Designing with and for Youth: A Participatory Design Research Approach for Critical Machine Learning Education

As big data algorithm usage becomes more ubiquitous, it will become critical for all young people, particularly those from historically marginalized populations, to have a deep understanding of data science that empowers them to enact change in their local communities and globally. In this study, we explore the concept of critical machine learning: integrating machine learning knowledge content with social, ethical, and political effects of algorithms. We modified an intergenerational participatory design approach known as cooperative inquiry to co-design a critical machine learning educational program with and for youth ages 9 - 13 in two after-school centers in the southern United States. Analyzing data from cognitive interviews, observations, and learner artifacts, we describe the roles of children and researchers as meta-design partners. Our findings suggest that cooperative inquiry and meta-design are suitable frameworks for designing critical machine learning educational environments that reflect children’s interests and values. This approach may increase youth engagement around the social, ethical, and political implications of large-scale machine learning algorithm deployment

Data‐enabled cognitive modeling: Validating student engineers’ fuzzy design‐based decision‐making in a virtual design problem

The ability of future engineering professionals to solve complex real‐world problems depends on their design education and training. Because engineers engage with open‐ended problems in which there are unknown parameters and multiple competing objectives, they engage in fuzzy decision‐making, a method of making decisions that takes into account inherent imprecisions and uncertainties in the real world. In the design‐based decision‐making field, few studies have applied fuzzy decision‐making models to actual decision‐making process data. Thus, in this study, we use datasets on student decision‐making processes to validate approximate fuzzy models of student decision‐making, which we call data‐enabled cognitive modeling. The results of this study (1) show that simulated design problems provide rich datasets that enable analysis of student design decision‐making and (2) validate models of student design cognition that can inform future design curricula and help educators understand how students think about design problems

Teaching and Assessing Engineering Design Thinking with Virtual Internships and Epistemic Network Analysis

An engineering workforce of sufficient size and quality is essential for addressing significant global challenges such as climate change, world hunger, and energy demand. Future generations of engineers will need to identify challenging issues and design innovative solutions. To prepare young people to solve big and increasingly global problems, researchers and educators need to understand how we can best educate young people to use engineering design thinking. In this paper, we explore virtual internships, online simulations of 21st-century engineering design practice, as one method for teaching engineering design thinking. To assess the engineering design thinking, we use epistemic network analysis (ENA), a tool for measuring complex thinking as it develops over time based on discourse analysis. The combination of virtual internships and ENA provides opportunities for students to engage in authentic engineering design, potentially receive concurrent feedback on their engineering design thinking, and develop the identity, values, and ways of thinking of professional engineers

In Search of Conversational Grain Size: Modelling Semantic Structure Using Moving Stanza Windows

Analyses of learning based on student discourse need to account not only for the content of the utterances but also for the ways in which students make connections across turns of talk. This requires segmentation of discourse data to define when connections are likely to be meaningful. In this paper, we present an approach to segmenting data for the purposes of modeling connections in discourse using epistemic network analysis. Specifically, we use epistemic network analysis to model connections in student discourse using a temporal segmentation method adapted from recent work in the learning sciences. We compare the results of this study to a purely conversation-based segmentation method to examine the affordances of temporal segmentation for modeling connections in discourse

Design of a Professional Practice Simulator for Educating and Motivating First-Year Engineering Students

Increasingly, first-year engineering curricula incorporate design projects. However, the faculty and staff effort and physical resources required for the number of students enrolled can be daunting and affect the quality of instruction. To reduce these costs, ensure a high quality educational experience, and reduce variability in student outcomes that occur with individual design projects, we developed a simulation of engineering professional practice, NephroTex, in which teams of students are guided through multiple design-build-test cycles by a mentor in a virtual internship. Here we report on the design process for the virtual internship and results of testing with first-year engineering students at a large, public university. Our results demonstrate that the novel virtual internship successfully educated and motivated first-year-engineering students. Importantly, the virtual environment captures rich discourse that can be used to assess the process of student learning with tools from existing learning theory

A Grounded Qualitative Analysis of the Effect of a Focus Group on Design Process in a Virtual Internship

