Teaching and assessment of physics measurement uncertainty in remote delivery during Covid-19 Lockdown

Authors

  • Sunil Dehipawala CUNY Queensborough Community College
  • Ian Schanning CUNY Queensborough Community College
  • Dodi Sukmayadi Universitas Terbuka
  • George Tremberger CUNY Queensborough Community College
  • tak cheung Queensborough Community College

DOI:

https://doi.org/10.33830/ijrse.v5i2.767

Keywords:

Uncertainty learning, Measurement Uncertainty, Physics Education, Remote Learning, Experiential Learning, Simulation Labs

Abstract

The teaching and assessment of measurement uncertainty in physics lab class has been an ongoing challenge under the Covid-19 no-access policy, especially in a Two-year community college setting with less budget. The tactile experience as a tacit knowledge must be delivered in words and students are presumed to be able to learn from reading and following the rules in a simulation, with an analogy of the learning of emotions in a literature class with the original words in the novel and the related movies. The transference learning process offers guidance to design the remote delivery of experiential learning in a lab class. The quantitative uncertainty in physics lab is an assessment of how well we know. The misconception that a simulation lab would carry zero uncertainty was found to be the more difficult for students to eliminate. When the teaching of uncertainty percent calculation be classified as a lesson at the average difficulty level, then the teaching of the uncertainty in graphical representation would be deemed to be at the next difficulty level. For the case with a single formula in several variables, the small change concept in algebra can be used to estimate the uncertainty when the small changes are in absolute magnitudes.  For the case with two or more cascade formulas, the use of simulation to estimate uncertainty from the variation of the simulation results would be practical. Teaching uncertainty examples and assessment rubric examples for experiential learning in remote delivery during Covid -19 pandemic are discussed.

Downloads

Download data is not yet available.

References

Bransford, John D., Ed.; Brown, Ann L., Ed.; Cocking, Rodney R., Ed. (2000). How People Learn: Brain, Mind, Experience, and School: Expanded Edition (2000). Chapter: 3 Learning and Transfer. ISBN-0-309-07036-8

DJB Microtech ltd. (2020). https://www.djb.co.uk/pdfs/Physics%20pdfs/Wheatstone%20Bridge.pdf

Eggen, P., & Kauchak, D. (2004). Educational Psychology: Windows on Classrooms (6th ed.). Merrill Prentice Hall.

Felder, R. M., & Brent, R. (2003). Learning and Teaching Styles In Engineering Education. Engineering Education, 78(7), 674-681.

Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Prentice-Hall.

Kusmawan, U. (2018). Online Microteaching: a Multifaceted Approach to Teacher Professional Development. Journal of Interactive Online Learning.

Kusmawan, U. (2022). A Virtual Lab As A Vehicle For Active Learning Through Distance Education. International Journal of Research in STEM Education (IJRSE), 4(2), 18-38.

Matoskova, Jana (2020). Tacit knowledge as an indicator of academic performance. Journal of Further and Higher Education Volume 44, 2020 - Issue 7, pp877-895, 2020 https://www.tandfonline.com/doi/abs/10.1080/0309877X.2019.1614544

Matthew, C.T.; Sternberg, R.J. (2009). Developing experience-based (tacit) knowledge through reflection. Learning and Individual Differences, Volume 19, Issue 4, December 2009, Pages 530-540 https://www.sciencedirect.com/science/article/pii/S1041608009000491

McCulley, F. (2014) Overview of Circular Forces Lab. Youtube: https://www.youtube.com/watch?v=fNmTLlMZ-fs

McGill University (2020) Guidelines for assessment of experiential learning. https://www.thephysicsaviary.com/Physics/Programs/Labs/ClassicCircularForceLab/index.html Last accessed December 2020

Ormrod, J. E. (2009). Human Learning (5th ed.). London: Pearson.

Polanyi, M. (1966). The Tacit Dimension. University of Chicago Press.

Roepke, A. M., & Seligman, M. E. (2016). Depression and prospection. The British journal of clinical psychology, 55(1), 23-48. https://doi.org/10.1111/bjc.12087

Schan, D. A. (1983). The Reflective Practitioner: How Professionals Think in Action. Basic Books.

Sherman, B.E.; and Turk-Browne, N.B. (2020) Statistical prediction of the future impairs episodic encoding of the present. PNAS September 15 2020 117 (37) 22760-22770 https://pubmed.ncbi.nlm.nih.gov/32859755/

Simon Fraser University (2012). Experiential education at SFU: wide but not deep. July 17, 2012 https://www.sfu.ca/tlc/blog/Experiential-education-at-SFU-wide-but-not-deep.html

Simon Fraser University. (2012). Teaching and Learning: Pedagogic Techniques. https://www.sfu.ca/students/studyabroad/fieldschools/leading-a-field-school-pedagogy.html

SSERC Scottish Schools Education Research Centre (2019). Out of balance Wheatstone Bridge Bulletin Number 267 Summer 2019. https://www.sserc.org.uk/wp-content/uploads/Publications/Bulletins/267/SSERC-bulletin-267_p12-13.pdf

Szpunar, K. K.; Spreng, R.N.; Schacter, D.L. (2014). A taxonomy of prospection: Introducing an organizational framework for future-oriented cognition. PNAS December 30, 2014 111 (52) 18414-18421. https://www.pnas.org/content/early/2014/11/20/1417144111).

Taylor, J. R., & Taylor, R. (1997). Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. University Science Books.

van Biezen (2013) Physics - Optics: Lenses (1 of 5) Lens Combinations Two Converging Lenses. Youtube: https://www.youtube.com/watch?v=aHHa0cK_3as

van Schaik, J.E.; Slim, T.; Franse, R.K.; Raijmakers, M. E. J. (2020). Hands-On Exploration of Cubes' Floating and Sinking Benefits Children's Subsequent Buoyancy Predictions. Front Psychol 2020 Jul 21;11: 1665. https://pubmed.ncbi.nlm.nih.gov/32793051/

Zimmerman, B. J., & Schunk, D. H. (2001). Self-Regulated Learning and Academic Achievement: Theoretical Perspectives (2nd ed.). Lawrence Erlbaum Associates.

Downloads

Published

2023-11-12

How to Cite

Dehipawala, S., Schanning, I., Sukmayadi, D., Tremberger, G., & cheung, tak. (2023). Teaching and assessment of physics measurement uncertainty in remote delivery during Covid-19 Lockdown. International Journal of Research in STEM Education, 5(2), 94–103. https://doi.org/10.33830/ijrse.v5i2.767

Issue

Section

Research Articles