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  1. Free, publicly-accessible full text available December 28, 2022
  2. There is little research or understanding of curricular differences between two- and four-year programs, career development of engineering technology (ET) students, and professional preparation for ET early career professionals [1]. Yet, ET credentials (including certificates, two-, and four-year degrees) represent over half of all engineering credentials awarded in the U.S [2]. ET professionals are important hands-on members of engineering teams who have specialized knowledge of components and engineering systems. This research study focuses on how career orientations affect engineering formation of ET students educated at two-year colleges. The theoretical framework guiding this study is Social Cognitive Career Theory (SCCT). SCCTmore »is a theory which situates attitudes, interests, and experiences and links self-efficacy beliefs, outcome expectations, and personal goals to educational and career decisions and outcomes [3]. Student knowledge of attitudes toward and motivation to pursue STEM and engineering education can impact academic performance and indicate future career interest and participation in the STEM workforce [4]. This knowledge may be measured through career orientations or career anchors. A career anchor is a combination of self-concept characteristics which includes talents, skills, abilities, motives, needs, attitudes, and values. Career anchors can develop over time and aid in shaping personal and career identity [6]. The purpose of this quantitative research study is to identify dimensions of career orientations and anchors at various educational stages to map to ET career pathways. The research question this study aims to answer is: For students educated in two-year college ET programs, how do the different dimensions of career orientations, at various phases of professional preparation, impact experiences and development of professional profiles and pathways? The participants (n=308) in this study represent three different groups: (1) students in engineering technology related programs from a medium rural-serving technical college (n=136), (2) students in engineering technology related programs from a large urban-serving technical college (n=52), and (3) engineering students at a medium Research 1 university who have transferred from a two-year college (n=120). All participants completed Schein’s Career Anchor Inventory [5]. This instrument contains 40 six-point Likert-scale items with eight subscales which correlate to the eight different career anchors. Additional demographic questions were also included. The data analysis includes graphical displays for data visualization and exploration, descriptive statistics for summarizing trends in the sample data, and then inferential statistics for determining statistical significance. This analysis examines career anchor results across groups by institution, major, demographics, types of educational experiences, types of work experiences, and career influences. This cross-group analysis aids in the development of profiles of values, talents, abilities, and motives to support customized career development tailored specifically for ET students. These findings contribute research to a gap in ET and two-year college engineering education research. Practical implications include use of findings to create career pathways mapped to career anchors, integration of career development tools into two-year college curricula and programs, greater support for career counselors, and creation of alternate and more diverse pathways into engineering. Words: 489 References [1] National Academy of Engineering. (2016). Engineering technology education in the United States. Washington, DC: The National Academies Press. [2] The Integrated Postsecondary Education Data System, (IPEDS). (2014). Data on engineering technology degrees. [3] Lent, R.W., & Brown, S.B. (1996). Social cognitive approach to career development: An overivew. Career Development Quarterly, 44, 310-321. [4] Unfried, A., Faber, M., Stanhope, D.S., Wiebe, E. (2015). The development and validation of a measure of student attitudes toward science, technology, engineeirng, and math (S-STEM). Journal of Psychoeducational Assessment, 33(7), 622-639. [5] Schein, E. (1996). Career anchors revisited: Implications for career development in the 21st century. Academy of Management Executive, 10(4), 80-88. [6] Schein, E.H., & Van Maanen, J. (2013). Career Anchors, 4th ed. San Francisco: Wiley.« less
  3. How to effectively represent camera pose is an essential problem in 3D computer vision, especially in tasks such as camera pose regression and novel view synthesis. Traditionally, 3D position of the camera is represented by Cartesian coordinate and the orientation is represented by Euler angle or quaternions. These representations are manually designed, which may not be the most effective representation for downstream tasks. In this work, we propose an approach to learn neural representations of camera poses and 3D scenes, coupled with neural representations of local camera movements. Specifically, the camera pose and 3D scene are represented as vectors andmore »the local camera movement is represented as a matrix operating on the vector of the camera pose. We demonstrate that the camera movement can further be parametrized by a matrix Lie algebra that underlies a rotation system in the neural space. The vector representations are then concatenated and generate the posed 2D image through a decoder network. The model is learned from only posed 2D images and corresponding camera poses, without access to depths or shapes. We conduct extensive experiments on synthetic and real datasets. The results show that compared with other camera pose representations, our learned representation is more robust to noise in novel view synthesis and more effective in camera pose regression.« less