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Plagiarism Detection Using Keystroke Logs

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Bibliographic Details
Title: Plagiarism Detection Using Keystroke Logs
Language: English
Authors: Scott Crossley, Yu Tian, Joon Suh Choi, Langdon Holmes, Wesley Morris
Source: International Educational Data Mining Society. 2024.
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: 2024
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: 2112532
Document Type: Speeches/Meeting Papers
Reports - Research
Descriptors: Plagiarism, Writing (Composition), Essays, Artificial Intelligence, Cheating, Keyboarding (Data Entry), Writing Processes, Persuasive Discourse, Adults, Prediction
Abstract: This study examines the potential to use keystroke logs to examine differences between authentic writing and transcribed essay writing. Transcribed writing produced within writing platforms where copy and paste functions are disabled indicates that students are likely copying texts from the internet or from generative artificial intelligence (AI) models. Transcribed texts should differ from authentic texts where writers follow a process that includes monitoring, evaluating, and revising texts. This study develops a transcription detection model by using keystroke logs within a machine learning model to predict whether an essay is authentic or transcribed. Results indicated that keystroke logs accurately predicted whether an essay was written or transcribed with 99% accuracy using a random forest model. Authentic writing included a greater number of pauses before sentences and words, had a greater number of insertions and longer insertions, deleted more words and characters, and had a greater number of revisions than transcribed writing. Transcribers, on the other hand, produced a greater number of writing bursts because they were simply copying language. Overall, the results indicated that authentic writing is a dynamic process where writers monitor their writing and evaluate whether the writing needs to be changed if problems are identified. Transcribed writing, on the other hand is much more linear. The results may have important implications for plagiarism detection. [For the complete proceedings, see ED675485.]
Abstractor: As Provided
Entry Date: 2025
Accession Number: ED675615
Database: ERIC
FullText Text:
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  Data: Plagiarism Detection Using Keystroke Logs
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  Data: <searchLink fieldCode="AR" term="%22Scott+Crossley%22">Scott Crossley</searchLink><br /><searchLink fieldCode="AR" term="%22Yu+Tian%22">Yu Tian</searchLink><br /><searchLink fieldCode="AR" term="%22Joon+Suh+Choi%22">Joon Suh Choi</searchLink><br /><searchLink fieldCode="AR" term="%22Langdon+Holmes%22">Langdon Holmes</searchLink><br /><searchLink fieldCode="AR" term="%22Wesley+Morris%22">Wesley Morris</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22International+Educational+Data+Mining+Society%22"><i>International Educational Data Mining Society</i></searchLink>. 2024.
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  Data: 8
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  Data: 2024
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  Data: National Science Foundation (NSF)
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  Data: <searchLink fieldCode="DE" term="%22Plagiarism%22">Plagiarism</searchLink><br /><searchLink fieldCode="DE" term="%22Writing+%28Composition%29%22">Writing (Composition)</searchLink><br /><searchLink fieldCode="DE" term="%22Essays%22">Essays</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Cheating%22">Cheating</searchLink><br /><searchLink fieldCode="DE" term="%22Keyboarding+%28Data+Entry%29%22">Keyboarding (Data Entry)</searchLink><br /><searchLink fieldCode="DE" term="%22Writing+Processes%22">Writing Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Persuasive+Discourse%22">Persuasive Discourse</searchLink><br /><searchLink fieldCode="DE" term="%22Adults%22">Adults</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: This study examines the potential to use keystroke logs to examine differences between authentic writing and transcribed essay writing. Transcribed writing produced within writing platforms where copy and paste functions are disabled indicates that students are likely copying texts from the internet or from generative artificial intelligence (AI) models. Transcribed texts should differ from authentic texts where writers follow a process that includes monitoring, evaluating, and revising texts. This study develops a transcription detection model by using keystroke logs within a machine learning model to predict whether an essay is authentic or transcribed. Results indicated that keystroke logs accurately predicted whether an essay was written or transcribed with 99% accuracy using a random forest model. Authentic writing included a greater number of pauses before sentences and words, had a greater number of insertions and longer insertions, deleted more words and characters, and had a greater number of revisions than transcribed writing. Transcribers, on the other hand, produced a greater number of writing bursts because they were simply copying language. Overall, the results indicated that authentic writing is a dynamic process where writers monitor their writing and evaluate whether the writing needs to be changed if problems are identified. Transcribed writing, on the other hand is much more linear. The results may have important implications for plagiarism detection. [For the complete proceedings, see ED675485.]
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
    Subjects:
      – SubjectFull: Plagiarism
        Type: general
      – SubjectFull: Writing (Composition)
        Type: general
      – SubjectFull: Essays
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Cheating
        Type: general
      – SubjectFull: Keyboarding (Data Entry)
        Type: general
      – SubjectFull: Writing Processes
        Type: general
      – SubjectFull: Persuasive Discourse
        Type: general
      – SubjectFull: Adults
        Type: general
      – SubjectFull: Prediction
        Type: general
    Titles:
      – TitleFull: Plagiarism Detection Using Keystroke Logs
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            NameFull: Scott Crossley
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            NameFull: Yu Tian
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            NameFull: Langdon Holmes
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            NameFull: Wesley Morris
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              M: 01
              Type: published
              Y: 2024
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