Category Archives: Written by: Sarah Hampton

CRAFTing Better Learning Experiences: Infusing GenAI in Education Effectively and Ethically

CRAFT Framework in table format

CRAFT Framework by Andrew Fenstermaker, Drew Olsson
and Sarah Hampton

by Andrew Fenstermaker, Drew Olsson, and Sarah Hampton

Introduction

Generative artificial intelligence (GenAI) stands to be a disruptive technology in education and all facets of our daily life. While this technology offers significant advantages for teaching and learning, it hinders the process when used without a full understanding of how the technology works and how to evaluate the content generated. The educator must remain the expert, advocate, arbiter, human in the loop identifying why and when the technology gets used, and the critical evaluator to uphold the best of our human ideals.

CRAFT Framework Overview

Using GenAI to augment the lesson design process can seem overwhelming. From composing and revising prompts to evaluating the outputs, integrating GenAI requires a new set of literacy skills. CRAFT was collaboratively designed by Andrew Fenstermaker, Drew Olsson, and Sarah Hampton and augmented using GenAI. The framework serves as a step-by-step roadmap that scaffolds the process of infusing GenAI with the learning sciences to improve learning experiences ethically.

1. Create
The first step in the framework is to create a lesson plan using GenAI prompting that is grounded in learning sciences based on a specific standard, age group or grade level, and time frame. Often, we start with a basic prompt providing no persona or context for GenAI to use in its algorithm as it generates the output. As you can see in this example of generating a lesson plan on the main idea, the chatbot makes inferences about the grade level being taught, length of time, and materials available. The chatbot is simply following its algorithm to predict the next word in its sequence of constructing a complete lesson plan.

Giving a chatbot a persona, such as an expert teacher, and providing more details up front can enhance its recommendations. Reviewing the output from the Detailed Prompt example, you will see that the targeted grade level, specific standard, and length of time are now tailored to our prompt. We can improve the outputs further by including a request for evidence-based best education practices from learning sciences research up front. Therefore, the goal of the create step in the CRAFT framework is to underpin a detailed prompt with learning sciences.

Comparison table of basic prompts, detailed prompts, and detailed + learning sciences + chain of thought prompts

In addition, the results from the prompt with learning sciences can be optimized using chain-of-thought prompting. Chain-of-thought prompting is a technique that allows us to take a complex task and decompose it into a series of logical steps to increase the performance of the large language model (chatbot). As you explore the Detailed Prompt + learning sciences + Chain-of-Thought prompts, you will see that a series of 11 chain-of-thought prompts were used to continually refine the output. Decomposition methods were used as we broke our complex task into smaller chunks, from identifying effect size and selecting an evidence-based learning strategy to incorporating elements aligned with student interests and crafting sentence starters to scaffold the process for students.

2. Review
Advancements in GenAI capabilities and detailed prompting strategies may lull the user in “falling asleep at the wheel.” That is, over-relying on outputs without going through the process of refining its outputs for a given context with intentional consideration. The CARE framework is designed to critically evaluate each output for Clarity, Accuracy, Relevance, and Ethics.

  • Clarity– A lack of clarity in GenAI output might mean the output is not appropriate for a given audience such as overly wordy sentences for a 5th grade class.
  • Accuracy– It is well known that AI makes things up, from citing studies that never existed to stating facts there are blatantly false. It is up to the user to fact check GenAI outputs so that misinformation is not shared in a classroom setting.
  • Relevance– Often, an AI’s first output will not generate a response that matches the intent of the input given. Continue refining your prompts until the output fits your needs.
  • EthicsGenAI is trained on biased data and can be racist. Inspecting outputs for possible stereotypical depictions or biased results is imperative to uphold integrity and respect for all.

3. Amplify
Once we have reviewed our lesson plan grounded in learning sciences using the CARE framework, we are ready for the Amplify stage of CRAFT. The recent National Ed Tech Plan highlights three different divides with instructional technology: Access, Design and Use. The COVID-19 pandemic sparked a rapid influx of instructional technology, decreasing the access divide but exposing prominent divides in how teachers were designing digital learning as well as how students were using the digital tools and resources.

The amplify stage fosters opportunities for teachers to work towards minimizing the Design and Use divides by using GenAI prompts centered around the SAMR (substitution, augmentation, modification, redefinition) model. The graphic below illustrates the progression from beginning with a standard, using chain-of-thought prompting grounded in learning sciences, reviewing with the CARE framework, and concluding with instructional technology enhancements through the SAMR model.

Flow chart demonstrating how a standard can be used to prompt AI with both the learning sciences and SAMR framework for lesson plan design

As outlined before, we continue to use chain-of-thought prompting to further refine our outputs. Within our amplified SAMR lesson plan, you can see that the additional prompts help refocus the large language model (chatbot) when the algorithm deviates from our original vision and allow us to select the specific level(s) of SAMR we wish to incorporate within our final lesson. Just as we did before, we must leverage the review stage to critically evaluate the outputs using the CARE framework.

4. Fine-tune
Leveraging the fine-tune stage allows us to ensure that our lesson is both effective and equitable by carefully evaluating the integration of instructional technology. This includes considering the context of use, implementation strategies, sustainability factors, and inclusivity for all learners. By focusing on these key areas, we can enhance the technology’s impact and ensure it aligns with our pedagogical goals.

Graphical representation of technology considerations in the fine-tune stage including context, implementation, sustainability, and inclusivity.

5. Transform
The final stage of the CRAFT framework does not require additional prompting or reviewing of outputs. Instead, by implementing the filtered enhancements, teachers transform the learning experience, making it more engaging, interactive, and effective for their students.

Conclusion and Call to Action

As GenAI continues to evolve, it is essential for educators to remain the experts in their classrooms and use technology as a tool to enhance, not dictate, their practices. We encourage you to employ the CRAFT Framework to center students in the learning design process. It is through the intentional underpinning of learning sciences that we can remove barriers to create rich learning experiences for all students.

Resources
Link to PDF of CRAFT (two pager)


About the Authors

Andrew Fenstermaker is the Instructional Technology Coordinator for the Iowa City School District. A perpetual learner who infused emerging technologies into his own classroom for ten years now works to empower educators through dynamic professional development, one-on-one coaching, and innovative lesson design that centers students and removes barriers to success. He is a Google Certified Coach and Innovator, leading efforts locally and nationally on adopting and scaling computational thinking and AI in education while sharing key deliverables through presentations and publications.

Drew Olsson is the Technology Integration Coordinator for the Agua Fria High School District. An advocate for staff and student AI Integration, mindful EdTech implementation, and building tech literacy for all. He taught math and computer science for 9 years before moving into his current role where he services 5 comprehensive high schools and over 10,000 students. He is invested in providing powerful learning opportunities for all students so that they may thrive in an increasingly techno-centric world. Drew holds Master’s Degrees in Secondary Education and Educational Leadership from Arizona State University.

Sarah Hampton is a Technology and Curriculum Specialist for the Greenbrier County School District specializing in secondary math education. Prior to her current role, she brought passion for evidence-based instructional strategies and thoughtful technology integration to her middle and high school math and science classrooms. A veteran educator of 15+ years, Sarah works to bring the benefits of education research to more students through embedded professional development in her district and through collaboration with researchers and educators at the Center for Integrative Research in Computing and Learning Sciences.

