Exploring the deep-level reasoning questions effect during vicarious learning among eighth to eleventh graders in the domains of computer literacy and Newtonian physics

Gholson, Barry; Witherspoon, Amy; Morgan, Brent; Brittingham, Joshua K.; Coles, Robert; Graesser, Arthur C.; Sullins, Jeremiah; Craig, Scotty D.

doi:10.1007/s11251-008-9069-2

Cited by 43 publications

(27 citation statements)

References 21 publications

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“…Based on correlational evidence from the fine-grained analysis of gist explanation dialogues, the questions, "what is breast cancer," and "how does breast cancer grow and spread" were associated with smaller gains in outcome measures. These results seem to corroborate other findings on deep-level reasoning questions (Craig et al, 2012; Gholson et al, 2009). The effectiveness of asking participants to provide explanations in their own words is worthy of future experimental work.…”

Section: 1 Discussionsupporting

confidence: 91%

Understanding genetic breast cancer risk: Processing loci of the BRCA Gist Intelligent Tutoring System

Wolfe

Reyna

Widmer

et al. 2016

Learning and Individual Differences

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The BRCA Gist Intelligent Tutoring System helps women understand and make decisions about genetic testing for breast cancer risk. BRCA Gist is guided by Fuzzy-Trace Theory, (FTT) and built using AutoTutor Lite. It responds differently to participants depending on what they say. Seven tutorial dialogues requiring explanation and argumentation are guided by three FTT concepts: forming gist explanations in one’s own words, emphasizing decision-relevant information, and deliberating the consequences of decision alternatives. Participants were randomly assigned to BRCA Gist, a control, or impoverished BRCA Gist conditions removing gist explanation dialogues, argumentation dialogues, or FTT images. All BRCA Gist conditions performed significantly better than controls on knowledge, comprehension, and risk assessment. Significant differences in knowledge, comprehension, and fine-grained dialogue analyses demonstrate the efficacy of gist explanation dialogues. FTT images significantly increased knowledge. Providing more elements in arguments against testing correlated with increased knowledge and comprehension.

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Section: 1 Discussionsupporting

confidence: 91%

Understanding genetic breast cancer risk: Processing loci of the BRCA Gist Intelligent Tutoring System

Wolfe

Reyna

Widmer

et al. 2016

Learning and Individual Differences

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“…Students' liking of an AutoTutor agent also had no correlation with learning as reported by Moreno et al (2002), whereas Jackson and Graesser (2007) reported a negative relationship between conditions that promote liking versus deep learning. Studies on vicarious learning have identified some conditions when the human does not even need to be directly interacting with the tutor, but can learn fairly well by watching a simulated student learn (Gholson et al 2009;Chi et al 2008). In total, this suggests that pedagogical agents do not need to be navigators or buddies to the learner, nor even liked by the learner.…”

Section: Discussion: Key Findings From Autotutormentioning

confidence: 99%

“…This vicarious learning research followed up on a study that reported that adaptive conversational interaction was only slightly more effective than presenting succinct, targeted script content that directly answered the main question (Graesser et al 2004b). Research on iDRIVE revealed that vicarious learning with deep questions performed comparably to AutoTutor on physics (Gholson et al 2009). Vicarious dialogs where the peer student modeled asking deep questions also increased question asking by students, which is a metacognitive strategy that improves learning (Craig et al 2006;Rosenshine et al 1996).…”

Section: Vicarious Agent Demonstrationsmentioning

confidence: 99%

AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring

Nye

Graesser

2014

Int J Artif Intell Educ

Self Cite

184

107

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AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages of natural language tutoring are presented. Next, we review three central themes in AutoTutor's development: human-inspired tutoring strategies, pedagogical agents, and technologies that support natural-language tutoring. Research on early versions of AutoTutor documented the impact on deep learning by co-constructed explanations, feedback, conversational scaffolding, and subject matter content. Systems that evolved from AutoTutor added additional components that have been evaluated with respect to learning and motivation. The latter findings include the effectiveness of deep reasoning questions for tutoring multiple domains, of adapting to the affect of low-knowledge learners, of content over surface features such as voices and persona of animated agents, and of alternative tutoring strategies such as collaborative lecturing and vicarious tutoring demonstrations. The paper also considers advances in pedagogical agent roles (such as trialogs) and in tutoring technologies, such semantic processing and tutoring delivery platforms. This paper summarizes and integrates significant findings produced by studies using AutoTutor and related systems.

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“…SKO and AutoTutor draw heavily on tutoring research about self-explanation and elaboration and has benefitted from continuous research and development for over twenty years. AutoTutor has been shown to be successful in multiple academic domains, such as physics and computer science (Craig, Sullins, Witherspoon, & Gholson, 2006;Gholson et al, 2009;Jackson, Ventura, Chewle, Graesser, & the Tutoring Research Group, 2004). AutoTutor's successful natural-language interaction style of expectation and misconception tailored dialogue (Graesser, Chipman, et al, 2005;Graesser, Person, & Magliano, 1995) uses scripts that include a set of expectations as well as misconceptions learners are likely to entertain.…”

Section: Tutoring and Self-explanationmentioning

confidence: 99%

Tutorial dialogues and gist explanations of genetic breast cancer risk

et al. 2015

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The intelligent tutoring system (ITS) BRCA Gist is a Web-based tutor developed using the Shareable Knowledge Objects (SKO) platform that uses latent semantic analysis to engage women in natural-language dialogues to teach about breast cancer risk. BRCA Gist appears to be the first ITS designed to assist patients' health decision making. Two studies provide fine-grained analyses of the verbal interactions between BRCA Gist and women responding to five questions pertaining to breast cancer and genetic risk. We examined how Bgist explanations^generated by participants during naturallanguage dialogues related to outcomes. Using reliable rubrics, scripts of the participants' verbal interactions with BRCA Gist were rated for content and for the appropriateness of the tutor's responses. Human researchers' scores for the content covered by the participants were strongly correlated with the coverage scores generated by BRCA Gist, indicating that BRCA Gist accurately assesses the extent to which people respond appropriately. In Study 1, participants' performance during the dialogues was consistently associated with learning outcomes about breast cancer risk. Study 2 was a field study with a more diverse population. Participants with an undergraduate degree or less education who were randomly assigned to BRCA Gist scored higher on tests of knowledge than those assigned to the National Cancer Institute website or than a control group. We replicated findings that the more expected content that participants included in their gist explanations, the better they performed on outcome measures. As fuzzy-trace theory suggests, encouraging people to develop and elaborate upon gist explanations appears to improve learning, comprehension, and decision making.

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Exploring the deep-level reasoning questions effect during vicarious learning among eighth to eleventh graders in the domains of computer literacy and Newtonian physics

Cited by 43 publications

References 21 publications

Understanding genetic breast cancer risk: Processing loci of the BRCA Gist Intelligent Tutoring System

Understanding genetic breast cancer risk: Processing loci of the BRCA Gist Intelligent Tutoring System

AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring

Tutorial dialogues and gist explanations of genetic breast cancer risk

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