Search Results (3 total)

Pre-testing and post-testing is a commonly used method in Physics Education Research to assess student learning gains. It is well recognized in the community that timings and incentives in delivering conceptual tests can impact test results. However, it is difficult to control these variables across different studies. As a common practice, a pre-test is often administered either at or near the beginning of a course, while a post-test can be given either at or near the end of a course. Also, in conducting such tests there often is no norm as to whether incentives should be offered to students. Because these variations can significantly affect test results, it is important to study and document their impact. We analyzed five years of data that were collected at The Ohio State University from over 2100 students, who took both the pre-test and post-test of the Conceptual Survey of Electricity and Magnetism under various timings and incentives. We observed that the actual time frame for giving a test has a marked effect on the test results and that incentive granting also has a significant influence on test outcomes. These results suggest that one should carefully monitor and document the conditions under which tests are administered.

We present mathematical learning models—predictions of student’s knowledge vs amount of instruction—that are based on assumptions motivated by various theories of learning: tabula rasa, constructivist, and tutoring. These models predict the improvement (on the post-test) as a function of the pretest score due to intervening instruction and also depend on the type of instruction. We introduce a connectedness model whose connectedness parameter measures the degree to which the rate of learning is proportional to prior knowledge. Over a wide range of pretest scores on standard tests of introductory physics concepts, it fits high-quality data nearly within error. We suggest that data from MIT have low connectedness (indicating memory-based learning) because the test used the same context and representation as the instruction and that more connected data from the University of Minnesota resulted from instruction in a different representation from the test.

Decades of education research have shown that students can simultaneously possess alternate knowledge frameworks and that the development and use of such knowledge are context dependent. As a result of extensive qualitative research, standardized multiple-choice tests such as Force Concept Inventory and Force-Motion Concept Evaluation tests provide instructors tools to probe their students’ conceptual knowledge of physics. However, many existing quantitative analysis methods often focus on a binary question of whether a student answers a question correctly or not. This greatly limits the capacity of using the standardized multiple-choice tests in assessing students’ alternative knowledge. In addition, the context dependence issue, which suggests that a student may apply the correct knowledge in some situations and revert to use alternative types of knowledge in others, is often treated as random noise in current analyses. In this paper, we present a model analysis, which applies qualitative research to establish a quantitative representation framework. With this method, students’ alternative knowledge and the probabilities for students to use such knowledge in a range of equivalent contexts can be quantitatively assessed. This provides a way to analyze research-based multiple choice questions, which can generate much richer information than what is available from score-based analysis.