Overcoming Barriers to Teaching Regulation of Learning

People at table

Photo by Allison Shelley for EDUimages

by Sarah Hampton and Dr. Dalila Dragnić-Cindrić

In our two previous blog posts, we talked about students’ individual self-regulated learning (SRL), group-level, social regulation of learning (SoRL), and why it’s important to explicitly teach both alongside our content (Hampton & Dragnić-Cindrić, 2023a, 2023b). The link between students’ effective self-regulated learning and successful academic and life outcomes has been well documented (Dent & Koenka, 2016). If that’s the case, and if we know the benefits, why don’t more teachers focus on teaching it?

In this post, we will explore some of the barriers and possible solutions for teaching regulation of learning that we have seen in K-12 and higher education classrooms. Importantly, some of the barriers that surfaced during our conversations are within a teacher’s control, and others are not (e.g., district or state policies). In the spirit of teacher empowerment, this post focuses on the barriers and solutions within teachers’ control.

Barrier 1: Comprehensive instruction of SRL and or SoRL requires the teacher to give up control, an uncomfortable idea for many of us.

Suggested Solution: Gradually but steadily release control of learning to the students, making them responsible for their own learning.

Elaboration: If we want students to take more responsibility for their own learning, then we must give responsibility back to them. Doing so gradually but steadily can help teachers overcome their own discomfort with releasing control as well as ease students into new, more active roles in their own learning.

In a recent study conducted in high school physics classrooms, Dalila and colleagues showed that the level of teachers’ control over collaborative groups’ dialogues impacted groups’ SoRL. Students in groups in which the teacher controlled the conversation engaged in less conversation with each other and enacted less SoRL (Dragnić-Cindrić et al., 2023).

For Sarah, our conversation about this study led to a somewhat sobering realization. As a reflective practitioner, she said, “I realized that I had been robbing my students of taking more responsibility for their learning because I was holding onto so much of it. In an effort to maximize our learning minutes, head off classroom disruptions at the pass, and ensure successful learning outcomes, I have hoarded control of my students’ learning experiences.”

If we want students to take more ownership, we must shift more control over learning back to them. Gradual release of control means providing more support and guidance at the beginning, then fading the support as students demonstrate increased capability to manage their own learning. During our conversations on this topic, Sarah said her “aha” moment came when Dalila pointed out that regulation happens whether a teacher acknowledges it or not. “You’re modeling regulation whether you’re intentional about it or not. You’re either modeling good examples or bad examples. It’s about taking advantage of the opportunity to help students learn how to regulate their learning individually and with others.”

That leads us to the next barrier…

Barrier 2: Teachers may not be sure how to teach regulation of learning.

Suggested Solution: To teach regulation of learning, include both modeling and direct instruction of regulation of learning.

Elaboration: As teachers, we have made a career in education and are most likely effective at regulating our own learning. We have probably automated many regulation strategies and don’t even need to think about them, which can make it difficult to understand the perspective of students who find learning how to learn challenging. Because we haven’t had to explicitly think about regulation to navigate learning challenges in our own lives, we may not know how to model and explicitly articulate learning strategies to our students.

Additionally, most teacher preparation programs do not include courses on how to teach regulation of learning. We also recognize that teachers with many demands on their time don’t have the luxury of independently learning about best practices for teaching regulation and developing worksheets, prompts, reflections, etc., to help their students with regulation of learning. Still, there are some steps that can be taken to improve students’ regulation of learning through modeling and direct instruction (Paris & Paris, 2001).

  1. Reflect on your own learning strategies and take time to model them for your students. Narrate your own thought processes and explain how you approach and solve problems. Learn more about regulation of learning and how to teach it. We gave a brief overview in the first post of the series, but we have included more teacher-friendly resources in the Additional Resources section below. For a self-paced professional learning experience, you might like the “Self-regulation professional development module” by the Students at the Center Hub.
  2. Explicitly teach students effective regulation of learning and learning strategies you’re already familiar with, such as:
  • Modify your learning environment and structure study time: Studying is more effective if you eliminate distractions and study in short time intervals followed by brief breaks. Put your phone away and engage in a focused 15-minute study session followed by a 5-minute break (Yes, this is the time to check that phone!)
  • Summarize text and tell someone about it: When studying new material, an effective approach is to read the text and then write a summary of the main points or tell someone else, a friend or a family member, about it. Go into details as much as you can. If there are things you cannot recall, that’s a sign you might want to read that part again. Many students rely exclusively on text highlighting and re-reading. These strategies are ineffective because they create “illusions of knowing,” a false sense that you have learned the material.
  • Quiz yourself to memorize new words or concepts: In subjects where memorizing content is needed (e.g., studying vocabulary), quizzing works! Quiz yourself and ask others to quiz you.
  • Seek help when you get stuck: It is okay to ask for help, and smart students do! If you are stuck, ask others to explain how they approach similar problems. Show your teacher your work and walk them through it — they will be happy to help you identify the rough spots and help you work through them.

We provide links to the additional learning strategy resources below.

Barrier 3: From a short-term perspective, teaching regulation of learning feels like a less valuable use of time than teaching content.

Suggested Solution: Embrace teaching regulation of learning as an inextricable part of teaching your content’s process standards. In other words, part of the standards we’re expected to teach requires students to engage in regulation of learning (see examples below).

Elaboration: Regulation of learning isn’t directly assessed, so when it comes to spending 10 minutes of class time, teachers are likely to choose learning content over learning how to learn. However, hyperfocusing on content standards over process standards is more short-sighted than short-term. The research suggests that teaching regulation will pay content learning dividends in a single school year (Dignath & Büttner, 2008). Beyond that, learning how to navigate challenges and find a way to learn alone and together will benefit learners their entire lives.

Many school districts are adopting big-picture mission statements and portraits of a graduate. Most have a line about creating self-sufficient lifelong learners. Teaching regulation of learning is a critically important way to spend your class time. Justify that time (to yourself and others!) using your existing state and national standards and school, district, and/or state mission statements. Here are some examples:

The National Council of Teachers of Mathematics (NCTM) problem-solving process standards call for teachers to:

  • Allow students to apply and adapt a variety of appropriate strategies to solve problems
  • Allow students to monitor and reflect on their own and others’ strategies for solving problems
  • The National Council for Teachers of English calls for students to:

  • Participate as knowledgeable, reflective, creative, and critical members of a variety of literacy communities.
  • The National Science Teaching Association (NSTA) emphasizes that:

  • Learning is an active, constructive process, and not a receptive one;
  • High quality science, engineering, mathematics, and technology education fosters students’ 21st-century skills of collaboration, problem solving, communication, and creative thinking;
  • North Carolina Department of Public Instruction’s “A Portrait of a Graduate” emphasizes that in addition to academic content, schools must be more intentional about fostering durable skills critical for students’ success, including learner’s mindset, personal responsibility, and collaboration.

    These are a few of the challenges we have identified. What other barriers prevent you or your colleagues from teaching regulation of learning? How have you navigated these challenges in your classroom? We would love to hear your thoughts — tweet us at @EducatorCIRCLS!

    References

    Dent, A.L., & Koenka, A.C. (2016). The Relation Between Self-Regulated Learning and Academic Achievement Across Childhood and Adolescence: A Meta-Analysis. Educational Psychology Review, 28, 425–474. https://doi.org/10.1007/s10648-015-9320-8

    Dignath, C. & Büttner, G. (2008). Components of fostering self-regulated learning among students. A meta-analysis on intervention studies at primary and secondary school level. Metacognition and Learning, 3, 231–264. https://doi.org/10.1007/s11409-008-9029-x.

