View in EDS

Dialogism Meets Language Models for Evaluating Involvement in CSCL Conversations

Saved in:
Bibliographic Details
Title: Dialogism Meets Language Models for Evaluating Involvement in CSCL Conversations
Language: English
Authors: Maria-Dorinela Dascalu, Stefan Ruseti, Mihai Dascalu, Danielle S. McNamara, Stefan Trausan-Matu
Source: Grantee Submission. 2022Paper presented at the International Conference on Smart Learning Ecosystems and Regional Development (6th, Bucharest, Romania, Jun 2021).
Peer Reviewed: Y
Page Count: 13
Publication Date: 2022
Sponsoring Agency: National Center for Education Research (NCER) (ED/IES)
Office of Naval Research (ONR) (DOD)
Contract Number: R305A180144
N000141912424
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Computational Linguistics, Longitudinal Studies, Technology Uses in Education, Teaching Methods, Learning Processes, Cooperative Learning, Computer Assisted Instruction, Dialogs (Language), Semantics, Computer Mediated Communication, Models, Algorithms, Intelligent Tutoring Systems, COVID-19, Pandemics, Online Courses, Undergraduate Students, Computer Science Education, Visual Aids
DOI: 10.1007/978-981-16-3930-2_6
Abstract: The use of technology as a facilitator in learning environments has become increasingly prevalent with the global pandemic caused by COVID-19. As such, computer-supported collaborative learning (CSCL) gains a wider adoption in contrast to traditional learning methods. At the same time, the need for automated tools capable of assessing and stimulating collaboration between participants has become more stringent, as human monitoring of the increasing volume of conversations becomes overwhelming. This paper introduces a method grounded in dialogism for evaluating students' involvement in chat conversations based on semantic chains computed using language models. These semantic chains reflect emergent voices from dialogism that span and interact throughout the conversation. Our integrated method uses contextual information captured by BERT transformer models to identify links in a chain that connects semantically related concepts from a voice uttered by one or more participants. Two types of visualizations were generated to depict the longitudinal propagation and the transversal inter-animation of voices within the conversation. In addition, a list of handcrafted features derived from the constructed chains and computed for each participant is introduced. Several machine learning algorithms were tested using these features to evaluate the extent to which semantic chains are predictive of student involvement in chat conversations. [This paper was published in: "Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Proceedings of the 6th International Conference on Smart Learning Ecosystems and Regional Development," edited by Ó. Mealha et al., Springer Nature Singapore Pte Ltd., 2022, pp. 67-78.]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2023
Accession Number: ED637572
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED637572
    Name: ERIC Full Text
    Category: fullText
    Text: Full Text from ERIC
Header DbId: eric
DbLabel: ERIC
An: ED637572
AccessLevel: 3
PubType: Conference
PubTypeId: conference
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Dialogism Meets Language Models for Evaluating Involvement in CSCL Conversations
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Maria-Dorinela+Dascalu%22">Maria-Dorinela Dascalu</searchLink><br /><searchLink fieldCode="AR" term="%22Stefan+Ruseti%22">Stefan Ruseti</searchLink><br /><searchLink fieldCode="AR" term="%22Mihai+Dascalu%22">Mihai Dascalu</searchLink><br /><searchLink fieldCode="AR" term="%22Danielle+S%2E+McNamara%22">Danielle S. McNamara</searchLink><br /><searchLink fieldCode="AR" term="%22Stefan+Trausan-Matu%22">Stefan Trausan-Matu</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2022Paper presented at the International Conference on Smart Learning Ecosystems and Regional Development (6th, Bucharest, Romania, Jun 2021).
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 13
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2022
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: National Center for Education Research (NCER) (ED/IES)<br />Office of Naval Research (ONR) (DOD)
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: R305A180144<br />N000141912424
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Speeches/Meeting Papers<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Computational+Linguistics%22">Computational Linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+Studies%22">Longitudinal Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Processes%22">Learning Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperative+Learning%22">Cooperative Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Instruction%22">Computer Assisted Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Dialogs+%28Language%29%22">Dialogs (Language)</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Mediated+Communication%22">Computer Mediated Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+Tutoring+Systems%22">Intelligent Tutoring Systems</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22Pandemics%22">Pandemics</searchLink><br /><searchLink fieldCode="DE" term="%22Online+Courses%22">Online Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+Education%22">Computer Science Education</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+Aids%22">Visual Aids</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/978-981-16-3930-2_6
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The use of technology as a facilitator in learning environments has become increasingly prevalent with the global pandemic caused by COVID-19. As such, computer-supported collaborative learning (CSCL) gains a wider adoption in contrast to traditional learning methods. At the same time, the need for automated tools capable of assessing and stimulating collaboration between participants has become more stringent, as human monitoring of the increasing volume of conversations becomes overwhelming. This paper introduces a method grounded in dialogism for evaluating students' involvement in chat conversations based on semantic chains computed using language models. These semantic chains reflect emergent voices from dialogism that span and interact throughout the conversation. Our integrated method uses contextual information captured by BERT transformer models to identify links in a chain that connects semantically related concepts from a voice uttered by one or more participants. Two types of visualizations were generated to depict the longitudinal propagation and the transversal inter-animation of voices within the conversation. In addition, a list of handcrafted features derived from the constructed chains and computed for each participant is introduced. Several machine learning algorithms were tested using these features to evaluate the extent to which semantic chains are predictive of student involvement in chat conversations. [This paper was published in: "Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Proceedings of the 6th International Conference on Smart Learning Ecosystems and Regional Development," edited by Ó. Mealha et al., Springer Nature Singapore Pte Ltd., 2022, pp. 67-78.]
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: CodeSource
  Label: IES Funded
  Group: SrcInfo
  Data: Yes
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2023
– Name: AN
  Label: Accession Number
  Group: ID
  Data: ED637572
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED637572
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/978-981-16-3930-2_6
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
    Subjects:
      – SubjectFull: Computational Linguistics
        Type: general
      – SubjectFull: Longitudinal Studies
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Teaching Methods
        Type: general
      – SubjectFull: Learning Processes
        Type: general
      – SubjectFull: Cooperative Learning
        Type: general
      – SubjectFull: Computer Assisted Instruction
        Type: general
      – SubjectFull: Dialogs (Language)
        Type: general
      – SubjectFull: Semantics
        Type: general
      – SubjectFull: Computer Mediated Communication
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Intelligent Tutoring Systems
        Type: general
      – SubjectFull: COVID-19
        Type: general
      – SubjectFull: Pandemics
        Type: general
      – SubjectFull: Online Courses
        Type: general
      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: Computer Science Education
        Type: general
      – SubjectFull: Visual Aids
        Type: general
    Titles:
      – TitleFull: Dialogism Meets Language Models for Evaluating Involvement in CSCL Conversations
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Maria-Dorinela Dascalu
      – PersonEntity:
          Name:
            NameFull: Stefan Ruseti
      – PersonEntity:
          Name:
            NameFull: Mihai Dascalu
      – PersonEntity:
          Name:
            NameFull: Danielle S. McNamara
      – PersonEntity:
          Name:
            NameFull: Stefan Trausan-Matu
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2022
          Titles:
            – TitleFull: Grantee Submission
              Type: main
ResultId 1