Artificial Intelligence (AI) enhanced systems are widely adopted in post-secondary education, however, tools and activities have only recently become accessible for teaching AI and machine learning (ML) concepts to K-12 students. Research on K-12 AI education has largely included student attitudes toward AI careers, AI ethics, and student use of various existing AI agents such as voice assistants; most of which has focused on high school and middle school. There is no consensus on which AI and Machine Learning concepts are grade-appropriate for elementary-aged students or how elementary students explore and make sense of AI and ML tools. AI is a rapidly evolving technology and as future decision-makers, children will need to be AI literate[1]. In this paper, we will present elementary students’ sense-making of simple machine-learning concepts. Through this project, we hope to generate a new model for introducing AI concepts to elementary students into school curricula and provide tangible, trainable representations of ML for students to explore in the physical world. In our first year, our focus has been on simpler machine learning algorithms. Our desire is to empower students to not only use AI tools but also to understand how they operate. We believe that appropriate activities can help late elementary-aged students develop foundational AI knowledge namely (1) how a robot senses the world, and (2) how a robot represents data for making decisions. Educational robotics programs have been repeatedly shown to result in positive learning impacts and increased interest[2]. In this pilot study, we leveraged the LEGO® Education SPIKE™ Prime for introducing ML concepts to upper elementary students. Through pilot testing in three one-week summer programs, we iteratively developed a limited display interface for supervised learning using the nearest neighbor algorithm. We collected videos to perform a qualitative evaluation. Based on analyzing student behavior and the process of students trained in robotics, we found some students show interest in exploring pre-trained ML models and training new models while building personally relevant robotic creations and developing solutions to engineering tasks. While students were interested in using the ML tools for complex tasks, they seemed to prefer to use block programming or manual motor controls where they felt it was practical.
more »
« less
Machine learning for the educational sciences
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.
- Award ID(s):
- 1758835
- NSF-PAR ID:
- 10447005
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Review of Education
- Volume:
- 9
- Issue:
- 3
- ISSN:
- 2049-6613
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)In June 2020, at the annual conference of the American Society for Engineering Education (ASEE), which was held entirely online due to the impacts of COVID-19 (SARS-CoV-2), engineering education researchers and social justice scholars diagnosed the spread of two diseases in the United States: COVID-19 and racism. During a virtual workshop (T614A) titled, “Using Power, Privilege, and Intersectionality as Lenses to Understand our Experiences and Begin to Disrupt and Dismantle Oppressive Structures Within Academia,” Drs. Nadia Kellam, Vanessa Svihla, Donna Riley, Alice Pawley, Kelly Cross, Susannah Davis, and Jay Pembridge presented what we might call a pathological analysis of institutionalized racism and various other “isms.” In order to address the intersecting impacts of this double pandemic, they prescribed counter practices and protocols of anti-racism, and strategies against other oppressive “isms” in academia. At the beginning of the virtual workshop, the presenters were pleasantly surprised to see that they had around a hundred attendees. Did the online format of the ASEE conference afford broader exposure of the workshop? Did recent uprising of Black Lives Matter (BLM) protests across the country, and internationally, generate broader interest in their topic? Whatever the case, at a time when an in-person conference could not be convened without compromising public health safety, ASEE’s virtual conference platform, furnished by Pathable and supplemented by Zoom, made possible the broader social impacts of Dr. Svihla’s land acknowledgement of the unceded Indigenous lands from which she was presenting. Svihla attempted to go beyond a hollow gesture by including a hyperlink in her slides to a COVID-19 relief fund for the Navajo Nation, and encouraged attendees to make a donation as they copied and pasted the link in the Zoom Chat. Dr. Cross’s statement that you are either a racist or an anti-racist at this point also promised broader social impacts in the context of the virtual workshop. You could feel the intensity of the BLM social movements and the broader political climate in the tone of the presenters’ voices. The mobilizing masses on the streets resonated with a cutting-edge of social justice research and education at the ASEE virtual conference. COVID-19 has both exacerbated and made more obvious the unevenness and inequities in our educational practices, processes, and infrastructures. This paper is an extension of a broader collaborative research project that accounts for how an exceptional group of engineering educators have taken this opportunity to socially broaden their curricula to include not just public health matters, but also contemporary political and social movements. Engineering educators for change and advocates for social justice quickly recognized the affordances of diverse forms of digital technologies, and the possibilities of broadening their impact through educational practices and infrastructures of inclusion, openness, and accessibility. They are makers of what Gary Downy calls “scalable scholarship”—projects in support of marginalized epistemologies that can be scaled up from ideation to practice in ways that unsettle and displace the dominant epistemological paradigm of engineering education.[1] This paper is a work in progress. It marks the beginning of a much lengthier project that documents the key positionality of engineering educators for change, and how they are socially situated in places where they can connect social movements with industrial transitions, and participate in the production of “undone sciences” that address “a structured absence that emerges from relations of inequality.”[2] In this paper, we offer a brief glimpse into ethnographic data we collected virtually through interviews, participant observation, and digital archiving from March 2019 to August 2019, during the initial impacts of COVID-19 in the United States. The collaborative research that undergirds this paper is ongoing, and what is presented here is a rough and early articulation of ideas and research findings that have begun to emerge through our engagement with engineering educators for change. This paper begins by introducing an image concept that will guide our analysis of how, in this historical moment, forms of social and racial justice are finding their way into the practices of engineering educators through slight changes in pedagogical techniques in response the debilitating impacts of the pandemic. Conceptually, we are interested in how small and subtle changes in learning conditions can socially broaden the impact of engineering educators for change. After introducing the image concept that guides this work, we will briefly discuss methodology and offer background information about the project. Next, we discuss literature that revolves around the question, what is engineering education for? Finally, we introduce the notion of situating engineering education and give readers a brief glimpse into our ethnographic data. The conclusion will indicate future directions for writing, research, and intervention.more » « less
-
more » « less
Abstract This paper describes a Human‐Centred Learning Analytics (HCLA) design approach for developing learning analytics (LA) dashboards for K‐12 classrooms that maintain both contextual relevance and scalability—two goals that are often in competition. Using mixed methods, we collected observational and interview data from teacher partners and assessment data from their students' engagement with the lesson materials. This DBR‐based, human‐centred design process resulted in a dashboard that supported teachers in addressing their students' learning needs. To develop the dashboard features that could support teachers, we found that a design refinement process that drew on the insights of teachers with varying teaching experience, philosophies and teaching contexts strengthened the resulting outcome. The versatile nature of the approach, in terms of student learning outcomes, makes it useful for HCLA design efforts across diverse K‐12 educational contexts.
