January 1, 1996

ASC-94.pdf

The goal of this evaluation was to determine what types of learning experiences were typical of students participating in the AiS program. In order to answer this question three types of data were collected and analyzed: demographic data describing the participating students, teachers and schools; contextual data describing the particular circumstances in which the AiS curriculum is implemented; and student learning data documenting the process and the outcomes of students' work.

The data documenting student learning outcomes - videotapes of student groups presenting their projects and being questioned about them - was analyzed according to performance criteria. Students were then clustered according to the scores they received on their presentations. There were found to be three resulting clusters which had distinctive profiles according to the quality of student performance on the five dimensions of the performance criteria: understanding, critical thinking, clarity, teamwork, and technical competence.

Clusters were then analyzed in relation to the demographic data and learning process data to isolate the variables that significantly correlated with membership in each of the clusters. Contextual data was used to aid in the interpretation of the significant variables.

Overall, the findings from this evaluation are extremely promising. Of the 137 students included in the performance assessment, a very high proportion - a little more than half (51percent) - were able to demonstrate an integrated understanding of the content area they were investigating and the computational techniques they employed. This capacity to bring together an understanding of content and computational methods of inquiry indicates that students are achieving the objectives of the AiS program. Additionally, findings indicate that the target population for this program - female students and students of color - are achieving on a par with other students in the program. The findings also indicate that the AiS program is succeeding at offering students opportunities to use authentic computational techniques to engage in substantive and complex scientific inquiry.