Curriculum Development Annotated Plan Excerpts

Excerpt 1 [University of Michigan]

Quantitative Analysis:
Specifies data quality control procedures

Data will be entered into files and verified at the Institute for Social Research. Primary analyses of predicted main effects will be through the use of multivariate analysis of variance. Other subsequent analyses will include multivariate analysis of covariance and multiple regression. Cohorts of calculus graduates will be followed until they leave the university. They will be given periodic questionnaires on their attitudes about mathematics, its usefulness, their ability to apply concepts from calculus to other fields, etc. Their grades, especially in science courses will be recorded and comparisons performed between experimental and control students. These data will also be used in regression analyses with attitudinal and cognitive style measures.

Excerpt 2 [University of Tennessee, Chattanooga]

Quantitative Analysis:
Describes schedule for analysis procedure

At the end of each semester, the Director will prepare a report analyzing the contents of the weekly course logs submitted by class-testers, as well as the responses to the questionnaires. In addition, the director will gather data from client department faculty to gauge their reactions both to our materials and the perceived effects of them on students in client disciplinary courses.

Excerpt 3 [University of Hartford]

Qualitative and Quantitative Analysis:
Overviews data to be collected, purposes behind the collection, and factors to be examined

The summer will conclude the evaluation with a comprehensive data analysis. The data will include information on follow-up courses from the first year, pre-post student attitude data, student data from comparative calculus tests, and faculty interviews (including interviews with faculty from other departments such as engineering). The data will be analyzed to determine what factors effect student and faculty attitudes and student performance. The factors will include gender, ethnicity, type of school, faculty training, degree of use of labs, and the use of technology for in-class work and testing.

Excerpt 4 [Anonymous 1]

Quantitative Analysis:
Describes purposes of proposed analysis

Analysis of responses to the three questionnaires given to students in our experimental Integrated Introductory Biology-Chemistry Lab will allow us to test for changes in students’ research knowledge, research skills, and commitment to science over the 11/2 years they are involved in the experimental program. We will also be able to test the relationship between process and outcome variables; that is, between students’ reactions to the integrated lab, their summer internships, and the career seminar course and the growth they exhibit in their science knowledge, skills, and involvement. Basic demographic data will also be gathered so that it will be possible to test for differences in experiences and outcomes by gender, racial background, and language (English versus non-native speakers). Of particular interest will be the ratings of women in the single-sex lab versus those of women in mixed-sex courses.

Excerpt 5 [SUNY Stony Brook]

Quantitative Analysis:
Proposes comparison of measures

Through surveys and college records we will document and compare the following measures: 1) grades in the quantitative courses; 2) grades in courses whose connections to quantitative disciplines have been illustrated in the quantitative courses; 3) continuation in courses with quantitative content; and 4) continuation in a career that requires quantitative skill. These students will also be questioned about specific reformed courses and coordination among courses.

Uses findings for project improvement

All survey results will be used for feedback to make continual improvements in individual courses and the overall educational environment that the project seeks to create.

Excerpt 6 [University of Colorado, Denver]

Quantitative Analysis:
Specifies hypothesis that will be tested through the analysis

In our preliminary analysis of the survey results, we designed a model to:

1. Test the null hypothesis that the course changed student beliefs concerning science, technology, and culture at a .05 significance level.
2. Test the null hypothesis that different student populations have differences in beliefs concerning science, technology, and culture at a .05 level of significance.
3. Give a percentage description with a margin of error between 5-10% of general student beliefs about science, technology, and culture from weighted samples.

Excerpt 7 [Gettysburg College]

Quantitative Analysis:
Describes quantitative analysis of data to control for pretest difference in comparison groups

Appropriate statistical procedures will be applied to the data. There will be a sufficient number of students in the samples to provide the statistical power to detect a significant difference if one is present. To control for pre-test differences, analysis of covariance will allow an adjustment to the scores to test for each of the two groups, "CLEA" and "controls". This is a relevant issue, because it is unlikely that students will be randomly assigned to CLEA and control classes.