This research explores the application of large language models (LLMs) to generate synthetic datasets for Product Desirability Toolkit (PDT) testing, a key component in evaluating user sentiment and product experience. Utilizing gpt-4o-mini, a cost-effective alternative to larger commercial LLMs, three methods, Word+Review, Review+Word, and Supply-Word, were each used to synthesize 1000 product reviews. The generated datasets were assessed for sentiment alignment, textual diversity, and data generation cost. Results demonstrated high sentiment alignment across all methods, with Pearson correlations ranging from 0.93 to 0.97. Supply-Word exhibited the highest diversity and coverage of PDT terms, although with increased generation costs. Despite minor biases toward positive sentiments, in situations with limited test data, LLM-generated synthetic data offers significant advantages, including scalability, cost savings, and flexibility in dataset production.

This innovative practice full paper describes the iterative use of service learning to develop, review, and improve computing-based artifacts for small rural organizations, over an extended period. It is well- known that computing students benefit from service-learning experiences as do the community partners. It is also well-known that computing artifacts rarely function well long-term without versioning and updates. Service-learning projects are often one-time engagements, completed by single teams of students over the course of a semester or year long course. This limits the benefit for the community partners, such as small rural organizations, that do not have the expertise or resources to review and update a project on their own. Over the course of several years, teams of undergraduate students in a computing capstone social media development course created tailored social media plans for numerous small rural organizations. The projects were required to meet the client's specific needs, with identified audiences, measurable goals, and a minimum of three recommended social media strategies and tactics to reach the identified goals. This paper builds on previously reported initial results for 60 projects conducted over several years. Nine clients were selected to participate in the iterative follow-up process, where new student teams conducted client interviews, reviewed the initial plans, and analyzed metrics from the social media strategies and tactics already in place to provide updated, improved artifacts. Using ABET computing learning objectives as a basis, clients reviewed the student teams and the artifacts created. Students also reflected on their experiences. This research provides a longitudinal study of the impact of the interventions in increasing implementation and sustained use rates of computing artifacts developed through service learning, along with lessons learned. Both students and clients reported high satisfaction levels, and clients were particularly satisfied with the iterative improvement process. This research demonstrates an innovative practice for creating and maintaining computing artifacts through iterative service learning, while addressing the resource constraints of small rural organizations.

This study explores the use of LLMs for providing quantitative zero-shot sentiment analysis of implicit software desirability, addressing a critical challenge in product evaluation where traditional review scores, though convenient, fail to capture the richness of qualitative user feedback. Innovations include establishing a method that 1) works with qualitative user experience data without the need for explicit review scores, 2) focuses on implicit user satisfaction, and 3) provides scaled numerical sentiment analysis, offering a more nuanced understanding of user sentiment, instead of simply classifying sentiment as positive, neutral, or negative. Data is collected using the Microsoft Product Desirability Toolkit (PDT), a well-known qualitative user experience analysis tool. For initial exploration, the PDT metric was given to users of two software systems. PDT data was fed through several LLMs (Claude Sonnet 3 and 3.5, GPT4, and GPT4o) and through a leading transfer learning technique, Twitter-Roberta-Base-Sentiment, and Vader, a leading sentiment analysis tool. Each system was asked to evaluate the data in two ways, by looking at the sentiment expressed in the PDT word/explanation pairs; and by looking at the sentiment expressed by the users in their grouped selection of five words and explanations, as a whole. Numerical analysis is used to provide insights into the magnitude of sentiment to drive high quality decisions regarding product desirability. Each LLM is asked to provide its confidence (low, medium, high) in its sentiment score, along with an explanation of its score. All LLMs tested were able to statistically detect user sentiment from the users' grouped data, whereas TRBS and Vader were not. The confidence and explanation of confidence provided by the LLMs assisted in understanding user sentiment. This study adds deeper understanding of evaluating user experiences, toward the goal of creating a universal tool that quantifies implicit sentiment.

