Engineering

We Strive: Initial Explorations of STEM Teachers' Successes and Challenges in Implementing Socioscientific Issues

This study explores two teachers participating in professional development workshops implementing SocioScientific Issues (SSI) into STEM classrooms. Two research questions were investigated: (a) To what extent did teachers implement SSI into their lesson plans and (b) In what ways did lessons change from the beginning of the workshop?

Author/Presenter

Joseph Johnson

Augusto Macalalag

Becky Mathers-Lowery

Gabrielle Ialacci

Year
2022
Short Description

This study explores two teachers participating in professional development workshops implementing SocioScientific Issues (SSI) into STEM classrooms. Two research questions were investigated: (a) To what extent did teachers implement SSI into their lesson plans and (b) In what ways did lessons change from the beginning of the workshop?

We Strive: Initial Explorations of STEM Teachers' Successes and Challenges in Implementing Socioscientific Issues

This study explores two teachers participating in professional development workshops implementing SocioScientific Issues (SSI) into STEM classrooms. Two research questions were investigated: (a) To what extent did teachers implement SSI into their lesson plans and (b) In what ways did lessons change from the beginning of the workshop?

Author/Presenter

Joseph Johnson

Augusto Macalalag

Becky Mathers-Lowery

Gabrielle Ialacci

Year
2022
Short Description

This study explores two teachers participating in professional development workshops implementing SocioScientific Issues (SSI) into STEM classrooms. Two research questions were investigated: (a) To what extent did teachers implement SSI into their lesson plans and (b) In what ways did lessons change from the beginning of the workshop?

Longitudinal Clustering of Students’ Self-Regulated Learning Behaviors in Engineering Design

It is vital to develop an understanding of students' self-regulatory processes in the domains of STEM (Science, Technology, Engineering, and Mathematics) for the quality delivery of STEM education. However, most studies have followed a variable-centered approach, leaving open the question of how specific SRL (Self-regulated Learning) behaviors group within individual learners. Furthermore, little is known about how students' SRL profiles unfold over time in STEM education, specifically in the context of engineering design.

Author/Presenter

Shan Li

Guanhua Chen

Wanli Xing

Juan Zheng

Charles Xie

Year
2020
Short Description

It is vital to develop an understanding of students' self-regulatory processes in the domains of STEM (Science, Technology, Engineering, and Mathematics) for the quality delivery of STEM education. However, most studies have followed a variable-centered approach, leaving open the question of how specific SRL (Self-regulated Learning) behaviors group within individual learners. Furthermore, little is known about how students' SRL profiles unfold over time in STEM education, specifically in the context of engineering design. In this study, we examined the change of students’ SRL profiles over time as 108 middle school students designed green buildings in a simulation-based computer-aided design (CAD) environment

Examining Temporal Dynamics of Self-Regulated Learning Behaviors in STEM Learning: A Network Approach

From a network perspective, self-regulated learning (SRL) can be conceptualized as networks of mutually interacting self-regulatory learning behaviors. Nevertheless, the research on how SRL behaviors dynamically interact over time in a network architecture is still in its infancy, especially in the context of STEM (sciences, technology, engineering, and math) learning.

Author/Presenter

Shan Li

Hanxiang Du

Wanli Xing

Juan Zheng

Guanhua Chen

Charles Xie

Year
2020
Short Description

From a network perspective, self-regulated learning (SRL) can be conceptualized as networks of mutually interacting self-regulatory learning behaviors. Nevertheless, the research on how SRL behaviors dynamically interact over time in a network architecture is still in its infancy, especially in the context of STEM (sciences, technology, engineering, and math) learning. In the present paper, we used a multilevel vector autoregression (VAR) model to examine the temporal dynamics of SRL behaviors as 101 students designed green buildings in Energy3D, a simulation-based computer-aided design (CAD) environment.

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.