Bao Fang Cai Wang Yang Cui Han Ding Luo - arxiv.org 2008

Learning of Content Knowledge and Development of Scientific Reasoning Ability: A Cross Culture Comparison
arxiv.org

Lei Bao, Kai Fang, Tianfang Cai, Jing Wang, Lijia Yang, Lili Cui, Jing Han, Lin Ding, and Ying Luo

Student content knowledge and general reasoning abilities are two important areas in education practice and research. However, there hasn't been much work in physics education that clearly documents the possible interactions between content learning and the development of general reasoning abilities. In this paper, we report one study of a systematic research to investigate the possible interactions between students' learning of physics content knowledge and the development of general scientific reasoning abilities. Specifically, this study seeks to answer the research question of whether and to what extent content learning may affect the development of general reasoning abilities. College entrance testing data of freshman college students in both USA and China were collected using three standardized tests, FCI, BEMA, and Lawson's Classroom Test of Scientific Reasoning (Lawson Test). The results suggest that years of rigorous training of physics knowledge in middle and high schools have made significant impact on Chinese students' ability in solving physics problems, while such training doesn't seem to have direct effects on their general ability in scientific reasoning, which was measured to be at the same level as that of the students in USA. Details of the curriculum structures in the education systems of USA and China are also compared to provide a basis for interpreting the assessment data.

To appear in AJP.

Ding Reach Lee Bao - PRST-PER 2008

Effects of testing conditions on conceptual survey results
Phys. Rev. ST Phys. Educ. Res. 4, 010112 (2008)

Lin Ding, Neville W. Reay, Albert Lee, and Lei Bao

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.

DOI: 10.1103/PhysRevSTPER.4.010112

Pritchard Lee Bao - Phys Rev 2008

Mathematical learning models that depend on prior knowledge and instructional strategies
Phys. Rev. ST Phys. Educ. Res. 4, 010109 (2008)

David E. Pritchard, Young-Jin Lee, Lei Bao

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.

Reay Li Bao - AJP 2008

Testing a new voting machine question methodology

N. W. Reay, Pengfei Li, and Lei Bao
Department of Physics, The Ohio State University, Columbus, Ohio 43210
(Received 15 April 2007; accepted 9 November 2007)

A new question methodology has been developed and used with voting machines in large physics lecture classrooms. The methodology was tested by comparing student performance in voting machine and non-voting machine lecture sections during three consecutive electricity and magnetism quarters of introductory calculus-based physics. Data from The Conceptual Survey of Electricity and Magnetism and common examination questions indicates that students using voting machines achieved a significant gain in conceptual learning, and that voting machines reduced the gap between male and female student performances on tests. Surveys indicated that students were positive about the use of voting machines and believed that they helped them learn. The surveys also suggested that grading voting machines responses and/or overusing voting machines may lower student enthusiasm.

©2008 American Association of Physics Teachers


doi:10.1119/1.2820392
PACS: 01.40.G-, 01.50.ht

Bao - arxiv.org 2007

Dynamic Models of Learning and Education Measurement
arxiv.org posting

Lei Bao
(Submitted on 6 Oct 2007)

Pre-post testing is a commonly used method in physics education for evaluating students' achievement and or the effectiveness of teaching through a short period of instruction. A popular method to analyze pre-post testing results is the normalized gain first brought to the physics education community in wide use by R. Hake. In his analysis with thousands of students' pre-post test results, it has been observed that students having very different pretest scores tend to have similar normalized gains when going through similar types of instruction, i.e., classes with traditional instruction often have systematically lower gains than classes with research-based collaborative types of instruction. This feature allows researchers to investigate the effectiveness of instruction using data collected from classes with different initial states. However, the question of why the normalized gain has this feature and to what extend this feature will be valid is not well understood. Recently, there have been debates on what the normalized gain is actually measuring and concerns that the normalized gain lacks a probability framework comparing to other methods such as Item Response Theory (IRT). Motivated by searching for answers to these questions, a theoretical model about the dynamic process of learning have been developed, which leads to an explanatory interpretation of the features of the normalized gain. Further the model also connects well to other models and methods such as IRT and shows that the normalized gain does have a probabilistic framework but one different from what the IRT emphasizes. This paper will report the basic theoretical formalism of the new model and explore its applications in data modeling and analysis.

Comments: Theoretical Models of Education Measurement
Subjects: Physics Education (physics.ed-ph); Data Analysis, Statistics and Probability (physics.data-an)
Cite as: arXiv:0710.1375v1 [physics.ed-ph]