Bringing Soil Mechanics to Elementary Schools
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Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle