An initial test of scientific inquiry skills revealed that students enrolled in a computer enhanced introductory college chemistry class using a guided discovery approach produced significantly larger gains after class instruction compared with two other introductory chemistry classes at the same institution and three introductory science classes at two other college institutions. The purpose of this study was to analyze the instructional strategy in this class to understand how it may have contributed to gains in inquiry skills. Classroom observations of the computer enhanced guided discovery class and two other lecture based chemistry classes, uncovered a pattern of instruction in the guided discovery case that was markedly different from the other two classes, yet more similar to model construction processes of scientists. The central pattern of instruction in the primary case was referred to as the guided discovery approach and was characterized by instructional strategies designed to trigger generate, evaluate, and modify or GEM cycles, other teacher guidance strategies, and the integration of an interactive computer tool. Analysis of classroom observation data and student surveys confirmed a higher frequency of students' generating ideas about chemistry, constructing explanations, and quantitative problem solving in the guided discovery case than the lecture-based classes and a higher rate of teacher requests for students to engage in several of these processes. Small group observations revealed students' reasoning processes as they interacted with their teacher and the computer during instruction. Overall, compared with more traditional forms of chemistry instruction, the evidence suggests that the instructional strategies in the guided discovery case were successful in sustaining student engagement with several fundamental processes of scientific inquiry and may have led to the development of important inquiry skills. The guided discovery case used classroom activities that included finding trends, evaluating extreme cases, using incremental values, making comparisons, asking why, providing discrepant information, designing new tests, working back from the data, and thinking of an individual molecule, as several different strategies to foster inquiry. Rich descriptions of such instructional strategies may offer prescriptive methods for teachers to foster these processes in their classrooms and may represent a promising model for inquiry based instruction.

Galileo's contemporaries as well as today's students have difficulty understanding relative motion. It is hypothesized that construction of visual models, resolution of these visual models with numeric models, and, in many cases, rejection of epistemological commitments such as the belief in one "true" velocity, are necessary for students to form integrated mental models of relative motion events. To investigate students' relative motion problem solving, high school science students were videotaped in classroom and laboratory settings as they performed collaborative predict-observe-explain activities with relative motion computer simulations. The activities were designed to facilitate conceptual change by challenging common alternative conceptions. Half of the students interacted with simulations that provided animated feedback; the other half received numeric feedback. Learning, as measured by a diagnostic test, occurred following both conditions. There was no statistically significant difference between groups on the measure. It is hypothesized that students did not show statistically significant performance differences on the relative motion test because (a) many students were able to solve numeric problems through algorithm use; (b) many numeric condition students were aided in their ability to visualize problems by interaction with the treatment; and (c) the animation condition fostered little learning because the activities were too easy for students to perform. Students' problem solving was examined through analyses of protocols and through statistical analyses of written responses. Evidence supported the following findings: (1) Numeric condition students had more difficulty with the computer activities than animation condition students. (2) Many students in both groups were able to construct accurate mental models of relative motion events. (3) A number of numeric condition students used faulty mechanical algorithms to solve problems. (4) A number of animation condition students used visualization to solve problems, mapping dynamic visual features of the animations onto posttest problems. Thus, there is evidence that presentation of numeric data can foster students' use of mechanical algorithms. Presentation of animations can foster visualization of target problems solved off-line. These results suggest that, in addition to the structure of the simulations, how computer simulations are used may have a great impact on students' cognition.