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| Author | : George R. Dean |
| Publisher | : |
| Total Pages | : 148 |
| Release | : 1983 |
| Genre | : Academic achievement |
| ISBN | : |
| Author | : Tai-Sheng Fan |
| Publisher | : |
| Total Pages | : 408 |
| Release | : 1996 |
| Genre | : College students |
| ISBN | : |
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.
| Author | : Vassil N. Alexandrov |
| Publisher | : Springer |
| Total Pages | : 1068 |
| Release | : 2003-05-15 |
| Genre | : Computers |
| ISBN | : 3540457186 |
LNCS volumes 2073 and 2074 contain the proceedings of the International Conference on Computational Science, ICCS 2001, held in San Francisco, California, May 27-31, 2001. The two volumes consist of more than 230 contributed and invited papers that reflect the aims of the conference to bring together researchers and scientists from mathematics and computer science as basic computing disciplines, researchers from various application areas who are pioneering advanced application of computational methods to sciences such as physics, chemistry, life sciences, and engineering, arts and humanitarian fields, along with software developers and vendors, to discuss problems and solutions in the area, to identify new issues, and to shape future directions for research, as well as to help industrial users apply various advanced computational techniques.
| Author | : M.G. Helander |
| Publisher | : Elsevier |
| Total Pages | : 1202 |
| Release | : 2014-06-28 |
| Genre | : Computers |
| ISBN | : 1483295133 |
This Handbook is concerned with principles of human factors engineering for design of the human-computer interface. It has both academic and practical purposes; it summarizes the research and provides recommendations for how the information can be used by designers of computer systems. The articles are written primarily for the professional from another discipline who is seeking an understanding of human-computer interaction, and secondarily as a reference book for the professional in the area, and should particularly serve the following: computer scientists, human factors engineers, designers and design engineers, cognitive scientists and experimental psychologists, systems engineers, managers and executives working with systems development. The work consists of 52 chapters by 73 authors and is organized into seven sections. In the first section, the cognitive and information-processing aspects of HCI are summarized. The following group of papers deals with design principles for software and hardware. The third section is devoted to differences in performance between different users, and computer-aided training and principles for design of effective manuals. The next part presents important applications: text editors and systems for information retrieval, as well as issues in computer-aided engineering, drawing and design, and robotics. The fifth section introduces methods for designing the user interface. The following section examines those issues in the AI field that are currently of greatest interest to designers and human factors specialists, including such problems as natural language interface and methods for knowledge acquisition. The last section includes social aspects in computer usage, the impact on work organizations and work at home.
| Author | : |
| Publisher | : |
| Total Pages | : 476 |
| Release | : 1998 |
| Genre | : Computer managed instruction |
| ISBN | : |
| Author | : Reece E. Newman |
| Publisher | : |
| Total Pages | : 613 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
Abstract Predicting Student Success in an Introductory Programming Course at an Urban Midwestern Community College with Computer Programming Experience, Self-Efficacy, and Hope Name: Newman, Reece Elton University of Dayton Advisor: Dr. Charles J. Russo This study of a convenience sample of 66 Introductory Computer Programming students at an urban Midwestern community college used age, computer programming experience, self-efficacy, and hope to predict overall course score. The age, computer programming experience, self-efficacy, and hope frequency distributions were not statistically normal or Gaussian in the sample. Computer programming experience statistically significantly correlated with both computer programming self-efficacy and computer programing hope. Age and computer programming experience, age and computer programming self-efficacy, and age and computer programming hope did not statistically significantly correlate. Computer programming self-efficacy and computer programming hope did not statistically significantly correlate. Relations between age and overall course score, computer programming experience and overall course score, computer programming self-efficacy and overall course score, and computer programming hope and overall course score were nonlinear, so the assumptions for correlation, simple linear regression, and hierarchical multiple linear regression did not hold for the sample data. Correlational, simple regression, and multiple hierarchical regression results were not statistically significant, nor were Student's independent samples t-tests, one-way ANOVAs, and twoway 2 X 2 and 3 X 2 ANOVAs. Despite the overall lack of statistical significance in the findings, there were novel contributions to human knowledge discovered through the observational study of the sample data. Instrument response patterns were internally consistent, providing evidence that the instruments are reliable in the introductory computer programming community college student sample. There were clustering and clear trends in the data indicating a broad range of responses to each instrument. The highly heterogeneous community college population was quite clearly distinct and different from much more homogeneous four-year college and university student populations.
| Author | : |
| Publisher | : |
| Total Pages | : 764 |
| Release | : 2001 |
| Genre | : Education |
| ISBN | : |
| Author | : University of Michigan |
| Publisher | : UM Libraries |
| Total Pages | : 164 |
| Release | : 1988 |
| Genre | : Education, Higher |
| ISBN | : |
Each number is the catalogue of a specific school or college of the University.
| Author | : |
| Publisher | : |
| Total Pages | : 1006 |
| Release | : 1973 |
| Genre | : Education |
| ISBN | : |
| Author | : Zieme Tatum |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023-05-05 |
| Genre | : |
| ISBN | : 9785996841646 |
Student success in computer programming courses has been a long-studied problem and computer science major retention has historically been substantially lower than other majors. The issue of retention for computer science majors has become more pronounced in two-year, open-enrollment institutions. This quantitative study, grounded in Gardner's theory of multiple intelligences, attempted to address some of the causes of poor retention for entry-level computer science majors at two-year colleges by looking for predictors of student success in their first computer programming course. Logical-Mathematical and Visual-Spatial, were used along with two factors: student success in previous mathematics courses and the student's own perception of his or her programming skill.
