Liu Zhang Liang Fulmer Kim Yuan - Science Education 2008

Alignment between the physics content standard and the standardized test: A comparison among the United States-New York State, Singapore, and China-Jiangsu
Sci Ed 1-21, 2008

Xiufeng Liu, Baohui Zhang, Ling L. Liang, Gavin Fulmer, Beaumie Kim, Haiquan Yuan

Alignment between content standards and standardized tests is a significant issue to society, science pedagogy, and test validation. To better understand the issues related to alignment, this study compares the alignment in physics among three education systems: Jiangsu (China), New York State (United States), and Singapore. The same coding framework for content standards and standardized tests is used to compute the alignment indices in the three education systems. It was found that there was a statistically significant alignment between the New York content standard and the standardized test for physics, but there was not a statistically significant alignment for Chinese and Singapore physics. The insignificant alignment for Chinese and Singapore physics was due to a shift toward higher level cognitive reasoning skills from content standards to standardized tests. For Chinese physics, the insignificant alignment was also due to a heavier emphasis on electricity in the test than in the content standard. Both significant and insignificant alignments may result in desirable and undesirable effects on guiding classroom instruction. Thus, ongoing study of the alignment between a science content standard and the standardized test is necessary for any education system, and alignment studies may be used as a means of teacher professional development for improving student achievement. © 2008 Wiley Periodicals, Inc.

Liu Ruiz - JRST 2008

Using data mining to predict K-12 students' performance on large-scale assessment items related to energy
J. Res. Sci. Teach 45: 554-573, 2008

Xiufeng Liu, Miguel E. Ruiz

This article reports a study on using data mining to predict K-12 students' competence levels on test items related to energy. Data sources are the 1995 Third International Mathematics and Science Study (TIMSS), 1999 TIMSS-Repeat, 2003 Trend in International Mathematics and Science Study (TIMSS), and the National Assessment of Educational Progress (NAEP). Student population performances, that is, percentages correct, are the object of prediction. Two data mining algorithms, C4.5 and M5, are used to construct a decision tree and a linear function to predict students' performance levels. A combination of factors related to content, context, and cognitive demand of items and to students' grade levels are found to predict student population performances on test items. Cognitive demands have the most significant contribution to the prediction. The decision tree and linear function agree with each other on predictions. We end the article by discussing implications of findings for future science content standard development and energy concept teaching.