View in EDS

Preserving the Integrity of Study Behaviour in Online Retrieval Practice Using Quantified Learner Dynamics

Saved in:
Bibliographic Details
Title: Preserving the Integrity of Study Behaviour in Online Retrieval Practice Using Quantified Learner Dynamics
Language: English
Authors: Maarten van der Velde, Malte Krambeer, Hedderik van Rijn
Source: International Educational Data Mining Society. 2025.
Availability: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Peer Reviewed: Y
Page Count: 8
Publication Date: 2025
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Secondary Education
Descriptors: Artificial Intelligence, Technology Uses in Education, Student Behavior, Information Retrieval, Integrity, Keyboarding (Data Entry), Secondary School Students, Foreign Countries, Cheating, Prevention, Deception
Geographic Terms: Netherlands
Abstract: Ensuring the integrity of results in online learning and assessment tools is a challenge, due to the lack of direct supervision increasing the risk of fraud. We propose and evaluate a machine learning-based method for detecting anomalous behaviour in an online retrieval practice task, using an XGBoost classifier trained on keystroke dynamics and task performance features to distinguish between genuine and fraudulent responses. The classifier requires only a modest amount of training data--approximately 100 short-answer responses, typically collected within 10 minutes of practice-- and maintains good performance when not all feature types are available. This method enhances the reliability of online learning and assessment by identifying anomalous response behaviour in a way that preserves learners' privacy. [For the complete proceedings, see ED675583.]
Abstractor: As Provided
Entry Date: 2025
Accession Number: ED675635
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED675635
    Name: ERIC Full Text
    Category: fullText
    Text: Full Text from ERIC
Header DbId: eric
DbLabel: ERIC
An: ED675635
AccessLevel: 3
PubType: Conference
PubTypeId: conference
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Preserving the Integrity of Study Behaviour in Online Retrieval Practice Using Quantified Learner Dynamics
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Maarten+van+der+Velde%22">Maarten van der Velde</searchLink><br /><searchLink fieldCode="AR" term="%22Malte+Krambeer%22">Malte Krambeer</searchLink><br /><searchLink fieldCode="AR" term="%22Hedderik+van+Rijn%22">Hedderik van Rijn</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22International+Educational+Data+Mining+Society%22"><i>International Educational Data Mining Society</i></searchLink>. 2025.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 8
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– 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="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Retrieval%22">Information Retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Integrity%22">Integrity</searchLink><br /><searchLink fieldCode="DE" term="%22Keyboarding+%28Data+Entry%29%22">Keyboarding (Data Entry)</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Students%22">Secondary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Cheating%22">Cheating</searchLink><br /><searchLink fieldCode="DE" term="%22Prevention%22">Prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Deception%22">Deception</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Netherlands%22">Netherlands</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Ensuring the integrity of results in online learning and assessment tools is a challenge, due to the lack of direct supervision increasing the risk of fraud. We propose and evaluate a machine learning-based method for detecting anomalous behaviour in an online retrieval practice task, using an XGBoost classifier trained on keystroke dynamics and task performance features to distinguish between genuine and fraudulent responses. The classifier requires only a modest amount of training data--approximately 100 short-answer responses, typically collected within 10 minutes of practice-- and maintains good performance when not all feature types are available. This method enhances the reliability of online learning and assessment by identifying anomalous response behaviour in a way that preserves learners' privacy. [For the complete proceedings, see ED675583.]
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: ED675635
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED675635
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Student Behavior
        Type: general
      – SubjectFull: Information Retrieval
        Type: general
      – SubjectFull: Integrity
        Type: general
      – SubjectFull: Keyboarding (Data Entry)
        Type: general
      – SubjectFull: Secondary School Students
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Cheating
        Type: general
      – SubjectFull: Prevention
        Type: general
      – SubjectFull: Deception
        Type: general
      – SubjectFull: Netherlands
        Type: general
    Titles:
      – TitleFull: Preserving the Integrity of Study Behaviour in Online Retrieval Practice Using Quantified Learner Dynamics
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Maarten van der Velde
      – PersonEntity:
          Name:
            NameFull: Malte Krambeer
      – PersonEntity:
          Name:
            NameFull: Hedderik van Rijn
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
          Titles:
            – TitleFull: International Educational Data Mining Society
              Type: main
ResultId 1