Combining Semantic Interpretation and Statistical Classification for Improved Explanation Processing in a Tutorial Dialogue System

Dzikovska, Myroslava O.; Farrow, Elaine; Moore, Johanna D.

doi:10.1007/978-3-642-39112-5_29

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…In Beetle II, students' utterances are analyzed by means of a process that includes two stages: in the first, the TRIPS dialog analyzer [85] generates a semantic representation that is domain independent; and in the second, the contextual interpreter applies a reference resolution approach and a set of rules to obtain a representation in terms of the Beetle II domain. Later, in [86], the group of researchers responsible for Beetle II presented a study of how to improve the robustness of the semantic interpreter. Basically, the improvement consisted of implementing a classifier based on lexical similarity within the symbolic approach.…”

Section: Rq4 Nlu Approachmentioning

confidence: 99%

A Systematic Literature Review of Intelligent Tutoring Systems With Dialogue in Natural Language

Paladines¹,

Ramírez²

2020

IEEE Access

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Intelligent tutoring systems (ITSs) are computer programs that provide instruction adapted to the needs of individual students. Dialog systems are computer programs that communicate with human users by using natural language. This paper presents a systematic literature review to address ITSs that incorporate dialog systems and have been implemented in the last twenty years. The review found 33 ITSs and focused on answering the following five research questions. a) What ITSs with natural language dialogue have been developed? b) What is the main purpose of the tutoring dialogue in each system? c) What are the pedagogical features of the teaching process performed by the ITSs with natural language dialogue? d) What natural language understanding approach does each system employ to understand students' utterances? e) What evidence exists related to the evaluation of ITSs with natural language dialogue? The results of this review reveal that most ITSs are directed toward science, technology, engineering, and mathematics (STEM) domains at the university level, and the majority of the selected ITSs implement the expectations and misconceptions tailored approach. Furthermore, most ITSs use dialog to help students learn how to solve a problem by applying rules, laws, etc. (the apply level in Bloom's taxonomy). With regard to the instructional approach, the selected ITSs help students write correct explanations or answers for deep questions; assist students in problem solving; or support a reflective dialogue motivated by either previously provided content or the result of a simulation. Additionally, we found empirical evaluations for 90.91% of the selected ITSs that measure the learning gains and/or assess the impacts of different tutoring strategies.

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Section: Rq4 Nlu Approachmentioning

confidence: 99%

A Systematic Literature Review of Intelligent Tutoring Systems With Dialogue in Natural Language

Paladines¹,

Ramírez²

2020

IEEE Access

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“…AutoTutor sessions primarily help students generate correct explanations that solve a problem, while remedying students' misconceptions is a secondary focus. Other successful dialog systems, such as WHY2/Atlas and BEETLE (Graesser et al 2001b;Dzikovska et al 2013), have emphasized targeting and repairing misconceptions. While both approaches are effective, misconceptions tend to be highly domaindependent and are hard for experts to predict.…”

Section: Human-inspired Tutoring Strategiesmentioning

confidence: 99%

“…This multi-faceted approach is shared by other contemporary natural language ITS families, which often have one central natural language understanding technology supported by complementary methods. For example, the BEETLE II tutor for circuit design combines semantic grammars with lexical databases (e.g., WordNet), reference resolution algorithms, and statistical classifications (Dzikovska et al 2013).…”

Section: Semantic Analysis and Natural Language Processingmentioning

confidence: 99%

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

Nye

Graesser

2014

Int J Artif Intell Educ

185

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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The joint student response analysis and recognizing textual entailment challenge: making sense of student responses in educational applications

Dzikovska

Nielsen

Leacock

2015

Lang Resources & Evaluation

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Combining Semantic Interpretation and Statistical Classification for Improved Explanation Processing in a Tutorial Dialogue System

Cited by 7 publications

References 13 publications

A Systematic Literature Review of Intelligent Tutoring Systems With Dialogue in Natural Language

A Systematic Literature Review of Intelligent Tutoring Systems With Dialogue in Natural Language

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

The joint student response analysis and recognizing textual entailment challenge: making sense of student responses in educational applications

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