A Procedure to Create a Pedagogic Conversational Agent in Secondary Physics and Chemistry Education

Marín, Diana Pérez; Boza, Antonio

doi:10.4018/ijicte.2013100107

Cited by 7 publications

(2 citation statements)

References 20 publications

(22 reference statements)

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“…The subjects of the dialogues or the topics that educational agents focus on them can be various, such as mathematics ( Melis and Siekmann, 2004 ; Sabo et al, 2013 ; Aguiar et al, 2014 ; Zhang and Jia, 2017 ), physics ( VanLehn et al, 2002 ; Pérez-Marín and Boza, 2013 ), medicine ( Frize and Frasson, 2000 ; Suebnukarn and Peter, 2004 ; Martin et al, 2009 ), computer science ( Wallace, 1995 ; Weerasinghe and Mitrovic, 2011 ; Koedinger et al, 2013 ; Wang et al, 2015 ). In these examples, conversational agents are used to support learning about a particular subject, and the key element in all these agents is their domain knowledge (implemented by different means).…”

Section: Background Knowledge and Related Workmentioning

confidence: 99%

Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments

Mirzababaei

Pammer-Schindler

2021

Front. Artif. Intell.

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This article discusses the usefulness of Toulmin’s model of arguments as structuring an assessment of different types of wrongness in an argument. We discuss the usability of the model within a conversational agent that aims to support users to develop a good argument. Within the article, we present a study and the development of classifiers that identify the existence of structural components in a good argument, namely a claim, a warrant (underlying understanding), and evidence. Based on a dataset (three sub-datasets with 100, 1,026, 211 responses in each) in which users argue about the intelligence or non-intelligence of entities, we have developed classifiers for these components: The existence and direction (positive/negative) of claims can be detected a weighted average F1 score over all classes (positive/negative/unknown) of 0.91. The existence of a warrant (with warrant/without warrant) can be detected with a weighted F1 score over all classes of 0.88. The existence of evidence (with evidence/without evidence) can be detected with a weighted average F1 score of 0.80. We argue that these scores are high enough to be of use within a conditional dialogue structure based on Bloom’s taxonomy of learning; and show by argument an example conditional dialogue structure that allows us to conduct coherent learning conversations. While in our described experiments, we show how Toulmin’s model of arguments can be used to identify structural problems with argumentation, we also discuss how Toulmin’s model of arguments could be used in conjunction with content-wise assessment of the correctness especially of the evidence component to identify more complex types of wrongness in arguments, where argument components are not well aligned. Owing to having progress in argument mining and conversational agents, the next challenges could be the developing agents that support learning argumentation. These agents could identify more complex type of wrongness in arguments that result from wrong connections between argumentation components.

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Section: Background Knowledge and Related Workmentioning

confidence: 99%

Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments

Mirzababaei

Pammer-Schindler

2021

Front. Artif. Intell.

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“…For instance, using Autotutor (Graesser et al 2008), it has been found out that mixed-dialogue interaction, in which both the agent and the student can change the turn of the conversation, can improve the score of the student up to one point; the adaptive intelligent tutorial dialogue module in the BEETLE II (Dzikovska et al 2011) pedagogic agent system provided significant learning gains for students interacting with the system; and, Mike (Lane et al 2011) behaves differently depending on the students' feelings and some pedagogic choices with good results. Some agents that have been used in different domains are the following: Herman the Bug (Lester et al 1997), Steve (Rickel and Johnson, 1999), Guilly (Nunes et al 2002), Sam (Ryokai et al 2003), Baldi (Massaro et al 2005), Betty (Biswas et al 2009;Segedy et al 2013), Agents in Active Worlds (Holmes, 2007), SBEL agents (Reategui et al 2007), MyPet (Chen et al 2009), Fisca (Pérez-Marín andBoza, 2013), Pascall (Da Costa Pinho et al 2013), MentorChat (Tegos et al 2014), and Metabots (Griol et al 2014).…”

Section: Related Workmentioning

confidence: 99%

A Procedure to Engage Children in the Morphological and Syntax Analysis of Pedagogic Conversational Agent-Generated Sentences to Study Language

Marín

2015

International Journal of Online Pedagogy and Course Design

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Pedagogic Conversational Agents are computer applications that interact with the students in natural language. They usually focus the dialogue on a certain topic under study. In this paper, the authors propose the possibility of children studying morphology and syntax by using a Pedagogic Conversational Agent. The main benefit is that the agent is able to generate an infinite number of sentences and, it automatically generates the morphological and syntactical analysis from a given grammar. That way, students can practise with all the sentences they need, receive immediate feedback with automatic evaluation, at their own rhythm and, the level of difficulty can be adapted to their particular competence of analysis. Given the originality of this new computer assisted learning initiative, the authors have devised a procedure to engage the students in the dialogue with the agent to carry out the morphological and syntax analysis at five different levels of difficulty, and test the validity of the approach with a limited number of users according to the principles of User-Centered Design. The results gathered provide evidence of the goodness of the procedure and, they encourage us to keep working on this promising field of using pedagogic agents as computer teaching language assistants.

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A critical review of state‐of‐the‐art chatbot designs and applications

Luo

Lau

et al. 2021

WIREs Data Min & Knowl

116

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Chatbots are intelligent conversational agents that can interact with users through natural languages. As chatbots can perform a variety of tasks, many companies have committed numerous resources to develop and deploy chatbots to enhance various business processes. However, we lack an up‐to‐date critical review that thoroughly examines both state‐of‐the‐art technologies and innovative applications of chatbots. In this review, we not only critically analyze the various computational approaches used to develop state‐of‐the‐art chatbots, but also thoroughly review the usability and applications of chatbots for various business sectors. We also identify gaps in chatbot‐related studies and propose new research directions to address the shortcomings of existing studies and applications. Our review advances both academic research and practical business applications of state‐of‐the‐art chatbots. We provide guidance for practitioners to fully realize the business value of chatbots and assist in making sensible decisions related to the development and deployment of chatbots in various business contexts. Researchers interested in the design and development of chatbots can also gain useful insights from our critical review and identify fruitful research topics and future research directions based on the research gaps discussed herein. This article is categorized under: Technologies > Machine Learning Application Areas > Business and Industry

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A Procedure to Create a Pedagogic Conversational Agent in Secondary Physics and Chemistry Education

Cited by 7 publications

References 20 publications

Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments

Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments

A Procedure to Engage Children in the Morphological and Syntax Analysis of Pedagogic Conversational Agent-Generated Sentences to Study Language

A critical review of state‐of‐the‐art chatbot designs and applications

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