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Arthur C Graesser

Publications and source records attributed to Arthur C Graesser.

5 recordsLinked to original sources

Question asking and eye tracking during cognitive disequilibrium: comprehending illustrated texts on devices when the devices break down.

The PREG model of question asking assumes that questions emerge when there is cognitive disequilibrium, as in the case of contradictions, obstacles, and anomalies. Participants read illustrated texts about everyday devices (e.g., a cylinder lock) and then were placed in cognitive disequilibrium through a breakdown scenario (e.g., the key turns but the bolt does not move). The participants asked questions when given the breakdown scenario, and an eyetracker recorded their fixations. As was predicted, deep comprehenders asked better questions and fixated on device components that explained the malfunction. The eye fixations were examined before, during, and after the participants' questions in order to trace the occurrence and timing of convergence on faults, causal reasoning, and other cognitive processes.

Adult↗

AutoTutor: a tutor with dialogue in natural language.

AutoTutor is a learning environment that tutors students by holding a conversation in natural language. AutoTutor has been developed for Newtonian qualitative physics and computer literacy. Its design was inspired by explanation-based constructivist theories of learning, intelligent tutoring systems that adaptively respond to student knowledge, and empirical research on dialogue patterns in tutorial discourse. AutoTutor presents challenging problems (formulated as questions) from a curriculum script and then engages in mixed initiative dialogue that guides the student in building an answer. It provides the student with positive, neutral, or negative feedback on the student's typed responses, pumps the student for more information, prompts the student to fill in missing words, gives hints, fills in missing information with assertions, identifies and corrects erroneous ideas, answers the student's questions, and summarizes answers. AutoTutor has produced learning gains of approximately .70 sigma for deep levels of comprehension.

Algorithms↗

Coh-metrix: analysis of text on cohesion and language.

Advances in computational linguistics and discourse processing have made it possible to automate many language- and text-processing mechanisms. We have developed a computer tool called Coh-Metrix, which analyzes texts on over 200 measures of cohesion, language, and readability. Its modules use lexicons, part-of-speech classifiers, syntactic parsers, templates, corpora, latent semantic analysis, and other components that are widely used in computational linguistics. After the user enters an English text, CohMetrix returns measures requested by the user. In addition, a facility allows the user to store the results of these analyses in data files (such as Text, Excel, and SPSS). Standard text readability formulas scale texts on difficulty by relying on word length and sentence length, whereas Coh-Metrix is sensitive to cohesion relations, world knowledge, and language and discourse characteristics.

Comprehension↗

Human use regulatory affairs advisor (HURAA): learning about research ethics with intelligent learning modules.

The Human Use Regulatory Affairs Advisor (HURAA) is a Web-based facility that provides help and training on the ethical use of human subjects in research, based on documents and regulations in United States federal agencies. HURAA has a number of standard features of conventional Web facilities and computer-based training, such as hypertext, multimedia, help modules, glossaries, archives, links to other sites, and page-turning didactic instruction. HURAA also has these intelligent features: (1) an animated conversational agent that serves as a navigational guide for the Web facility, (2) lessons with case-based and explanation-based reasoning, (3) document retrieval through natural language queries, and (4) a context-sensitive Frequently Asked Questions segment, called Point & Query. This article describes the functional learning components of HURAA, specifies its computational architecture, and summarizes empirical tests of the facility on learners.

Artificial Intelligence↗

ETAT: Expository Text Analysis Tool.

Qualitative methods that analyze the coherence of expository texts not only are time consuming, but also present challenges in collecting data on coding reliability. We describe software that analyzes expository texts more rapidly and produces a notable level of objectivity. ETAT (Expository Text Analysis Tool) analyzes the coherence of expository texts. ETAT adopts a symbolic representational system, known as conceptual graph structures. ETAT follows three steps: segmentation of a text into nodes, classification of the unidentified nodes, and linking the nodes with relational arcs. ETAT automatically constructs a graph in the form of nodes and their interrelationships, along with various attendant statistics and information about noninterrelated, isolated nodes. ETAT was developed in Java, so it is compatible with virtually all computer systems.

Artificial Intelligence↗