Cyber Space Odyssey (CSO) is a novel serious game supporting computer networking education by engaging students in a race to successfully perform various cybersecurity tasks in order to collect clues and solve a puzzle in virtual near-Earth 3D space. Each team interacts with the game server through a dedicated client presenting a multimodal interface, using a game controller for navigation and various desktop computer networking tools of the trade for cybersecurity tasks on the game's physical network. Specifically, teams connect to wireless access points, use packet monitors to intercept network traffic, decrypt and reverse engineer that traffic, craft well-formed and meaningful responses, and transmit those responses. Successful completion of these physical network actions to solve a sequence of increasingly complex problems is necessary to progress through the virtual, story-driven adventure. Use of the networking tools reinforces networking theory and offers hands-on practical training requisite for today's cyberoperators. This paper presents the learning outcomes targeted by a classroom intervention based on CSO, the design and implementation of the game, a pedagogical overview of the overall intervention, and four years of quantitative and qualitative data assessing its effectiveness.

E-learning courses often suffer from high dropout rates and low student satisfaction. One way to address this issue is to use Personalized Learning Paths (PLPs), which are sequences of learning materials that meet the individual needs of students. However, creating PLPs is difficult and often involves combining knowledge graphs, student profiles, and learning materials. Researchers typically assume that the problem of creating PLPs belong to the NP-Hard class of computational problems. However, previous research in this field has neither defined the different variations of the PLP problem nor formally established their computational complexity. Without clear definitions of the PLP variations, researchers risk making invalid comparisons and conclusions when they use different metaheuristics for different PLP problems. In order to unify this conversation, this paper formally proves the NP-completeness of two common PLP variations and their generalizations and uses them to categorize recent research in the PLP field. It then presents an instance of the PLP problem using real-world data and shows how this instance can be cast into two different NP-complete variations. This paper then presents three AI strategies, solving one of the PLP variations with back-tracking and branch and bound heuristics and also converting the PLP variation instance to XCSP ³ , an intermediate constraint satisfaction language to be resolved with a general Constraint Optimization solver. This paper solves the other PLP variation instance using a greedy search heuristic. The paper finishes by comparing the results of the two different PLP variations.

This paper describes a project for an introductory computer architecture class. The project involves designing and implementing a "miniscule" processor that can run programs within certain boundaries. In this paper, we describe the goals of the project, the process, and the challenges faced by students in successfully completing the project.

Many colleges and universities throughout the world offer introductory computer science courses with significant student enrollment. There are certainly plenty of challenges for the administrators of those courses; ensuring equitable grading across different instructors and offering times for the course, accomplishing the massive material preparation and other logistical tasks required for the course, grading the large number of assessments associated with the large enrollment, and managing the numerous instructors in the course are a few examples. This paper discusses the processes we have implemented to address these and other management challenges at the U.S. Air Force Academy.