    Dragnić-Cindrić, D., Lobczowski, N. G., Greene, J. A., & Murphy, P. K. (2023). Exploring the teacher’s role in discourse and social regulation of learning: Insights from collaborative sessions in high-school physics classrooms. Cognition and Instruction, 1–32. https://doi.org/10.1080/07370008.2023.2266847

    Hampton S., & Dragnić-Cindrić, D. (2023a). Regulation of learning: What is it, and why is it important? Center for Integrative Research in Computing and Learning Sciences.

    Hampton S., & Dragnić-Cindrić, D. (2023b). Social Regulation of Learning and Insights for Educators. Center for Integrative Research in Computing and Learning Sciences.

    North Carolina Department of Public Instruction. (n.d.). Portrait of a graduate. https://www.dpi.nc.gov/districts-schools/operation-polaris/portrait-graduate#Tab-DurableSkills-4800

    Paris, S. G., & Paris, A. H. (2001). Classroom applications of research on self-regulated learning. Educational Psychologist, 36(2), 89–101. https://doi.org/10.1207/S15326985EP3602_4

    Acknowledgements

    This material is based upon work supported by the National Science Foundation grant number 2101341 and grant number 2021159. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

    Additional Resources:

    Elaboration | How Expanding On Ideas Increase Outcomes | Science of Learning Series
    Interleaving | Mixed up Practice | Science of Learning Series
    Self-regulated learning: The technique smart students use.
    Spacing | Revisit Material To Boost Outcomes | Science of Learning Series
    Teacher Support of Co- and Socially-Shared Regulation of Learning in Middle School Mathematics Classrooms

    Social Regulation of Learning and Insights for Educators

    by Sarah Hampton and Dr. Dalila Dragnić-Cindrić

    In the first post of this series (Hampton & Dragnić-Cindrić, 2023), we focused primarily on individual student’s self-regulated learning (SRL), explained the related key terms and ideas, and discussed why it is important to teach SRL alongside subject content. In this post, we will focus on regulation of learning in small, collaborative groups.

    Social regulation of learning (SoRL) occurs when students in collaborative groups purposefully select, use, and, if necessary, adjust their collective actions and behaviors to achieve shared learning goals (Hadwin et al., 2018). Navigating group dynamics and collaborating well are skills all students need. SoRL is an essential prerequisite for successful collaboration (Dragnić-Cindrić & Greene, 2021).

    Social regulation of learning (Figure 1) unfolds through the same three loose phases of learning as SRL (i.e., preparation, execution, reflection), and it has the added complexity of coordinating with others. Collaborative groups enact their SoRL by relying on one or more of the following modes of regulation: self-regulated learning (SRL), coregulated learning (CoRL), and socially-shared regulation of learning (SSRL).

    These three modes of regulation vary in their focus:

  • SRL focuses on what “I” do within the group related to my own learning;
  • CoRL focuses on what “you” do and how I can temporarily help you with your regulation; and
  • SSRL focuses on what “we” do together to propel joint learning.
  • CoRL occurs when one group member temporarily supports one or more others in the group, with the goal of eventually transitioning regulation of learning to the regulated student(s). For example, if a student is repeatedly distracted by looking at another group, a teammate might prompt them a few times to pay attention to their own group. After a few prompts, the “regulated” student might decide to switch seats to fully engage with the group and avoid further disruption.

    SSRL is characterized by the equal and balanced participation of all group members in the group’s regulation of learning. During SSRL, group members build on each other’s actions and statements to create synergistic outcomes.

    Figure 1. Social regulation of learning infographic



    Note: This graphic shows a three-person collaborative group engaging in social regulation of learning. The group first plans how to do the task. Then, they attempt to execute their plan and fail. They reflect on what went wrong and what they needed to change. Finally, they try again and achieve their goal.

    When you consider all the ways learners must regulate during group work—self, others, and each other—it’s not surprising that successful collaboration can be challenging. Importantly, students don’t have to regulate their learning all the time. In fact, when students are satisfied with their learning progress, there is no need to regulate. Typically, regulation unfolds as a response to an encountered challenge. For example, some group members might lose their motivation for the task and want to quit. Other group members might need to actively encourage them and point out the progress the group made so far to get them to re-engage.

    Most of what we’ve discussed so far has been about what students in the group are doing to regulate their own learning. It is also possible that someone outside the group—the teacher or even a student from another group—might need to help with the group’s regulation of learning. This is called external regulation of learning. For example, a teacher may decide to step in if a group is engaging in excessive off-task behavior or if they are repeatedly trying an ineffective learning strategy.

    Such an intervention involves trade-offs between the teacher’s control over the group’s learning and allowing adequate space and time for the students to learn how to socially regulate their own learning (Dragnić-Cindić et al., 2023). Think of it like this— when a child first learns to tie shoelaces, it’s clumsy and time-consuming and requires multiple tries with some help. It would be much faster if a parent tied them instead. However, if the parent repeatedly makes the choice to step in and tie the child’s shoelaces, then the child never has the opportunity to learn. Given enough space and time, the child eventually learns to tie them quickly, and the parent never has to intervene again. Similarly, the teacher’s job is to discern when and how to offer the least assistance possible to help students grow in SRL, CoRL, and SSRL and recognize which mode of regulation is the most appropriate in a given situation.

    The quality of a group’s regulation of learning is closely connected to the group climate (Dragnić-Cindrić & Greene, 2021), a persistent pattern of group members’ interactions, emotions, and behaviors that remains stable over time. Successful groups tend to have a positive group climate characterized by positive interactions. For example, group members praise each others’ ideas, offer encouragement when mistakes are made, and joke and laugh together. It is important to establish a positive climate from the first collaborative session, and clear group norms and teacher modeling of desired interactions can help with that. Teachers should step in when off-task or negative behaviors hurt the group climate or even the classroom culture in ways that make growth unlikely.

    In other words, rather than managing students directly, teachers should manage the classroom conditions that allow students to manage their own learning. We include research-based teachers’ moves in the table below (Table 1).

    Table 1. Research-based recommendations for teachers

    In the final blog post of this series, we’ll explore some barriers and potential solutions for teaching regulation of learning in our classrooms. Meanwhile, we would love to hear from you. Are you already incorporating some teacher moves that facilitate regulation of learning in your classroom? If so, which ones? If not, which moves could you implement easily? Let us know by engaging with us on social media @EducatorCIRCLS!

    Educator CIRCLS posts are licensed under a Creative Commons Attribution 4.0 International License. If you use content from this site, please cite the post and consider adding: “Used under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).”
    Suggested citation format: Hampton, S., & Dragnić-Cindrić, D. (2023). Social Regulation of Learning and Insights for Educators. Educator CIRCLS Blog. Retrieved from https://circleducators.org/social-regulation-of-learning-and-insights-for-educators

    Acknowledgements
    This material is based upon work supported by the National Science Foundation grant number 2101341 and grant number 2021159. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

    Resources

    Dragnić-Cindrić, D., & Greene, J. A. (2021). Social regulation of learning as a base for successful collaboration. (Rapid Community Report Series). Digital Promise, International Society of the Learning Sciences, and the Center for Integrative Research in Computing and Learning Sciences. https://repository.isls.org//handle/1/6854

    Dragnić-Cindrić, D., Lobczowski, N. G., Greene, J. A., & Murphy, P. K. (2023). Exploring the teacher’s role in discourse and social regulation of learning: Insights from collaborative sessions in high-school physics classrooms. Cognition and Instruction, 1-32. https://doi.org/10.1080/07370008.2023.2266847

    Hadwin, A. F., Järvelä, S., & Miller, M. (2018). Self-regulation, co-regulation, and shared regulation in collaborative learning environments. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (pp. 83–105). Routledge.

