With the increasing use of Artificial Intelligence (AI) technologies in education, the number of published studies in the field has increased. However, no large-scale reviews have been conducted to comprehensively investigate the various aspects of this field. Based on 4,519 publications from 2000 to 2019, we attempt to fill this gap and identify…

Academic discourse communities and learning circles are characterized by collaboration, sharing commonalities in terms of social interactions and language. The discourse of these communities is composed of jargon, common terminologies, and similarities in how they construe and communicate meaning. This study examines the extent to which discourse…

This study examines the extent to which instructions to self-explain vs. "other"-explain a text lead readers to produce different forms of explanations. Natural language processing was used to examine the content and characteristics of the explanations produced as a function of instruction condition. Undergraduate students (n = 146)…

Background: Pedagogical agents are computerized talking heads or embodied animated avatars that help students learn by performing actions and holding conversations with the students in natural language. Dialogues occur between a tutor agent and the student in the case of AutoTutor and other intelligent tutoring systems with natural language…

A number of studies have demonstrated strong links between students' language features (as found in spoken and written production) and their math performance. However, no studies have examined links between the students' language features and measures of their Math Identity. This project extends prior studies that use natural language processing…

This study builds upon previous work aimed at developing a student model of reading comprehension ability within the intelligent tutoring system, iSTART. Currently, the system evaluates students' self-explanation performance using a local, sentence-level algorithm and does not adapt content based on reading ability. The current study leverages…

Post-problem reflective tutorial dialogues between human tutors and students are examined to predict when the tutor changed the level of abstraction from the student's preceding turn (i.e., used more general terms or more specific terms); such changes correlate with learning. Prior work examined lexical changes in abstraction. In this work, we…

Various computer tools have been developed to support educators' assessment of student writing, including automated essay scoring and automated writing evaluation systems. Research demonstrates that these systems exhibit relatively high scoring accuracy but uncertain instructional efficacy. Students' writing proficiency does not necessarily…

We annotated transcripts of human tutoring dialogue for the purpose of constructing a dialogue-based intelligent tutoring system, CIRCSIM-Tutor. The tutors were professors of physiology who were also expert tutors. The students were 1st year medical students who communicated with the tutors using typed communication from separate rooms. The tutors…

Describes those features that distinguish human tutorial explanations from those produced by computer-based instructional systems utilizing natural language interfaces. This is illustrated through a study conducted on an intelligent instruction system, drawing comparisons between student interaction with a human tutor and the system feedback.…

The Reflective Tutorial Dialogue System (ReTuDiS) is a system for learner modelling historical text comprehension through reflective dialogue. The system infers learners' cognitive profiles and constructs their learner models. Based on the learner model the system plans the appropriate--personalized for learners--reflective tutorial dialogue in…