This Work in Progress outlines a framework of a new intelligent e-learning platform for quantum cybersecurity education. The platform uses intelligent notebooks to support multiple modes of learning through a rich set of media that includes text, videos, interactive widgets, simulations and can even incorporate serious games. The new platform, named Quark, is designed for undergraduate and graduate students, professionals and self-learners wanting to learn the basics of emerging areas in cybersecurity. The Quark platform consists of a learning bank data repository based on FAIR (Findable, Accessible, Inter-operable, and Reusable) principles storing learning objects designed using object oriented (OO) methods and incorporated using contemporary pedagogical methods to support a multiple, varied learning experiences for each topic. The overarching vision of this work is the development of a novel e-learning platform that synthesizes engaging learning experiences so that learners can achieve targeted proficiency in quantum cybersecurity education. In contrast to e-books and other elearning repositories, Quark offers a dynamic way to create subject content satisfying a given set of student-learning objectives for achieving the desired student learning outcomes with high levels of engagement and proficiency. Domain experts drive the content creation enriched with metadata that allows automatic processing of content through algorithms in Quark to synthesize a family of Python-based Jupyter Notebooks and lesson plans. Students select their learning objectives and outcomes, time to completion and student learning choices. The system then dynamically builds the lesson plan based on the dependencies in the metadata defined by the domain experts. This work in progress describes the Quark framework using sample skeleton content. Quark is the first intelligent notebook platform of its kind designed for quantum cybersecurity. In the future, Quark will be able to customize content continuously based on student interactions and informed learning choices and can be set-up for use with any topic.

This research paper introduces a unique system called ZORQ that is a combination of a game development framework and a gamification framework (GDGF). The ZORQ GDGF acts as a catalyst to help motivate students by increasing student engagement and success within undergraduate Computer Science (CS) education, regardless of student experience and background. The dynamic gamification elements utilized within the GDGF make it an attractive learning method for students. After collaborative game space customization, ZORQ gameplay sees each student tasked with designing a ship movement philosophy and then implementing their own code to autonomously control the ship in an interstellar game space filled with supplies, obstacles, and enemy ships. The particulars of engagements between ships can vary greatly by semester, along with the resources/objects present in the game, depending on the collaborative customization and the independent ship strategies implemented.A preliminary ZORQ trial was conducted over five years in an undergraduate Data Structures and Algorithms (DSA) course. The ZORQ trial is designed to fulfill the following objectives: 1) implement DSA concepts discussed within the course, 2) identify appropriate problem-solving approaches, 3) apply one or more solutions, 4) build depth with a coding language, 5) bridge the gap between limited concept assignments and large, multi-developer software systems by allowing students to build code within a larger architecture, 6) introduce students to version control, 7) illustrate the use of prior mathematics coursework in practical applications, and 8) introduce unit testing in software systems. In exit surveys, students expressed overwhelming satisfaction with this approach. More than 84% of the students surveyed found the system useful in their educational experience and saw benefit to inspecting a completed software project. 82% of the students found that ZORQ increased software development comprehension. 80% enjoyed using their own personal creativity in designing a ship controller, 76% found ZORQ helped them learn how to implement and use DSAs. 71% found the system engaging and found the system interaction to be clear and understandable. Observations of student performance in later courses suggest better student maturity and comprehension in preparation for proposing and implementing their own independent projects.

This research full paper presents two main outcomes: 1) a novel classification system for gamification implementations including proposed genres, and 2) a comprehensive study and categorization of existing DSA gamification applications and a discussion of genres absent existing applications. Gamification presents a great potential to improve user engagement, motivation, and learning in nearly all fields of study including computer science (CS) education. However, it lacks formalized study and comprehensive analysis in CS education, and thus what makes for effective gamification is still a key question. Rather than initially trying to examine and catalog existing gamification applications and studies across the breadth of CS education as a whole, this paper instead focuses on Data Structures and Algorithms (DSA) courses. In general, DSA courses tend to be difficult due to the inherent complexity and abstraction exhibited by the fundamental concepts. As such, gamification presents a potential opportunity to convey these complex ideas in meaningful and unique ways. To carry out this work, a literature review of current DSA gamification applications is presented, the applications are categorized, and the pros and cons analyzed. Based on this analysis, a classification system is created and two new abstract genres are identified: dynamic gamification and collaborative gamification development. Potential uses, benefits and detriments are suggested for these newly identified genres. With this analysis and classification of gamification along with the identification of new abstract genres, the practice of gamification in DSA coursework can be made more efficient and effective. Upon a more thorough understanding of DSA gamification, pedagogical considerations can be made to better aid teachers and instructors in the integration of gamification into existing curriculum. The paper also touches on the applicability of the classification system to CS gamification examples outside of DSA.