SolvE engineering problems

Rather than validating a previously known theory using the outcome of an experiment, consider asking students to determine the application of the results, such as "what if...?" or "how could...?". This would replicate the function of the staff member posing the question, as an example of remote staff support. In addition, a digital artefact to process will also be required, such as a video of the phenomena or a dataset with results.

An example could be identifying unknown samples from experimental data or data from an online simulation e.g. which material is being used a a lens, considering its refractive properties? This can be done using photographs taken in the lab and developed as an online activity using an interactive online optics bench.

Start by posing a question: Using the data given, which of these material samples would be best as a choice for x purpose? For each dataset give a brief methodological description or link to some information on the purpose of that testing method.

For example, in materials Identification, students are given previously measured density and hardness data from unknown samples. The students must identify the materials from the data provided and the relative applications (e.g. which material could be used as rope on a boat?) . The students will need images of the unknown samples to help with the identification.

As this learning outcome relates to understanding the value of experimentation as a problem solving tool, set a problem that can be investigated at the student's home that can be solved using either analytical or experimental means. For example, determine the time required to melt an ice cube of a certain size in a glass of water. This can be solved both analytically (using empirical correlations) and simply through experimentation.

This method can be used to help students understand the advantages and drawbacks of experimentation compared to alternative methods and identify when experimentation is the most appropriate tool.

Get the students to generate an assessment criteria or give them a specification for the problem that the prototype is designed to solve. The students then need to work out if the design will be able to achieve the intended goal. This could be informed by simple digital data like schematics (if a mechanical movement problem), simulated models, or remote interaction with a physical device. The prototype could also be designed by the students and built/tested remotely.

This type of practical can vary considerably in complexity; keeping the design variables constrained is advised, especially with design/build variants.