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Computing systems are consuming an increasing and unsustainable fraction of society’s energy footprint, notably in data centers. Meanwhile, energy-efficient software engineering techniques are often absent from undergraduate curricula. We propose to develop a learning module for energy-efficient software, suitable for incorporation into an undergraduate software engineering class. There is one major problem with such an endeavor: undergraduate curricula have limited space for mastering energy-related systems programming aspects. To address this problem, we propose to leverage the domain expertise afforded by large language models (LLMs). In our preliminary studies, we observe that LLMs can generate energy-efficient variations of basic linear algebra codes tailored to both ARM64 and AMD64 architectures, as well as unit tests and energy measurement harnesses. On toy examples suitable for classroom use, this approach reduces energy expenditure by 30–90%. These initial experiences give rise to our vision of LLM-based metacompilers as a tool for students to transform high-level algorithms into efficient, hardware-specific implementations. Complementing this tooling, we will incorporate systems thinking concepts into the learning module so that students can reason both locally and globally about the effects of energy optimizations. 

Abstract Self-report assessments are used frequently in higher education to assess a variety of constructs, including attitudes, opinions, knowledge, and competence. Systems thinking is an example of one competence often measured using self-report assessments where individuals answer several questions about their perceptions of their own skills, habits, or daily decisions. In this study, we define systems thinking as the ability to see the world as a complex interconnected system where different parts can influence each other, and the interrelationships determine system outcomes. An alternative, less-common, assessment approach is to measure skills directly by providing a scenario about an unstructured problem and evaluating respondents’ judgment or analysis of the scenario (scenario-based assessment). This study explored the relationships between engineering students’ performance on self-report assessments and scenario-based assessments of systems thinking, finding that there were no significant relationships between the two assessment techniques. These results suggest that there may be limitations to using self-report assessments as a method to assess systems thinking and other competencies in educational research and evaluation, which could be addressed by incorporating alternative formats for assessing competence. Future work should explore these findings further and support the development of alternative assessment approaches. 

Abstract There is an increasing emphasis on assessing student learning outcomes from study abroad experiences, but this assessment often focuses on a limited range of outcomes and assessment methods. We argue for shifting to assessing student learningprocessesin study abroad and present the critical incident technique as one approach to achieve this goal. We demonstrate this approach in interviews with 79 students across a range of global engineering programs, through which we identified 173 incidents which were analyzed to identify common themes. This analysis revealed that students described a wide range of experiences and outcomes from their time abroad. Students’ experiences were messy and complex, making them challenging to understand through typical assessment approaches. Our findings emphasize the importance of using a range of assessment approaches and suggest that exploring students’ learning processes in addition to learning outcomes could provide new insights to inform the design of study abroad programs. 

Abstract We introduce the Lake Urmia Vignette (LUV) as a tool to assess individuals' understanding of complexity in socio‐environmental systems. LUV is based on a real‐world case and includes a short vignette describing an environmental catastrophe involving a lake. Over a few decades, significant issues have manifested themselves at the lake because of various social, political, economic, and environmental factors. We design a rubric for assessing responses to a prompt. A pilot test with a sample of 30 engineering graduate students is conducted. We compare responses to LUV with other measures. Our findings suggest that students' understanding of complexity is positively associated with their understanding of systems concepts such as feedback loops but not with other possible variables such as self‐reported systems thinking skills or systems‐related coursework. Based on the provided instructions, researchers can use LUV as a novel assessment tool to examine understanding of complexity in socio‐environmental systems. © 2020 System Dynamics Society