The Adaptation of Online Project-Based Learning in Computer Engineering Education Settings

While many universities offered some online courses in computer engineering prior to the COVID-19 pandemic, few offered project-based courses in a fully virtual mode. This has changed with pandemic-related restrictions. When converting to an online-only mode, active-learning projects and lab components of computer engineering courses pose additional challenges because they require face-to-face interaction and specialized equipment. In this paper, we present how we adapted a year-long capstone course to be fully online at a public university in California, U.S., and report our findings based on students’ perceptions. Our major findings are: 1) students’ satisfaction rates of online courses improved over the one year of online-only learning, and 2) students tended to prefer synchronous lectures, but the difference in preference between synchronous vs. asynchronous teaching was not significant. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

ZOOM Sandwich: An Adaptable Model for Distance Learning

Distance learning is the education of students who may not often be physically present. Internet technology has made many possible ways of distance learning by open educational tools and services, such as e-learning and MOOCs. The distance learning technologies are divided into two types of delivery: synchronous learning and asynchronous learning. The two methods can be combined to create a hybrid learning model. In this work, we will discuss an adaptable hybrid teaching model we call it Zoom Sandwich. The model utilizes the benefits of both methods and integrates active learning techniques where students learn the concepts and topics online and then come to the classroom for application through interactive groups and class level discussions utilizing technology breakout rooms and chat platforms. We also consider the best practices to humanize online learning and increase students' and instructors' social presence. Covid-19 Pandemic changed the way institutions conduct business and provide services. To fight the spread of coronavirus, schools across the country have temporarily closed and transitioned to distance learning. This provided an opportunity for cybercriminals to hack and expose human vulnerabilities. We are proposing a set of security best practices and policies to countermeasures the cyberattacks. © 2020 IEEE.

Evaluation of the COVID-19 shock on STEM laboratory courses

Universities across the U.S. have moved to various virtual teaching models in response to the health threats caused by the COVID-19 pandemic. When converting to an online-only mode, STEM and related fields face additional challenges over the lab portion of courses because laboratory courses and active-learning projects frequently require specialized equipment and manual dexterity interactions. In this paper, we report the results of a study on students' perceptions about online learning during the initial phase of the pandemic at a public university in California, U.S. We focus on the overall reaction to the rapid conversion to online, the negative impressions created, 'structural' concerns that would be difficult to mitigate, concerns readily amenable to mitigation, and side effects such as impact on equity. Twenty-five recommendations for those factors deemed improvable are provided. © 2021 IEEE.

Predicting and Preventing Cyber Attacks during COVID-19 Time Using Data Analysis and Proposed Secure IoT layered Model

The global spread of the COVID-19 pandemic and its unprecedented impact not only on health and economy but almost on all aspects of our lives, including how we work, meet, communicate, collaborate, etc. Unfortunately, these changes and the transition to the virtual space in such a short time without proper planning created opportunities for bad actors in cyberspace. In the last few months, we have witnessed new treads and waves of cyber-Attacks targeting businesses, governments, health, and other critical services. Attackers try to take advantage of people's fear of the virus, vulnerabilities associated with data collection sensors and IoT devices, and eagerness to look for solutions or protections. In this study, we will survey the nature of cyberattacks related to the COVID-19 outbreak. Them, we will analyze related data to phishing attacks using Neural Networks. This analysis is covering different technical and socio-economical aspects. We will also evaluate states' countermeasures in response to such attacks. We propose a new IoT model. We define three layers;End User, Device or Sensors, and Cloud. We can combine the proposed model with the security and privacy policies to countermeasure the cybersecurity threats facing each layer. © 2020 IEEE.