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Data analytics and computational thinking are essential for processing and analyzing data from sensors, and presenting the results in formats suitable for decision-making. However, most undergraduate construction engineering and management students struggle with understanding the required computational concepts and workflows because they lack the theoretical foundations. This has resulted in a shortage of skilled workforce equipped with the required competencies for developing sustainable solutions with sensor data. End-user programming environments present students with a means to execute complex analysis by employing visual programming mechanics. With end-user programming, students can easily formulate problems, logically organize, analyze sensor data, represent data through abstractions, and adapt the results to a wide variety of problems. This paper presents a conceptual system based on end-user programming and grounded in the Learning-for-Use theory which can equip construction engineering and management students with the competencies needed to implement sensor data analytics in the construction industry. The system allows students to specify algorithms by directly interacting with data and objects to analyze sensor data and generate information to support decision-making in construction projects. An envisioned scenario is presented to demonstrate the potential of the system in advancing students’ data analytics and computational thinking skills. The study contributes tomore »existing knowledge in the application of computational thinking and data analytics paradigms in construction engineering education.« less

CONTEXT There is today a broad consensus that emotions influence all forms of teaching and learning, and scholarship on Emotions in Engineering Education (EEE) is an emerging and rapidly growing field. However, this nascent research is currently very dispersed and not well consolidated. There is also a lack of knowledge about the state of the art, strengths, and limitations of the existing literature in the field, gaps, and future avenues for research. PURPOSE We have conducted a scoping review of EEE research, aiming to provide a first overview of the EEE scholarship landscape. We report here on preliminary findings related to (1) the status of the field, (2) geographical representation of authors, and (3) emerging hot spots and blind spots in terms of research approaches, contexts, and topics. METHODS The scoping review is part of a larger, systematic review of the EEE literature. Using an inclusive search strategy, we retrieved 2,175 items mentioning emotions and engineering education, including common synonyms. Through abstract screening and full text sifting, we identified 184 items that significantly focus on engineering education and emotion. From these items, we extracted and synthesized basic quantitative and qualitative information on publication outlets, author origins, keywords, research approaches, andmore »research contexts. PRELIMINARY RESULTS Surprised by the large number of EEE publications, we found that EEE is a rapidly expanding, but internationally dispersed field. Preliminary results also suggest a dominance of research on higher education, often exploring students’ academic emotions or emotional competences. Research on emotional intelligence and anxiety is particularly common while studies focusing on cultural and sociological aspects of EEE are largely absent. CONCLUSIONS The EEE literature is expanding exponentially. However, the field is not well consolidated, and many blind spots remain to be explored in terms of research approaches, contexts, and foci. To accelerate the development of the field, we invite current and prospective EEE researchers to join our emerging, international community of EEE researchers.« less

This Work-In-Progress research paper presents preliminary results and next steps of a study that aims to identify institutional data and resources that instructors find helpful in facilitating learning in large foundational engineering courses. The work is motivated by resource-driven compromises made in response to increasing engineering student populations. One such compromise is teaching some courses (usually foundational courses taken by students across multiple disciplines) in large sections, despite research suggesting that large class environments may correspond with unfavorable student learning experiences. Examples of courses often taught in large class environments are mathematics, physics, and mechanics. We are currently working with a cohort of instructors of foundational engineering courses as part of an NSF Institutional Transformation project. We have collected qualitative data through semi-structured interviews to explore the following research question: What data and/or resources do STEM faculty teaching large foundational classes for undergraduate engineering identify as being useful to enhance students' experiences and outcomes a) within the classes that they teach, and b) across the multiple large foundational engineering classes taken by students? Our inquiry and analysis are guided by Lattuca and Stark's Academic Plan Model. Preliminary analysis indicated that instructors would like more opportunities to interact and collaborate withmore »instructors from other departments. These results will inform activities for our Large Foundational Courses Summit scheduled for Summer 2018 as part of the project.« less