skip to main content


Title: Machine Learning and the Five Big Ideas in AI
This article provides an in-depth look at how K-12 students should be introduced to Machine Learning and the knowledge and skills they will develop as a result. We begin with an overview of the AI4K12 Initiative, which is developing national guidelines for teaching AI in K-12, and briefly discuss each of the “Five Big Ideas in AI” that serve as the organizing framework for the guidelines. We then discuss the general format and structure of the guidelines and grade band progression charts and provide a theoretical framework that highlights the developmental appropriateness of the knowledge and skills we want to impart to students and the learning experiences we expect them to engage in. Development of the guidelines is informed by best practices from Learning Sciences and CS Education research, and by the need for alignment with CSTA’s K-12 Computer Science Standards, Common Core standards, and Next Generation Science Standards (NGSS). The remainder of the article provides an in-depth exploration of the AI4K12 Big Idea 3 (Learning) grade band progression chart to unpack the concepts we expect students to master at each grade band. We present examples to illustrate the progressions from two perspectives: horizontal (across grade bands) and vertical (across concepts for a given grade band). Finally, we discuss how these guidelines can be used to create learning experiences that make connections across the Five Big Ideas, and free online tools that facilitate these experiences.  more » « less
Award ID(s):
1846073
NSF-PAR ID:
10357450
Author(s) / Creator(s):
; ;
Editor(s):
Wang, Ning; Lester, James
Date Published:
Journal Name:
International journal of artificial intelligence in education
ISSN:
1560-4292
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Wang, Ning ; Lester, James C. (Ed.)
    This article provides an in-depth look at how K-12 students should be introduced to Machine Learning and the knowledge and skills they will develop as a result. We begin with an overview of the AI4K12 Initiative, which is developing national guidelines for teaching AI in K-12, and briefly discuss each of the “Five Big Ideas in AI” that serve as the organizing framework for the guidelines. We then discuss the general format and structure of the guidelines and grade band progression charts and provide a theoretical framework that highlights the developmental appropriateness of the knowledge and skills we want to impart to students and the learning experiences we expect them to engage in. Development of the guidelines is informed by best practices from Learning Sciences and CS Education research, and by the need for alignment with CSTA’s K-12 Computer Science Standards, Common Core standards, and Next Generation Science Standards (NGSS). The remainder of the article provides an in-depth exploration of the AI4K12 Big Idea 3 (Learning) grade band progression chart to unpack the concepts we expect students to master at each grade band. We present examples to illustrate the progressions from two perspectives: horizontal (across grade bands) and vertical (across concepts for a given grade band). Finally, we discuss how these guidelines can be used to create learning experiences that make connections across the Five Big Ideas, and free online tools that facilitate these experiences. 
    more » « less
  2. 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. 
    more » « less
  3. Abstract

    The Framework for K‐12 science education (TheFramework) and Next Generation Science Standards (NGSS) emphasize the usefulness of learning progressions in helping align curriculum, instruction, and assessment to organize the learning process. TheFrameworkdefines three dimensions of science as the basis of theoretical learning progressions described in the document and used to develop NGSS. The three dimensions include disciplinary core ideas, scientific and engineering practices, and crosscutting concepts. TheFrameworkdefines three‐dimensional learning (3D learning) as integrating scientific and engineering practices, crosscutting concepts, and disciplinary core ideas to make sense of phenomena. Three‐dimensional learning leads to the development of a deep, useable understanding of big ideas that students can apply to explain phenomena and solve real‐life problems. While theFrameworkdescribes the theoretical basis of 3D learning, and NGSS outlines possible theoretical learning progressions for the three dimensions across grades, we currently have very limited empirical evidence to show that a learning progression for 3D learning can be developed and validated in practice. In this paper, we demonstrate the feasibility of developing a 3D learning progression (3D LP) supported by qualitative and quantitative validity evidence. We first present a hypothetical 3D LP aligned to a previously designed NGSS‐based curriculum. We further present multiple sources of validity evidence for the hypothetical 3D LP, including interview analysis and item response theory (IRT) analysis to show validity evidence for the 3D LP. Finally, we demonstrate the feasibility of using the assessment tool designed to probe levels of the 3D LP for assigning 3D LP levels to individual student answers, which is essential for the practical applicability of any LP. This work demonstrates the usefulness of validated 3D LP for organizing the learning process in the NGSS classroom, which is essential for the successful implementation of NGSS.

     
    more » « less
  4. null (Ed.)
    K-12 teachers serve a critical role in their students’ development of interest in engineering, especially as engineering content is emphasized in curriculum standards. However, teachers may not be comfortable teaching engineering in their classrooms as it can require a different set of skills from which they are trained. Professional development activities focused on engineering content can help teachers feel more comfortable teaching the subject in their classrooms and can increase their knowledge of engineering and thus their engineering teaching self-efficacy. There are many different types of professional development activities teachers might experience, each one with a set of established best practices. VT PEERS (Virginia Tech Partnering with Educators and Engineers in Rural Communities) is a program designed to provide recurrent hands-on engineering activities to middle school students in or near rural Appalachia. The project partners middle school teachers, university affiliates, and local industry partners throughout the state region to develop and implement engineering activities that align with state defined standards of learning (SOLs). Throughout this partnership, teachers co-facilitate engineering activities in their classrooms throughout the year with the other partners, and teachers have the opportunity to participate in a two-day collaborative workshop every year. VT PEERS held a workshop during the summer of 2019, after the second year of the partnership, to discuss the successes and challenges experienced throughout the program. Three focus groups, one for each grade level involved (grades 6-8), were held during the summit for teachers and industry partners to discuss their experiences. None of the teachers involved in the partnership have formal training in engineering. The transcripts of these focus groups were the focus of the exploratory qualitative data analyses to answer the following research question: How do middle-school teachers develop teaching engineering self-efficacy through professional development activities? Deductive coding of the focus group transcripts was completed using the four sources of self-efficacy: mastery experience, vicarious experience, verbal persuasion and physiological states. The analysis revealed that vicarious experiences can be particularly valuable to increasing teachers’ teaching engineering self-efficacy. For example, teachers valued the ability to play the role of a student in an engineering lesson and being able to share ideas about teaching engineering lessons with other teachers. This information can be useful to develop engineering-focused professional development activities for teachers. Additionally, as teachers gather information from their teaching engineering vicarious experiences, they can inform their own teaching practices and practice reflective teaching as they teach lessons. 
    more » « less
  5. Abstract Practitioner notes

    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).

     
    more » « less