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1. Moving Toward Transdisciplinary Learning Around Topics of Convergence: Is it really Possible in Higher Education Today?

   Strimel, G. ; Pruim, D. ; Briller, S. ; Martinez, R. ; Lucas, D. ; Kelley, T. ; Sohn, J. ( June 2023 , Review directory American Society for Engineering Education)

   There have been numerous demands for enhancements in the way undergraduate learning occurs today, especially at a time when the value of higher education continues to be called into question (The Boyer 2030 Commission, 2022). One type of demand has been for the increased integration of subjects/disciplines around relevant issues/topics—with a more recent trend of seeking transdisciplinary learning experiences for students (Sheets, 2016; American Association for the Advancement of Science, 2019). Transdisciplinary learning can be viewed as the holistic way of working equally across disciplines to transcend their own disciplinary boundaries to form new conceptual understandings as well as develop new ways in which to address complex topics or challenges (Ertas, Maxwell, Rainey, & Tanik, 2003; Park & Son, 2010). This transdisciplinary approach can be important as humanity’s problems are not typically discipline specific and require the convergence of competencies to lead to innovative thinking across fields of study. However, higher education continues to be siloed which makes the authentic teaching of converging topics, such as innovation, human-technology interactions, climate concerns, or harnessing the data revolution, organizationally difficult (Birx, 2019; Serdyukov, 2017). For example, working across a university’s academic units to collaboratively teach, or co-teach, around topics of convergence are likely to be rejected by the university systems that have been built upon longstanding traditions. While disciplinary expertise is necessary and one of higher education’s strengths, the structures and academic rigidity that come along with the disciplinary silos can prevent modifications/improvements to the roles of academic units/disciplines that could better prepare students for the future of both work and learning. The balancing of disciplinary structure with transdisciplinary approaches to solving problems and learning is a challenge that must be persistently addressed. These institutional challenges will only continue to limit universities seeking toward scaling transdisciplinary programs and experimenting with novel ways to enhance the value of higher education for students and society. This then restricts innovations to teaching and also hinders the sharing of important practices across disciplines. To address these concerns, a National Science Foundation Improving Undergraduate STEM Education project team, which is the topic of this paper, has set the goal of developing/implementing/testing an authentically transdisciplinary, and scalable educational model in an effort to help guide the transformation of traditional undergraduate learning to span academics silos. This educational model, referred to as the Mission, Meaning, Making (M3) program, is specifically focused on teaching the crosscutting practices of innovation by a) implementing co-teaching and co-learning from faculty and students across different academic units/colleges as well as b) offering learning experiences spanning multiple semesters that immerse students in a community that can nourish both their learning and innovative ideas. As a collaborative initiative, the M3 program is designed to synergize key strengths of an institution’s engineering/technology, liberal arts, and business colleges/units to create a transformative undergraduate experience focused on the pursuit of innovation—one that reaches the broader campus community, regardless of students’ backgrounds or majors. Throughout the development of this model, research was conducted to help identify institutional barriers toward creating such a cross-college program at a research-intensive public university along with uncovering ways in which to address these barriers. While data can show how students value and enjoy transdisciplinary experiences, universities are not likely to be structured in a way to support these educational initiatives and they will face challenges throughout their lifespan. These challenges can result from administration turnover whereas mutual agreements across colleges may then vanish, continued disputes over academic territory, and challenges over resource allotments. Essentially, there may be little to no incentives for academic departments to engage in transdisciplinary programming within the existing structures of higher education. However, some insights and practices have emerged from this research project that can be useful in moving toward transdisciplinary learning around topics of convergence. Accordingly, the paper will highlight features of an educational model that spans disciplines along with the workarounds to current institutional barriers. This paper will also provide lessons learned related to 1) the potential pitfalls with educational programming becoming “un-disciplinary” rather than transdisciplinary, 2) ways in which to incentivize departments/faculty to engage in transdisciplinary efforts, and 3) new structures within higher education that can be used to help faculty/students/staff to more easily converge to increase access to learning across academic boundaries. 

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2. Cogenerative Dialogues and Development of the Culturally Relevant Pedagogical Guidelines for Computational Thinking and Computer Science.