Practitioner notes What is already known about this topic
Learning analytics that are aligned to both a learning theory and learning design support student learning.
LA dashboards that support users to understand the associated learning analytics data provide actionable insight.
Design‐based research is a promising methodology for Human‐Centred Learning Analytics design, particularly in the K‐12 educational context.
What this paper adds
Leveraging a longstanding, yet fluid, research‐practice partnership is an effective design‐based research adaptation for addressing the high variation in instructional practices that characterize K‐12 education.
Using both quantitative and qualitative data that reflects students' developing knowledge effectively supports teachers' inquiry into student learning.
Teachers' use of learning analytics dashboards is heavily influenced by their perspectives on teaching and learning.
Implications for practice and/or policy
Impact on student learning outcomes, alongside usability and feasibility, should be included as a necessary metric for the effectiveness of LA design.
LA dashboard developers should both leverage learning data that reflect students' developing knowledge and position teachers to take responsive pedagogical action to support student learning.
LA researchers and developers should utilize a long‐term, yet fluid, research‐practice partnership to form a multi‐stakeholder, multidisciplinary design team for Human‐Centred Learning Analytics design.
-
more » « less
Abstract Machine learning (ML) has become commonplace in educational research and science education research, especially to support assessment efforts. Such applications of machine learning have shown their promise in replicating and scaling human‐driven codes of students' work. Despite this promise, we and other scholars argue that machine learning has not yet achieved its transformational potential. We argue that this is because our field is currently lacking frameworks for supporting creative, principled, and critical endeavors to use machine learning in science education research. To offer considerations for science education researchers' use of ML, we present a framework, Distributing Epistemic Functions and Tasks (DEFT), that highlights the functions and tasks that pertain to generating knowledge that can be carried out by either trained researchers or machine learning algorithms. Such considerations are critical decisions that should occur alongside those about, for instance, the type of data or algorithm used. We apply this framework to two cases, one that exemplifies the cutting‐edge use of machine learning in science education research and another that offers a wholly different means of using machine learning and human‐driven inquiry together. We conclude with strategies for researchers to adopt machine learning and call for the field to rethink how we prepare science education researchers in an era of great advances in computational power and access to machine learning methods.
-
The biodesign studio: Constructions and reflections of high school youth on making with living mediamore » « less
Abstract Most constructionist efforts have focused on supporting learners with tools for designing with digital and/or physical media. Recent developments in life sciences now allow K‐12 learners to design with living materials, or to biodesign. In this paper, we report on the development of a biodesign studio activity called biocakes wherein teams of high school youth genetically modified a yeast strain to bake a nutrient fortified food product. Using a qualitative deductive analysis of classroom observations, interviews and projects, we examined high school youth experiences and reflections of these activities to answer three research questions: (1) What kind of artefacts did youth make with living media? (2) How did youth engage in biodesign? and (3) What did youth have to say about their biodesign experiences? We discuss how our analyses of biodesign applications highlight the importance of assembly practices, social engagement and imaginative design for constructionist learning. These insights provide compelling examples for designing with living media in K‐12 education.
Practitioner notes What is already known about this topic?
Constructionist perspectives shape important ideas about what we know about science learning, and notably in computer science fields.
One widely taken up example includes making, where learners use crafts to make interactive computational objects.
In life science, constructionism also provides insights about learning using digital media to model biological systems or interact with living materials.
What this paper adds?
This paper extends these perspectives by examining production and engagement when learners construct
with living materials—an approach that has only recently been possible with the development of accessible wet lab tools.We frame learning activities as a studio model—that emphasised application design, iteration and critique—to better assess the roles assembly, construction and speculative design play in production that uses living materials.
Our findings suggest that assembly is important for creating accessible points of entry to complex biological fabrication processes.
We also find that speculative design provides an opportunity for learners to extend their existing abilities beyond what is otherwise available given their expertise or access to resources, and thus expansively explore related ideas.
Implications for practice and/or policy
Assembly and speculative design have important places in constructionist‐driven production with living materials and could, therefore, be leveraged in practice.
