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1. Building an Online Learning Module for Satellite Remote Sensing Applications in Hydrologic Science

   https://doi.org/10.3390/rs12183009  

   Maggioni, Viviana ; Girotto, Manuela ; Habib, Emad ; Gallagher, Melissa A. ( September 2020 , Remote Sensing)

   null (Ed.)

   This article presents an online teaching tool that introduces students to basic concepts of remote sensing and its applications in hydrology. The learning module is intended for junior/senior undergraduate students or junior graduate students with no (or little) prior experience in remote sensing, but with some basic background of environmental science, hydrology, statistics, and programming. This e-learning environment offers background content on the fundamentals of remote sensing, but also integrates a set of existing online tools for visualization and analysis of satellite observations. Specifically, students are introduced to a variety of satellite products and techniques that can be used to monitor and analyze changes in the hydrological cycle. At completion of the module, students are able to visualize remote sensing data (both in terms of time series and spatial maps), detect temporal trends, interpret satellite images, and assess errors and uncertainties in a remote sensing product. Students are given the opportunity to check their understanding as they progress through the module and also tackle complex real-life problems using remote sensing observations that professionals and scientists commonly use in practice. The learning tool is implemented in HydroLearn, an open-source, online platform for instructors to find and share learning modules and collaborate on developing teaching resources in hydrology and water resources. 

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2. Developing two-year college student engineering technology career profiles

   https://doi.org/10.18260/1-2--36945  

   Frady, K. ; Brown, C. ; High, K. ; Hughes, C. ; O’Hara, R. ; & Huang, S. ( July 2021 , Annual American Society for Engineering Education Conference)

   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). SCCT 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. 

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3. Analyzing teacher supports for collective argumentation in integrative STEM classrooms.

   Welji, S. ( June 2022 , ASEE annual conference)

   The Next Generation Science Standards [1] recognized evidence-based argumentation as one of the essential skills for students to develop throughout their science and engineering education. Argumentation focuses students on the need for quality evidence, which helps to develop their deep understanding of content [2]. Argumentation has been studied extensively, both in mathematics and science education but also to some extent in engineering education (see for example [3], [4], [5], [6]). After a thorough search of the literature, we found few studies that have considered how teachers support collective argumentation during engineering learning activities. The purpose of this program of research was to support teachers in viewing argumentation as an important way to promote critical thinking and to provide teachers with tools to implement argumentation in their lessons integrating coding into science, technology, engineering, and mathematics (which we refer to as integrative STEM). We applied a framework developed for secondary mathematics [7] to understand how teachers support collective argumentation in integrative STEM lessons. This framework used Toulmin’s [8] conceptualization of argumentation, which includes three core components of arguments: a claim (or hypothesis) that is based on data (or evidence) accompanied by a warrant (or reasoning) that relates the data to the claim [9], [8]. To adapt the framework, video data were coded using previously established methods for analyzing argumentation [7]. In this paper, we consider how the framework can be applied to an elementary school teacher’s classroom interactions and present examples of how the teacher implements various questioning strategies to facilitate more productive argumentation and deeper student engagement. We aim to understand the nature of the teacher’s support for argumentation—contributions and actions from the teacher that prompt or respond to parts of arguments. In particular, we look at examples of how the teacher supports students to move beyond unstructured tinkering (e.g., trial-and-error) to think logically about coding and develop reasoning for the choices that they make in programming. We also look at the components of arguments that students provide, with and without teacher support. Through the use of the framework, we are able to articulate important aspects of collective argumentation that would otherwise be in the background. The framework gives both eyes to see and language to describe how teachers support collective argumentation in integrative STEM classrooms. 

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4. Workshops and Co-design Can Help Teachers Integrate Computational Thinking into Their K-12 STEM Classes

   Wu, S. ; Peel, A. ; Bain, C. ; Anton, G. ; Horn, M. ; Wilensky, U. ( April 2020 , Proceedings of International Conference on Computational Thinking Education 2020)

   Kong, S.C. (Ed.)

   This work aims to help high school STEM teachers integrate computational thinking (CT) into their classrooms by engaging teachers as curriculum co-designers. K-12 teachers who are not trained in computer science may not see the value of CT in STEM classrooms and how to engage their students in computational practices that reflect the practices of STEM professionals. To this end, we developed a 4-week professional development workshop for eight science and mathematics high school teachers to co-design computationally enhanced curriculum with our team of researchers. The workshop first provided an introduction to computational practices and tools for STEM education. Then, teachers engaged in co-design to enhance their science and mathematics curricula with computational practices in STEM. Data from surveys and interviews showed that teachers learned about computational thinking, computational tools, coding, and the value of collaboration after the professional development. Further, they were able to integrate multiple computational tools that engage their students in CT-STEM practices. These findings suggest that teachers can learn to use computational practices and tools through workshops, and that teachers collaborating with researchers in co-design to develop computational enhanced STEM curriculum may be a powerful way to engage students and teachers with CT in K-12 classrooms. 

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5. Developing Subgoal Labels for Imperative Programming to Improve Student Learning Outcomes

   Decker, Adrienne ; Morrison, Briana B. ; Margulieux, Lauren E. ( June 2019 , ASEE Annual Conference proceedings)

   There have been many calls recently for computing for all across the nation. While there are many opportunities to study and use computing to advance the fields of computer science, software development, and information technology, computing is also needed in a wide range of other disciplines, including engineering. Most engineering programs require students take a course that teaches them introductory programming, which covers many of the same topics as an introductory course for computing majors (and at times may be the same course). However, statistics about the success of a course that is an introductory programming course are sobering; approximately half the students will fail, forcing them to either repeat the course or leave their chosen field of study if passing the course is required. This NSF IUSE project incorporates instructional techniques identified through educational psychology research as effective ways to improve student learning and retention in introductory programming. The research team has developed worked examples of problems that incorporate subgoal labels, which are explanations that describe the function of steps in the problem solution to the learner and highlight the problem-solving process. Using subgoal labels within worked examples, which has been effective in other STEM fields, students are able to see an expert's problem solving process, which helps students learn to solving problems before they can solve problem themselves. Experts, including instructors, teaching introductory level courses are often unable to explain the process they use in problem solving at a level that learners can grasp because they have automated much of the problem-solving processes after many years of practice. This submission will present the results of the first part of development of subgoals and will explain how to integrate them into classroom lessons in introductory computing classes. 

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