A key component associated with the development of an entrepreneurial mindset is the ability to understand customerneeds and consider this when developing a product. This study sought to understand whether the inclusion of a customerfocus group as part of a virtual internship created any differences in the design processes of sophomore engineeringstudents (114 students). The Nephrotex virtual internship requires that students design a dialysis membrane by optimizinga selection of four components: membrane polymer, polymerization process, processing surfactant, and carbon nanotubepercentage. We found that sophomores who engaged in a focus group during the virtual internship Nephrotex showed(statistically) equal focus on cost versus technical measures of design performance during the focus group. Despite this,design cost was lower in the section that participated in a focus group, with no decrease in product quality. This indicatesthat customer voice may be an important factor in decreasing product cost. We also found that sophomore studentsprioritized their interviewing of customers within the focus group towards end users, such as the patient and nephrologist.Qualitative analysis of sophomore responses demonstrated that they found utility in the focus group (30% of participants)but did not necessarily believe that the customers had useful knowledge of the relevant design attributes (17% ofparticipants). Such realizations may have contributed to the equivalent quality and decreased costs associated with thedesigns of sophomores who participated in a focus group

Connected design rationale: a model for measuring design learning using epistemic network analysis

Virtual learning environments have the potential to support students’ development of design skills in engineering education. However, few approaches exist for modeling and measuring design learning as it emerges in authentic practices, which often includes collaboration. This study merges learning sciences research with engineering design education to develop an approach for modeling and measuring design thinking. I propose a connected design rationale model which identifies relationships among design moves and rationale. Results from a qualitative examination of how professional engineers make connections among moves and rationales were used as the foundation to examine students in virtual internships. Using digital collaborative chat data and Epistemic Network Analysis (ENA), the discourse networks of students who had high and low scores in the virtual internship were compared to the discourse patterns of professional engineers to determine if measuring connected design rationale reveals meaningful differences between expert and novice design thinking. The results show a significant difference between high and low-performing students in terms of their patterns of connections and that high-performing students in the virtual internship made connections that were more like experts than low-performing students. Results suggest that a connected design rationale model distinguishes between experts and novices in meaningful ways and can be a robust approach for research in learning sciences and engineering education

Using Knowledgeable Agents of the Digital and data feminism to uncover social identities in the #blackgirlmagic Twitter community

Online spaces have the capacity to be powerful informal learning and identity development spaces for marginalized communities. However, there is still much work to be done to uncover these complex social identities using ethical big data analyses. In this study, I draw on the theory of Knowledgeable Agents of the Digital, data feminism, and critical reflexivity practices to engage with a #blackgirlmagic Twitter dataset from 2016 to 2019. Using Epistemic Network Analysis, findings suggest that the #blackgirlmagic community self-defined their social identities around Black beauty, academic/professional accomplishments, and social justice. Because the women and girls of #blackgirlmagic were agentive in rewriting and sharing narratives of themselves, they were acting as knowledgeable agents of the digital. These findings may be useful for (1) uncovering other instances of knowledgeable agents from non-dominant populations and how they navigate a racialized and gendered society, and (2) providing suggestions for analyzing online big data through an ethical, intersectional feminist lens

Designing a critical machine learning educational program with and for children

Presented online via Bluejeans Events on February 10, 2022 at 12:30 p.m.Dr. Arastoopour Irgens is Assistant Professor of Learning Sciences, Director of the IDEA Lab at Clemson University, and Vice President of the International Society for Quantitative Ethnography. In her learning analytics work, she uses quantitative ethnography, computational linguistics, and discourse networks to make sense of how learners engage with digital technologies.Runtime: 49:10 minutesThe world is becoming increasingly reliant on artificial intelligence (AI) technologies that collect, store, and analyze our data. Such technologies improve our quality of life, but they also (re)perpetuate inequities and harm marginalized populations. As digital technologies become more ubiquitous, it will become critical for all young people to have a deep understanding of AI that empowers them to enact change in their local communities and globally. In this talk, I discuss how researchers and children collaborated to develop a critical machine learning after-school education program, in which children explored the social and ethical consequences of large-scale algorithm deployment and applied machine learning content knowledge. Findings show that children were able to 1) explain how biased training datasets could be harmful and 2) build robots for social good that used their own designed classification algorithms. Reflecting on these findings, I argue for the benefits of participatory design methods in designing critical machine learning educational environments, as well as the unresolved tensions that emerge

Data Detectives: A Data Science Program for Middle Grade Learners

Data science is a highly interdisciplinary field that comprises various principles, methodologies, and guidelines for the analysis of data. The creation of appropriate curricula that use computational tools and teaching activities is necessary for building skills and knowledge in data science. However, much of the literature about data science curricula focuses on the undergraduate university level. In this study, we developed an introductory data science curriculum for an out of school enrichment program aimed at middle grade learners (ages 11–13). We observed how the participants in the program (n = 11) learned data science practices through the combination of nonprogramming activities and programming activities using the language R. The results revealed that participants in the program were able to investigate statistical questions of their creation, perform data analysis using statistics and the creation of data visuals, make meaning from their results, and communicate their findings. These results suggest that a series of learner-centered nonprogramming and programming activities using R can facilitate the learning of data science skills for middle-school age students