The Effect of Motivational Learning Companions on Low Achieving Students and Students with Disabilities

Woolf, Beverly Park; Arroyo, Ivon; Müldner, Kasia; Burleson, Winslow; Cooper, David G.; Dolan, Robert; Christopherson, Robert

doi:10.1007/978-3-642-13388-6_37

Cited by 92 publications

(51 citation statements)

References 9 publications

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“…Work with AutoTutor-AS revealed that only low knowledge students benefited from affect sensitivity (D'Mello and Graesser 2012a;D'Mello et al 2011b). This work mirrors findings with the Wayang Output tutoring system, which found that low knowledge students benefit more from affective support provided by animated agents (Woolf et al 2010). Similar results have been found for factors such as politeness of animated agents (Wang et al 2008).…”

Section: Discussion: Key Findings From Autotutorsupporting

confidence: 81%

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

Nye

Graesser

2014

Int J Artif Intell Educ

189

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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Section: Discussion: Key Findings From Autotutorsupporting

confidence: 81%

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

Nye

Graesser

2014

Int J Artif Intell Educ

189

107

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“…Natural language processing (NLP) is clearly an AI area and many systems now try to do this (e.g., Person et al 2001;Wang et al 2011). Another use of AI is in trying to infer and respond to student emotion (e.g., Baker et al 2010;du Boulay et al 2010;D'Mello et al 2010;Woolf et al 2010). Still others have used speech recognition technology to try to better teach students (Chen et al 2011;Johnson 2010).…”

Section: Assistments and The Existing Aied Literaturementioning

confidence: 99%

The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching

Heffernan

2014

Int J Artif Intell Educ

361

149

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The ASSISTments project is an ecosystem of a few hundred teachers, a platform, and researchers working together. Development professionals help train teachers and get teachers to participate in studies. The platform and these teachers help researchers (sometimes explicitly and sometimes implicitly) simply by using content the teacher selects. The platform, hosted by Worcester Polytechnic Institute, allows teachers to write individual ASSISTments (composed of questions with answers and associated hints, solutions, web-based videos, etc.) or to use pre-built ASSISTments, bundle them together in a problem set, and assign these to students. The system gives immediate feedback to students while they are working and provides student-level data to teachers on any assignment. The word "ASSISTments" blends tutoring "assistance" with "assessment" reporting to teachers and students. While originally focused on mathematics, the platform now has content from many other subjects (e.g., science, English, Statistics, etc.). Due to the large library of mathematics content, however, it is mostly used by math teachers. Over 50,000 students used ASSISTments last school year (2013-4) and this number has been doubling each year for the last 8 years. The platform allows any user, mostly researchers, to create randomized controlled trials in the content, which has helped us use the tool in over 18 published and an equal number of unpublished studies. The data collected by the system has also been used in a few dozen peer-reviewed data mining publications. This paper will not seek to review these publications, but instead we will share why ASSISTments has been successful and what lessons were learned along the way. The first lesson learned was to build a platform for learning sciences, not a product that focused on a math topic. That is, ASSISTments is a tool, not a curriculum. A second lesson learned is expressed by the mantra "Put the teacher in charge, not the computer." This second lesson is about building a flexible system that allows teachers to use the tool in concert with the classroom routine. Once teachers are using the tool they are more likely to want to participate in research studies. These lessons were born from the design decisions about Int

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“…They consist of the aspects of the visual and auditory presence of the APA that are hypothesized to ''make the learning experience more interesting, believable, or natural'' (p. 508). Empirical studies of these properties have explored the influence of the APA's age, gender, ethnicity, politeness, responsiveness, dynamism, and visual appeal, among others (e.g., André et al 1996;Arroyo et al 2011Arroyo et al , 2009Baylor 2011;Baylor and Kim 2004;Baylor and Ryu 2003;Craig et al 2002;Domagk 2010;Kim et al 2007;Lester et al 1997;Moreno 2004;Moreno et al 2000;Plant et al 2009;Wang et al 2008;Woolf et al 2010). Given our special interest in gender and science education, we summarize below the empirical studies that have investigated gender and motivation in relation to APAs in virtual science learning environments.…”

Section: Animated Pedagogical Agents and Student Motivationmentioning

confidence: 99%

Animated pedagogical agents effects on enhancing student motivation and learning in a science inquiry learning environment

Meij

Harmsen

2015

Education Tech Research Dev

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This study focuses on the design and testing of a motivational animated pedagogical agent (APA) in an inquiry learning environment on kinematics. The aim of including the APA was to enhance students' perceptions of task relevance and selfefficacy. Given the under-representation of girls in science classrooms, special attention was given to designing an APA that would appeal to the female students. A review of the literature suggested that the best design solution would be an agent who was female, young, attractive, and ''cool''. An experiment compared three conditions: agent (image and voice), voice (no image), and control (no image and no voice). The research question was whether students' motivation and knowledge changed over time as they worked in the inquiry learning environment, and whether condition and gender affected such changes. Participants were 61 third-year students (mean age 14.7 years) from a secondary school. Gender was distributed evenly within and across conditions. A significant main effect of time on self-efficacy was found, with self-efficacy beliefs increasing significantly for both boys and girls. In addition, there was a significant interaction between time, condition, and gender for self-efficacy. About halfway during training, girls' self-efficacy beliefs significantly increased in both experimental conditions and decreased in the control condition. For boys the opposite pattern was found. Girls also gave higher appraisals for the agent. Students in all three conditions realized significant knowledge gains, which did not differ by gender. The discussion critically considers the need for, and design of motivational scaffolding in inquiry learning environments.

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The Effect of Motivational Learning Companions on Low Achieving Students and Students with Disabilities

Cited by 92 publications

References 9 publications

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

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

The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching

Animated pedagogical agents effects on enhancing student motivation and learning in a science inquiry learning environment

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