In recent years, Wyoming has developed Computer Science (CS) standards for adoption and use within K-12 classrooms. These standards, adopted in January of 2022, go into effect for the 2022-2023 school year. The University of Wyoming has offered two different computer science week-long professional developments for teachers. Many K-12 teachers do not have a CS background, so developing CS lessons plans can be a challenge in these PDs.This research study is centered around three central questions: 1) To what extent did K-12 teachers integrate computing topics into their PD created lesson plans; 2) How do the teacher perceptions from the two CS PDs compare to each other; and 3) How was the CS PD translated to classroom activity? The first PD opportunity (n=14), was designed to give hands-on learning with CS topics focused on cybersecurity. The second PD opportunity (n=28), focused on integrating CS into existing curricula. At the end of each of these PDs, teacher K-12 teachers incorporated CS topics into their selected existing lesson plan(s). Additionally, a support network was implemented to support excellence in CS education throughout the state. This research study team evaluated the lesson plans developed during each PD event, by using a rubric on each lesson plan. Researchers collected exit surveys from the teachers. Implementation metrics were also gathered, including, how long each lesson lasted, how many students were involved in the implementation, what grades the student belonged to, the basic demographics of the students, the type of course the lesson plan was housed in, if the K-12 teacher reached their intended purpose, what evidence the K-12 teacher had of the success of their lesson plan, data summaries based on supplied evidence, how the K-12 teachers would change the lesson, the challenges and successes they experienced, and samples of student work. Quantitative analysis was basic descriptive statistics. Findings, based on evaluation of 40+ lessons, taught to over 1500 K-12 students, indicate that when assessed on a three point rubric of struggling, emerging, or excellent - certain components (e.g., organization, objectives, integration, activities & assessment, questions, and catch) of K-12 teacher created lessons plans varied drastically. In particular, lesson plan organization, integration, and questions each had a significant number of submissions which were evaluated as "struggling" [45%, 46%, 41%] through interesting integration, objectives, activities & assessment, and catch all saw submissions which were evaluated as "excellent" [43%, 48%, 43%, 48%]. The relationship between existing K-12 policies and expectations surfaces within these results and in combination with other findings leads to implications for the translation of current research practices into pre-collegiate PDs.
more »
« less
Designing a Collaborative Game-Based Learning Environment for AI-Infused Inquiry Learning in Elementary School Classrooms
Recent years have seen growing recognition of the importance of enabling K-12 students to learn computer science. Meanwhile, artificial intelligence, a field of computer science, has with the potential to profoundly reshape society. This has generated increasing demand for fostering an AI-literate populace. However, there is little work exploring how to introduce K-12 students to AI and how to support K-12 teachers in integrating AI into their classrooms. In this work, we explore how to introduce AI learning experiences into upper elementary classrooms (student ages 8 to 11). With a focus on integrating AI and life science, we present initial work on a collaborative game-based learning environment that features rich problem-based learning scenarios that enable students to gain experience with AI applied toward solving real-world life-science problems.
more »
« less
- NSF-PAR ID:
- 10188722
- Date Published:
- Journal Name:
- Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education
- Page Range / eLocation ID:
- 566 to 566
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract The Institute for Student‐AI Teaming (iSAT) addresses the foundational question:
how to promote deep conceptual learning via rich socio‐collaborative learning experiences for all students ?—a question that is ripe for AI‐based facilitation and has the potential to transform classrooms. We advance research in speech, computer vision, human‐agent teaming, computer‐supported collaborative learning, expansive co‐design, and the science of broadening participation to design and study next generation AI technologies (called AI Partners) embedded in student collaborative learning teams in coordination with teachers. Our institute ascribes to theoretical perspectives that aim to create a normative environment of widespread engagement through responsible design of technology, curriculum, and pedagogy in partnership with K–12 educators, racially diverse students, parents, and other community members. -
With the increasing prevalence of large language models (LLMs) such as ChatGPT, there is a growing need to integrate natural language processing (NLP) into K-12 education to better prepare young learners for the future AI landscape. NLP, a sub-field of AI that serves as the foundation of LLMs and many advanced AI applications, holds the potential to enrich learning in core subjects in K-12 classrooms. In this experience report, we present our efforts to integrate NLP into science classrooms with 98 middle school students across two US states, aiming to increase students’ experience and engagement with NLP models through textual data analyses and visualizations. We designed learning activities, developed an NLP-based interactive visualization platform, and facilitated classroom learning in close collaboration with middle school science teachers. This experience report aims to contribute to the growing body of work on integrating NLP into K-12 education by providing insights and practical guidelines for practitioners, researchers, and curriculum designers.more » « less
-
