Large dataset offers view of math and computer self-efficacy among computer science undergraduates

ABSTRACT

First-year data from a large, nationwide, three-year longitudinal study of undergraduates in the computing disciplines have been obtained and are in the process of being analyzed. Participants were from both Historically Black Colleges and Universities and Predominantly White Institutions. This paper presents an initial analysis of just two of the twelve variables being investigated -- math self-efficacy and computer self-efficacy -- and focuses primarily on computer science undergraduates. Comparisons are made between the first-year computer science subjects and first-year subjects from non-computing disciplines who also participated in the survey in order to verify commonly held views about these two variables. Among the computer science subjects, the two variables are examined more closely with respect to gender, ethnicity, university type, and year in school.

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INDEX TERMS

Primary Classification:
  K. Computing Milieux
  K.3 COMPUTERS AND EDUCATION
      K.3.2 Computer and Information Science Education

General Terms:
Experimentation, Measurement, Verification

Keywords:
ethnicity, gender, university type