    Hampton S., & Dragnić-Cindrić, D. (2023). Regulation of learning: What is it, and why is it important? Center for Integrative Research in Computing and Learning Sciences. https://circleducators.org/regulation-of-learning-what-is-it-and-why-is-it-important

    Regulation of Learning: What is it, and why is it Important?

    by Sarah Hampton and Dr. Dalila Dragnić-Cindrić

    How many of us want our students to be highly motivated learners? Proactive? Goal-directed? Strategic? Perseverant? Adaptive? We’ve heard teachers across subjects and grade bands say that these are difference-making characteristics that students need to be successful in and out of the classroom. In educational research terms, students who demonstrate these qualities are skilled at regulating their learning. Researchers have dedicated significant efforts to understanding self-regulated learning skills and the underlying processes. In a series of three blog posts, we take a closer look at regulation of learning, why it matters to students and teachers, and how educators might foster it in classroom settings.

    As Timothy Cleary describes in The Self-Regulated Learning Guide (2018), self-regulated learners “want to perform well on some activity” and “purposefully and strategically figure out ways to achieve their goals…despite experiencing challenges, barriers, or struggles, [they] continuously find a way to learn” (pp. 9-10). Interestingly, they do this by repeating three fairly simple phases (Figure 1):

    1. Before the Learning (Preparation Phase)–self-motivating and figuring out how to approach the task;
    2. During the Learning (Execution Phase)–maintaining motivation, using strategies to complete the task, and self-monitoring thinking and actions during learning;
    3. After the Learning (Reflection Phase)–determining how well the selected strategies helped accomplish the task and deciding how to improve next time.
    Three phases of self-regulated learning cycle: preparation, execution, reflection.

    Figure 1. Phases of self-regulated learning.

    Note: This figure shows phases of self-regulated learning and steps students can take throughout this cyclical process. Adapted from the original figure The Cycle of Self-Regulated Learning by Karen Kirk from Develop Self-Regulated Learners: Choosing and Using the Best Strategies for the Task. Published under the Creative Commons license.

    Self-regulated learning is cyclical and its phases are iterative and loosely sequenced; students might move from one phase to the next or revisit previous phases as needed. Thinking about strategies involves thinking about learning strategies (e.g., ignoring distractions, re-reading task instructions) as well as best content area strategies to use in a given task.

    But what do we do when our students aren’t particularly skilled in regulating their learning? Can regulation be learned? Can regulation be taught? Should it be taught?

    Some learners figure out how to regulate their learning on their own and then go on to do it automatically without much thought. That can lead us to believe that some people just get it and some people don’t. However, that kind of fixed mindset thinking isn’t accurate. Regulation of learning can be learned and strengthened when people become aware of the principles and processes behind it and consciously reflect on how to do it better. In fact, when learners realize that the strategies they select are directly linked to how successful they are with tasks, they experience greater self-efficacy, motivation, and success on future tasks (Greene, 2018).

    Likewise, regulation of learning can be taught when we explicitly talk about it with our students, model it for them, and prompt them to engage in it before, during, and after learning activities in our classes. For example, a mathematics teacher might ask her students to fill out a task planning sheet before starting a task (Figure 2) to help them prepare for learning.

    An example of a student mathematics task planning sheet.

    Figure 2. Student task planning sheet by D. Dragnić-Cindrić and S. Hampton

    The purpose of engaging students in task planning is to get them to think about the task and their own goals for it, which might differ from the teacher’s goals. It connects the doing of the task to the time the teacher allotted for it and the materials students will need to use to get it done. Lastly, it leads the students to think about and plan the steps needed to complete the task beforehand. Of course, this plan is a starting point and should remain flexible as students work through the task. The idea behind scaffolds like this planning sheet is that they help students internalize and learn how to engage in self-regulated learning, and over time, begin to do it on their own in other classes.

    So regulation can be learned and taught, but should it? In our conversations on this topic, we relied on our combined expertise, Sarah, as a practitioner and teacher coach with 15 years of experience, and Dalila, as a learning scientist, who studies individual and group regulation of learning. During our conversation, we discussed current regulation of learning literature, Dalila’s own research findings, and Sarah’s deep knowledge of classroom contexts. We concluded that teaching regulation is so important because, immediately, it helps students see what they do in the classroom as something they do for themselves vs. something they do for the teacher, parents, school, etc., and, ultimately, it prepares students for success in any career path. The bottom line is that regulation empowers students and prepares them for life.

    Everyone is going to encounter a difficult moment, an exceptional challenge, and regulation is critical in that moment. Regulation of learning strategies are for everyone. Even if you don’t need them today, I promise you, a day will come when you’ll need them. – Dalila Dragnić-Cindrić

    In addition to the benefits for students, an upfront investment in teaching regulation of learning returns dividends for teachers, too. Imagine having a classroom full of students who are active and confident self-regulated learners rather than passive recipients of knowledge. Some of the time you currently spend motivating learners and managing your classroom could be repurposed for more personalized instruction as students begin diagnosing their own learning barriers and requesting specific kinds of help. Teaching regulation of learning alleviates teachers of the sole responsibility for ensuring students’ progress, while equipping students to assume more ownership of their learning success.

    Research supports what we intuitively know–helping students learn to be highly motivated, proactive, goal-directed, strategic, perseverant, adaptive learners is a game changer for them, and we can accomplish it by explicitly teaching and modeling regulation of learning skills. Because the benefits transcend subject areas and career paths, we would argue that teaching regulation is even more important than teaching subject specific content. Thankfully, teaching content and regulation of learning together is the best way to teach them both.

    In the coming blog posts within this series, we’ll explore some barriers and potential solutions for teaching regulation of learning in our classrooms. We’ll also discuss regulation of learning in collaborative groups (i.e., social regulation of learning) and hear more from Dalila about her research on this topic and from Sarah about her experiences with managing collaborative groups in her math and science classrooms. Together we will offer insights and recommendations for educators.

    Do you think it’s important to explicitly teach regulation of learning? Why or why not? If you’re already teaching it, let us know your favorite strategies by tweeting @EducatorCIRCLS!

    Educator CIRCLS posts are licensed under a Creative Commons Attribution 4.0 International License. If you use content from this site, please cite the post and consider adding: “Used under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).”
    Suggested citation format: [Authors] ([Year]). [Title]. Educator CIRCLS Blog. Retrieved from [URL]

    Resources

    Cleary, T. J. (2018). The self-regulated learning guide: Teaching students to think in the language of strategies. Routledge. https://doi.org/10.4324/9781315693378

    Greene, J. A. (2018). Self-regulation in education. Routledge. https://doi.org/10.4324/9781315537450

    Kirk, K. (n.d.) The cycle of self-regulated learning. [Figure]. The Supporting and Advancing Geoscience Education at Two-Year Colleges (SAGE 2YC) project website. Retrieved July 13, 2023, from https://serc.carleton.edu/sage2yc/self_regulated/index.html

    Apprentice Learner: Artificial Intelligence (AI) in the Classroom

    by Sarah Hampton

    One of my favorite things about CIRCLS is the opportunity to collaborate with education researchers and technology developers. Our goal as a community is to innovate education using technology and the learning sciences to give more learners engaging educational experiences to help them gain deep understanding. To reach that goal, we need expertise from many areas: researchers who study how we learn best, teachers who understand how new technologies can be integrated, and developers who turn ideas into hardware or software.

