Scaffolding Design to Bridge the Gaps between Machine Learning and Scientific Discovery for K-12 STEM Education

2021 ◽  
Author(s):  
Xiaofei Zhou ◽  
Kaixin Li ◽  
Abdul Moid Munawar ◽  
Zhen Bai
Keyword(s):  
Machine Learning ◽  
Stem Education ◽  
Scientific Discovery ◽  
K 12

Voice Recognition for STEM Education Using Robotics

2017 ◽  
Author(s):  
Shubo Chen ◽  
Binsen Qian ◽  
Harry Cheng
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Stem Education ◽  
Basic Concept ◽  
Voice Recognition ◽  
Voice Control ◽  
K 12 ◽  
The Voice ◽  
Recognition Technique ◽  
Machine Learning Tool

In this paper, we provide a new voice recognition framework which allows K-12 students to write programs to solve problems using voice control. The framework contains the voice recognition module SPHINX which is based on an open source machine learning tool developed by Carnegie Mellon University and a wrapper function which is written in C/C++ interpreter Ch. The wrapper function allows students to interact the module in Ch. Along with Ch programming and robotic coursework, students will get the chance to learn the basic concept of machine learning and voice recognition technique. In order to bring students attention and interest in machine learning, various tasks have been designed for students to accomplish based on the framework. The framework is also flexible for them to explore other interesting projects.

Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

2021 ◽  
pp. 109434202110293
Author(s):  
Francis J Alexander ◽  
James Ang ◽  
Jenna A Bilbrey ◽  
Jan Balewski ◽  
Tiernan Casey ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
New Technologies ◽  
Scientific Discovery ◽  
Language Translation ◽  
Learning Technologies ◽  
Department Of Energy ◽  
Experimental Science ◽  
Exascale Computing ◽  
Self Driving Cars

Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.

scholarly journals How STEM Academy Teachers Conceptualize and Implement STEM Education

2015 ◽  
Vol 1 (1) ◽  
pp. 45-58
Author(s):  
Teruni Lamberg ◽  
Nicole Trzynadlowski
Keyword(s):  
Elementary Teachers ◽  
Stem Education ◽  
Teacher Interviews ◽  
Science Technology ◽  
Hands On ◽  
Subject Areas ◽  
And Mathematics ◽  
K 12 ◽  
Reading Activities ◽  
Science Activities

STEM (science, technology, engineering and mathematics) education has been gaining increasing nationwide attention. While the STEM movement has ambitious goals for k-12 education, a lack of shared understanding exists of what STEM is as well as how to implement STEM in the elementary classroom. This study investigates how seven elementary teachers in three STEM academy schools conceptualize and implement STEM in their classrooms. Teacher interviews were conducted. The findings reveal that the majority of teachers believe that STEM education involves integrating STEM subject areas. STEM activities consisted of student-led research and reading activities on STEM topics. Two teachers described STEM as involving “hands-on” science activities. Teachers at each STEM academy school conceptualized and implemented STEM differently. How STEM was implemented at each school was based on how teachers interpreted STEM and the resources they had access to. The STEM coaches played a central role in supporting the elementary teachers to plan and implement lessons. Teachers relied on them for ideas to plan and teach STEM lessons. The results of this study indicate that as more schools embrace the STEM movement, a unified understanding and resources are needed to support teachers.

Advancing the “E” in K-12 STEM Education

2010 ◽  
Vol 36 (1) ◽  
Author(s):  
Rockland Ronald ◽  
Diane S Bloom ◽  
John Carpinelli ◽  
Levelle Burr-Alexander ◽  
Linda S Hirsch ◽  
...  
Keyword(s):  
Stem Education ◽  
K 12

scholarly journals Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research

BioScience ◽  
2020 ◽  
Vol 70 (7) ◽  
pp. 610-620 ◽  
Author(s):  
Katelin D Pearson ◽  
Gil Nelson ◽  
Myla F J Aronson ◽  
Pierre Bonnet ◽  
Laura Brenskelle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scientific Discovery ◽  
Herbarium Specimens ◽  
Plant Fitness ◽  
Science And Society ◽  
Ecological Processes ◽  
Critical Determinant ◽  
Phenological Data ◽  
Life On Earth ◽  
Growth And Reproduction

