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Predicting Performance in a Computer Programming Course.

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Bibliographic Details
Title: Predicting Performance in a Computer Programming Course.
Authors: Bauer, Roger
Peer Reviewed: N
Page Count: 9
Publication Date: 1968
Descriptors: Ability Identification, Academic Achievement, Academic Aptitude, Achievement Tests, Aptitude Tests, Competitive Selection, Data Processing Occupations, Educational Testing, Grade Prediction, Interest Inventories, Performance Tests, Predictive Measurement, Predictive Validity, Programers, Psychometrics
Assessment and Survey Identifiers: Strong Vocational Interest Blank
Abstract: Since the need for good programers exists and will increase, their identification before training is desirable. Until now only single tests of potential ability have been evaluated. In this study several tests used in various combinations were evaluated as test batteries. The IBM Aptitude Test for Programmer Personnel (ATPP) and the Strong Vocational Interest Blank (SVIB) were administered to 68 students enrolled in an introductory computer science course at Michigan State University. Grade point average (GPA) and College Qualification Test (CQT) scores for participants were available from college records. All test scores correlated significantly with course grade (p .05). GPA was found to be the best single predictor of success. Among total test scores, general scholastic aptitude (the CQT) predicted achievement as well as specialized aptitude (the ATPP). However, the best results were found to be obtainable with a judicious choice of subtests emphasizing numerical and spatial reasoning (ATPP Part III, CQT Numerical). An "interest" variable as assessed by the SVIB appeared to identify a dimension discrete from aptitude that was significantly related to course achievement. The results were taken to indicate that instruments presently available can be used effectively to predict achievement in computer programing. (SS/MF)
Entry Date: 1969
Accession Number: ED026872
Database: ERIC
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  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED026872
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  Data: Predicting Performance in a Computer Programming Course.
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  Data: <searchLink fieldCode="AR" term="%22Bauer%2C+Roger%22">Bauer, Roger</searchLink>
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  Data: N
– Name: Pages
  Label: Page Count
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  Data: 9
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 1968
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  Label: Descriptors
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  Data: <searchLink fieldCode="DE" term="%22Ability+Identification%22">Ability Identification</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Aptitude%22">Academic Aptitude</searchLink><br /><searchLink fieldCode="DE" term="%22Achievement+Tests%22">Achievement Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Aptitude+Tests%22">Aptitude Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Competitive+Selection%22">Competitive Selection</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Processing+Occupations%22">Data Processing Occupations</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Testing%22">Educational Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+Prediction%22">Grade Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Interest+Inventories%22">Interest Inventories</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+Tests%22">Performance Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+Measurement%22">Predictive Measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+Validity%22">Predictive Validity</searchLink><br /><searchLink fieldCode="DE" term="%22Programers%22">Programers</searchLink><br /><searchLink fieldCode="DE" term="%22Psychometrics%22">Psychometrics</searchLink>
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  Data: Since the need for good programers exists and will increase, their identification before training is desirable. Until now only single tests of potential ability have been evaluated. In this study several tests used in various combinations were evaluated as test batteries. The IBM Aptitude Test for Programmer Personnel (ATPP) and the Strong Vocational Interest Blank (SVIB) were administered to 68 students enrolled in an introductory computer science course at Michigan State University. Grade point average (GPA) and College Qualification Test (CQT) scores for participants were available from college records. All test scores correlated significantly with course grade (p .05). GPA was found to be the best single predictor of success. Among total test scores, general scholastic aptitude (the CQT) predicted achievement as well as specialized aptitude (the ATPP). However, the best results were found to be obtainable with a judicious choice of subtests emphasizing numerical and spatial reasoning (ATPP Part III, CQT Numerical). An "interest" variable as assessed by the SVIB appeared to identify a dimension discrete from aptitude that was significantly related to course achievement. The results were taken to indicate that instruments presently available can be used effectively to predict achievement in computer programing. (SS/MF)
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  Label: Entry Date
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  Data: 1969
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  Data: ED026872
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RecordInfo BibRecord:
  BibEntity:
    PhysicalDescription:
      Pagination:
        PageCount: 9
    Subjects:
      – SubjectFull: Ability Identification
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Academic Aptitude
        Type: general
      – SubjectFull: Achievement Tests
        Type: general
      – SubjectFull: Aptitude Tests
        Type: general
      – SubjectFull: Competitive Selection
        Type: general
      – SubjectFull: Data Processing Occupations
        Type: general
      – SubjectFull: Educational Testing
        Type: general
      – SubjectFull: Grade Prediction
        Type: general
      – SubjectFull: Interest Inventories
        Type: general
      – SubjectFull: Performance Tests
        Type: general
      – SubjectFull: Predictive Measurement
        Type: general
      – SubjectFull: Predictive Validity
        Type: general
      – SubjectFull: Programers
        Type: general
      – SubjectFull: Psychometrics
        Type: general
      – SubjectFull: Strong Vocational Interest Blank
        Type: general
    Titles:
      – TitleFull: Predicting Performance in a Computer Programming Course.
        Type: main
  BibRelationships:
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          Name:
            NameFull: Bauer, Roger
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      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 1968
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