    Recently I’ve been reminded of an opportunity when Judi, Pati, and I meet with Daniel Weitekemp in June of 2020. Daniel, a PhD student at Carnegie Mellon University at the time, was developing an AI tool for teachers called Apprentice Learner.

    A stacked addition problem demonstrating carrying.

    Figure 1. Apprentice Learner Interface that students use when interacting with Apprentice Learner. The user can request a hint or type in their answer and then hit done.

    Apprentice Learner looks a bit like a calculator at first glance, so an onlooker might be tempted to say, “What’s so cutting edge about this? We’ve been able to do basic math on calculators for years.” But we need to understand the difference between traditional software and software using artificial intelligence (AI) to appreciate the new benefits this kind of tool can bring to the education table.

    In a basic calculator, there’s an unchanging program that tells the screen to display “1284” when you type in “839+445.” There’s no explanation given for how and why the programming behind a calculator works. Yet, for each math problem someone could type in a calculator, there is an answer that has been explicitly programmed to be displayed on the screen.

    Contrast a calculator to Apprentice Learner, which uses machine learning (a type of artificial intelligence). No one tells Apprentice Learner to display “1284” when it sees 839+445.” Instead, it has some basic explicit instructions and is given lots of examples of correctly solved problems adding 2 or more columns of numbers. Then it has to figure out how to answer new questions. The examples it is given are called training data. In this case, Apprentice Learner was given explicit instructions about adding single digit numbers and then lots of training data–multidigit addition problems with their answers–maybe problems like “21+43=64,” “49+8=57,” and “234+1767=2001.” Then, it starts guessing at ways to arrive at the answers given from the training data.

    The first guess might be to stack the numbers and add each column from left to right. That works perfectly for “21+43,” but gives an incorrect answer of “129” for “49+8.”

    Two guesses for adding numbers

    The second guess might be to stack the numbers and add each column from right to left. Again, that works perfectly for “21+43.” Unfortunately, that would give an answer of “417” for “49+8.”

    The software continues finding patterns and trying out models until it finds one that fits the training data best. You can see below that, eventually, Apprentice Learner “figured out” how to regroup (aka carry) so it could arrive at the correct answer.

    A stacked addition problem

    So what are the implications for something like this in education? Here are a few of my thoughts.

    Apprentice Learner models inductive learning which can help pre-service teachers

    Induction is the process of establishing a general law by observing multiple specific examples. It’s the basic principle machine learning uses. In addition, inductive reasoning tasks such as identifying similarities and differences, pattern recognition, generalization, and hypothesis generation play important roles when learning mathematics. (See Haverty, Koedinger, Klahr, Alibali). Multiple studies have shown that greater learning occurs when students induce mathematical principles themselves first rather than having the principles directly explained at the onset. (See Zhu and Simon, Klauer, and Koedinger and Anderson)

    However, instructional strategies that promote students to reason inductively prior to direct instruction can be difficult for math teachers to implement if they haven’t experienced learning math this way themselves. Based on conversations with multiple math teacher colleagues throughout the years, most of us learned math in more direct manners i.e., the teacher shows and explains the procedure first and then the learner imitates it with practice problems. (Note: even this language communicates that there is “one right way” to do math unlike induction in which all procedures are evaluated for usefulness. This could be a post in its own right.)

    Apprentice Learner could provide a low-stakes experience to encourage early-career teachers to think through math solutions inductively. Helping teachers recognize and honor multiple student pathways to a solution empowers students, helps foster critical thinking, and increases long-term retention. (See Atta, Ayaz, and Nawaz and Pokharel) This could also help teachers preempt student misconceptions (like column misalignment caused by a misunderstanding of place values and digits) and be ready with counterexamples to show why those misconceptions won’t work for every instance, much like I demonstrated above with Apprentice Learner’s possible first and second guess at how multi-digit addition works. Ken Koedinger, professor of human-computer interaction and psychology at CMU put it like this, “The machine learning system often stumbles in the same places that students do. As you’re teaching the computer, we can imagine a teacher may get new insights about what’s hard to learn because the machine has trouble learning it.”

    The right training data is crucial

    What would have happened if there were only some types of problems in the training data? What if they were all two digit numbers? Then it wouldn’t have mattered if you stacked them left to right or right to left. What if none required regrouping/carrying? Then adding right to left is a perfectly acceptable way to add in every instance. But when all the edge cases are included, the model is more accurate and robust.

    Making sure the training data has enough data and a wide array of data to cover all the edge cases is crucial to the success of any AI model. Consider what has already happened when insufficient training data was used for facial recognition software. “A growing body of research exposes divergent error rates across demographic groups, with the poorest accuracy consistently found in subjects who are female, Black, and 18-30 years old.” Some of the most historically excluded people were most at risk for negative consequences of the AI failing. What’s important for us as educators? We need to ask questions about things like training data before using AI tools, and do our best to protect all students from negative consequences of software.

    Feedback is incredibly advantageous

    A flowchart demonstrating different ways a user can give direct input to Apprentice Learner

    Figure 2. Diagram of how it works to give feedback to the Apprentice Learner system.

    One of the most interesting things about Apprentice Learner is how it incorporates human feedback while it develops models. Instead of letting the AI run its course after the initial programming, it’s designed for human interaction throughout the process. The developers’ novel approach allows Apprentice Learner to be up and running in about a fourth of the time compared to similar systems. That’s a significant difference! (You can read about their approach in the Association for Computing Machinery’s Digital Library.)

    It’s no surprise that feedback helps the system learn, in fact, there’s a parallel between helping the software learn and helping students learn. Feedback is one of the most effective instructional strategies in our teacher toolkit. As I highlighted in a former post, feedback had an average effect size of 0.79 standard deviation – an effect greater than students’ prior cognitive ability, socioeconomic background, and reduced class size on students’ performance. I’ve seen firsthand how quickly students can learn when they’re given clear individualized feedback exactly when they need it. I wasn’t surprised to see that human intervention could do the same for the software.

    I really enjoyed our conversation with Daniel. It was interesting to hear our different perspectives around the same tool. (Judi is a research scientist, Pati is a former teacher and current research scientist, Daniel is a developer, and I am a classroom teacher.) I could see how this type of collaboration during the research and development of tools could amplify their impacts in classrooms. We always want to hear from more classroom teachers! Tweet @EducatorCIRCLS and be part of the conversation.

    Thank you for your time in talking and reviewing this post, Daniel Weitekamp, PhD Candidate, Carnegie Mellon University.

    Learn More about Apprentice Learner:

    Learn More about Math Teaching and Learning:

    Educator Spotlight: Marni Landry

    Headshot of woman with short hair, glasses, earings wearing a white shirt with a pointy collar and a blazer.Who is Marni Landry?

