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1. An empirical analysis of high school students' practices of modelling with unstructured data

   https://doi.org/10.1111/bjet.13253  

   Jiang, Shiyan ; Nocera, Amato ; Tatar, Cansu ; Yoder, Michael Miller ; Chao, Jie ; Wiedemann, Kenia ; Finzer, William ; Rosé, Carolyn P. ( July 2022 , British Journal of Educational Technology)

   To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making.

   What is already known about this topic

   Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.

   While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.

   There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data.

   What this paper adds

   Results show that students developed nuanced understandings of models learning patterns in data for automated decision making.

   Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.

   Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in.

   Implications for practice and/or policy

   It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.

   Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.

   To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).

    

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2. Using Messy, Authentic Data to Promote Data Literacy & Reveal the Nature of Science

   https://doi.org/10.1525/abt.2020.82.7.439  

   Schultheis, Elizabeth H. ; Kjelvik, Melissa K. ( September 2020 , The American Biology Teacher)

   null (Ed.)

   Authentic, “messy data” contain variability that comes from many sources, such as natural variation in nature, chance occurrences during research, and human error. It is this messiness that both deters potential users of authentic data and gives data the power to create unique learning opportunities that reveal the nature of science itself. While the value of bringing contemporary research and messy data into the classroom is recognized, implementation can seem overwhelming. We discuss the importance of frequent interactions with messy data throughout K–16 science education as a mechanism for students to engage in the practices of science, such as visualizing, analyzing, and interpreting data. Next, we describe strategies to help facilitate the use of messy data in the classroom while building complexity over time. Finally, we outline one potential sequence of activities, with specific examples, to highlight how various activity types can be used to scaffold students' interactions with messy data. 

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3. Developing a Model of Disciplinary Literacy Instruction for K-12 Engineering Education: Comparing the Literacy Practices of Electrical and Mechanical Engineers (Fundamental)

   Green, T. ; Wilson-Lopez, A. ; Minichiello, A. ( July 2019 , ASEE Annual Conference proceedings)

   Despite efforts to diversify the engineering workforce, the field remains dominated by White, male engineers. Research shows that underrepresented groups, including women and minorities, are less likely to identify and engage with scientific texts and literacy practices. Often, children of minority groups and/or working-class families do not receive the same kinds of exposure to science, technology, engineering, and mathematics (STEM) knowledge and practices as those from majority groups. Consequently, these children are less likely to engage in school subjects that provide pathways to engineering careers. Therefore, to mitigate the lack of diversity in engineering, new approaches able to broadly support engineering literacy are needed. One promising approach is disciplinary literacy instruction (DLI). DLI is a method for teaching students how advanced practitioners in a given field generate, interpret, and evaluate discipline-specific texts. DLI helps teachers provide access to to high quality, discipline-specific content to all students, regardless of race, ethnicity, gender, or socio-economic status, Therefore, DLI has potential to reduce literacy-based barriers that discourage underrepresented students from pursuing engineering careers. While models of DLI have been developed and implemented in history, science, and mathematics, little is known about DLI in engineering. The purpose of this research is to identify the authentic texts, practices, and evaluative frameworks employed by professional engineers to inform a model of DLI in engineering. While critiques of this approach may suggest that a DLI model will reflect the literacy practices of majority engineering groups, (i.e., White male engineers), we argue that a DLI model can directly empower diverse K-16 students to become engineers by instructing them in the normed knowledge and practices of engineering. This paper presents a comparative case study conducted to investigate the literacy practices of electrical and mechanical engineers. We scaffolded our research using situated learning theory and rhetorical genre studies and considered the engineering profession as a community of practice. We generated multiple types of data with four participants (i.e., two electrical and two mechanical engineers). Specifically, we generated qualitative data, including written field notes of engineer observations, interview transcripts, think-aloud protocols, and engineer logs of literacy practices. We used constant comparative analysis (CCA) coding techniques to examine how electrical and mechanical engineers read, wrote, and evaluated texts to identify the frameworks that guide their literacy practices. We then conducted within-group and cross-group constant comparative analyses (CCA) to compare and contrast the literacy practices specific to each sub-discipline Findings suggest that there are two types of engineering literacy practices: those that resonate across both mechanical and electrical engineering disciplines and those that are specific to each discipline. For example, both electrical and mechanical engineers used test procedures to review and assess steps taken to evaluate electrical or mechanical system performance. In contrast, engineers from the two sub-disciplines used different forms of representation when depicting components and arrangements of engineering systems. While practices that are common across sub-disciplines will inform a model of DLI in engineering for K-12 settings, discipline-specific practices can be used to develop and/or improve undergraduate engineering curricula. 

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4. What should students be able to do? Helping students recognize professional skills in our courses

   https://doi.org/10.1093/jmammal/gyad035  

   Yahnke, Christopher J. ; Lanier, Hayley ; Flaherty, Elizabeth A. ; Varner, Johanna ; Munroe, Karen ; Duggan, Jennifer M. ; Erb, Liesl ; Dizney, Laurie ; Connors, Patrice K. ; Powell, ed., Roger ( April 2023 , Journal of Mammalogy)

   While the traditional goals of undergraduate courses are often content-based, the development of career-readiness and professional skills, such as those listed by the National Association of Colleges and Employers, are increasingly recognized as important learning outcomes. As Mammalogy courses embrace more hands-on learning activities, they provide the opportunity to embed these professional skills, which are directly relevant to many careers in science. For example, many Mammalogy courses may include projects that incorporate experimental design and data analysis that focus on quantitative literacy, in addition to technical skills including small mammal trapping and handling, or preparing voucher specimens, that focus on problem-solving and attention to detail. Here, we review the professional skills that can be developed through a Mammalogy course and evaluate evidence-based approaches to build those skills into our courses. One approach, using Course-based Undergraduate Research Experiences (CUREs), provides opportunities for both student skill development and instructor research program development. Because they invite students to participate in authentic scientific inquiry—from study design and data collection, to analysis and reporting of results—students participating in CUREs reported significant gains in their comfort with several important professional skills, including conducting field procedures, formulating and analyzing data, normalizing failure, and attempting new procedures on their own. Finally, we review the literature to demonstrate how active learning approaches inherent in CUREs can help students to build familiarity with technologies and techniques for collecting and assessing data from wild mammal populations, as well as to build important professional skills such as teamwork, leadership, problem-solving, and written and oral communication.

    

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5. 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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