We explore how different components of an Automatic Short Answer Grading (ASAG) model affect the model's ability to generalize to questions outside of those used for training. For supervised automatic grading models, human ratings are primarily used as ground truth labels. Producing such ratings can be resource heavy, as subject matter experts…

As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present…

Background: Competency models provide useful frameworks for organizing learning and assessment programs, but their construction is both time intensive and subject to perceptual biases. Some aspects of model development may be particularly well-suited to automation, specifically natural language processing (NLP), which could also help make them…

Similarity is one of the most important relations humans perceive, arguably subserving category learning and categorization, generalization and discrimination, judgment and decision making, and other cognitive functions. Researchers have proposed a wide range of representations and metrics that could be at play in similarity judgment, yet have not…

Misconceptions have been an important area of study in STEM education towards improving our understanding of learners' construction of knowledge. The advent of largescale tutoring systems has given rise to an abundance of data in the form of learner question-answer logs in which signatures of misconceptions can be mined. In this work, we explore…

Students who take an online course, such as a MOOC, use the course's discussion forum to ask questions or reach out to instructors when encountering an issue. However, reading and responding to students' questions is difficult to scale because of the time needed to consider each message. As a result, critical issues may be left unresolved, and…

Collection and analysis of students' writing samples on a large scale is a part of the research agenda of the emerging writing analytics community that promises to deliver an unprecedented insight into characteristics of student writing. Yet with a large scale often comes variability of contexts in which the samples were produced--different…

Infants' disposition to learn repetitions in the input structure has been demonstrated in pattern generalization (e.g., learning the pattern ABB from the token "ledidi"). This study tested whether a repetition advantage can also be found in lexical learning (i.e., learning the word "lele" vs. "ledi"). Twenty-four…