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The PEERSIST (Peer-led, Student Instructed STudy group) model promotes academic achievement through peer dialogue, in which disciplinary knowledge is socially co-constructed and refined over successive sessions. The study presents and utilizes an observation protocol to evaluate the implementation of peer learning in a mezzanine-level thermodynamics course at a large southwestern public university. The observation protocol was used to measure and compare the student interactions in two types of recitation sections, one of which used Peer-Led Study Groups (PLSGs) and the other of which followed a traditional TA-led format, to help demonstrate that student interactions were the main source of learning in the PLSG sections. The results showed that student interactions occurred at a significantly higher rate in the PLSG sections than in the TA-led sections. The study also analyzed peer interactions by incoming course preparedness in the PLSG sections and found a non-significant relationship between each group's mean pre-course grade point average (GPA) and number of peer interactions. [ABSTRACT FROM AUTHOR]

Achieving the United States goal of net-zero emissions by 2050 requires further development in technology due to the temporal mismatch between power/energy supply from renewable energy sources and power/energy demand of citizens and industry. A promising solution is through the use of energy storage systems (ESSs) to mitigate this mismatch and reduce excessive peak demand. Helping futures engineers understand the differences between energy storage technologies and areas for growth can be challenging due to a multiplicity of technological and application specific constraints. For example, in the area of energy storage, common technologies include conventional batteries (Li-ion, NaS, Pb-acid), flow batteries (PSB, VRB, ZnBr), supercapacitors, superconducting magnetic, flywheel, pumped hydro and compressed air. Technological constraints include power capacity, specific energy, energy density, specific power, power density, round trip efficiency, suitable depth of discharge and lifetime, among others. Application specific constraints relate to the user's energy demand profile. Due to the importance and complexity of this issue, a meaningful approach to teaching ESS analysis within the classroom is vital. Therefore, a simple Matlab application is presented in this paper to size the energy storage capacity of an ESS given a single month of demand data and desired peak reduction (power capacity). The program is designed for educational purposes but can also have practical use in sizing ESSs. [ABSTRACT FROM AUTHOR]