EAGER: Social Media Participation as Indicator of Actors, Awareness, Attitudes, and Activities Related to STEM Education

EAGER：社交媒体参与度作为与 STEM 教育相关的参与者、意识、态度和活动的指标

基本信息

批准号：
1707837
负责人：
Aditya Johri
金额：
$ 29.93万
依托单位：
George Mason University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-15 至 2020-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1707837&HistoricalAwards=false
关键词：
EAGER Social Media Participation Indicator

项目摘要

A strong and productive STEM workforce is essential for the socio-economic well-being of the nation. Consequently, efforts to improve STEM education are undertaken each year across a range of federal and state agencies and they support thousands of STEM education events, activities, and initiatives. These efforts have been successful to some degree but participation by populations historically underrepresented in STEM continues to lag at a time when STEM workforce requirements are increasing. In order to improve future efforts, it is critical to build a nuanced and empirical understanding of the overall STEM space: What is the nature of issues related to STEM that garner interest? Who shows an interest and participates? What are the outcomes and impact of different STEM related initiatives? This proposed project will use social media data to study these issues and answer such questions. Social media data is an underutilized resource as almost 87% of the American population now participates in some form of social media activity. This project has the potential to improve and increase the impact of STEM education related efforts by illuminating ideas and activities of interest to people, current efforts in place, and what participants share and where. This information can be used for efforts such as broadening participation by better targeting campaigns intended to increase interest in STEM and by connecting individuals and organizations to create more momentum for an idea, event or topic.This study will use Twitter data and will focus on studying: (1) Actors - who participates in STEM education issues? (2) Awareness - what do participants know? (3) Attitudes - what attitude and beliefs do participants hold? (4) Activities - what do the participants do? what activities do they participate in? More specifically, the investigative team will use novel methodology to (a) analyze interaction patterns of the categorized users and create interaction networks by considering nodes as users and edges as the interaction strength; (b) use topic modeling techniques to gain insight about users' awareness of STEM; (c) employ target-specific sentiment mining techniques to gain insight about users' attitudes; (d) use geographic and spatio-temporal analysis, such as Density Based Spatial Clustering of Applications with Noise (DBSCAN), to gain insight about STEM activities. This project has the potential to contribute to our understanding of STEM education from the lens of social media. There is novelty in the approach and the methods of this project that serves to potentially transform how social media in STEM education is utilized.

强大而富有成效的 STEM 劳动力对于国家的社会经济福祉至关重要。因此，每年一系列联邦和州机构都会努力改善 STEM 教育，并支持数以千计的 STEM 教育活动、活动和倡议。这些努力在某种程度上取得了成功，但在 STEM 劳动力需求不断增加的情况下，历史上 STEM 代表性不足的人群的参与仍然滞后。为了改进未来的工作，对整个 STEM 领域建立细致入微的实证理解至关重要：引起人们兴趣的与 STEM 相关的问题的本质是什么？谁表现出兴趣并参与？不同 STEM 相关举措的成果和影响是什么？该拟议项目将使用社交媒体数据来研究这些问题并回答这些问题。社交媒体数据是一种未得到充分利用的资源，因为现在近 87% 的美国人参与某种形式的社交媒体活动。该项目有潜力通过阐明人们感兴趣的想法和活动、当前所做的努力以及参与者分享的内容和地点，来改善和增加 STEM 教育相关工作的影响。这些信息可用于通过更好的针对性活动来扩大参与范围，以提高对 STEM 的兴趣，以及通过联系个人和组织为想法、活动或主题创造更多动力。本研究将使用 Twitter 数据，并将重点研究：（1）参与者——谁参与STEM教育问题？ (2) 意识——参与者知道什么？ (3) 态度——参与者持有什么态度和信念？ (4) 活动——参与者做什么？他们参加什么活动？更具体地说，调查团队将使用新颖的方法来（a）分析分类用户的交互模式，并通过将节点视为用户、将边缘视为交互强度来创建交互网络； (b) 使用主题建模技术来深入了解用户对 STEM 的认识； (c) 采用针对特定目标的情绪挖掘技术来深入了解用户的态度； (d) 使用地理和时空分析，例如基于噪声的应用程序的密度空间聚类 (DBSCAN)，来深入了解 STEM 活动。该项目有可能有助于我们从社交媒体的角度理解 STEM 教育。该项目的方法和方法很新颖，有可能改变 STEM 教育中社交媒体的使用方式。