    Some of us at Educator CIRCLS recently had the pleasure of talking to educator Marni Landry. Marni has been the K-12 STEM Outreach Manager at Grand Canyon University for about three and half years where she spends her time coordinating STEM professional development for teachers and amazing summer camps like GenCyber cybersecurity, with partner Cori Araza for students and teachers. Before coming to GCU, Marni taught high school science for 16 years. She wrote the STEM Integrated curriculum for, and taught in the Center for Research, Engineering, Science, and Technology program on the Paradise Valley High School campus.

    Outside the classroom, Marni has been a leader in the teaching community. She served on the Paradise Valley Technology Committee, designing and delivering technology PD to staff and delivering biotechnology PD as a BioRad fellow. She has also presented STEM PD for the National and Arizona Science Teachers Association (ASTA) and has served as their committee chair. In addition, she partners with MESA (Math Engineering Science Achievement), HOSA-Future Health Professionals, and the Society of Women Engineers (SWE).

    Marni’s passion for teaching and learning was evident throughout our conversation, so it was no surprise to learn that her passion and impact have been widely recognized by various organizations. Marni is a recipient of the Presidential Award for Excellence in Science and Math Teaching, a Nobel Top 10 Teacher of the Year, AZ High School Science Teacher of the Year, Arizona Tech Council Teacher of the Year, IEEE Pre-College Teacher of the Year, Arizona Bioindustry Association Educator of the Year, and a Fellow of the Fulbright Teachers for Global Classrooms.

    What’s one thing you really care about getting right as an educator?
    Even though she’s been out of the classroom for a few years, Marni definitely maintains the heart of a teacher and still works through that lens. When asked what she really cares about getting right as an educator, she said, “Getting people to love learning and getting people who say ‘I can’t’ to say ‘I will.’ Learning is not a task. It’s an adventure! I want them to say, ‘Yeah, this is hard, but that’s the fun part!’”

    What are you most proud of in your career?
    When asked what she’s most proud of in her career, Marni first pointed to her students’ successes. She said that she has been fortunate to build relationships with so many students and to still be part of many of their lives. “Seeing their success is what I’m most proud of. Other people may not always have seen what I saw in them, but I fought tooth and nail for them. So to see them succeed is what makes me most proud.”

    Marni also pointed to a proud personal moment–winning the Presidential Award for Excellence in Math and Science Teaching. She took away something profound from that experience in addition to the recognition. “I didn’t think I was PAEMST material, but my mentor was convinced that I was. I didn’t even think I could go through the application process, but my mentor said I could do it and that she would help.” Marni realized her mentor’s investment in her had a trickle down effect on her students. “They might not think they’re the right material, but I believe they are. They might not think they can accomplish certain things, but I think they can and I can help.”

    What are some of your favorite educational technologies?
    You can tell Marni frequently uses tech tools because she had several favorites in her back pocket. Here are a few she mentioned:

    You can check out Marni’s Tech Tools Wakelet and GCU’s “Educator Tip of the Day” YouTube channel for more tech tools, tech tips, and general professional development, too!

    What is your ideal vision for how the learning sciences and/or educational technologies could shape teaching and learning in the future?
    Marni had some great thoughts surrounding the ideal partnership between the learning sciences, technologies, and education. She pointed out that teachers have to overcome several obstacles before they can meaningfully incorporate technology and research into their teaching practices. For one, she said teachers don’t have time to try out several new technologies and get comfortable with them. “Before teachers can use technologies wisely, they have to have time and permission to use them messily. With the demands teachers face, there’s no chance for trying; there’s no chance for messy.” She also talked about the challenges of using educational research to create standardized policies. “The perfect research-based method, strategy, tool, etc. isn’t going to work for everybody. In an ideal world, educators would be valued and given the freedom to motivate their students in the way their students need to be motivated–and that might look different from classroom to classroom. We need the system to come to terms with that.”

    Takeaway
    We have several great takeaways from our conversation with Marni. One thing we appreciate most is that she highly values the quantitative aspects of the STEM fields she champions, and she equally values the qualitative aspects of being human and of teaching as a human endeavor. While she respects data, her students are more than numbers to her. They are names and faces and personalities and individuals. As the 2021-2022 school year starts, I hope we’re all inspired to be an educator like that.

    Contact Info
    You can connect with Marni via email at marni.landry@gcu.edu, through GCU’s Outreach program at CayonPD.com, or on social media @marni_landry

    Reflections on the AI and Learning Environments Webinar: Things to Consider When Making Purchasing and/or Adoption Decisions for AI Tools

    Eduators, Artificial Intelligence, and the future of Learning
    By Sarah Hampton

    On April 21, I was able to participate in something really exciting! I joined some amazing researchers and former teachers in the Educators, Artificial Intelligence, and the Future of Learning webinar on Learning Environments facilitated by James Lester. The webinar was designed to help practitioners, AI researchers, and developers share their perspectives on how artificial intelligence can be used in the classroom. As you may know, I am a middle and secondary math teacher. My fellow panelists included:

    • Diane W. Doersch, Technical Project Director, Digital Promise
    • Cindy Hmelo Silver, Learning and Technology Researcher, Indiana University
    • Kylie Peppler and Emily Schindler, Learning and Technology Researchers, University of California, Irvine

    The webinar focused on how AI can enhance learning environments. It started with James who discussed the advancements in educational AIs during his 25 years of work in the field, the significant benefit they can provide, and the current demand for AI in educational settings. In other words, this is a hot topic in education right now!

    Next, Diane Doersch shared her thoughts on AI in education, drawing from her experiences as a former classroom teacher, a Director of Technology for a large school district, and Chief Technology and Information Officer. She called for optimism yet caution and thoughtful vetting processes before incorporating AI in classrooms. She also stressed how important it is for school decision makers to know and understand what artificial intelligence is and the impacts that it has in order to properly vet products.

    In this initial post, I want to camp out on Diane’s thoughts; we’ll discuss Cindy Hmelo-Silver and Kylie Peppler and Emily Schindler’s work in later posts. At Educator CIRCLS, we’ve really been digging into artificial intelligence so we can participate in the important conversation happening right now around how AI can be used in classrooms, and, perhaps more importantly, when AI should and shouldn’t be used in classrooms. We want to offer our educator perspectives to the communities developing, researching, and creating policy around AI in education. Furthermore, we want you to understand artificial intelligence so you can offer your unique perspectives and advocate for your students, too. Our friends at Digital Promise recently posted Artificial Intelligence 101: Covering the Basics for Educators. It’s a great introduction to AI and has points to ponder for veteran AI folks, too.

    I’ve spent a lot of time reflecting since the webinar. I’ve specifically been thinking about things to consider when making purchasing and/or adoption decisions for AI products. Diane and I offered some suggestions during the webinar (timestamp 39:55), and I have added more below. You will notice some common themes from AI 101 and from this school procurement guide by Edtech Equity. I hope these can be useful resources for you and your school decision makers as you’re sure to see more and more AI products coming your way!

    Is it safe? Is it secure? Is it ethical?

    • How is the company funded? Do they sell the data they collected? How is the data safeguarded?
    • What was the training data for the AI like? Was it sufficient in volume and diversity? Has it had adversarial training?
    • What was the fitness model like when training the AI? What was the goal and how was fitness measured?
    • What are the consequences if the AI fails? How does it fail?

    Does it align with the mission of the district/school?

    • Does it promote the kind of district/school culture you want?
    • Does it create a significantly better learning experience that you couldn’t gain otherwise? Will it lead to substantial time saving or learning gains or meaningful learning experiences? Is it more than a wow factor?
    • Does it promote the kind of assessments and standards you want to grow toward, or does it increase performance on your current assessments and standards?

    Is it classroom/teacher friendly?

    • Was it developed in collaboration with teachers? If not, it might work really well in the lab but may not extend to the complexity of a real classroom.
    • Has it been tested in a classroom context similar to your own?
    • Can the teacher override the AI if necessary?
    • Does the tool free up the teacher to do what the teacher does best? You don’t want to offload what humans do best onto a machine. You want to maximize what machines do best and what people do best.
    • Does the tool have a thoughtful approach to classroom management?
    • Does the tool have a simple but thoughtful teacher dashboard?
    • Will implementing the tool require teachers to change their pedagogy? If so, what supports, training, and time will be offered to make that shift successful?
    • Does it promote the kind of classroom culture/activities you want? For example, does it help with collaboration, critical thinking, engaging all students, etc.?

    What do you think? Did I leave something out? Feel free to tweet us @EducatorCIRCLS with any comments or suggestions! Stay tuned for future posts unpacking important topics from the webinar and sign up for the CIRCLS newsletter to stay updated on emerging technologies for teaching and learning. I’ll leave you with a question Diane posed, “If AI is the solution, then what’s the problem we’re trying to solve?”

    Related

    We also have resources from the other webinars in this series and additional posts on AI.

    Learning from Gaming

    by Sarah Hampton

    In the previous post of this series, we explored why pedagogy really matters.

    The more pedagogies we know → the more we can choose from → the more targeted we can make the approach to hit the learning goals → the better our students can learn

    In this post, let’s see how pedagogy comes into play when incorporating educational games.

    Consider two different game-based approaches I have used for a middle school physical science unit on chemical reactions. One year, I created a quiz show toward the end of the unit complete with teams and buzzers. The questions for the game came from the unit’s notes and textbook. Another year, I used a design competition in the middle of the unit in which students created the best reptile egg incubator by chemically engineering a heat pack with optimal amounts of calcium chloride, baking soda, and water. Which do you think was more effective?

    That was actually a trick question. The answer should have been–more effective for what? If my learning goal was to promote low level recall of multiple concepts, then my quiz show was the better choice. If my learning goal was to promote collaboration, problem solving, and deep learning of fewer concepts, then the design competition was the hands down winner.

    sentiment from Mike Sharples: Your pedagogy should be thoughtfully chosen based on what best supports your learning goal.Your pedagogy should be thoughtfully chosen based on what best supports your learning goal. That was my number one takeaway from our book study on Practical Pedagogy 40 New Ways to Teach and Learn. This applies to game-based learning like any other kind of learning. I like how author Mike Sharples explained it in our conversation with him last January.

    The idea that pedagogy underpins effective games is also discussed in Motivating Children to Learn Effectively: Exploring the Value of Intrinsic Integration in Educational Games. The paper describes two different kinds of games. One type of game tacks fun onto learning like “chocolate covered broccoli.” (My quiz show is an example of this kind of gamification.) In contrast, intrinsically integrated games (like the incubator design competition):

    1. deliver learning material through the parts of the game that are the most fun to play, riding on the back of the flow experience produced by the game, and not interrupting or diminishing its impact and;
    2. embody the learning material within the structure of the gaming world and the player’s interactions with it, providing an external representation of the learning content that is explored through the core mechanics of the gameplay.

    My students love learning through gaming. I bet yours do, too! Just remember–simply incorporating a game doesn’t mean your students will reach the learning goal. That depends on the underlying pedagogy. Evaluate potential games to see if the fun elements are sugar coating to make your learning goals more palatable or if the learning goals are intrinsically linked to the fun of the game.

    You can find examples of games we like below. Do you already use effective games in your classes? Share them with us @EducatorCIRCLS!

    Post Title and laptop
    Intrinsically Integrated Educational Games
    Crystal Island (middle school microbiology)
    Geniverse (middle school/high school genetics)
    Graspable Math (several different algebraic ideas)
    eRebuild (middle school ratios and proportions)
    Euclid the Game (geometry constructions)
    Human Resource Machine (computational thinking)
    Zoombinis (computational thinking)
    Game Builder Garage (computational thinking Switch game)
    Robot Turtles (computational thinking board game)
    Lemonade Stand (entrepreneurship)
    Institute of Play (multiple subjects and grade levels)
    Absolute Blast (multiplayer math board game for grades 6-8)
    Socratic Smackdown (discussion-based humanities game to practice argumentation)
    Self on the Stand (middle school ELA)
    Conditionals with Cards (elementary computer science)

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    Suggested citation format: [Authors] ([Year]). [Title]. Educator CIRCLS Blog. Retrieved from [URL]

    Pedagogy Really Matters

    book and tiled Zoom on laptop screenby Sarah Hampton

    Last January, we were lucky enough to have a conversation with Mike Sharples, the author of Practical Pedagogy 40 New Ways to Teach and Learn, 1st Edition. While we apologize for the delay in posting — we got a little busy with transitioning to remote and hybrid teaching — we think that pedagogy is more important than ever and want to share some timely insights from the conversation. This is the first of a series of posts and is a little about math and a lot about pedagogy. If you’re here for the pedagogy but not the math, just stick with us past the example from my own math teaching journey.

    I’ve always liked math as a subject, but as a student math class was never my favorite. Every year, math class went something like this.

    • I showed up for class.
    • I took out my home work and we reviewed it.
    • My teacher would go over questions.
    • I’d sit and listen to a lecture while I took notes.
    • I tried a few guided practice questions.
    • I went home and did my homework.
    • Wash, rinse, repeat.

    When I accidentally fell into teaching math, that was all I had ever known so that’s how I taught, too. Fortunately, a few years into teaching, I was introduced to a professional development program specifically for math teachers hosted by a local college. The program professors allowed me to experience a different kind of math classroom! I learned new concepts by exploring first, then reflecting and articulating my nascient thoughts and defending my reasoning. In fact, the entire process was accomplished by me, the learner. That’s not to say that the professors were absent or that their roles weren’t important. They…

    • created the learning environment and tasks
    • motivated me
    • intervened at critical moments
    • prodded me to reflect and refine my thoughts
    • helped me internalize the learning.

    This experience changed how I thought math class could look. That summer, I fully understood that how we teach is just as important as what we teach. In other words, pedagogy really matters.

    We all use pedagogies every day, but it may be something we do unintentionally. Take a minute to think about your “go to” approach to teaching and learning. What happens on a typical day in your classroom? Maybe you’re likely to teach through lectures followed by guided practice. Maybe you facilitate class-wide conversations about your content. Maybe you organize your classroom into pods of students (or breakout rooms these days!) for collaborative learning.

    Something to consider–no matter what you typically do, that teaching approach is well-suited for some types of learning goals but ineffective for others. You know the typical math classroom I talked about in the beginning of the post? That’s a type of direct instruction pedagogy. In fact, parts of it are really effective for some things. For one, teachers can disseminate information quickly. For another, it’s great when students have very little prior knowledge about a topic. Unfortunately, this pedagogy is not the most effective for things like long-term retention or transfer. By contrast, constructivist pedagogies like those used by the program professors are effective for deep learning, motivation, and engagement. (You can read more constructivist pedagogies in three previous posts–Learning Scientists and Classroom Practice, Practitioner POV of Constructivist Approaches, and The Benefits and Obstacles of Constructivism.)

