In this half-day workshop, we seek to establish a continuing dialogue across classrooms about the challenges faced from a rapidly expanding student body, such as is happening in Computer Science departments. We plan to catalog a list of these challenges, the tools developed internally to overcome these challenges, and the barriers to adoption of these tools for the learning community at large. We invite practitioners who are in the process or creating or adapting educational technologies for scale to share their challenges and experiences.

Cheating detection in large classes with online, take-home exams is an extremely difficult problem. As class size increases, the process of detection and evidence building becomes a significant investment of time. To identify cheating without invasive real-time monitoring, Examinator v4.0 uses answer rarity and submission timestamps. Creative question design allows detection of cheating even when answers are correct. Automatic report generation saves instructors time when compiling evidence for cases. Examinator v4.0 streamlines the identification and reporting of cheating students in online take-home exams, analyzing 7683 unique submissions from 1923 students across two semesters and resulting in 52 convictions of academic misconduct.

Cheating detection in large classes with online, take-home exams is an extremely difficult problem. While some cheating can be identified through statistical analysis of all student responses, this analysis can easily be fooled by "smart cheaters'' actively attempting to hide evidence of their unauthorized collaboration. We demonstrate the effectiveness of watermarks combined with creative question design to provide evidence of cheating. We provide results from an initial deployment of our answer watermarking method and do a case study into how "smart cheaters'' attempt to cover their tracks, demonstrating the need for more advanced methods of catching cheating in online, take-home exams.

Interactive teaching methods often lead to higher levels of student engagement with course material. Yet, as class sizes increase, the demand on teaching staff becomes unsustainable. Our solution, Socratic Mind, employs Large Language Models to provide scalable, interactive oral assessments by functioning as a virtual instructor. This paper discusses the outcomes and user feedback from the preliminary implementation of our system in a large classroom environment with 600 students.

The proliferation of powerful large language models with human-like abilities, like ChatGPT, pose serious challenges for educators to enforce academic integrity policies. To address this problem, we propose a novel approach that uses past students' essay submissions dated before the popularization of ChatGPT, and ChatGPT generated essay responses as ground truth to train classifiers to detect ChatGPT usage for current student submissions. Our case study found that, for the same question prompt, student written answers and ChatGPT generated answers are very different. Testing on the ground truth data shows very simple machine learning methods, including multinomial naive Bayes, linear discriminant analysis, and logistic regression, can achieve close to perfect accuracies in detecting ChatGPT generated responses. Using this approach, we suspect around 7% of current student submissions are ChatGPT generated.

Examinator v3.0 detects cheating in online take-home exams by comparing answers and the timestamps they were entered. A web interface enables efficient manual inspection. Use of the tool reveals that certain question types substantially enhance cheating detection, demonstrating the potential of automated algorithmic detection at scale. Examinator v3.0 has analyzed 915,831 pairs of exam submissions across three courses over two semesters at a top U.S. institution, identifying 46 instances of cheating.

PopSign is a smartphone-based bubble-shooter game that helps hearing parentsof deaf infants learn sign language. To help parents practice their ability to sign,PopSign is integrating sign language recognition as part of its gameplay. Fortraining the recognizer, we introduce the PopSign ASL v1.0 dataset that collectsexamples of 250 isolated American Sign Language (ASL) signs using Pixel 4Asmartphone selfie cameras in a variety of environments. It is the largest publiclyavailable, isolated sign dataset by number of examples and is the first dataset tofocus on one-handed, smartphone signs. We collected over 210,000 examplesat 1944x2592 resolution made by 47 consenting Deaf adult signers for whomAmerican Sign Language is their primary language. We manually reviewed 217,866of these examples, of which 175,023 (approximately 700 per sign) were the signintended for the educational game. 39,304 examples were recognizable as a signbut were not the desired variant or were a different sign. We provide a training setof 31 signers, a validation set of eight signers, and a test set of eight signers. Abaseline LSTM model for the 250-sign vocabulary achieves 82.1% accuracy (81.9%class-weighted F1 score) on the validation set and 84.2% (83.9% class-weightedF1 score) on the test set. Gameplay suggests that accuracy will be sufficient forcreating educational games involving sign language recognition.