Abstract Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.

scholarly journals Understanding intrinsic challenges to STEM instructional practices for Chinese teachers based on their beliefs and knowledge base

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Yan Dong ◽  
Jing Wang ◽  
Yunying Yang ◽  
Premnadh M. Kurup
Keyword(s):  
Knowledge Base ◽  
Stem Education ◽  
Structural Equation ◽  
Mathematical Problem ◽  
Equation Model ◽  
Chinese Society ◽  
Stem Learning ◽  
Chinese Teachers ◽  
Teachers Beliefs ◽  
K 12

Abstract Background China has great student participation in STEM education. Chinese society has a progressive and positive attitude towards STEM as it is considered to provide more opportunities in life. Teachers play a vital role in the success of any STEM program in K-12 schools. However, teachers are facing instructional challenges because of the interdisciplinary nature of the STEM curriculum and the current typical school structure. The success of the STEM programs depends on teachers’ beliefs and their knowledge in adapting to instructional implementation of STEM concepts. Results The data (n = 216) was collected from STEM primary and secondary teachers from 25 provinces in mainland China. Exploratory factor analysis (EFA) was applied, and Pearson’s correlation analysis was used to examine the correlation between Chinese STEM teachers’ beliefs, knowledge, implementation, and the intrinsic challenges of STEM education; t tests and analysis of variance (ANOVA) were performed to ascertain whether there were differences. The structural equation model (SEM) was applied to identify interrelationships. The results indicated that Chinese STEM teachers encounter higher-level intrinsic challenges to instructional implementations based on their beliefs and knowledge. Teachers who utilize their experience of teaching science as their main discipline and then attempt to integrate STEM using mathematics and engineering are likely to encounter higher-level intrinsic challenges in implementation. Conclusion The intrinsic challenges perceived by Chinese teachers in the practice of STEM education can be predicted by their beliefs and knowledge base. Teachers who understand the nature and pedagogy of STEM education are more likely to encounter lower-level intrinsic challenges of STEM teaching, while teachers who utilize their main discipline when conducting integrated STEM learning activities through modeling based on science, technology, engineering, and mathematical problem situations are more likely to encounter higher-level intrinsic challenges. This study also reveals that there are some significant differences in the level of STEM teachers’ beliefs, knowledge base, instructional practice, and their intrinsic challenges based on their teaching grade, seniority, and experience of STEM training and teaching.

scholarly journals Making sense of “STEM education” in K-12 contexts

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Tamara D. Holmlund ◽  
Kristin Lesseig ◽  
David Slavit
Keyword(s):  
Stem Education ◽  
Making Sense ◽  
K 12

Allies From Within: I-O Practitioners in Organizations

2018 ◽  
Vol 11 (4) ◽  
pp. 582-585
Author(s):  
Meghan Lowery ◽  
Joel Nadler ◽  
Dan J. Putka
Keyword(s):  
Machine Learning ◽  
Academic Language ◽  
Scientific Discovery ◽  
Organizational Psychology ◽  
Mutual Interest ◽  
Industrial And Organizational Psychology ◽  
Set Up ◽  
The Right

The focal article (Lapierre et al., 2018) highlights many good suggestions but only briefly mentions partnering with an academically trained internal industrial and organizational (I-O) practitioner. We believe beginning a partnership with a similarly trained ally well-versed through training in academic language and through experience in “business speak” will yield a stronger end result. The appreciation for an internal I-O practitioner should not go overlooked; when an academic partners with the right practitioner in the right environment, the partnership can be mutually beneficial and more rewarding than other options. For instance, recently we collaborated to set up a partnership for scientific discovery and mutual interest that involved 12 teams representing 14 different institutions spanning academe and practice to conduct a machine learning competition. This partnership enabled many academics and practitioners access to a complex organizational dataset in order to contribute to both an organization and the Society for Industrial and Organizational Psychology (SIOP) community (see Putka et al., 2018).

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