AutoTutor: A tutor with dialogue in natural language

Graesser, Arthur C.; Lu, Shulan; Jackson, G. Tanner; Mitchell, Heather H.; Ventura, M. J.; Olney, Andrew; Louwerse, Max M.

doi:10.3758/bf03195563

Cited by 361 publications

(223 citation statements)

References 38 publications

Supporting

Mentioning

198

Contrasting

Unclassified

Order By: Relevance

“…Consequently, in the text-based adjective analyses, the principle components analyses were based on the correlation matrix of the 88 adjective occurrences across the 1165 participants' essays. Note that the assumptions underlying this approach are congruent with those in latent semantic analysis (Landauer, Foltz, & Laham, 1998), computational modeling of semantic spaces (e.g., Buchanan, Westbury, & Burgess, 2001), and other approaches to explore natural language patterns (e.g., Graesser, Cai, Louwerse, & Daniel, 2006;Graesser, Lu, Jackson, Mitchell, Ventura, Olney, & Louwerse, 2004;Graesser, McNamara, Louwerse, & Cai, 2004). Although we report the results from principal components analyses with varimax rotation, virtually identical results were obtained using principal axis analyses and with promax, oblique, and equamax rotations.…”

Section: Text Analytic Strategymentioning

confidence: 45%

Revealing dimensions of thinking in open-ended self-descriptions: An automated meaning extraction method for natural language

Chung

Pennebaker

2008

Journal of Research in Personality

192

178

View full text Add to dashboard Cite

A new method for extracting common themes from written text is introduced and applied to 1,165 open-ended self-descriptive narratives. Drawing on a lexical approach to personality, the most commonly-used adjectives within narratives written by college students were identified using computerized text analytic tools. A factor analysis on the use of these adjectives in the selfdescriptions produced a 7-factor solution consisting of psychologically meaningful dimensions. Some dimensions were unipolar (e.g., Negativity factor, wherein most loaded items were negatively valenced adjectives); others were dimensional in that semantically opposite words clustered together (e.g., Sociability factor, wherein terms such as shy, outgoing, reserved, and loud all loaded in the same direction). The factors exhibited modest reliability across different types of writ writing samples and were correlated with self-reports and behaviors consistent with the dimensions. Similar analyses with additional content words (adjectives, adverbs, nouns, and verbs) yielded additional psychological dimensions associated with physical appearance, school, relationships, etc. in which people contextualize their self-concepts. The results suggest that the meaning extraction method is a promising strategy that determines the dimensions along which people think about themselves. Keywords LIWC; Meaning Extraction Method; natural language; self-descriptionsIn a job or clinical interview, meeting an office mate for the first time, or talking to someone at a party, we usually ask others to tell us about themselves. Directly or indirectly, we elicit people's descriptions of themselves to construct a coherent sense of them. With the exception of a small group of social and personality psychologists, most people consider free descriptions as the currency of everyday life. From the media describing politicians and celebrities, reference letters, or through casual observation and gossip, much of what we think and know about others is shaped by everyday descriptions of them.Although free descriptions are the natural form of appraising what people are like, most formal personality assessment methods use itemized questionnaires. With statistical methods, questionnaires make it easy to summarize people's personality trait levels, to make comparisons across respondents, and to generalize across groups. With a common vocabulary of personality factors, it is easy for researchers to communicate with one another about the major dimensions of personality that are associated with a variety of behaviors. A recurring

show abstract

Section: Text Analytic Strategymentioning

confidence: 45%

Revealing dimensions of thinking in open-ended self-descriptions: An automated meaning extraction method for natural language

Chung

Pennebaker

2008

Journal of Research in Personality

192

178

View full text Add to dashboard Cite

show abstract

“…The effect of dialogue-based interaction on learning is further supported by studies that show that prompting students with little or no content in the tutoring interface is associated with an increase in the learning curves and that forcing them to generate words rather than simply reading them promotes subsequent recall of those words (Slamecka & Graf, 1978). There are at least two successful tutors that utilize conversational interface; Autotutor (A. C. Graesser et al, 2004) -a computer literacy tutor that simulates conversational dialogue and Atlas Andes and Why2 (Gertner & VanLehn, 2000;VanLehn K. et al, 2002) -a set of physics tutors that attempt to comprehend natural language and plan dialogue moves.…”

Section: Introductionmentioning

confidence: 90%

A natural language intelligent tutoring system for training pathologists: implementation and evaluation

Saadawi

Tseytlin

Legowski

et al. 2007

Adv in Health Sci Educ

View full text Add to dashboard Cite

Introduction-We developed and evaluated a Natural Language Interface (NLI) for an Intelligent Tutoring System (ITS) in Diagnostic Pathology. The system teaches residents to examine pathologic slides and write accurate pathology reports while providing immediate feedback on errors they make in their slide review and diagnostic reports. Residents can ask for help at any point in the case, and will receive context-specific feedback.

show abstract

“…Instructional methods applied through APAs include directing learner attention through gestures (Moreno, 2004;Moreno, Reisslein, & Ozogul, 2010) and delivering feedback messages, verbal guidance, and modeling (Azevedo et al, 2009;Graesser et al, 2004;Moreno, Mayer, Spires, & Lester, 2001). External properties of APAs relate to the image and voice of the agent, and include such agent characteristics as gender, age, ethnicity, and tone of voice.…”

Section: Introductionmentioning

confidence: 99%

Investigating the impact of pedagogical agent gender matching and learner choice on learning outcomes and perceptions

Özoğul

Johnson

Atkinson

et al. 2013

Computers & Education

View full text Add to dashboard Cite

The similarity attraction hypothesis posits that humans are drawn toward others who behave and appear similar to themselves. Two experiments examined this hypothesis with middle school students learning electrical circuit analysis in a computer -based environment with an Animated Pedagogical Agent (APA). Experiment 1 was designed to determine whether matching the gender of the APA to the student has a positive impact on learning outcomes or student perceptions. One hundred ninety-seven middle-school students learned with the computer-based environment using an APA that matched their gender or one which was opposite in gender. Female students reported higher program ratings when the APA matched their gender.Male students, on the other hand, reported higher program ratings than females when the APA did not match their gender. Experiment 2 systematically tested the impact of providing learners the choice among four APAs on learning outcomes and student perceptions. Three hundred thirty-four middle-school students received either a pre-assigned random APA or were free to choose from four APA options: young male agent, older male agent, young female agent, or older female agent. Learners had higher far transfer scores when provided a choice of animated agent, but student perceptions were not impacted by having the ability to make this choice. We suggest that offering students learner control positively impacts student motivation and learning by increasing student perceptions autonomy, responsibility for the success of the instructional materials, and global satisfaction with the design of materials.

show abstract

AutoTutor: A tutor with dialogue in natural language

Cited by 361 publications

References 38 publications

Revealing dimensions of thinking in open-ended self-descriptions: An automated meaning extraction method for natural language

Revealing dimensions of thinking in open-ended self-descriptions: An automated meaning extraction method for natural language

A natural language intelligent tutoring system for training pathologists: implementation and evaluation

Investigating the impact of pedagogical agent gender matching and learner choice on learning outcomes and perceptions

Contact Info

Product

Resources

About