Undergraduate

Creating Inclusive PreK–12 STEM Learning Environments

Brief CoverBroadening participation in PreK–12 STEM provides ALL students with STEM learning experiences that can prepare them for civic life and the workforce.

Author/Presenter

Malcom Butler

Cory Buxton

Odis Johnson Jr.

Leanne Ketterlin-Geller

Catherine McCulloch

Natalie Nielsen

Arthur Powell

Year
2018
Short Description

This brief offers insights from National Science Foundation-supported research for education leaders and policymakers who are broadening participation in science, technology, engineering, and/or mathematics (STEM). Many of these insights confirm knowledge that has been reported in research literature; however, some offer a different perspective on familiar challenges.

Qualifying Domains of Student Struggle in Undergraduate General Chemistry Laboratory

Learning and learning goals in undergraduate chemistry laboratory have been a popular research topic for the past three decades due to calls for curriculum reform, cost justification, and overall efficacy of necessary skill development. While much work has been done to assess curricular interventions on students’ learning and attitudes towards lab, few have discussed the increased difficulties of these non-traditional laboratory activities or the obstacles students must overcome in the laboratory setting.

Author/Presenter

Clarissa Keen

Hannah Sevian

Year
2021
Short Description

The work presented here focuses on student struggles in undergraduate general chemistry laboratory activities, the source of these struggles, and the actions students take to overcome them. Using an activity theoretical lens and multiple domains (cognitive, epistemological, socioemotional, and psychomotor), we developed a domains-of-struggle framework which encompasses how struggles emerge through contradictions within the laboratory activity system.

Exploring the Viral Spread of Disease and Disinformation

The worldwide COVID-19 pandemic has highlighted the importance of mathematical models in predicting the spread of the coronavirus (Srinivas 2020; Stevens & Muyskens 2020) and assessing the effectiveness of various safety measures in reducing that spread (Li et al 2020). These models can be extremely sophisticated, drawing on the expertise of applied mathematicians, epidemiologists, public health experts, and others, but at its core, there is a notion of exponential growth that is relevant for the secondary mathematics curriculum.

Author/Presenter

Samuel Otten

Julia Bemke

Jerred Webb

Lead Organization(s)
Year
2022
Short Description

The tasks described in this chapter are intended to build connections between these real-world dangers of viral spread and some relevant topics from the secondary mathematics curriculum. We also explore a link between mathematical reasoning and media literacy—the ability to discern the commercial, ideological, or political motivations of media and the recognition that receivers negotiate the meaning of messages (Aufderheide, 1993)—so that, just as we know to take safety precautions with regard to an airborne coronavirus, we can also help our students learn to take precautions against the spread of misinformation on social media.

Classroom Orchestration of Computer Simulations for Science and Engineering Learning: A Multiple-Case Study Approach

This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels.

Author/Presenter

Jennifer Chiu

Ying Ying Seah

James P. Bywater

Corey Schimpf

Tugba Karabiyik

Sanjay Rebello

Charles Xie

Short Description

This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels.

Classroom Orchestration of Computer Simulations for Science and Engineering Learning: A Multiple-Case Study Approach

This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels.

Author/Presenter

Jennifer Chiu

Ying Ying Seah

James P. Bywater

Corey Schimpf

Tugba Karabiyik

Sanjay Rebello

Charles Xie

Short Description

This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels.

Classroom Orchestration of Computer Simulations for Science and Engineering Learning: A Multiple-Case Study Approach

This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels.

Author/Presenter

Jennifer Chiu

Ying Ying Seah

James P. Bywater

Corey Schimpf

Tugba Karabiyik

Sanjay Rebello

Charles Xie

Short Description

This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels.

Using Machine Learning to Predict Engineering Technology Students’ Success with Computer-Aided Design

Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students.

Author/Presenter

Jasmine Singh

Viranga Perera

Alejandra J. Magana

Brittany Newell

Jin Wei-Kocsis

Ying Ying Seah

Greg J. Strimel

Charles Xie

Year
2022
Short Description

Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students. To encourage CAD programs to build in assistance to students, we used data generated from students using a free, open-source CAD software called Aladdin to demonstrate how student data combined with machine learning techniques can predict how well a particular student will perform in a design task.

Using Machine Learning to Predict Engineering Technology Students’ Success with Computer-Aided Design

Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students.

Author/Presenter

Jasmine Singh

Viranga Perera

Alejandra J. Magana

Brittany Newell

Jin Wei-Kocsis

Ying Ying Seah

Greg J. Strimel

Charles Xie

Year
2022
Short Description

Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students. To encourage CAD programs to build in assistance to students, we used data generated from students using a free, open-source CAD software called Aladdin to demonstrate how student data combined with machine learning techniques can predict how well a particular student will perform in a design task.

Using Machine Learning to Predict Engineering Technology Students’ Success with Computer-Aided Design

Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students.

Author/Presenter

Jasmine Singh

Viranga Perera

Alejandra J. Magana

Brittany Newell

Jin Wei-Kocsis

Ying Ying Seah

Greg J. Strimel

Charles Xie

Year
2022
Short Description

Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students. To encourage CAD programs to build in assistance to students, we used data generated from students using a free, open-source CAD software called Aladdin to demonstrate how student data combined with machine learning techniques can predict how well a particular student will perform in a design task.

The Development of Critical Teaching Skills for Preservice Secondary Mathematics Teachers Through Video Case Study Analysis

Using social learning theory with the central concept of a community of practice, we situate this work within a secondary mathematics methods course to unpack preservice secondary mathematics teachers (PSMTs) development through the use of video case studies. We analyzed six sessions of the course in which PSMTs engaged in discussions about video segments of mathematics teaching rooted in the Teaching for Robust Understanding (TRU) framework for high-quality instruction. Analysis of this data showed opportunities for PSMTs to develop critical skills for teaching (Hiebert et al., 2007).

Author/Presenter

Helene S. Leonard

Youngjun Kim

Su San Lim

Victoria D. Bonaccorso

Joseph DiNapoli

Eileen Murray

Lead Organization(s)
Year
2021
Short Description

Using social learning theory with the central concept of a community of practice, we situate this work within a secondary mathematics methods course to unpack preservice secondary mathematics teachers (PSMTs) development through the use of video case studies. We analyzed six sessions of the course in which PSMTs engaged in discussions about video segments of mathematics teaching rooted in the Teaching for Robust Understanding (TRU) framework for high-quality instruction.