the swift acceleration of the transformation of education from traditional face-to-face classes to virtual learning environments. It also highlights the implications of this transformation, particularly in reshaping teaching models and advocating for a hybrid approach encompassing both face-to-face and virtual learning.   Research, Practical & Social implications: The implications of this research extend to informing educational institutions about the need for digital adaptation, guiding policymakers in supporting adaptable learning models, and empowering educators with a deeper understanding of the changing educational landscape. Moreover, it considers the broader societal impact, including equity and access issues in education.   Originality/Value: This research is unique in its contribution to understanding the profound impact of the COVID-19 pandemic on education. It emphasizes the significance of adaptability and hybrid learning models while providing a foundation for future educational research and policy development