Agent Technologies Designed to Facilitate Interactive Knowledge Construction

Graesser, Arthur C.; Jeon, Moongee; Dufty, David F.

doi:10.1080/01638530802145395

Cited by 102 publications

(57 citation statements)

References 40 publications

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“…In several experiments, Craig and colleagues (Craig et al, 2004;Craig, Sullins, Witherspoon, & Gholson, 2006) contrasted pretest to posttest gains of learners on 12 computer literacy topics. The learners either interacted directly with an intelligent tutoring system, AutoTutor (Graesser et al, 2004;Graesser, Jeon, & Dufty, 2008;Graesser, Person, Harter, & the Tutoring Research Group, 2001) or observed recordings of those tutoring sessions. Whereas learners in both conditions showed significant learning gains from pretest to posttest, participants in the computer tutoring condition significantly outperformed those in the observing tutoring condition in two experiments, with effect sizes of d ϭ .50 in Study 1 and d ϭ .84 in Study 2 (Craig et al, 2004).…”

Section: Observational Learningmentioning

confidence: 99%

Improving classroom learning by collaboratively observing human tutoring videos while problem solving.

Craig

Michelene

VanLehn

2009

Journal of Educational Psychology

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Collaboratively observing tutoring is a promising method for observational learning (also referred to as vicarious learning). This method was tested in the Pittsburgh Science of Learning Center's Physics LearnLab, where students were introduced to physics topics by observing videos while problem solving in Andes, a physics tutoring system. Students were randomly assigned to three groups: (a) pairs collaboratively observing videos of an expert human tutoring session, (b) pairs observing videos of expert problem solving, or (c) individuals observing expert problem solving. Immediate learning measures did not display group differences; however, long-term retention and transfer measures showed consistent differences favoring collaboratively observing tutoring.

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Section: Observational Learningmentioning

confidence: 99%

Improving classroom learning by collaboratively observing human tutoring videos while problem solving.

Craig

Michelene

VanLehn

2009

Journal of Educational Psychology

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“…Another type of meaning-making responses occurs in tutorial dialogue when a human or computer tutor interacts with the student in natural language (Graesser, D'Mello, & Cade, in press;Graesser et al, 2008;VanLehn et al, 2007). That is, the student is given a difficult question to answer or a problem to solve.…”

Section: Meaning-making Student Responsesmentioning

confidence: 99%

“…We define student-constructed responses as those that require a student to produce an answer in natural language that may range from a couple of sentences to several paragraphs. These advances have been in the context of computer-based assessments of explanations and think-aloud protocols during reading comprehension (Gilliam, Magliano, Millis, Levinstein, & Boonthum, 2007;Magliano, Millis, the RSAT Development Team, Levinstein, & Boonthum, 2011), the grading of essays and text summaries (Attali & Burstein, 2006;Burstein, Marcu, & Knight, 2003;Franzke, Kintsch, Caccamise, Johnson, & Dooley, 2005;Landauer, Laham, & Foltz, 2003), the grading of short-answer questions (Leacock & Chodorow, 2003), and intelligent tutoring systems and trainers that require students to produce constructed responses during interactive conversations (Graesser, Jeon, & Dufty, 2008;Litman et al, 2006;McNamara, Levinstein, & Boonthum, 2004;VanLehn et al, 2007). These can take the form of directed responses to specific questions or less directed thinkaloud and self-explanation responses.…”

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confidence: 99%

Computer-based assessment of student-constructed responses

Magliano

Graesser

2012

Behav Res

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Student-constructed responses, such as essays, short-answer questions, and think-aloud protocols, provide a valuable opportunity to gauge student learning outcomes and comprehension strategies. However, given the challenges of grading student-constructed responses, instructors may be hesitant to use them. There have been major advances in the application of natural language processing of student-constructed responses. This literature review focuses on two dimensions that need to be considered when developing new systems. The first is type of response provided by the student-namely, meaning-making responses (e.g., think-aloud protocols, tutorial dialogue) and products of comprehension (e.g., essays, open-ended questions). The second corresponds to considerations of the type of natural language processing systems used and how they are applied to analyze the student responses. We argue that the appropriateness of the assessment protocols is, in part, constrained by the type of response and researchers should use hybrid systems that rely on multiple, convergent natural language algorithms.

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“…Research shows that computer agents can be successfully used for tutoring, collaborative learning, co-construction of knowledge, and CPS (e.g., Biswas et al 2010;Graesser et al 2008;Millis et al 2011). A computer agent can be capable of generating goals, performing actions, communicating messages, sensing its environment, adapting to changing environments, and learning (Franklin and Graesser 1996).…”

Section: Human-to-human and Human-to-agent Approach In Cps Assessmentmentioning

confidence: 99%

“…These include measures of the quality of the solutions and the objects generated during the collaboration (Avouris et al 2003); analyses of log files, intermediate results, paths to the solutions (Adejumo et al 2008), team processes and structure of interactions (O'Neil et al 1997a, b); and quality and type of collaborative communication (Cooke et al 2003;Foltz and Martin 2008;Graesser et al 2008). There are distinct tradeoffs between the large amount of information that can be collected in a collaborative activity and what can be measured.…”

Section: Assessing Collaborative Problem Solving Skillsmentioning

confidence: 99%

Computer-based Assessment of Collaborative Problem Solving: Exploring the Feasibility of Human-to-Agent Approach

Rosen

2015

Int J Artif Intell Educ

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How can activities in which collaborative skills of an individual are measured be standardized? In order to understand how students perform on collaborative problem solving (CPS) computer-based assessment, it is necessary to examine empirically the multi-faceted performance that may be distributed across collaboration methods. Theaim of this study was to explore possible differences in student performance in humanto-agent (H-A), compared to human-to-human (H-H) CPS assessment tasks. One hundred seventy nine 14 years-old students from the United States, Singapore and Israel participated in the study. Students in both H-H and H-A modes were able to collaborate and communicate by using identical methods and resources. However, while in the H-A mode, students collaborated with a simulated computerdriven partner, and in the H-H mode students collaborated with another student to solve a problem. Overall, the findings showed that CPS with a computer agent involved significantly higher levels of shared understanding, progress monitoring, and feedback. However, no significant difference was found in a student's ability to solve the problem or in student motivation with a computer agent or a human partner. One major implication of CPS score difference in collaboration measures between the two modes is that in H-A mode one can program a wider range of interaction possibilities than would be available with a human partner. Thus, H-A approach offers more opportunities for students to demonstrate their CPS skills. This study is among the first of its kind to investigate systematically the effect of collaborative problem solving in standardized assessment settings.

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Agent Technologies Designed to Facilitate Interactive Knowledge Construction

Cited by 102 publications

References 40 publications

Improving classroom learning by collaboratively observing human tutoring videos while problem solving.

Improving classroom learning by collaboratively observing human tutoring videos while problem solving.

Computer-based assessment of student-constructed responses

Computer-based Assessment of Collaborative Problem Solving: Exploring the Feasibility of Human-to-Agent Approach

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