It’s Not Easy Being Green: Supporting Collaborative “Green Design” Learning

Chaudhuri, Sourish; Kumar, Rohit; Joshi, Mahesh; Terrell, Elon J.; Higgs, Fred; Aleven, Vincent; Rosé, Carolyn Penstein

doi:10.1007/978-3-540-69132-7_114

Cited by 18 publications

(15 citation statements)

References 1 publication

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“…In this paper, we build on a paradigm for dynamic support for group learning that has proven effective for improving interaction and learning in a series of online group learning studies conducted in classroom settings. In particular we refer to using tutorial dialogue agent technology to provide interactive support within a synchronous collaborative chat environment (Kumar et al, 2007;Chaudhuri et al, 2008;Chaudhuri et al, 2009;Kumar et al, 2010;Ai et al, 2010;Kumar & Rosé, 2011). Introduction of such technology in a classroom setting has consistently led to significant improvements in student learning (Adamson et al, 2014), and even positive impacts on the classroom environment outside of the collaborative activities (Clarke et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Education

Conati¹,

Heffernan²,

Mitrović³

et al. 2015

Lecture Notes in Computer Science

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Abstract. Peer assessment has become popular in recent years. However, in peer assessment, a problem remains that reliability depends on the rater characteristics. For this reason, some item response models that incorporate rater parameters have been proposed. However, in previous models, the parameter estimation accuracy decreases as the number of raters increases because the number of rater parameters increases drastically. To solve that problem, this article presents a proposal of a new item response model for peer assessment that incorporates rater parameters to maintain as few rater parameters as possible.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Education

Conati¹,

Heffernan²,

Mitrović³

et al. 2015

Lecture Notes in Computer Science

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show abstract

“…Scheuer, de Laat, Hever, de Groot & Rosé, 2007;Mu, Stegman, Mayfield, Rosé, & Fischer, 2012). This work has enabled a whole series of studies where interactive support for collaborative learning is triggered by real time analysis of collaborative processes and yields significant positive impact on learning (Kumar, Rosé, Wang, Joshi, & Robinson, 2007;Chaudhuri et al, 2008;Ai, Kumar, Nguyen, Nagasunder, & Rosé, 2010;Kumar & Rosé, 2011).…”

Section: Introductionmentioning

confidence: 99%

Measuring prevalence of other-oriented transactive contributions using an automated measure of speech style accommodation

Gweon

Jain

McDonough

et al. 2013

Computer Supported Learning

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This paper contributes to a theory-grounded methodological foundation for automatic collaborative learning process analysis. It does this by illustrating how insights from the social psychology and sociolinguistics of speech style provide a theoretical framework to inform the design of a computational model. The purpose of that model is to detect prevalence of an important group knowledge integration process in raw speech data. Specifically, this paper focuses on assessment of transactivity in dyadic discussions, where a transactive contribution is operationalized as one where reasoning is made explicit, and where that reasoning builds on a prior reasoning statement within the discussion. Transactive contributions can be either self-oriented, where the contribution builds on the speaker's own prior contribution, or other-oriented, where the contribution builds on a prior contribution of a conversational partner. Other-oriented transacts are particularly central to group knowledge integration processes. An unsupervised Dynamic Bayesian Network model motivated by concepts from Speech Accommodation Theory is presented and then evaluated on the task of estimating prevalence of other-oriented transacts in dyadic discussions. The evaluation demonstrates a significant positive correlation between an automatic measure of speech style accommodation and prevalence of other-oriented transacts (R = .36, p< .05).

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“…The results have been quite encouraging so far, indicating that the use of conversational agents for providing collaborative learning support can significantly enhance the learning outcomes (e.g. Chaudhuri et al 2008;Dyke et al 2012;Kumar et al 2007b).…”

Section: Conversational Agents In Csclmentioning

confidence: 96%

“…For example, the use of tutorial dialogue agents that encourage peers to engage in directed lines of reasoning, also known as knowledge construction dialogues, have been reported to significantly improve learning outcomes (Chaudhuri et al 2008;Kumar et al 2007b). In addition, it was revealed that the unsolicited supportive prompts of an agent can be more effective than solicited supportive prompts (Chaudhuri et al 2009).…”

Section: Conversational Agents In Csclmentioning

confidence: 99%

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A Configurable Conversational Agent to Trigger Students’ Productive Dialogue: A Pilot Study in the CALL Domain

Τέγος

Demetriadis

Tsiatsos

2013

Int J Artif Intell Educ

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Conversational agents constitute a specific type of ITSs that has been reportedly proven successful in helping students in one-to-one settings, while recently their impact has also been explored in computer-supported collaborative learning (CSCL). In this work, we present MentorChat, a dialogue-based system that employs a configurable and domain-independent conversational agent for triggering students' productive dialogue. After a system overview with an emphasis on design rationale and system architecture, we present a pilot study, where the agent is evaluated in the context of the computer-assisted language learning (CALL) domain. Thirty students collaborated in small groups trying to accomplish three different tasks. MentorChat conversational agent supported each group discussion differently in each task providing (a) 'weak'-directed interventions and/or (b) undirected interventions. The study results indicate that 'weak'-directed agent interventions can be more effective than undirected interventions by means of increasing the level of explicit reasoning, and thus productive dialogue. Some encouraging results concerning the system usability and user acceptance are also presented.

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It’s Not Easy Being Green: Supporting Collaborative “Green Design” Learning

Cited by 18 publications

References 1 publication

Artificial Intelligence in Education

Artificial Intelligence in Education

Measuring prevalence of other-oriented transactive contributions using an automated measure of speech style accommodation

A Configurable Conversational Agent to Trigger Students’ Productive Dialogue: A Pilot Study in the CALL Domain

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