Bringing Soil Mechanics to Elementary Schools
Bibliographic record
Abstract
Abstract Bringing Soil Mechanics to Elementary SchoolsIntroducing elementary school students to real engineering activities inspires their creativity at anearly age. Hands-on engineering activities stimulate positive feelings towards engineering, sincenovel tools and techniques are used to deliver curricular content aligned with standards.Likewise, exposure to what engineers do, allows students to aspire to become engineers whilethey are at an impressionable age.The main goal of this paper is to describe several soil mechanics-related activities conductedwith elementary school students. The activities were designed and conducted by a graduatestudent (Fellow) and his partner teacher under an NSF GK-12 Fellows grant. The Fellowexposed second, third, and fourth grade students to fundamental concepts of soil mechanicswithin the geotechnical engineering context as experienced by students in their own surroundingsand environment. Applications of soil mechanics in construction were also presented. Theactivities presented in this paper include: (1) soil permeability studies where students learn thatthe flow rate of water in soils depends on soil composition and grain size; (2) erosion in riverswhere students design and make buildings, and place them in the vicinity of a stream to predicttheir stability; and (3) shallow and deep foundations where students make their own soil profiles,and test the bearing capacity of various foundation systems. All activities support the requiredscience curriculum at the elementary school level. The Fellow and teacher conducted the labexperiments and challenged students and helped them to understand their assignments. Pre- andpost-evaluations were employed to assess the gain in students’ engineering knowledge as a resultof participation in the described activities.Robotic tools including LEGO NXT and 3-D printers were utilized for data collection and tofabricate scaled-models of student-designed residential and commercial buildings. For example,for the soil permeability investigations, an experimental setup was devised that uses a LEGONXT controller and an ultrasonic sensor to facilitate automated data collection. Moreover, whenconducting the “river erosion model” studies, the students took ownership of their learning sincethey: (1) proposed designs for their buildings; (2) had their designs digitally fabricated; and (3)made decisions about the placement of their buildings on a river bank, modeled on a table-topusing a variety of clay and sand. The river erosion model demonstrated water’s ability to changethe surface of the Earth and students could visualize the impact of erosion on their builtenvironment. The setup can also enable students to investigate the effects of foundation-types,foundation material, etc., to withstand a flood event. Students found these experimental toolsparticularly attractive, which made the class more enjoyable. Moreover, hands-on activitiesmotivated the students to learn the required basic concepts of science, e.g., a unit on Earthmaterials. Furthermore, the automated data acquisition system provided an interactive learningenvironment that allowed students to focus on the technical concepts rather than the drudgery ofmanual data collection.The paper demonstrates that students are motivated to study science, technology, engineering,and mathematics (STEM) through simplified geotechnical engineering exercises. Moreover, theuse of LEGO NXT toolkit enhances student learning, reasoning, and analytical judgment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".