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Relationship of Middle School Student STEM Interest to Career Intent

Understanding middle school students’ perceptions regarding STEM dispositions, and the role attitudes play in establishing STEM career aspirations, is imperative to preparing the STEM workforce of the future. Data were gathered from more than 800 middle school students participating in a hands-on, real world application curriculum to examine the relationship of the students’ interest in STEM and their intentions to pursue a career in a STEM field.

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Festival was just the start

Debuted in 2012, the Arizona SciTech Festival celebrated its sixth season with more than 800 organizations planning to stage nearly 1,500 events for an audience total exceeding 400,000.

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Honeywell hosts 175 students to advance STEM in Arizona

Honeywell hosted the second-annual Arizona Science Officer technology day and statewide cabinet meeting. The event is designed to inspire interest in technology careers and allow students to work on collective action projects that impact their respective schools and communities. The students were all elected by their peers to serve as chief science officers.

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Conversations: Startups need our students to buck up

In December, software companies WebPT and Galvanize are set to bring more than 800 new employees to the area as they renovate a 120,000 square foot building on Grant Street. How can the Arizona Commerce Authority continue to attract even more companies and convince them that the Phoenix region is the center of STEM and innovation? How will they convince startups that we’ve got the talent to build their workforce? Arizona SciTech is collaborating with ACA to help.

 

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SIUE’s “Digital East St. Louis” inspires local students to explore city’s history and culture

Students from East St. Louis are documenting the culture and history of their hometown, while gaining valuable knowledge and computer skills, by participating in Southern Illinois University Edwardsville's Digital East St. Louis program. Now in its third year, the program continues to incorporate new interactive projects for the students, with the underlying goal of inspiring a love for STEM through creative digital humanities content.

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Pursuing Science: Pathways from Middle School to Careers

This video presentation uses Conceptual Metaphor theory with the aim to increase the flow within the STEM pipeline

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Fun with GIS 2016: GRACE

Michigan's GRACE Project (GIS Resources and Applications for Career Education) helps students use GIS. GRACE uses a coordinated and multi-tiered approach for educators and students alike, and works with communities across the state to identify partners interested in high school student interns with GIS knowledge and skills.

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STEM Learning Activation survey

Designed to be used with 10-14 year olds, these survey scales are used to assess an individual across multiple dimensions of STEM learning activation (Fascination, Values, Competency Belief, Innovation Stance). Please note that these can be used in conjunction with the scientific sensemaking scale included in Science Learning Activation survey. These scales can be used concurrently to measure the multi-dimensional construct of STEM learning activation or separately to measure individual dimensions.

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Career Maturity Inventory Form C

It is a 24-item scale that measures change along four scales of student career awareness and planning, along with open-response items about specific schooling intentions and career aspirations. Four scales are Concern, Curiosity, Confidence, and Consultation.

 

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Measuring Cognitive and Psychological Engagement: Validation of the Student Engagement Instrument

A review of relevant literatures led to the construction of a self-report instrument designed to measure two subtypes of student engagement with school: cognitive and psychological engagement. The psychometric properties of this measure, the Student Engagement Instrument (SEI), were assessed based on responses of an ethnically and economically diverse urban sample of 1931 ninth grade students. Factor structures were obtained using exploratory factor analyses (EFAs) on half of the dataset, with model fit examined using confirmatory factor analyses (CFAs) on the other half of the dataset.

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