(Georgia Institute of Technology, 2022-05)
Sonubi, Imanuel Oluseyi
Cheating is an issue that affects more than just the student doing it, no matter the format of the assessment being cheated on. Take-home exams provide more flexibility for the instructor and student than regular proctored exams, but it is that lack of proctoring during the exam that makes cheating trickier to detect -- students may meet up outside of the classroom and inappropriately collaborate on these tests even though they are to be done individually. Examinator aims to detect cheating on Canvas take-home exams by examining the times at which students view questions and comparing them with other students' times to find any exam attempts that have suspiciously similar timestamps for each question.
(Georgia Institute of Technology, 2022-05)
Ravi, Prerna
Deaf children born to hearing parents lack continuous access to language, leading to weaker working memory compared to hearing children and deaf children born to deaf parents. CopyCat is a game where children communicate with the computer via American Sign Language (ASL), and it has been shown to improve language skills and working memory. Previously, CopyCat depended on unscalable hardware such as custom gloves for sign verification, but modern 4K cameras and pose estimators present new opportunities. This thesis focuses on the current version of the CopyCat game using off-the-shelf hardware, as well as the state-of-the-art sign language recognition system we have developed to augment game play. Using Hidden Markov Models (HMMs), user independent word accuracies were 90.6%, 90.5%, and 90.4% for AlphaPose, Kinect, and MediaPipe, respectively. Transformers, a state- of-the-art model in natural language processing, performed 17.0% worse on average. Given these results, we believe our current HMM-based recognizer can be successfully adapted to verify children’s signing while playing CopyCat.
(Georgia Institute of Technology, 2022-05)
Agrawal, Pranay
95% of deaf children are born to hearing parents and lack continuous exposure to language, which often inhibits learning. We are developing Adaptive CopyCat, an educational game where Deaf children communicate with the computer via American Sign Language (ASL) in order to improve their language skills and working memory. While previous versions of CopyCat relied on custom hardware such as colored gloves with accelerometers for sign verification, our current version of the game utilizes off-the-shelf 4K RGB depth cameras and pose estimators. Before re-creating the game for Deaf children, we evaluate the efficacy of our current CopyCat ASL recognition system with 12 adults. Average user-independent sentence and word accuracies were 85.1% and 95.4%, respectively. To improve the accuracy when new users are introduced, we developed a progressive training model that can adapt to a new user's signing as they play the game. This approach produced a 5% absolute increase in sentence accuracy. To test for generality, a 13th user was recruited six months after the initial experiment and achieved similarly high accuracies. These promising results suggest that our recognizer will be sufficiently accurate for verifying children's signing while playing Adaptive CopyCat.