The Road to Becoming a Scientist: A Mixed-Methods Investigation of Supports and Barriers Experienced by First-Year Community College Students
The Road to Becoming a Scientist: A Mixed-Methods Investigation of Supports and Barriers Experienced by First-Year Community College Students
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成为科学家之路：对社区学院一年级学生所经历的支持和障碍的混合方法调查
DOI：
10.1177/016146812012200208
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Amy C. Prevost
中科院分区：
文献类型：
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作者：
Xueli Wang;Kelly Wickersham;S. Y. Lee;Na Lor;Ashley N. Gaskew;Amy C. Prevost
尽管对转学途径进行了大量的研究，但社区学院培养STEM学士学位转学的能力方面的工作仍然有限。特别是，无论是定量还是定性的证据，都极其缺乏关于在社区大学开始的有抱负的STEM学生在追求STEM学士学位的过程中如何经历支持和障碍的证据。这项混合方法研究解决了以下问题：哪些显著因素与社区学院STEM学生决定转入STEM学士学位课程有关，学生如何描述他们在这些因素方面所经历的支持和障碍？在STEM迁移模型的指导下，我们采用解释性序列混合方法设计进行了本研究。我们结合了来自中西部一个州的三所大型两年制大学的调查、管理和访谈数据。我们应用人工神经网络（ANN）技术来识别与社区学院STEM学生决定转入STEM学士学位课程相关的因素。基于人工神经网络产生的因素，我们分析了访谈数据，利用学生对其经历的丰富描述来赋予识别出的因素意义。人工神经网络的结果显示，学生对科学的初始态度是影响STEM迁移的最显著因素。其次，GPA、学生对数学的初始态度、转移资本、是否全职工作、专业申报、高中理科准备、中等以上收入水平、转移效能也是影响学生STEM迁移的重要变量。定性结果进一步说明了来自人工神经网络的因素如何发挥其影响。这种混合方法的研究阐明了影响成为科学家之路的重要因素，以及这些因素如何在学生的教育历程中表现出它们的影响。通过这种方法，我们能够在不假设方向性的情况下确定影响因素的重要性，并利用访谈数据来解决这些因素如何以复杂而微妙的方式独立或共同起作用。我们的研究加深了对社区大学生STEM道路的理解，包括克服挑战和保持进步所涉及的许多情节曲折和过程。
Background Although a long line of research has been devoted to transfer pathways in general, there remains limited work on the capacity for community colleges to cultivate STEM baccalaureate transfer. In particular, both quantitative and qualitative evidence is extremely sparse on how STEM-aspiring students beginning at community colleges experience supports and barriers on their journey to pursue a STEM baccalaureate. Purpose This mixed-methods study addresses the question: What salient factors are associated with beginning community college STEM students’ decisions to transfer into baccalaureate STEM programs, and how do students describe the supports and barriers they experienced specifically pertaining to these factors? Research Design Guided by the STEM Transfer model, we carried out this research using an explanatory sequential mixed-methods design. We incorporated survey, administrative, and interview data from three large two-year institutions in a Midwestern state. We applied Artificial Neural Network (ANN) techniques to identify factors associated with beginning community college STEM students’ decisions to transfer into baccalaureate STEM programs. Based on the factors that emerged from ANN, we analyzed the interview data to give meaning to the identified factors using students’ rich descriptions of their experiences. Findings Results from the ANN revealed that students’ initial attitudes toward science was the most salient factor related to transfer in STEM. Following that, GPA, students’ initial attitudes toward math, transfer capital, being employed full time, major declaration, science preparation in high school, income levels above middle level, and transfer efficacy also turned out to be important variables shaping students’ transfer in STEM. Qualitative results further illustrated how the factors from the ANN exerted their impact. Conclusions This mixed-methods research illuminated significant factors shaping the road to becoming a scientist, as well as how those factors manifested their influences within the contexts of students’ educational journeys. Through this approach, we were able to establish the significance of influential factors without presuming directionality and leverage the interview data to disentangle how these factors functioned independently and together in sophisticated and nuanced ways. Our study brings forth a deeper understanding of community college students’ STEM pathways, including the many plot twists and processes involved to overcome challenges and maintain progress.