SSreenivasan-02
Multifunctional carbon-based electrocatalysts for energy applications
Energy
Preferred major field of study or minimum required skills
This project requires basic introductory chemistry coursework (general chemistry & organic at minimum). [Experience in social science research methods (ethnography, interviewing, survey development) and technologies (MaxQDA) encouraged but not required]. Understanding and interest in history and culture of the region are considered an asset.
Scholarly significance/intellectual merit
Recently, the US National Academies' Board on Science Education released a report of the future directions of STEM education for the year 2040. Futurists in the report illustrated a vision of equity-centered, individualized learning pathways that focused on project/problem-based learning (PBL) to tackle critical challenges facing our planet. Such a vision is unlikely to be realized with the topic-focused introductory chemistry curriculum that favors rote memorization and mindless algorithmic calculations over a deliberative and theory-based scaffolding of core-idea centered curriculum units. Using a design-based research methodology and the existing curricular framework CLUE, this project will design a PBL sequence to incorporate place-based education design principles into evidence-based curricula.
Research question(s)
- What El Paso/Borderlands history and phenomena can be leveraged as a driving 'need-to-know' in a core-idea centered curriculum?
- How can existing activities be (re)scaffolded to incorporate local communities' knowledge and history in the building blocks of a chemistry perspective on a STEM degree?
- Which assessment prompts can serve as internal and external metrics to evaluate the curriculum design?
Methods/techniques/instruments to be learned/utilized
Human subjects research methods -- assessment development and validation through interviews and online activities, navigating IRB requirements, qualitative coding, if good agreement (k >= 0.7) is reached, perhaps how to use lexical analysis models such as AACR to process large datasets