The son of Paul Christie and Sonya Stagnoli, Ben and his sister Bella are home-schooled students who also take college courses. He'll graduate with an associate's degree from Germanna Community College next spring, at about the same time that he receives his high school diploma. She takes classes at Rappahannock Community College.
Computational Thinking (CT) is considered a core competency in problem formulation and problem solving. We have developed the Computational Thinking using Simulation and Modeling (CTSiM) learning environment to help middle school students learn science and CT concepts simultaneously. In this paper, we present an approach that leverages multiple linked representations to help students learn by constructing and analyzing computational models of science topics. Results from a recent study show that students successfully use the linked representations to become better modelers and learners.
This week the Allen Institute for Artificial Intelligence announced a breakthrough for a BERT-based model, passing an eighth-grade science test. The GPU-accelerated system called Aristo can read, learn, and reason about science, in this case emulating the decision making of students. For this milestone, Aristo answered more than 90 percent of the questions on an eighth-grade science exam correctly, and 83 percent on a 12th-grade exam. "Although Aristo only answers multiple choice questions without diagrams, and operates only in the domain of science, it nevertheless represents an important milestone towards systems that can read and understand," the researchers stated in a newly published paper on ArXiv. "The momentum on this task has been remarkable, with accuracy moving from roughly 60% to over 90% in just three years," Though no diagrams were used for this particular task, the work as a whole integrates multiple AI-based technologies including natural language processing, information extraction, knowledge representation and reasoning, commonsense knowledge, and diagram understanding.
This paper reports on the Design Compass, a classroom tool for helping students record and reflect on their design process as they work on and complete a design challenge. The Design Compass software provides an interface where students can identify and record the various design steps they used while performing them, and add digital notes and pictures to document their work. In the Design Log view, students can review steps taken, and print the record of work done, which can be shared and discussed with their instructor or classmates. The paper describes the concepts underlying the creation of the Design Compass, its features as a metacognitive tool and how it works, and provides scenarios of its use as a teaching and assessment tool with eighth-grade technology education students, and in teacher professional development workshops.
Modeling player engagement is a key challenge in games. However, the gameplay signatures of engaged players can be highly context-sensitive, varying based on where the game is used or what population of players is using it. Traditionally, models of player engagement are investigated in a particular context, and it is unclear how effectively these models generalize to other settings and populations. In this work, we investigate a Bayesian hierarchical linear model for multi-task learning to devise a model of player engagement from a pair of datasets that were gathered in two complementary contexts: a Classroom Study with middle school students and a Laboratory Study with undergraduate students. Both groups of players used similar versions of Crystal Island, an educational interactive narrative game for science learning. Results indicate that the Bayesian hierarchical model outperforms both pooled and context-specific models in cross-validation measures of predicting player motivation from in-game behaviors, particularly for the smaller Classroom Study group. Further, we find that the posterior distributions of model parameters indicate that the coefficient for a measure of gameplay performance significantly differs between groups. Drawing upon their capacity to share information across groups, hierarchical Bayesian methods provide an effective approach for modeling player engagement with data from similar, but different, contexts.