Physical Sciences & Engineering

A series of interactive Excel spreadsheets or Excelets for discovery learning in general chemistry has been produced. Students are engaged in numerous higherorder thinking and science process skills as they work through the Excelets and accompanying activities. All of this is done computationally in Excel using formulas.

Experimental measurement is a vital component of the scientific method; and
interpreting the error in a measurement is also very important to grasp. Collecting data to understand the behavior of a system leads to developing mathematical
models, which has been recognized as a crucial process for students to learn in mathematics (AMATYC, 2006; NCTM, 2000) and the sciences (Siebert and McIntosh,
2001). We present a simple, guided-inquiry investigation using stacked cookies to
develop a simple linear mathematical model and to explore measurement error by
incorporating errors as part of the investigation. The model is then investigated
further by exploring an interactive Excel simulation. A conceptual understanding is
developed by hands-on manipulation and virtual experimentation. Numerous higher-order thinking and science process skills are used throughout the investigation. This
activity has usefulness in a variety of mathematics classes, as well as the sciences,
and illustrates the elegant connection between both disciplines.

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Excelet for article: https://academic.pgcc.edu/~ssinex/cookies_error.xls

Many materials science concepts can be developed into animated, interactive spreadsheets to create engaging discovery learning tools. These Excel spreadsheets do not require programming expertise. Learning how to create and use these didactically useful spreadsheets is simple and new examples can be quickly created by instructors.

Radiometric dating is introduced to beginning students in a real-world fashion through animated spreadsheets. Students discover the behavior of the parent-daughter relationship in radioactive decay, how the parent-daughter ratio yields the age, and a number of assumptions involved in radiometric dating. After exploring the concepts, and with some elementary mathematical modeling skills, students analyze data from the literature.

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For a discussion of carbon dating, which is NOT a parent-daughter ratio method, see "Radiocarbon Dating: Developing a Conceptual Understanding via Animated Spreadsheets" in Chem. Educator 2012, 17, 190–194.

As a follow-up to the classic Boyle’s law experiment, students can investigate experimental error in the virtual realm of a spreadsheet. Students engage in numerous higher-order thinking and science process skills as they work through the simulation.

Measurements in many natural systems can produce data with considerable noise or scatter. Getting students into this modeling process involves a number of steps that have been incorporated in many mathematics textbooks from algebra through calculus, as recommended by AMATYC (2004) and NCTM (2000). At the beginning level, mathematics textbooks have done a much better job at introducing modeling than the science textbooks.

At some point students reach the course that addresses the propagation of errors after answering the question “What errors influence your results?” in umpteen different experiments plus maybe a few percent error calculations. Dealing with error is a mindset that requires developing over time. Why not start the process as soon as measurement occurs? This is in line with the Analyzing and Interpreting Data section of the Next Generation Science Standards. To study errors, the novice learner needs to make experimental errors in relatively simple situations that allow comparison to near errorless situations. Now students can discover the influence of the error and study its behavior by further numerical experimentation.

Over the last decade as mathematical modeling of data has become a larger part
of the scientific experience for students, more investigation of errors has been
incorporated. The approach includes both actual experimentation with analysis
and the use of simulations. The tool of choice for modeling data and constructing interactive animated simulations is the spreadsheet. A variety of discovery-learning tools to examine random and systematic errors have been developed. Students gain hands-on experience with a number of experiments and subsequent data analysis. Then they extend their experience using prebuilt interactive animated simulations to enhance their conceptual understanding and foster deeper learning of multivariable systems.

How do instructors change their pedagogical approach? They learn how the
spreadsheet simulations can be used to explore and discover concepts, enhance
science process, and foster higher-order thinking. Instructors can convert a
classic lecture into a discussion using dynamic spreadsheet simulations and
asking “what if” questions. Students experience a predict-test-analyze cycle
where they must explain what they observe with instructor guidance. The
simple computational mechanics of inducing the various errors and how they
influence linear models are discussed. Students deal with errors in a large
variety of situations including error simulations and analysis for numerous
topics.

All materials and numerous other prebuilt spreadsheets plus instructions for
developing interactive Excel spreadsheets available free to download are
accessible at https://academic.pgcc.edu/~ssinex/excelets/matsci_excelets.htm.

We describe how students explore materials science concepts using animated interactive spreadsheets. An engaging pedagogy is created in the classroom using spreadsheets in a way that initially camouflages mathematical complexity, which can later be revealed and taught. Use of off-the-shelf spreadsheet software, including freeware makes these spreadsheets universally available.

In the case of General Chemistry, many engineering students only take a one semester class with​ ​ important topics such as kinetics and equilibrium being given limited coverage. Considerable time is spent covering materials already covered​ ​ in other courses such as General Physics and Introduction to Engineering. Moreover, most GChem courses are oriented toward health science majors and lack a materials focus relevant to engineering. Taking an atoms first approach, we developed and now run a one-semester course in general chemistry for engineers emphasizing relevant materials topics. Laboratory exercises integrate practical examples of materials science enriching the course for engineering students. First-semester calculus and a calculus-based introduction to engineering course are prerequisites, which enables teaching almost all the topics from a traditional two semester GChem course in this new course with advance topics as well. To support this course, an open access textbook in LibreText, formerly ChemWiki was developed entitled ​ General Chemistry for Engineering. Many of the topics were supported using Chemical Excelets and Materials Science Excelets, which are interactive Excel/Calc spreadsheets. The laboratory includes data analysis and interpretation, calibration, error analysis, reactions, kinetics, electrochemistry, and spectrophotometry. To acquaint the students with online collaboration typical of today's technical workplace Google Drive was used for data analysis and report preparation in the laboratory.

Updated course website:  https://sites.google.com/view/ssinex/home/chm-2000-new-stuff

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A follow-up paper, "Modernizing the Engineering Curriculum:  A Community College Approach to Integrate Materials", was published in J. Materials Education 40 (3-4), 125-132 (2018).

Students measure the height of a variety of stacks of bricks and construct a mathematical model via a spreadsheet using linear regression. They predict the height of a 100 bricks. Then they go outside and measure brick heights in a real brick wall. Students quickly discover that mortar in between the brick is going to modify their model. They take measurements of bricks plus mortar and produce a second or revised model. Students compare models, consider random and systematic errors using a pre-built spreadsheet simulation, and then develop a model to count the number of bricks in a specified area of a wall (and can verify). From multiple measurements of bricks and mortar joints in a brick wall, students can get a feel for real variation. In the end, a multivariable model is developed and explored as an interactive spreadsheet simulation. A version using Lego bricks and plates (mortar) is also considered.

As engineering, science, and industry in general become increasingly global, online collaboration has become a normal part of the professional's workday. It has, however, not been integrated into the US educational system. In this paper we show how Google Sheets, a part of Google Drive can be used to familiarize students in beginning courses with the practice of online collaboration and enhance their computational spreadsheet skills.