   Sirrakos, G. ( January 2023 , American Association for the Advancement of Curriculum Studies Annual Meeting)

   In this proposal, we will share some initial findings about how teacher and student engagement in cogenerative dialogues influenced the development of the Culturally Relevant Pedagogical Guidelines for Computational Thinking and Computer Science (CRPG-CSCT). The CRPG-CSCT’s purpose is to provide computer science teachers with tools to enhance their instruction by accurately reflecting students’ diverse cultural resources in the classroom. Additionally, the CRPG-CSCT will provide guidance to non-computer science teachers on how to facilitate the integration of computational thinking skills to a broad spectrum of classes in the arts, humanities, sciences, social sciences, and mathematics. Our initial findings shared here are part of a larger NSF-funded research project (Award No. 2122367) which aims to better understand the barriers to entry and challenges for success faced by underrepresented secondary school students in computer science, through direct engagement with the students themselves. Throughout the 2022-23 academic year, the researchers have been working with a small team of secondary school teachers, students, and instructional designers, as well as university faculty in computer science, secondary education, and sociology to develop the CRPG-CSCT. The CRPG-CSCT is rooted in the tenets of culturally relevant pedagogy (Ladson-Billings, 1995) and borrows from Muhammad’s (2020) work in Cultivating Genius: An Equity Framework for Culturally and Historically Responsive Literacy. The CRPG-CCT is being developed over six day-long workshops held throughout the academic year. At the time of this submission, five of the six workshops had been completed. Each workshop utilized cogenerative dialogues (cogens) as the primary tool for organizing and sustaining participants’ engagement. Through cogens, participants more deeply learn about students’ cultural capital and the value of utilizing that capital within the classroom (Roth, Lawless, & Tobin, 2000). The success of cogens relies on following specific protocols (Emdin, 2016), such as listening attentively, ensuring there are equal opportunities for all participants to share, and affirming the experiences of other participants. The goal of a cogen is to reach a collective decision, based on the dialogue, that will positively impact students by explicitly addressing barriers to their engagement in the classroom. During each workshop, one member of the research team and one undergraduate research assistant observed the interactions among cogen participants and documented these in the form of ethnographic field notes. Another undergraduate research assistant took detailed notes during the workshop to record the content of small and large group discussions, presentations, and questions/responses throughout the workshops. A grounded theory approach was used to analyze the field notes. Additionally, at the conclusion of each workshop, participants completed a Cogen Feedback Survey (CFS) to gather additional information. The CFS were analyzed through open thematic coding, memos, and code frequencies. Our preliminary results demonstrate high levels of engagement from teacher and student participants during the workshops. Students identified that the cogen structure allowed them to participate comfortably, openly, and honestly. Further, students described feeling valued and heard. Students’ ideas and experiences were frequently affirmed, which served as an important step toward dismantling traditional teacher-student boundaries that might otherwise prevent them from sharing freely. Another result from the use of cogens was the shared experience of participants comprehending views from the other group’s perspective in the classroom. Students appreciated the opportunity to learn from teachers about their struggles in keeping students engaged. Teachers appreciated the opportunity to better understand students’ schooling experiences and how these may affirm or deny aspects of their identity. Finally, all participants shared meaningful suggestions and strategies for future workshops and for the collective betterment of the group. Initial findings shared here are important for several reasons. First, our findings suggest that cogens are an effective approach for fostering participants’ commitment to creating the conditions for students’ success in the classroom. Within the context of the workshops, cogens provided teachers, students, and faculty with opportunities to engage in authentic conversations for addressing the recruitment and retention problems in computer science for underrepresented students. These conversations often resulted in the development of tangible pedagogical approaches, examples, metaphors, and other strategies to directly address the recruitment and retention of underrepresented students in computer science. Finally, while we are still developing the CRPG-CSCT, cogens provided us with the opportunity to ensure the voices of teachers and students are well represented in and central to the document. 

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3. Machine learning for the educational sciences

   https://doi.org/10.1002/rev3.3310  

   Hilbert, Sven ; Coors, Stefan ; Kraus, Elisabeth ; Bischl, Bernd ; Lindl, Alfred ; Frei, Mario ; Wild, Johannes ; Krauss, Stefan ; Goretzko, David ; Stachl, Clemens ( November 2021 , Review of Education)

   Machine learning (ML) provides a powerful framework for the analysis of high‐dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non‐linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows, as larger and more complex datasets become available through massive open online courses (MOOCs) and large‐scale investigations. The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. To provide educational researchers with an elaborate introduction to the topic, we provide an instructional summary of the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. Specifically, we (1) provide an overview of the types of data suitable for ML and (2) give practical advice for the application of ML methods. In each section, we provide analytical examples and reproducible R code. Also, we provide an extensive Appendix on ML‐based applications for education. This instructional summary will help educational scientists and practitioners to prepare for the promises and threats that come with the shift towards digitisation and large‐scale assessment in education.

   In 2020, the worldwide SARS‐COV‐2 pandemic forced the educational sciences to perform a rapid paradigm shift with classrooms going online around the world—a hardly novel but now strongly catalysed development. In the context of data‐driven education, this paper demonstrates that the widespread adoption of machine learning techniques is central for the educational sciences and shows how these methods will become crucial tools in the collection and analysis of data and in concrete educational applications. Helping to leverage the opportunities and to avoid the common pitfalls of machine learning, this paper provides educators with the theoretical, conceptual and practical essentials.