    Too often, we get stuck in a pedagogical rut and force our daily learning goals to fit our routine teaching style. Instead, we need to start purposefully thinking about which pedagogies best support our daily goals. In this series, I want to draw your attention to the pedagogies you use and introduce you to some you may not be familiar with so you can expand your teaching toolkit.

    Teaching should be about meeting students where they are, and different pedagogies help us reach different students at different points in their learning journeys. The more learning theory and instructional strategies we understand, the more intentional we can be about selecting approaches that engage more of our students and meet their needs.

    The more pedagogies we know → the more we can choose from → the more targeted we can make the approach to hit the learning goals → the better our students can learn

    How often do you change up your pedagogies? When are you most likely to make changes to your pedagogies? Are you in a teaching rut now? Think about the best teacher you’ve ever had. What pedagogies did that teacher use?

    In the next post, we’ll look at the role of pedagogy when teaching with technology and how we can use different pedagogies to up our pandemic teaching game. If necessity is the mother of invention, then learning about different pedagogies is more important now than ever. You don’t have to be an expert in a new pedagogy to use it in your classroom. I hope you find one that you are excited to try!

    Related Resources:
    Innovating Pedagogy 2021
    Innovating Pedagogy website with links to all Open University Innovation Reports
    Mike Sharples Keynote at Cyberlearning 2019

    What do you think? Let us know @EducatorCIRCLS.

    This post is part of the Practical Pedagogy Series
    In Educator CIRCLS, we’ve been doing the messy, fun, and challenging work of learning through discussions of our reading of Practical Pedagogy 40 New Ways to Teach and Learn, 1st Edition. We were lucky enough to have a conversation with Mike Sharples, the author. We feel we are emerging from those conversations as more informed and effective educators! We would love you to share your thoughts and join the conversation.

    AI and Formative Assessment

    by Sarah Hampton

    In my last post, I talked about effective formative assessments and their powerful impact on student learning. In this post, let’s explore why AI is well-suited for formative assessment.

    1. AI can offer individualized feedback on specific content.
    2. AI can offer individualized feedback that helps students learn how to learn.
    3. AI can provide meaningful formative assessment outside of school.
    4. AI might be able to assess complex and messy knowledge domains.

    Individualized Feedback on Content Learning

    I think individualized feedback is the most powerful advantage of AI for assessment. As a teacher, I can only be in one place at a time looking in one direction at a time. That means I have two choices for feedback: I can take some time to assess how each student is doing and then address general learning barriers as a class, or I can assess and give feedback to students one at a time. In contrast, AI allows for simultaneous individualized feedback for each student.

    “AI applications can identify pedagogical materials and approaches adapted to the level of individual students, and make predictions, recommendations and decisions about the next steps of the learning process based on data from individual students. AI systems assist learners to master the subject at their own pace and provide teachers with suggestions on how to help them.” (Trustworthy artificial intelligence (AI) in education: promises and challenges)

    Going one step further, AI has the ability to assess students without disrupting their learning by something called stealth assessment. While students work, AI can quietly collect data in the background such as the time it takes to answer questions, which incorrect strategies they tried before succeeding, etc. and organize them into a dashboard so teachers can use that data to inform what to focus on or clear up the next day in class. Note: As a teacher, I want the AI to help me do what I do best. I definitely want to see what each student needs in their learning. Also, as a teacher, I want to be able to control when the AI should alert me about intervening (as a caring human) instead of it trying to do something on its own that it isn’t capable of doing well.

    Feedback That Helps Students Learn How to Learn

    “Two experimental research studies have shown that students who understand the learning objectives and assessment criteria and have opportunities to reflect on their work show greater improvement than those who do not (Fontana & Fernandes, 1994; Frederikson & White, 1997).” (The Concept of Formative Assessment)

    In the last post, I noted that including students in the process of self-assessment is critical to effective formative assessment. After all, we ultimately want students to be able to self-regulate their own learning. But, as one teacher, it can sometimes be difficult to remind students individually to stop and reflect on their work and brainstorm ways to close the gap between their current understanding and their learning goal. By contrast, regulation prompts can be built into AI software so students routinely stop and check for understanding and defend their reasoning, giving students a start on learning how to self-regulate.

    For example, this is done in Crystal Island, an AI game-based platform for learning middle school microbiology, “students were periodically prompted to reflect on what they had learned thus far and what they planned to do moving forward…Students received several prompts for reflection during the game. After completing the game or running out of time, students were asked to reflect on their problem-solving experience as a whole, explaining how they approached the problem and whether they would do anything differently if they were asked to solve a similar problem in the future.” (Automated Analysis of Middle School Students’ Written Reflections During Game-Based Learning)

    In-game reflection prompt presented to students in Crystal Island

    Meaningful Formative Assessment Outside of School

    Formative assessment and feedback can come from many sources, but, traditionally, the main source is the teacher. Students only have access to their teacher inside the classroom and during class time. In contrast, AI software can provide meaningful formative assessment anytime and anywhere which means learning can occur anytime and anywhere, too.

    In the next post, we’ll look at how one AI tool, ASSISTments, is using formative assessment to transform math homework by giving meaningful individualized feedback at home.

    Assessing Complexity and Messiness

    In the first post of the series, I discussed the need for assessments that can measure the beautiful complexity of what my students know. I particularly like the way Griffin, McGaw, and Care state it in Assessment and Teaching of 21st Century Skills:

    “Traditional assessment methods typically fail to measure the high-level skills, knowledge, attitudes, and characteristics of self-directed and collaborative learning that are increasingly important for our global economy and fast-changing world. These skills are difficult to characterize and measure but critically important, more than ever. Traditional assessments are typically delivered via paper and pencil and are designed to be administered quickly and scored easily. In this way, they are tuned around what is easy to measure, rather than what is important to measure.”

    We have to have assessments that can measure what is important and not just what is easy. AI has the potential to help with that.

    For example, I can learn more about how much my students truly understand about a topic from reading a written response than a multiple choice response. However, it’s not possible to frequently assess students this way because of the time it takes to read and give feedback on each essay. (Consider some secondary teachers who see 150+ students a day!)

    Fortunately, one major area for AI advancement has been in natural language processing. AIs designed to evaluate written and verbal ideas are quickly becoming more sophisticated and useful for providing helpful feedback to students. That means that my students could soon have access to a more thorough way to show what they know on a regular basis and receive more targeted feedback to better their understanding.

    While the purpose of this post is to communicate the possible benefits of AI in education, it’s important to note that my excitement about these possibilities is not a carte blanche endorsement for them. Like all tools, AI has the potential to be used in beneficial or nefarious ways. There is a lot to consider as we think about AI and we’re just starting the conversation.

    As AI advances and widespread classroom implementation becomes increasingly more possible, it’s time to seriously listen to those at the intersection of the learning sciences and artificial intelligence like Rose Luckin. “Socially, we need to engage teachers, learners, parents and other education stakeholders to work with scientists and policymakers to develop the ethical framework within which AI assessment can thrive and bring benefit.” (Towards artificial intelligence-based assessment systems)

    Thank you to James Lester for reviewing this post. We appreciate your work in AI and your work to bring educators and researchers together on this topic.

    We are still at the beginning of our conversation around AI in Education. What do you think? Do the possible benefits excite you? Do the possible risks concern you? Both? Let us know @EducatorCIRCLS.