Teammate Relationships Improve Help-Seeking Behavior in an Intelligent Tutoring System

Tai, Minghui; Arroyo, Ivon; Woolf, Beverly Park

doi:10.1007/978-3-642-39112-5_25

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…Students in the experimental condition also had a lower frequency of quick-guessing on problems (quickly attempting an answer with not much time to even read the problem). Students in the experimental group saw many more hints, and apparently they used them in a positive wayas there was no significant difference with regard to hint abuse across conditions (Tai et al 2013).…”

Section: % Correct Answersmentioning

confidence: 90%

“…Specifically, we wondered whether students: 1) would have better learning outcomes when they learned with Wayang Outpost in the role of a teammate and 2) would engage in more effective help-seeking behaviors when they worked as a team to solve problems with the computer. The following is a summary of studies further described in Tai et al (2013).…”

Section: % Correct Answersmentioning

confidence: 99%

“…The field of AI and Education has made great strides in devising computational models that recognize student emotion (Graesser et al 2007;Lester et al 1999;Robison et al 2009) and is starting to explore how to use that information to respond to student emotion (D'Mello et al 2010;Rai et al 2013;Tai et al 2013). We have created a series of affect detectors, specifically classifiers based on linear regression models that predict four different student emotions from recent student behavior with the tutor.…”

Section: Affective Scaffolds and Interventionsmentioning

confidence: 99%

See 2 more Smart Citations

A Multimedia Adaptive Tutoring System for Mathematics that Addresses Cognition, Metacognition and Affect

Arroyo

Woolf

Burelson

et al. 2014

Int J Artif Intell Educ

Self Cite

176

103

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This article describes research results based on multiple years of experimentation and real-world experience with an adaptive tutoring system named Wayang Outpost. The system represents a novel adaptive learning technology that has shown successful outcomes with thousands of students, and provided teachers with valuable information about students' mathematics performance. We define progress in three areas: improved student cognition, engagement, and affect, and we attribute this improvement to specific components and interventions that are inherently affective, cognitive, and metacognitive in nature. For instance, improved student cognitive outcomes have been measured with pre-post tests and state standardized tests, and achieved due to personalization of content and math fluency training. Improved student engagement was achieved by supporting students' metacognition and motivation via affective learning companions and progress reports, measured via records of student gaming of the system. Student affect within the tutor was measured through sensors and

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Section: % Correct Answersmentioning

confidence: 90%

Section: % Correct Answersmentioning

confidence: 99%

Section: Affective Scaffolds and Interventionsmentioning

confidence: 99%

See 1 more Smart Citation

A Multimedia Adaptive Tutoring System for Mathematics that Addresses Cognition, Metacognition and Affect

Arroyo

Woolf

Burelson

et al. 2014

Int J Artif Intell Educ

Self Cite

176

103

View full text Add to dashboard Cite

show abstract

“…Research on intelligent tutoring systems has recently turned its attention on agents that can act socially and respond to tutees' affective states (e.g., [14,15]). When intelligent tutors and tutees are teammates, for instance, tutoring seems to be more effective [16]. Put differently, how intelligent tutors-and SDS in general-communicate plays an important role.…”

Section: Technology That Interacts: Insights Into the Mechanismsmentioning

confidence: 99%

Student Evaluations of a (Rude) Spoken Dialogue System Insights from an Experimental Study

Jucks¹,

Linnemann²,

Brummernhenrich³

2018

Advances in Human-Computer Interaction

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Communicating with spoken dialogue systems (SDS) such as Apple’s Siri® and Google’s Now is becoming more and more common. We report a study that manipulates an SDS’s word use with regard to politeness. In an experiment, 58 young adults evaluated the spoken messages of our self-developed SDS as it replied to typical questions posed by university freshmen. The answers were either formulated politely or rudely. Dependent measures were both holistic measures of how students perceived the SDS as well as detailed evaluations of each single answer. Results show that participants not only evaluated the content of rude answers as being less appropriate and less pleasant than the polite answers, but also evaluated the rude system as less accurate. Lack of politeness also impacted aspects of the perceived trustworthiness of the SDS. We conclude that users of SDS expect such systems to be polite, and we then discuss some practical implications for designing SDS.

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“…Very little research has tried to foster students' motivation for applying the metacognitive strategies in ITSs. One study promoted a teammates relationship between students and the tutor, which motivated the students to engage in more effective help-seeking behaviors [16]. However, it is still largely an open question how we can use motivational design in ITSs to help students want to use the metacognitive strategies.…”

Section: Introductionmentioning

confidence: 99%

Motivational Design in an Intelligent Tutoring System that Helps Students Make Good Task Selection Decisions

Long

Aman

Aleven

2015

Lecture Notes in Computer Science

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Abstract. Making effective problem selection decisions is an important yet challenging self-regulated learning (SRL) skill. Although efforts have been made to scaffold students' problem selection in intelligent tutoring systems (ITS), little work has tried to support students' learning of the transferable problem selection skill that can be applied when the scaffolding is not in effect. The current work uses a user-centered design approach to extend an ITS for equation solving, Lynnette, so the new designs may motivate and help students learn to apply a general, transferable rule for effective problem selection, namely, to select problem types that are not fully mastered ("Mastery Rule"). We conducted user research through classroom experimentation, interviews and storyboards. We found that the presence of an Open Learner Model significantly improves students' problem selection decisions, which has not been empirically established by prior work; also, lack of motivation, especially lack of a masteryapproach orientation, may cause difficulty in applying the Mastery Rule. Based on our user research, we designed prototypes of tutor features that aim to foster a mastery-approach orientation as well as transfer of the learned Mastery Rule when the scaffolding is faded. The work contributes to the research of supporting SRL in ITSs through a motivational design perspective, and lays foundation for future controlled experiments to evaluate the transfer of the problem selection skill in new tutor units where there is no scaffolding.

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Teammate Relationships Improve Help-Seeking Behavior in an Intelligent Tutoring System

Cited by 7 publications

References 14 publications

A Multimedia Adaptive Tutoring System for Mathematics that Addresses Cognition, Metacognition and Affect

A Multimedia Adaptive Tutoring System for Mathematics that Addresses Cognition, Metacognition and Affect

Student Evaluations of a (Rude) Spoken Dialogue System Insights from an Experimental Study

Motivational Design in an Intelligent Tutoring System that Helps Students Make Good Task Selection Decisions

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