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Application of Neighborhood Components Analysis to Process and Survey Data to Predict Student Learning of Statistics

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Title: Application of Neighborhood Components Analysis to Process and Survey Data to Predict Student Learning of Statistics
Language: English
Authors: Yikai Lu, Teresa M. Ober (ORCID 0000-0001-9698-9543), Cheng Liu (ORCID 0000-0002-2787-1653), Ying Cheng (ORCID 0000-0002-8654-1443)
Source: Grantee Submission. 2022.
Peer Reviewed: Y
Page Count: 6
Publication Date: 2022
Sponsoring Agency: Institute of Education Sciences (ED)
National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL)
Contract Number: R305A180269
1350787
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: High Schools
Secondary Education
Descriptors: Prediction, Statistics Education, Data Analysis, Learning Analytics, Trend Analysis, Educational Trends, Learning Management Systems, High School Students, Advanced Placement, Scores, Mathematics Tests, Evaluation Criteria, Guidelines, Data Interpretation, Online Courses, Learning Problems
DOI: 10.1109/ICALT55010.2022.00051
Abstract: Machine learning methods for predictive analytics have great potential for uncovering trends in educational data. However, simple linear models still appear to be most widely used, in part, because of their interpretability. This study aims to address the issues of interpretability of complex machine learning classifiers by conducting feature extraction by neighborhood components analysis (NCA). Our dataset comprises 287 features from both process data indicators (i.e., derived from log data of an online statistics learning platform) and self-report data from high school students enrolled in Advanced Placement (AP) Statistics (N=733). As a label for prediction, we use students' scores on the AP Statistics exam. We evaluated the performance of machine learning classifiers with a given feature extraction method by evaluation criteria including F1 scores, the area under the receiver operating characteristic curve (AUC), and Cohen's Kappas. We find that NCA effectively reduces the dimensionality of training datasets, stabilizes machine learning predictions, and produces interpretable scores. However, interpreting the NCA weights of features, while feasible, is not very straightforward compared to linear regression. Future research should consider developing guidelines to interpret NCA weights.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2024
Accession Number: ED661382
Database: ERIC
FullText Text:
  Availability: 0
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  Data: Application of Neighborhood Components Analysis to Process and Survey Data to Predict Student Learning of Statistics
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Yikai+Lu%22">Yikai Lu</searchLink><br /><searchLink fieldCode="AR" term="%22Teresa+M%2E+Ober%22">Teresa M. Ober</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9698-9543">0000-0001-9698-9543</externalLink>)<br /><searchLink fieldCode="AR" term="%22Cheng+Liu%22">Cheng Liu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2787-1653">0000-0002-2787-1653</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ying+Cheng%22">Ying Cheng</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8654-1443">0000-0002-8654-1443</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2022.
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  Label: Peer Reviewed
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  Data: Y
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  Label: Page Count
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  Data: 6
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  Label: Publication Date
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  Data: 2022
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  Label: Sponsoring Agency
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  Data: Institute of Education Sciences (ED)<br />National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL)
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  Data: R305A180269<br />1350787
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  Data: Speeches/Meeting Papers<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22High+Schools%22">High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics+Education%22">Statistics Education</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Trend+Analysis%22">Trend Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Trends%22">Educational Trends</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Management+Systems%22">Learning Management Systems</searchLink><br /><searchLink fieldCode="DE" term="%22High+School+Students%22">High School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Advanced+Placement%22">Advanced Placement</searchLink><br /><searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+Tests%22">Mathematics Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Criteria%22">Evaluation Criteria</searchLink><br /><searchLink fieldCode="DE" term="%22Guidelines%22">Guidelines</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Interpretation%22">Data Interpretation</searchLink><br /><searchLink fieldCode="DE" term="%22Online+Courses%22">Online Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Problems%22">Learning Problems</searchLink>
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  Label: DOI
  Group: ID
  Data: 10.1109/ICALT55010.2022.00051
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Machine learning methods for predictive analytics have great potential for uncovering trends in educational data. However, simple linear models still appear to be most widely used, in part, because of their interpretability. This study aims to address the issues of interpretability of complex machine learning classifiers by conducting feature extraction by neighborhood components analysis (NCA). Our dataset comprises 287 features from both process data indicators (i.e., derived from log data of an online statistics learning platform) and self-report data from high school students enrolled in Advanced Placement (AP) Statistics (N=733). As a label for prediction, we use students' scores on the AP Statistics exam. We evaluated the performance of machine learning classifiers with a given feature extraction method by evaluation criteria including F1 scores, the area under the receiver operating characteristic curve (AUC), and Cohen's Kappas. We find that NCA effectively reduces the dimensionality of training datasets, stabilizes machine learning predictions, and produces interpretable scores. However, interpreting the NCA weights of features, while feasible, is not very straightforward compared to linear regression. Future research should consider developing guidelines to interpret NCA weights.
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  Label: Abstractor
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  Data: As Provided
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  Data: Yes
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  Label: Entry Date
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  Data: 2024
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  Label: Accession Number
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  Data: ED661382
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      – Type: doi
        Value: 10.1109/ICALT55010.2022.00051
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 6
    Subjects:
      – SubjectFull: Prediction
        Type: general
      – SubjectFull: Statistics Education
        Type: general
      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: Learning Analytics
        Type: general
      – SubjectFull: Trend Analysis
        Type: general
      – SubjectFull: Educational Trends
        Type: general
      – SubjectFull: Learning Management Systems
        Type: general
      – SubjectFull: High School Students
        Type: general
      – SubjectFull: Advanced Placement
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      – SubjectFull: Scores
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      – SubjectFull: Mathematics Tests
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      – SubjectFull: Evaluation Criteria
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      – SubjectFull: Guidelines
        Type: general
      – SubjectFull: Data Interpretation
        Type: general
      – SubjectFull: Online Courses
        Type: general
      – SubjectFull: Learning Problems
        Type: general
    Titles:
      – TitleFull: Application of Neighborhood Components Analysis to Process and Survey Data to Predict Student Learning of Statistics
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          Name:
            NameFull: Yikai Lu
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            NameFull: Teresa M. Ober
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            NameFull: Cheng Liu
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            NameFull: Ying Cheng
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              M: 08
              Type: published
              Y: 2022
          Titles:
            – TitleFull: Grantee Submission
              Type: main
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