Nam-Hwa Kang

Achieving Excellence in Teacher Workforce and Equity in Learning Opportunities in South Korea

Akiba, LeTendre, and Scribner (2007) identified two problems with mathematics education in the United States: (a) a shortage of qualified mathematics teachers and (b) unequal access to those teachers by students of high and low socioeconomic status. Akiba et al. called for further research on how South Korea and other countries have achieved excellence in their teacher workforces and equity in access to qualified teachers. They also called for research on what mediates the relationship between opportunity and achievement gaps. In response, the authors of this article describe pertinent South Korean educational contexts and policies. To ensure teacher quality in the United States, the authors propose establishing teaching as a professional occupation by offering competitive salaries, improving working conditions, and increasing teachers’ out-of-class time for planning and professional development. As a way to close the achievement gap, they recommend that accessible supplementary learning opportunities be provided for students who lack family and community resources.

Understanding Teachers’ Conceptions of Classroom Inquiry With a Teaching Scenario Survey Instrument

A survey instrument using everyday teaching scenarios was developed to measure teacher conceptions of inquiry. Validity of the instrument was established by comparing responses for a group of secondary teachers to narrative writing and group discussion. Participating teachers used only three of the five essential features of inquiry detailed in the standards documents (NRC 2000) when expressing their ideas of classroom inquiry. The features of ‘evaluating explanations in connection with scientific knowledge’ and ‘communicating explanations’ were rarely mentioned. These missing components indicate a gap between the teachers’ conceptions of inquiry and the ideals of the reform movement.

Elementary Teachers' Teaching for Conceptual Understanding: Learning from Action Research.

This study reports teachers’ learning through action research on students’ conceptual understanding. The study examined (a) the teachers’ views about science teaching and learning, (b) the teachers’ learning about their teaching practices and (c) the conditions that supported the teachers’ learning through action research. A total of 14 elementary in-service teachers’ course discussion, self-video reflection, action research reports, and learning reflection were analyzed. Findings revealed that (a) the teachers in this study commonly espoused the importance of probing and utilizing students’ preconceptions in science teaching, but they demonstrated various levels of epistemological understanding of student learning and teaching, (b) the teachers experienced the action research as a means to evaluate science teaching methods and changing their teaching practices, and (c) the teachers identified sharing goals, problems, and solutions as an essential supporting condition for their learning through action research. Implications for professional development and further research are discussed.

The Structure of Scientific Arguments by Secondary Science Teachers: Comparison of experimental and historical science topics

Just as scientific knowledge is constructed using distinct modes of inquiry (e.g. experimental or historical), arguments constructed during science instruction may vary depending on the mode of inquiry underlying the topic. The purpose of this study was to examine whether and how secondary science teachers construct scientific arguments during instruction differently for topics that rely on experimental or historical modes of inquiry. Four experienced high-school science teachers were observed daily during instructional units for both experimental and historical science topics. The main data sources include classroom observations and teacher interviews. The arguments were analyzed using Toulmins argumentation pattern revealing specific patterns of arguments in teaching topics relying on these 2 modes of scientific inquiry. The teachers presented arguments to their students that were rather simple in structure but relatively authentic to the 2 different modes. The teachers used far more evidence in teaching topics based on historical inquiry than topics based on experimental inquiry. However, the differences were implicit in their teaching. Furthermore, their arguments did not portray the dynamic nature of science. Very few rebuttals or qualifiers were provided as the teachers were presenting their claims as if the data led straightforward to the claim. Implications for classroom practice and research are discussed.

INSTANCES: incorporating computational scientific thinking advances into education and science courses

The conceptual framework and initial steps taken by a project that aims to incorporate computational scientific thinking into the university‐level classes taken by preservice and in‐service teachers (education majors) are described. The project is called INSTANCES, an almost‐acronym for incorporating computational scientific thinking advances into education and science courses, and is supported by National Science Foundation as part of their Transforming Undergraduate Education in Science, Technology, Engineering, and Mathematics program. The overall goal of the project is to provide an introduction to scientific thinking with computation. Mathematics, programming, algorithmic thinking, and computing accuracy are explicit elements of the science education curriculum, and they are included as integral elements in modules that walk teachers through various examples of computational scientific thinking. Brief descriptions of the modules and their use are presented. Copyright

INSTANCES: incorporating computational scientific thinking advances into education & science courses

The INSTANCES project strives to create science educational materials that incorporate computation as an essential element [1]. Figure 1 illustrates how the authors incorporate this modern approach of scientific problem solving. Although a decade ago the combination of computing, science and applied mathematics known as computational science was rarely known beyond a few research universities, today K-12 organizations such as the Computer Science Teachers Association [2] and the National Science Teachers Association [3] recommend that secondary school classrooms teach simulation as a cornerstone of scientific inquiry.

Instances: Incorporating Computational Scientific Thinking Advances into Education & Science Courses

Teaching computation and science in the context of scientific inquiry and problem solving promotes interest in STEM and increases appreciation for computation in science. The work presented here is the result of multi-institutional and multi-disciplinary collaboration among Computational Physics educator, Science & Math educator, Computational Science Education foundation, Computational Biologist, two community college science teachers, and CS usability expert.We have created a collection of modules that have been piloted in a pre-service education course and are currently being modified for use in an online course for pre- and in- service teachers. The Computational Scientific Thinking & Modeling course will provide practical computation integrated into the scientific problem-solving paradigm. We assume that the students have varied knowledge of physics, biology, algebra, and Calculus1 at a high school level.The following module topics have been selected for the online course: Exponential Decay and Growth, Logistic Growth, Computer Precision, Predator Prey Models, Projectile Motion with Drag, Random Numbers, Random Walk.Students learn to create models and perform computations using Excel, Python language or Vensim simulation software. Our modules start with a scientific problem and then lead the students through its solution via a computational science approach. A typical module includes: Learning Objectives/Skills/Activities, Scientific problem, Concept map and system statements, Computational model, Background information on the computational model, Simulating the model, and Assessment.We found that the module topics are easily described in the context of physical examples. Yet biological examples are less obvious. The pilot of the Exponential Decay and Growth and Logistic Growth revealed that the science was masked in the process of learning the software and the students desired a greater understanding of computation in science. In this poster we present the modules that have been piloted.