   The process of teaching and learning is complex, multifaceted and dynamic. This paper contributes a seminal resource to highlight the digitisation of the educational sciences by demonstrating how new machine learning methods can be effectively and reliably used in research, education and practical application.

   The progressing digitisation of societies around the globe and the impact of the SARS‐COV‐2 pandemic have highlighted the vulnerabilities and shortcomings of educational systems. These developments have shown the necessity to provide effective educational processes that can support sometimes overwhelmed teachers to digitally impart knowledge on the plan of many governments and policy makers. Educational scientists, corporate partners and stakeholders can make use of machine learning techniques to develop advanced, scalable educational processes that account for individual needs of learners and that can complement and support existing learning infrastructure. The proper use of machine learning methods can contribute essential applications to the educational sciences, such as (semi‐)automated assessments, algorithmic‐grading, personalised feedback and adaptive learning approaches. However, these promises are strongly tied to an at least basic understanding of the concepts of machine learning and a degree of data literacy, which has to become the standard in education and the educational sciences.

   Demonstrating both the promises and the challenges that are inherent to the collection and the analysis of large educational data with machine learning, this paper covers the essential topics that their application requires and provides easy‐to‐follow resources and code to facilitate the process of adoption.

    

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4. Lessons and recommendations from three decades as an NSF REU site: A call for systems‐based assessment

   https://doi.org/10.1002/ece3.6136  

   McDevitt, Andrew L. ; Patel, Manisha V. ; Ellison, Aaron M. ( March 2020 , Ecology and Evolution)

   For more than 30 years, the US National Science Foundation's Research Experiences for Undergraduates (REU) program has supported thousands of undergraduate researchers annually and provides many students with their first research experiences in field ecology or evolution. REUs embed students in scientific communities where they apprentice with experienced researchers, build networks with their peers, and help students understand research cultures and how to work within them. REUs are thought to provide formative experiences for developing researchers that differ from experiences in a college classrooms, laboratories, or field trips. REU assessments have improved through time but they are largely ungrounded in educational theory. Thus, evaluation of long‐term impacts of REUs remains limited and best practices for using REUs to enhance student learning are repeatedly re‐invented. We describe how one sociocultural learning framework, cultural–historical activity theory (CHAT), could be used to guide data collection to characterize the effects of REU programs on participant's learning in an educationally meaningful context. CHAT embodies a systems approach to assessment that accounts for social and cultural factors that influence learning. We illustrate how CHAT has guided assessment of the Harvard Forest Summer Research Program in Ecology (HF‐SRPE), one of the longest‐running REU sites in the United States. Characterizing HF‐SRPE using CHAT helped formalize thoughts and language for the program evaluation, reflect on potential barriers to success, identify assessment priorities, and revealed important oversights in data collection.

    

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5. Identifying Supports and Barriers in Engineering Students’ Processes Toward Career Attainment

   https://doi.org/10.1007/s43683-023-00119-1  

   Desing, Renee M. ; Shermadou, Amena S. ; Nocera, Tanya M. ; Delaine, David A. ; Atiq, S. Zahra ; Ortiz-Rosario, Alexis ; Childers, Rachel C. ; Lewis, Racheida S. ( August 2023 , Biomedical Engineering Education)

   Evidence shows that biomedical engineering students face unique challenges in entering the workforce compared to peers in other engineering majors. The purpose of this study is to explore the factors impacting undergraduate engineering students’ career attainment, or the acquisition of employment in a chosen field, and how the students experienced these factors in their pathways towards post-graduate employment. By studying differences in processes towards career attainment, there is an opportunity to promote equity and better support the inclusion and persistence of women and racially minoritized groups in engineering, particularly their readiness to enter the engineering workforce. We sought to answer two research questions: (RQ1) What contextual factors are identified by engineering students as supports and barriers to their attainment of a career in the engineering field? (RQ2) How are engineering students experiencing inequities in their processes toward career attainment? We conducted six focus groups with undergraduate engineering students at a large Midwestern University. Participants were purposefully sampled based on demographics from four engineering disciplines: biomedical engineering and the three preferred majors for students transferring out of biomedical engineering at our university, chemical engineering, materials science and engineering, and mechanical engineering. We used social cognitive career theory (SCCT) to inform our data collection and analysis and interpret the findings. The transcripts were analyzed by developing a codebook containing theory-driven codes from SCCT and emergent codes from the data. We identified five themes representing the contextual factors impacting engineering students’ processes toward career attainment: implications of (1) interpersonal relationships; (2) institutional infrastructure; (3) academics; (4) social identity; and (5) out-of-class experiences. We also found that these contextual factors may act as either a support or barrier depending on personal factors such as demographics, personality, or identity. The nuance revealed in this study, that a contextual factor may be both a support and barrier, presents implications for universities to provide more individualized career preparedness resources and recognize the ways that students’ positionalities impact their processes toward career attainment.

    

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