Inclusive STEAM education in diverse disciplines of sustainable energy and AI

Skowronek, Michelle; Gilberti, Renée M.; Petro, Michael; Sancomb, Christopher; Maddern, Stacy Warner; Janković, Jasna

doi:10.1016/j.egyai.2021.100124

Cited by 22 publications

(15 citation statements)

References 28 publications

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“…Further exploration of the impact of AI-driven interventions on student learning and quality of instructor-student interactions could also include case studies from other subject disciplines that we have not tapped into, as well as narrowing efforts onto unrepresented or more diverse student demographics, given the dearth of evidence-based recommendations in AI-driven STEM education applicable to a diverse student body (Skowronek et al, 2022 ) and concerns over bias and equity in AI implementations (E. Gilbert, 2021 ). Furthermore, while no direct effect of praising or featuring was observed on the students who submitted the posts, the present study does not account for the public nature of praising or featuring on the posts of other students.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

A Validation of AI-Enabled Discussion Platform Metrics and Relationships to Student Efforts

Archibald

Hudson²,

Heap

et al. 2023

TechTrends

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Asynchronous discussions are a popular feature in online higher education as they enable instructor-student and student–student interactions at the users’ own time and pace. AI-driven discussion platforms are designed to relieve instructors of automatable tasks, e.g., low-stakes grading and post moderation. Our study investigated the validity of an AI-generated score compared to human-driven methods of evaluating student effort and the impact of instructor interaction on students’ discussion post quality. A series of within-subjects MANOVAs was conducted on 14,599 discussion posts among over 800 students across four classes to measure post ‘curiosity score’ (i.e., an AI-generated metric of post quality) and word count. After checking assumptions, one MANOVA was run for each type of instructor interaction: private coaching, public praising, and public featuring. Instructor coaching appears to impact curiosity scores and word count, with later posts being an average of 40 words longer and scoring an average of 15 points higher than the original post that received instructor coaching. AI-driven tools appear to free up time for more creative human interventions, particularly among instructors teaching high-enrollment classes, where a traditional discussion forum is less scalable.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

A Validation of AI-Enabled Discussion Platform Metrics and Relationships to Student Efforts

Archibald

Hudson²,

Heap

et al. 2023

TechTrends

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“…This parameter is about investigating AI governance's role in promoting sustainable energy and sustainable development without putting essential energy requirements at risk and considering strategies to fight climate change. It captures different dimensions of "Sustainable Energy and Climate," including AI-powered solutions to achieve sustainable energy (Saheb et al, 2022), the aspects of water governance in urban areas (Goulas et al, 2022), the significance of AI in promoting sustainable development in the water, food, and energy industries (D'amore et al, 2022), sustainable education and society in the energy domain (Skowronek et al, 2022), and the governance of AI to confront climate change and achieve sustainable development (Raper et al, 2022).…”

Section: Sustainable Energy and Climatementioning

confidence: 99%

AI explainability and governance in smart energy systems: A review

2023

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Traditional electrical power grids have long suffered from operational unreliability, instability, inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the energy sector with emerging technologies, renewable energy sources, and other trends. Artificial intelligence (AI) is being applied to smart energy systems to process massive and complex data in this sector and make smart and timely decisions. However, the lack of explainability and governability of AI is a major concern for stakeholders hindering a fast uptake of AI in the energy sector. This paper provides a review of AI explainability and governance in smart energy systems. We collect 3,568 relevant papers from the Scopus database, automatically discover 15 parameters or themes for AI governance in energy and elaborate the research landscape by reviewing over 150 papers and providing temporal progressions of the research. The methodology for discovering parameters or themes is based on “deep journalism,” our data-driven deep learning-based big data analytics approach to automatically discover and analyse cross-sectional multi-perspective information to enable better decision-making and develop better instruments for governance. The findings show that research on AI explainability in energy systems is segmented and narrowly focussed on a few AI traits and energy system problems. This paper deepens our knowledge of AI governance in energy and is expected to help governments, industry, academics, energy prosumers, and other stakeholders to understand the landscape of AI in the energy sector, leading to better design, operations, utilisation, and risk management of energy systems.

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“…We analyzed STEM education forms in the world and identified the most common: solving practical cases in the field [29] and at home [30]; practical classes using narrative and interdisciplinary approaches in senior school [31]; special courses [32]; consulting in the project activities [33].…”

Section: Stem Education Around the World And Features Of Its Implemen...mentioning

confidence: 99%

Stem Project as a Means of Learning Modeling for Pre-Service Mathematics and Computer Science Teachers

Семеніхіна¹,

Друшляк²,

Shyshenko³

2022

ITLT

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Modern science operates with various methods, among which modeling is one of the most popular. The development of information technology allows the study of analogues (models) with the most significant characteristics of the real object. Modeling activities have been considered as useful teaching method in STEM education. Cloud services (like GeoGebra) are effective means for STEM education. The paper features a methodology of forming modeling skills based on STEM projects, which is grounded on modeling interesting curves of Analytic Geometry course. The content of the methodology is a course in Computer Modeling, which includes a module "STEM education and modeling". The module idea is based on the formation of skills required to model interesting curves (ellipse, hyperbola, parabola, conchoid of Nicomedes, limaçon of Pascal, strophoid, cissoid of Diocles, lemniscate of Bernoulli, Cassini oval, cycloidal curves, folium of Descartes, witch of Agnesi, logarithmic spiral). The methodology provides 4 steps (Step 1 – the teacher offers an example of a STEM project, which is discussed in class and solved by the teacher using GeoGebra; Step 2 – students are divided into groups of 3-4 people; Step 3 – the teacher offers a short STEM project (7-10 days), in which students model the curve; Step 4 – students offer their own STEM project (15-20 days), the solution of which is based on the modeling of an interesting curve). To test the effectiveness of the developed methodology, a pedagogical experiment was organized (2019-2021), which was joined by Master's students majoring in "Secondary Education (Mathematics)" and "Secondary Education (Computer Science)". Makarenko Sumy State Pedagogical University (Ukraine) was the experimental base. The effectiveness of the proposed methodology is proved by the sign test at the significant level of 0.05.

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Inclusive STEAM education in diverse disciplines of sustainable energy and AI

Cited by 22 publications

References 28 publications

A Validation of AI-Enabled Discussion Platform Metrics and Relationships to Student Efforts

A Validation of AI-Enabled Discussion Platform Metrics and Relationships to Student Efforts

AI explainability and governance in smart energy systems: A review

Stem Project as a Means of Learning Modeling for Pre-Service Mathematics and Computer Science Teachers

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