David C. Shepherd | Searcharxiv

arXiv2026

Adoption-Ready Project-Based Learning for Computing Education: The FORAP Framework and a Multi-Scale Project Portfolio

This innovative practice full paper presents FORAP (Framework for Organizing Reusable and Adaptable PjBL Projects) and a portfolio of 14 adoption-ready project-based learning (PjBL) project packages built with the framework. PjBL in computing education offers strong educational benefits, yet its adoption remains limited by high instructor workload and recurring student technical challenges. FORAP addresses these barriers by organizing each package around a project designed with aligned learning objectives and described through project attributes, along with coordinated instructor, student, and assessment materials that support adoption and adaptation across diverse computing courses. We report on four years of deployment across 44 classroom trials at seven universities, drawing on feedback from students, instructors, and advisory board members. Results suggest that structured project packaging supports feasible adoption with limited modification effort and that targeted support materials help reduce the technical barriers that commonly hinder student engagement. The contributions of this work include FORAP and a multi-scale portfolio that demonstrates its use across diverse computing domains and project scopes, offering practical guidance for instructors who wish to design, adopt, or adapt reusable PjBL projects in computing education.

cs.CY↗

arXiv2017

Detecting Plagiarism based on the Creation Process

All methodologies for detecting plagiarism to date have focused on the final digital "outcome", such as a document or source code. Our novel approach takes the creation process into account using logged events collected by special software or by the macro recorders found in most office applications. We look at an author's interaction logs with the software used to create the work. Detection relies on comparing the histograms of multiple logs' command use. A work is classified as plagiarism if its log deviates too much from logs of "honestly created" works or if its log is too similar to another log. The technique supports the detection of plagiarism for digital outcomes that stem from \emph{unique} tasks, such as theses and \emph{equal} tasks such as assignments for which the same problem sets are solved by multiple students. Focusing on the latter case, we evaluate this approach using logs collected by an interactive development environment (IDE) from more than sixty students who completed three programming assignments.

cs.OH↗