The program For Inspiration and Recognition of Science and Technology (FIRST) for young students incorporates project-based learning (PBL) with designing and building wireless-controlled robots. The students are guided by experts, mostly engineers. The FIRST organization determines the theme of the robot annual competition. The goal of this research is to characterize and evaluate the effect of the FIRST program on graduates’ self-efficacy, interpersonal skills, and career choices in science, technology, engineering, and mathematics (STEM). The research participants included 297 FIRST graduates, mostly high schoolers, who responded to questionnaires, and five of them were interviewed. Analysis of the data showed that the FIRST program improved graduates’ interpersonal skills such as time management, teamwork skills, and self-efficacy, as well as had an impact on the graduates’ STEM career choices. The main factors impacting the graduates’ career choice was their exposure to robotics and to experts from the industry. The theoretical contribution is to the social cognitive theory (SCT) in the context of the FIRST program. Our study explains students’ career choice through correlations among students’ aspirations for choosing a career, their self-efficacy, their interpersonal skills, and their actual choice. The practical contribution lies in better understanding the robotic PBL program and expanding the STEM work force.
Purpose
A social media user behavior model is presented as a function of different user types, i.e. light and heavy users. The users’ behaviors are analyzed in terms of knowledge creation, framing and targeting.
Design/methodological approach
Data consisting of 160,000 tweets by nearly 40,000 twitter users in the city of Newark (NJ, USA) were collected during the year 2014. An analysis was conducted to examine the hypothesis that different user types exhibit distinct behaviors driven from different motivations.
Findings
There are three important findings of this study. First, light users reuse existing content more often, while heavy and automated users create original content more often. Light users also use more sentiments than the heavy and automated users. Second, automated users frame more than heavy users, who frame more than light users. Third, light users tend to target a specific audience, while heavy and automated users broadcast to a general audience.
Research implications
Decision-makers can use this study to improve communication with their customers (the public) and allocate resources more effectively for better public services. For example, they can better identify subsets of users and then share and track specialized content to these subsets more effectively.
Originality/value
Despite the broad interest, there is insufficient research on many aspects of social media use, and very limited empirical research examining the relevance and impact of social media within the public sector. The social media user behavior model was established as a framework that can provide explanations for different social media knowledge behaviors exhibited by various subsets of users, in an e-government context.
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