The authors of the current investigation developed and evaluated the effects of a tutoring system based on a small-group arrangement to two young adults with autism spectrum disorder on the acquisition, maintenance, and generalization of sight words. The tutoring system was comprised of a virtual teacher to instruct sight words, and a humanoid…

Bayesian Knowledge Tracing (BKT) is a commonly used approach for student modeling, and Long Short Term Memory (LSTM) is a versatile model that can be applied to a wide range of tasks, such as language translation. In this work, we directly compared three models: BKT, its variant Intervention-BKT (IBKT), and LSTM, on two types of student modeling…

This chapter addresses students' data interpretation, a key NGSS inquiry practice, with which students have several different types of difficulties. In this work, we unpack the difficulties associated with data interpretation from those associated with warranting claims. We do this within the context of Inq-ITS (Inquiry Intelligent Tutoring…

This study evaluates high school students' perceptions of automated writing feedback, and the influence of these perceptions on revising, as a function of varying modes of computer-based writing instruction. Findings indicate that students' perceptions of automated feedback accuracy, ease of use, relevance, and understandability were favorable.…

Model tracing tutors represent a technology designed to mimic key elements of one-on-one human tutoring. We examine the situations in which such supportive computer technologies may devolve into mindless student work with little conceptual understanding or student development. To analyze the support of student intellectual work in the model…

The purpose of intelligent tutoring systems is to provide students with personalized instruction and feedback. The focus of these systems typically rests in the adaptability of the feedback provided to students, which relies on automated assessments of performance in the system. A large focus of my previous work has been to determine how natural…

This paper proposes a methodology for authoring of intelligent tutoring systems using human computation. The methodology embeds authoring tasks in existing educational tasks to avoid the need for monetary authoring incentives. Because not all educational tasks are equally motivating, there is a tension between designing the human computation task…

Many problems related to synthesis with intelligent tutoring may be phrased as program synthesis problems using AI-style search and formal reasoning techniques. The _x000C_first two results in this dissertation focus on problem synthesis as an aspect of intelligent tutoring systems applied to STEM-based education frameworks, specifically high…

Intelligent tutors based on expert systems often struggle to provide formative feedback on complex, ill-defined problems where answers are unknown. Hybrid crowdsourcing systems that combine the intelligence of multiple novices in face-to-face settings might provide an alternate approach for providing intelligent formative feedback. The purpose of…

This ethnographical study aims to interpret how an intelligent tutorial system, geogebraTUTOR, mediates to the student's argumentative processes. Data consisted of four geometrical problems proposed to a group of four students aged 16-17. Qualitative analysis of two selected cases led to the identification of the development of argumentative…

We show how the novel use of a semantic representation based on Osgood's semantic differential scales can lead to effective features in predicting short- and long-term learning in students using a vocabulary learning system. Previous studies in students' intermediate knowledge states during vocabulary acquisition did not provide much information…

The future of education is human expertise and artificial intelligence working in conjunction, a revolution that will change the education as we know it. The Intelligent Tutoring System is a key component of this future. A quantitative measurement of efficacies of practice to heterogeneous learners is the cornerstone of building an effective…

The current study introduces a model for measuring student diligence using online behaviors during intelligent tutoring system use. This model is validated using a full academic year dataset to test its predictive validity against long-term academic outcomes including end-of-year grades and total work completed by the end of the year. The model is…

Early prediction of student difficulty during long-duration learning activities allows a tutoring system to intervene by providing needed support, such as a hint, or by alerting an instructor. To be effective, these predictions must come early and be highly accurate, but such predictions are difficult for open-ended programming problems. In this…

A common drawback in traditional language education is that all students in the same class use the same content. Since students may have different backgrounds such as prior knowledge and learning speed, one single curriculum may not be able to accommodate every student. Unfortunately, most students cannot afford personalized language learning,…