Automated Algorithmic Machine-to-Machine Negotiation for Lane Changes Performed by Driverless Vehicles at the Edge of the Internet of Things

This dissertation creates and examines algorithmic models for automated machine-to-machine negotiation in localized multi-agent systems at the edge of the Internet of Things. It provides an implementation of two such models for unsupervised resource allocation for the application domain of autonomous vehicle traffic as it pertains to lane changing and speed setting selection. The first part concerns negotiation via abstract argumentation. A general model for the arbitration of conflict based on abstract argumentation is outlined and then applied to a scenario where autonomous vehicles on a multi-lane highway use expert systems in consultation with private objectives to form arguments and use them to compete for lane positions. The conflict resolution component of the resulting argumentation framework is augmented with social voting to achieve a community supported conflict-free outcome. The presented model heralds a step toward independent negotiation through automated argumentation in distributed multi-agent systems. Many other cyber-physical environments embody stages for opposing positions that may benefit from this type of tool for collaboration. The second part deals with game-theoretic negotiation through mechanism design. It outlines a mechanism providing resource allocation for a fee and applies it to autonomous vehicle traffic. Vehicular agents apply for speed and lane assignments with sealed bids containing their private feasible action valuations determined within the context of their governing objective. A truth-inducing mechanism implementing an incentive-compatible strategyproof social choice functions achieves a socially optimal outcome. The model can be adapted to many application fields through the definition of a domain-appropriate operation to be used by the allocation function of the mechanism. Both presented prototypes conduct operations at the edge of the Internet of Things. They can be applied to agent networks in just about any domain where the sharing of resources is required. The social voting argumentation approach is a minimal but powerful tool facilitating the democratic process when a community makes decisions on the sharing or rationing of common-pool assets. The mechanism design model can create social welfare maximizing allocations for multiple or multidimensional resources

Factors Affecting the Adoption of Peer Instruction in Computing Courses

Peer Instruction (PI) as defined by Mazur, and variations on this pedagogic technique, have been in use in computing courses for about a decade. Despite dozens of educational research publications documenting positive learning effects, improved retention, student acceptance, and effectiveness for large classes; PI does not appear to be widely adopted for computing courses. This paper reports on a three-way investigation into this apparent contradiction. First, the authors reflect on their own adoption, practice, experience, and abandonment of the use of PI in computing courses. Second, we surveyed the literature regarding the use of PI in computing courses and present a summary of the research findings, variations, and extensions to PI used in computing courses. Third, a survey of computing instructors was conducted to gauge the attitude toward PI in computing courses. To add context, this report considers publications documenting usage of PI in STEM courses, and the adoption of other pedagogic techniques in computing. Particular effort was made to identify the reasons computing instructors don’t adopt PI. This report also includes advice to instructors considering adopting PI in computing courses

Novice programmers and the problem description effect.

It is often debated whether a problem presented in a straightforward minimalist fashion is better, or worse, for learning than the same problem presented with a real-life or concrete context. The presentation, contextualization, or problem description has been well studied over several decades in disciplines such as mathematics education and psychology; however, little has been published in the field of computing education. In psychology it has been found that not only the presence of context, but the type of context can have dramatic results on problem success. In mathematics education it has been demonstrated that there are non-mathematical factors in problem presentation that can affect success in solving the problem and learning. The contextual background of a problem can also impact cognitive load, which should be considered when evaluating the effects of context. Further, it has been found that regarding cognitive load, computer science has unique characteristics compared to other disciplines, with the consequence that results from other disciplines may not apply to computer science, thus requiring investigation within computer science. This paper presents a multi-national, multi-institutional study of the effects of problem contextualization on novice programmer success in a typical CS1 exercise

The Impact of COVID-19 on the CS Student Learning Experience

Students have experienced incredible shifts in their learning environments, brought about by the response of universities to the ever-changing public health mandates driven by waves and stages of the coronavirus pandemic (COVID-19). Initially, these shifts in learning (mode of course delivery, course availability, etc.) were considered emergency responses. However, as the pandemic pressed on, students have had to repeatedly adapt to the continuously evolving educational landscape. This working group builds upon foundations and structure created by a 2021 ITiCSE Working Group exploring the effects of COVID-19 on teaching and learning from a faculty perspective. That working group identified the incorporation of some pandemic-induced changes into future teaching practices. This working group examines the existing literature and insights gained from responses to a multi-national survey to explore the new student experience emerging from the continuously evolving teaching practices catalyzed by the global pandemic. Traditionally, computing is a subject full of experiential learning opportunities, rich with in-person labs and exercises. We investigate how the changes within the COVID-affected academic landscape have altered that student experience. The current group of computing students will have had experiences under both typical (i.e. pre-pandemic) and COVID-affected teaching practices. It is, therefore, timely that we understand how each has impacted how they perceive their learning environment and educational experience. In turn, identifying those practices that have most benefited the student learning experience will help computing faculty improve their educational methods going forward

COVID-19, students and the new educational landscape.

Students have experienced incredible shifts in the in their learning environments, brought about by the response of universities to the ever-changing public health mandates driven by waves and stages of the coronavirus pandemic (COVID-19). Initially, these shifts in learning (mode of course delivery, course availability, etc.) were considered emergency responses. However, as the pandemic presses on, students have had to repeatedly adapt to the continuously evolving educational landscape as this global health crisis forced an "unprecedented global shift within higher education in the ways that we communicate with and educate students". This working group builds upon foundations and structure created by a 2021 ITICSE Working Group exploring the effects of COVID-19 on teaching and learning from a faculty perspective. That Working Group identified the incorporation of some pandemic-induced changes into future teaching practices. In this Working Group, we explore existing literature regarding the student experience in response to the evolving teaching practices catalyzed by COVID-19). Traditionally, computing is a subject full of experiential learning opportunities, rich with in-person labs and exercises. We explore how the changes within the COVID-affected academic landscape have altered that student experience. The current group of computing students will have had experiences under both typical (i.e. pre-pandemic) and COVID-affected teaching practices. It is, therefore, timely that we understand how each has impacted how they perceive their learning environment and educational experience. In turn, identifying those practices that have most benefited the student learning experience will help computing faculty improve their practices going forward