null (Ed.)Today’s classrooms are remarkably different from those of yesteryear. In place of individual students responding to the teacher from neat rows of desks, one more typically finds students working in groups on projects, with a teacher circulating among groups. AI applications in learning have been slow to catch up, with most available technologies focusing on personalizing or adapting instruction to learners as isolated individuals. Meanwhile, an established science of Computer Supported Collaborative Learning has come to prominence, with clear implications for how collaborative learning could best be supported. In this contribution, I will consider how intelligence augmentation could evolve to support collaborative learning as well as three signature challenges of this work that could drive AI forward. In conceptualizing collaborative learning, Kirschner and Erkens (2013) provide a useful 3x3 framework in which there are three aspects of learning (cognitive, social and motivational), three levels (community, group/team, and individual) and three kinds of pedagogical supports (discourse-oriented, representation-oriented, and process-oriented). As they engage in this multiply complex space, teachers and learners are both learning to collaborate and collaborating to learn. Further, questions of equity arise as we consider who is able to participate and in which ways. Overall, this analysis helps us see the complexity of today’s classrooms and within this complexity, the opportunities for augmentation or “assistance to become important and even essential. An overarching design concept has emerged in the past 5 years in response to this complexity, the idea of intelligent augmentation for “orchestrating” classrooms (Dillenbourg, et al, 2013). As a metaphor, orchestration can suggest the need for a coordinated performance among many agents who are each playing different roles or voicing different ideas. Practically speaking, orchestration suggests that “intelligence augmentation” could help many smaller things go well, and in doing so, could enable the overall intention of the learning experience to succeed. Those smaller things could include helping the teacher stay aware of students or groups who need attention, supporting formation of groups or transitions from one activity to the next, facilitating productive social interactions in groups, suggesting learning resources that would support teamwork, and more. A recent panel of AI experts identified orchestration as an overarching concept that is an important focus for near-term research and development for intelligence augmentation (Roschelle, Lester & Fusco, 2020). Tackling this challenging area of collaborative learning could also be beneficial for advancing AI technologies overall. Building AI agents that better understand the social context of human activities has broad importance, as does designing AI agents that can appropriately interact within teamwork. Collaborative learning has trajectory over time, and designing AI systems that support teams not just with a short term recommendation or suggestion but in long-term developmental processes is important. Further, classrooms that are engaged in collaborative learning could become very interesting hybrid environments, with multiple human and AI agents present at once and addressing dual outcome goals of learning to collaborate and collaborating to learn; addressing a hybrid environment like this could lead to developing AI systems that more robustly help many types of realistic human activity. In conclusion, the opportunity to make a societal impact by attending to collaborative learning, the availability of growing science of computer-supported collaborative learning and the need to push new boundaries in AI together suggest collaborative learning as a challenge worth tackling in coming years.more » « less
-
more » « less
Abstract This paper provides an experience report on a co‐design approach with teachers to co‐create learning analytics‐based technology to support problem‐based learning in middle school science classrooms. We have mapped out a workflow for such applications and developed design narratives to investigate the implementation, modifications and temporal roles of the participants in the design process. Our results provide precedent knowledge on co‐designing with experienced and novice teachers and co‐constructing actionable insight that can help teachers engage more effectively with their students' learning and problem‐solving processes during classroom PBL implementations.
Practitioner notes What is already known about this topic
Success of educational technology depends in large part on the technology's alignment with teachers' goals for their students, teaching strategies and classroom context.
Teacher and researcher co‐design of educational technology and supporting curricula has proven to be an effective way for integrating teacher insight and supporting their implementation needs.
Co‐designing learning analytics and support technologies with teachers is difficult due to differences in design and development goals, workplace norms, and AI‐literacy and learning analytics background of teachers.
What this paper adds
We provide a co‐design workflow for middle school teachers that centres on co‐designing and developing actionable insights to support problem‐based learning (PBL) by systematic development of responsive teaching practices using AI‐generated learning analytics.
We adapt established human‐computer interaction (HCI) methods to tackle the complex task of classroom PBL implementation, working with experienced and novice teachers to create a learning analytics dashboard for a PBL curriculum.
We demonstrate researcher and teacher roles and needs in ensuring co‐design collaboration and the co‐construction of actionable insight to support middle school PBL.
Implications for practice and/or policy
Learning analytics researchers will be able to use the workflow as a tool to support their PBL co‐design processes.
Learning analytics researchers will be able to apply adapted HCI methods for effective co‐design processes.
Co‐design teams will be able to pre‐emptively prepare for the difficulties and needs of teachers when integrating middle school teacher feedback during the co‐design process in support of PBL technologies.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3341525.3393981
