Prediction of academic achievement for college computer science majors in the Republic of China Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/kh04ds43s

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • The purpose of this research was to determine whether student academic achievement in college computer science programs in the Republic of China (ROC) could be predicted by factors reported to be effective in US studies. The relationship between these factors and course performance in computer science programs was examined. Gender differences were also interrogated. Sophomore, junior, and senior students enrolled in five universities offering computer science programs in the ROC constituted the population. A researcher-designed questionnaire was used to collect background information. Validity and reliability issues were addressed by the conduct of validity assessment, questionnaire pilot testing, and interviews with selected pilot test subjects. Scores from the College Entrance Examination (CEE) and college computer science courses were accessed through university registrar's offices. A total of 940 questionnaires were collected, representing more than 81% of the population. From data analysis, the predictive powers of CEE test scores in relation to subsequent college performance appeared to be limited. The CEE math component was negatively correlated to performance in college computer science programs. The positive relation of math ability to academic achievement in complete computer science programs was confirmed. High school overall achievement as well as math course averages were identified as effective performance predictors for college computer science programs. Prior computer experience showed no conclusive relationship to subsequent performance in college computer science courses. The close relationship between performance in beginning computer science courses and performance in complete computer science programs was validated. Significant linear prediction models with limited predictive powers (R2 ranged from 0.19 to 0.30) were generated for overall performance, but not for introductory computer science course performance. Model predictive powers were significantly improved (R2 range from 0.59 to 0.63) when performance in introductory computer science courses was included in the models. Significant gender differences were not found for CEE performance, prior computer experience, and prediction models. However, female subjects outperformed male counterparts in course performance at both the high school and college levels.
Resource Type
Date Available
Date Copyright
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Non-Academic Affiliation
Subject
Rights Statement
Peer Reviewed
Language
Digitization Specifications
  • File scanned at 300 ppi (Monochrome) using ScandAll PRO 1.8.1 on a Fi-6670 in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR.
Replaces
Additional Information
  • description.provenance : Made available in DSpace on 2012-10-16T17:52:34Z (GMT). No. of bitstreams: 1 FanTaiSheng1997.pdf: 8170513 bytes, checksum: bf070c01be8592eb5e720e735b6a87aa (MD5) Previous issue date: 1996-04-05
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2012-10-16T17:52:34Z (GMT) No. of bitstreams: 1 FanTaiSheng1997.pdf: 8170513 bytes, checksum: bf070c01be8592eb5e720e735b6a87aa (MD5)
  • description.provenance : Submitted by John Valentino (valentjo@onid.orst.edu) on 2012-10-15T23:56:24Z No. of bitstreams: 1 FanTaiSheng1997.pdf: 8170513 bytes, checksum: bf070c01be8592eb5e720e735b6a87aa (MD5)
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2012-10-16T17:50:34Z (GMT) No. of bitstreams: 1 FanTaiSheng1997.pdf: 8170513 bytes, checksum: bf070c01be8592eb5e720e735b6a87aa (MD5)

Relationships

In Administrative Set:
Last modified: 08/09/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items