Of course! A match made in cyberspace. First met virtually on suggestion of a common colleague. Nosofsky's expertise in cognitive science and the theory of category learning provides match to test Brande's technology in the messy real-world environment of students in labs.

Every human develops abstract categories based on experience with concrete objects that stand as examples with diagnostic properties of the category: birds, pots and pans, friends, even minerals and rocks.

Which learning experience can lead to superior performance in rock identification? Can students learn to identify a granite, for example, from studying multiple examples of interactive 3D models of physical samples? We're getting close from data of experiments designed by Robert Nosofsky through collaborative award and programmed by Brande-directed software engineers in Rockviewer.

Learning to identify rock type as normally practiced in an introductory physical geology lab. For training under Condition #1, student engages only rocks-in-the-box for the same amount of time as the other conditions.

For training under Condition #2, student engages only 3D interactive models for the same amount of time as the other conditions.

For training under Condition #3, student engages physical rocks-in-the-box for 1/2 the time, and 3D interactive models for 1/2 the time.

There are benefits of training with high-quality interactive virtual rocks over learning from traditional kit of physical samples. Important to understand that "feeling" a rock sample does not convey any information (haptic) that needed for a diagnostic identification of the rock name. "Rough" vs "Smooth" does not lead to a particular rock type identification.

All students experience all three training conditions, once each for igneous, sedimentary, and metamorphic rock identification. Each rock type uses one lab period. Experiment runs across three labs. Some students miss one or more labs in real-world lab experiments. Statistical analysis modified for unequal student group sample sizes.

Second cohort completed spring semester 2022. Logistic regression results show that performance is equal by physical sample and 3D training conditions. Performance by hybrid training is significantly (P<0.05) greater than training by either of other conditions.

Rock samples about 1-3 inches in size. Rocks for 3D models totaled 4 samples of each of 10 rock types - igneous, sedimentary, metamorphic. 40 igneous rocks, 40 sedimentary rocks, 36 metamorphic rocks (we couldn't acquire 4 samples of one metamorphic rock type).

NSF funds for equipment to produce high quality images needed for high quality models. Rock is positioned, and a focus-stacked shoot of 10 images is made. Rock is rotated 10 to 15 degrees, another shoot. Rock is rotated through 360 degrees (manually on a turntable -not shown). Rock is flipped upside down and rotated to shoot bottom. Rock is rotated 90 degrees to shoot the sides (if needed).

Focus-stacking is needed for detail of small 1 to 2 inch rock samples. Maintaining focus at nearly 1-to1 magnification across a rock sample is difficult. Camera body has built-in software to shoot 10 multiple images at each focal distance of rock from front to back. Focus stacking processing software (Canon) composites into a single image only the in-focus portion of each of the 10 images in the stack.

Our custom code is needed to eliminate background before model making.

Commercial software needed for processing composited images (60 to 100+) into 3D model. Processing time can take up to 1 hour per model.

40 rock samples of 10 types of igneous rock. 40 rock samples of of 10 types of sedimentary rock. 36 rock samples of 10 types of metamorphic rock.

Example 3D model (not part of research set). Public model served on Sketchfab platform.

Funding used for software engineers to use gaming engine (Unity) and Microsoft database to administer functions for student training sequences.

2 minute video showing deployment in the classroom.

With links to resources.

PDF of presentation at AGU Fall, Dec. 12, 2022, Chicago IL.