As part of a group project, myself and four teammates created an interactive children's storybook based off of the "Young Lady's Illustrated Primer" in Neal Stephenson's novel The Diamond Age. This electronic book is meant to be read aloud by a caregiver with their child, and is designed for reading…
As part of a group project, myself and four teammates created an interactive children's storybook based off of the "Young Lady's Illustrated Primer" in Neal Stephenson's novel The Diamond Age. This electronic book is meant to be read aloud by a caregiver with their child, and is designed for reading over long distances through the use of real-time voice and video calling. While one part of the team focused on building the electronic book itself and writing the program, myself and two others wrote the story and I provided illustrations. Our Primer tells the story of a young princess named Charname (short for character name) who escapes from a tower and goes on a mission to save four companions to help her on her quest. The book is meant for reader-insertion, and teaches children problem-solving, teamwork, and critical thinking skills by presenting challenges for Princess Charname to solve. The Primer borrows techniques from modern video game design, focusing heavily on interactivity and feelings of agency through offering the child choices of how to proceed, similar to choose-your-own-adventure books. If brought to market, the medium lends itself well to expanded quests and storylines for the child to explore as they learn and grow. Additionally, resources are provided for the narrator to help create an engaging experience for the child, based off of research on parent-child cooperative reading and cooperative gameplay. The final version of the Primer included a website to run the program, a book-like computer to access the program online, and three complete story segments for the child and narrator to read together.
Machine learning is a rapidly growing field, with no doubt in part due to its countless applications to other fields, including pedagogy and the creation of computer-aided tutoring systems. To extend the functionality of FACT, an automated teaching assistant, we want to predict, using metadata produced by student activity, whether…
Machine learning is a rapidly growing field, with no doubt in part due to its countless applications to other fields, including pedagogy and the creation of computer-aided tutoring systems. To extend the functionality of FACT, an automated teaching assistant, we want to predict, using metadata produced by student activity, whether a student is capable of fixing their own mistakes. Logs were collected from previous FACT trials with middle school math teachers and students. The data was converted to time series sequences for deep learning, and ordinary features were extracted for statistical machine learning. Ultimately, deep learning models attained an accuracy of 60%, while tree-based methods attained an accuracy of 65%, showing that some correlation, although small, exists between how a student fixes their mistakes and whether their correction is correct.
