Interdisciplinary Computational Science – Chemical and Biological Physics: Advanced Course for Science High School Students
Leading team:
- Prof. Edit Yerushalmi
- Dr. Yarden Ben Horin
- Dr. Hila Damari- Weissler
- Prof. Samuel Safran
Project team:
- Dr. Ariel Abrashkin
- Lavi Bigman
- Moti Biton
- Prof. Ruth Chabay
- Dr. Ohad Cohem
- Dr. Haim Edri
- Pnina Hagag
- Dr. Elon Langbeheim
- Stanislav Levchenko
- Dr. Shelly Livne
- Alex Ribstein
- Dr. Nava Schulmann
- Dr. Ariel Steiner
Secretariat and Administration:
Yael Ben-HaimBrief
In the world of contemporary research and technology, there is a growing interest for interdisciplinary fields. In particular, simplified models that are grounded in physical concepts and principles serve to explain structure formation phenomena in multi-particle systems, which are central to the fields of chemistry, biology and material engineering. However, the current high school science curriculum does not engage students in simplifying complex phenomena, nor does it equip them with either the conceptual framework or numerical tools required to analyze such phenomena.
The novel school subject of “Interdisciplinary Computational Science (ICS) – Chemical and Biological Physics” aims to respond to these challenges. It comprises of a three-year learning sequence (10th to 12th grade) intended for capable and motivated high school science students who express interest in interdisciplinary, project-based learning. The conceptual framework combines computational models (e.g. molecular dynamics and random walk models for the time evolution of multi-particle systems) and analytical models (applying statistical mechanics to describe system behavior in equilibrium), both of which provide students with tools for analyzing self-organization and structure formation phenomena that are at the core of current interdisciplinary science.
- The ICS learning sequence incorporates novel characteristics which assist in reducing cognitive load as well as overcoming the conceptualization gaps and inconsistencies accompanying traditional sequence of introductory and thermal physics curricula:
- Newtonian-based models of the time evolution of multi-particle systems precede statistical modelling. A coarse-grained spatial and temporal process helps clarify the transition from deterministic to stochastic behavior in such systems.
- Analysis of energy dispersion phenomenon (thermal contact) is presented as analogical to spatial distribution of particles in diffusion, based on the concept of entropy and the 2nd law of thermodynamics.
- A reordering of the statistical mechanics sequence separates kinetic energy from potential energy, corresponding to their non-dependence in multi-particle systems. Kinetic energy is discussed with respect to thermal contact, while potential energy is presented later – in the context of structure formation for finite-range interacting particles.
The research-based development of the ICS program was based on the Soft Matter pilot program, The design of the ICS program was carried out within the MER (Educational Reconstruction) framework, consisting of three iterative dimensions:
- Analysis of content structure: clarification of the program’s scientific subject matter, selection of central ideas, and organization of these ideas into a coherent knowledge structure;
- Construction of instruction: the implementation of pedagogical principles in order to design a learning environment (given the constraints of implementation).
- Empirical investigations of students’ conceptions, practices, achievements and learning processes in the program context.
The MER dimensions were applied in 3 consecutive development cycles, each modified by the empirical studies of its previous implementations. The first development cycle was based on the findings of the Soft and Messy Matter pilot program.
The research done on students’ perception and comprehension – with respect to both the statistical mechanics content matter and the modelling practices – consisted of the following studies:
- Examining students’ perceptions regarding modelling practices: how they distinguish between scientific principles, computational procedures, and the epistemological conceptualization (e.g., simplification assumptions) in the context of two and multi-particle systems (Haim Edri thesis, supervisors: Prof. Edit Yerushalmi, Prof. Bat-Sheva Eylon).
- Studying students’ understanding of concepts and principles in statistical mechanics in the context of spatial distributions, as well as their preferences of statistical vs. dynamical reasoning (Ariel Steiner thesis, supervisors: Prof. Edit Yerushalmi, Prof. Samuel Safran).
- Investigating the students’ ability to invoke and apply the statistical mechanics framework, as well as their knowledge organization coherence, alternative reasoning patterns and preference of dynamical vs. statistical modelling. This was done in contexts of energy dispersion (thermal contact), and structure formation (adsorption) phenomena (Ariel Abrashkin thesis, supervisors: prof. Edit Yerushalmi, prof. Samuel Safran).
Further reading:
- Abrashkin A. (2021). Statistical thermodynamics – Research-Based Development of a Curricular Unit in an Interdisciplinary Computational Science Program. (Unpublished doctoral dissertation). Rehovot, Israel: The Weizmann Institute of Science
- Langbeheim, E., Abrashkin, A., Steiner, A., Edri, H., Safran, S., & Yerushalmi, E. (2020). Shifting the learning gears: redesigning a project-based course on soft matter through the perspective of constructionism. Phys Rev – PER, 16(2), 020147.
- Langbeheim, E. (2020). Simulating the Effects of Excluded-Volume Interactions in Polymer Solutions. Journal of Chemical Education, 97(6), 1613-1619.
- Edri H. (2019). Bringing Simplification Assumptions to the Forefront in Chemical and Biological Physics: Research-Based Development of an Introductory Computational Science Curriculum. (Unpublished doctoral dissertation). Rehovot, Israel: The Weizmann Institute of Science.
- Steiner A. (2019). Research based design of an instroctural unit Statistical mechanics – model of the diffusion phenomenon. (Unpublished doctoral dissertation). Rehovot, Israel: The Weizmann Institute of Science.
- Langbeheim, E., Edri, H., Schulmann, N., Safran, S., & Yerushalmi, E. (2019). Extending the Boundaries of High-School Physics: Introducing Computational Modeling of Complex Systems. In Sunal C. S., Sunal D. W., Harrell J. W. & Shemwell J. T. (Eds.). Physics Teaching and Learning. (pp. 111-134) Charlotte, NY.
- Langbeheim, E., Safran, S. A. & Yerushalmi, E. (2016). Engagement in theoretical modelling in research apprenticeships for capable high school students. In Taber, K. S., & Sumida, M. (Eds.). International Perspectives on Science Education for the Gifted: Key issues and challenges. (pp. 61-74). Routledge.
- Langbeheim, E., Safran, S. A., & Yerushalmi, E. (2014), Visualizing the Entropy Change of a Thermal Reservoir, J. Chem. Educ., 91 (3), pp 380–385.
- Yerushalmi, E., (2013), Editorial: The challenge of teaching soft matter at the introductory level, Soft matter, 9, (pp 5316-5318), RSC publication.
- Langbeheim, E., Livne, S., Safran, S. A., & Yerushalmi, E., (2013), Evolution in students' understanding of thermal physics with increasing complexity, Phys Rev – ST PER ,9, 020117.
- Langbeheim, E., Livne, S., Safran, S. A., & Yerushalmi, E. (2012) Introductory physics going soft, Am. J. Phys. 80, 51-60.
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