Modeling Unstructured Data: Teachers as Learners and Designers of Technology-enhanced Artificial Intelligence Curriculum. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), (pp. 617-620). Bochum, Germany: International Society of the Learning Sciences.
Title: Modeling Unstructured Data: Teachers as Learners and Designers of Technology-enhanced Artificial Intelligence Curriculum. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), (pp. 617-620). Bochum, Germany: International Society of the Learning Sciences.
In this paper, we present a co-design study with teachers to contribute towards development of a technology-enhanced Artificial Intelligence (AI) curriculum, focusing on modeling unstructured data. We created an initial design of a learning activity prototype and explored ways to incorporate the design into high school classes. Specifically, teachers explored text classification models with the prototype and reflected on the exploration as a user, learner, and teacher. They provided insights about learning opportunities in the activity and feedback for integrating it into their teaching. Findings from qualitative analysis demonstrate that exploring text classification models provided an accessible and comprehensive approach for integrated learning of mathematics, language arts, and computing with the potential of supporting the understanding of core AI concepts including identifying structure within unstructured data and reasoning about the roles of human insight in developing AI technologies. more »« less
Tatar, C.; Yoder, M. M.; Coven, M.; Wiedemann, K.; Chao, J.; Finzer, W.; Jiang, S.; Rosé, C. P.
(, International Society of the Learning Sciences Annual Meeting 2021)
null
(Ed.)
In this paper, we present a co-design study with teachers to contribute towards the development of a technology-enhanced Artificial Intelligence (AI) curriculum, focusing on modeling unstructured data. We created an initial design of a learning activity prototype and explored ways to incorporate the design into high school classes. Specifically, teachers explored text classification models with the prototype and reflected on the exploration as a user, learner, and teacher. They provided insights about learning opportunities in the activity and feedback for integrating it into their teaching. Findings from qualitative analysis demonstrate that exploring text classification models provided an accessible and comprehensive approach for integrated learning of mathematics, language arts, and computing with the potential of supporting the understanding of core AI concepts including identifying structure within unstructured data and reasoning about the roles of human insight in developing AI technologies.
Abstract 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. Practitioner notesWhat is already known about this topicScholarly 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 addsResults 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 policyIt 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).
Jiang, Shiyan; Tang, Hengtao; Tatar, Cansu; Rosé, Carolyn P.; Chao, Jie
(, Learning, Media and Technology)
It’s critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through developing machine learning models, few provided in-depth insights into the nuanced learning processes. In this study, we examined high school students’ data modeling practices and processes. Twenty-eight students developed machine learning models with text data for classifying negative and positive reviews of ice cream stores. We identified nine data modeling practices that describe students’ processes of model exploration, development, and testing and two themes about evaluating automated decisions from data technologies. The results provide implications for designing accessible data modeling experiences for students to understand data justice as well as the role and responsibility of data modelers in creating AI technologies.
Holyfield, Christine; MacNeil, Stephen; Caldwell, Nicolette; O'Neill_Zimmerman, Tara; Lorah, Elizabeth; Dragut, Eduard; Vucetic, Slobodan
(, American Journal of Speech-Language Pathology)
Purpose:Augmentative and alternative communication (AAC) technology innovation is urgently needed to improve outcomes for children on the autism spectrum who are minimally verbal. One potential technology innovation is applying artificial intelligence (AI) to automate strategies such as augmented input to increase language learning opportunities while mitigating communication partner time and learning barriers. Innovation in AAC research and design methodology is also needed to empirically explore this and other applications of AI to AAC. The purpose of this report was to describe (a) the development of an AAC prototype using a design methodology new to AAC research and (b) a preliminary investigation of the efficacy of this potential new AAC capability. Method:The prototype was developed using a Wizard-of-Oz prototyping approach that allows for initial exploration of a new technology capability without the time and effort required for full-scale development. The preliminary investigation with three children on the autism spectrum who were minimally verbal used an adapted alternating treatment design to compare the effects of a Wizard-of-Oz prototype that provided automated augmented input (i.e., pairing color photos with speech) to a standard topic display (i.e., a grid display with line drawings) on visual attention, linguistic participation, and (for one participant) word learning during a circle activity. Results:Preliminary investigation results were variable, but overall participants increased visual attention and linguistic participation when using the prototype. Conclusions:Wizard-of-Oz prototyping could be a valuable approach to spur much needed innovation in AAC. Further research into efficacy, reliability, validity, and attitudes is required to more comprehensively evaluate the use of AI to automate augmented input in AAC.
