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Factors influencing university performance of Associate in Arts graduates transferring to the state university system.

机译：影响艺术系本科毕业生转入州立大学系统的大学绩效的因素。

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摘要

The purpose of this study was to examine the factors behind the failure rates of Associate in Arts (AA) graduates from Miami-Dade Community College (M-DCC) transferring to the Florida State University System (SUS). In M-DCC's largest disciplines, the university failure rate was 13% for Business & Management, 13% for Computer Science, and 14% for Engineering. Hypotheses tested were: Hypothesis 1 (H1): The lower division (LD) overall cumulative GPA and/or the LD major field GPA for AA graduates are predictive of the SUS GPA for the Business Management, Computer Science, and Engineering disciplines. Hypothesis 2 (H2): Demographic variables (age, race, gender) are predictive of performance at the university among M-DCC AA graduates in Engineering, Business & Management, and Computer Science. Hypothesis 3 (H3): Administrative variables (CLAST--College Level Academic Skills Test subtests) are predictive of university performance (GPA) for the Business/Management, Engineering, and Computer Science disciplines. Hypothesis 4 (H4): LD curriculum variables (course credits, course quality points) are predictive of SUS performance for the Engineering, Business/Management and Computer Science disciplines.;Multiple Regression was the inferential procedure selected for predictions. Descriptive statistics were generated on the predictors. Results for H1 identified the LD GPA as the most significant variable in accounting for the variability of the university GPA for the Business & Management, Computer Science, and Engineering disciplines.;For H2, no significant results were obtained for the age and gender variables, but the ethnic subgroups indicated significance at the.0001 level. However, differentials in GPA may not have been due directly to the race factor but, rather, to curriculum choices and performance outcomes while in the LD.;The CLAST computation variable (H3) was a significant predictor of the SUS GPA. This is most likely due to the mathematics structure pervasive in these disciplines.;For H4, there were two curriculum variables significant in explaining the variability of the university GPA (number of required critical major credits completed and quality of the student's performance for these credits). Descriptive statistics on the predictors indicated that 78% of those failing in the State University System had a LD major GPA (calculated with the critical required university credits earned and quality points of these credits) of less than 3.0; and 83% of those failing at the university had an overall community college GPA of less than 3.0.
机译：这项研究的目的是调查从迈阿密-戴德社区学院（M-DCC）转移到佛罗里达州立大学系统（SUS）的艺术专业（AA）毕业生的失败率背后的因素。在M-DCC最大的学科中，大学的业务和管理失败率是13％，计算机科学的失败率是13％，工程专业的失败率是14％。检验的假设是：假设1（H1）：AA毕业生的较低部门（LD）总体累积GPA和/或LD专业领域GPA可以预测SUS GPA用于业务管理，计算机科学和工程学科。假设2（H2）：人口统计学变量（年龄，种族，性别）是工程，商业与管理和计算机科学领域M-DCC AA毕业生中大学表现的预测。假设3（H3）：管理变量（CLAST –大学水平的学术技能测试子测验）可预测商业/管理，工程和计算机科学学科的大学绩效（GPA）。假设4（H4）：LD课程变量（课程学分，课程质量点）可预测工程，业务/管理和计算机科学学科的SUS绩效。多元回归是为进行预测而选择的推论程序。对预测变量进行描述性统计。 H1的结果确定了LD GPA是解释商业，管理，计算机科学和工程学科的大学GPA变异性的最大变量; H2的年龄和性别变量未获得显着结果，但是种族亚组在.0001级别上具有显着意义。但是，GPA的差异可能不是直接由种族因素引起的，而是在LD时归因于课程选择和绩效结果。CLAST计算变量（H3）是SUS GPA的重要预测指标。这很可能是由于这些学科中普遍存在的数学结构所致。;对于H4，有两个课程变量对解释大学GPA的变异性具有重要意义（所需的重要专业学分的完成数量和这些学分的学生表现质量） 。对预测变量的描述性统计表明，在州立大学系统中，有78％的失败者的LD主要GPA（根据所获得的关键要求的大学学分和这些学分的质量分数计算）低于3.0；在大学失败的学生中，有83％的社区大学总GPA低于3.0。

著录项

  • 作者

    Baldwin, Anne Lillie.;

  • 作者单位

    Florida International University.;

  • 授予单位 Florida International University.;
  • 学科 Community college education.;Curriculum development.;Higher education.
  • 学位 Ed.D.
  • 年度 1992
  • 页码 208 p.
  • 总页数 208
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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