Touretzky, David; Gardner-McCune, Christina; Martin, Fred; Seehorn, Deborah
(, Proceedings of the AAAI Conference on Artificial Intelligence)
The ubiquity of AI in society means the time is ripe to consider what educated 21st century digital citizens should know about this subject. In May 2018, the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) formed a joint working group to develop national guidelines for teaching AI to K-12 students. Inspired by CSTA's national standards for K-12 computing education, the AI for K-12 guidelines will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The AI for K-12 working group is also creating an online resource directory where teachers can find AI- related videos, demos, software, and activity descriptions they can incorporate into their lesson plans. This blue sky talk invites the AI research community to reflect on the big ideas in AI that every K-12 student should know, and how we should communicate with the public about advances in AI and their future impact on society. It is a call to action for more AI researchers to become AI educators, creating resources that help teachers and students understand our work.
Tatar, C., Yoder, M. M., Coven, M., Wiedemann, K., Chao, J., Finzer, W., Jiang, S., and Rosé, C. P. Modeling Unstructured Data: Teachers as Learners and Designers of Technology-enhanced Artificial Intelligence Curriculum. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), (pp. 617-620). Bochum, Germany: International Society of the Learning Sciences.. Retrieved from https://par.nsf.gov/biblio/10327961. Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. .Jun-2021 Web. doi:10.22318/icls2021.617.
Tatar, C., Yoder, M. M., Coven, M., Wiedemann, K., Chao, J., Finzer, W., Jiang, S., & Rosé, C. P. Modeling Unstructured Data: Teachers as Learners and Designers of Technology-enhanced Artificial Intelligence Curriculum. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), (pp. 617-620). Bochum, Germany: International Society of the Learning Sciences.. Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021., (Jun-2021). Retrieved from https://par.nsf.gov/biblio/10327961. https://doi.org/10.22318/icls2021.617
Tatar, C., Yoder, M. M., Coven, M., Wiedemann, K., Chao, J., Finzer, W., Jiang, S., and Rosé, C. P.
"Modeling Unstructured Data: Teachers as Learners and Designers of Technology-enhanced Artificial Intelligence Curriculum. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), (pp. 617-620). Bochum, Germany: International Society of the Learning Sciences.". Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. (Jun-2021). Country unknown/Code not available. https://doi.org/10.22318/icls2021.617.https://par.nsf.gov/biblio/10327961.
@article{osti_10327961,
place = {Country unknown/Code not available},
title = {Modeling Unstructured Data: Teachers as Learners and Designers of Technology-enhanced Artificial Intelligence Curriculum. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), (pp. 617-620). Bochum, Germany: International Society of the Learning Sciences.},
url = {https://par.nsf.gov/biblio/10327961},
DOI = {10.22318/icls2021.617},
abstractNote = {In this paper, we present a co-design study with teachers to contribute towards development of a technology-enhanced Artificial Intelligence (AI) curriculum, focusing on modeling unstructured data. We created an initial design of a learning activity prototype and explored ways to incorporate the design into high school classes. Specifically, teachers explored text classification models with the prototype and reflected on the exploration as a user, learner, and teacher. They provided insights about learning opportunities in the activity and feedback for integrating it into their teaching. Findings from qualitative analysis demonstrate that exploring text classification models provided an accessible and comprehensive approach for integrated learning of mathematics, language arts, and computing with the potential of supporting the understanding of core AI concepts including identifying structure within unstructured data and reasoning about the roles of human insight in developing AI technologies.},
journal = {Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021.},
number = {Jun-2021},
author = {Tatar, C. and Yoder, M. M. and Coven, M. and Wiedemann, K. and Chao, J. and Finzer, W. and Jiang, S. and Rosé, C. P.},
editor = {de Vries, E. and Hod, Y. and Ahn, J.}
}
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