Category Archives: Teachers & Researchers

iPads used by students in school classrooms — image by Arthur Lambillotte via Unsplash

by Courtney Teague, Rita Fennelly-Atkinson, and Jillian Doggett

Courtney Teague, EdD, Deputy Director of Internal Professional Learning and Coaching with Verizon Innovative Learning Schools program based in Atlanta, GA.
Rita Fennelly-Atkinson,EdD, Director Micro-credentials with the Pathways and Credentials team based in Austin, TX
Jillian Doggett M.Ed, Project Director of Community Networks with Verizon Innovative Learning Schools program based in Columbus, OH

What is microlearning?
Microlearning is a teaching and learning approach that delivers educational content in short, focused bursts of information. Microlearning tends to focus on one objective, and the learning doesn’t require more than 1-20 minutes of the learner’s time. Schools and teachers can use microlearning to supplement traditional instruction or as a standalone learning tool. microlearning has been around for a long time–remember those flashcards at kindergarten that helped us learn numbers, the alphabet, and colors? However, schools were largely unaware of how powerful this learning strategy can be for a teacher.

Microlearning has several potential benefits for both learners and teachers. For learners, microlearning can provide a more engaging and interactive learning experience. This type of instruction can also help to reduce distractions for students who become disengaged with unnecessary learning information. For teachers, microlearning can be used to differentiate instruction and address the needs of all learners. Additionally, microlearning can save instructional time by allowing teachers to deliver targeted information in a concise format. Teachers can tailor microlearning content to focus on specific skills or knowledge gaps (Teague, 2021).

Microlearning is flexible and can be accessed anytime, anywhere. Learners can complete microlearning activities on their own time, at their own pace. Microlearning is like a seasoning for learning; it seasons and heats up information to make the process of comprehending new knowledge easier. It has been part-and-parcel in many schools’ instructional strategies since time immemorial, but only recently have we begun paying attention to how powerful this strategy really can be when used correctly.

microchip being held — image by Brian Kostiuk via Unsplash

What does microlearning look like?
Microlearning can come in many forms. Below is a list of 10 microlearning examples:

Short, Focused Videos
Infographics
Podcasts or Audio Recordings
Social Media Posts and Feeds
Interactive Multimedia
Animations
Flashcards
Virtual Simulations
Assessment Activities: Polls, Multiple-Choice Questions, Open Response Questions
Games

How can teachers use microlearning effectively to maximize content retention, personalize learning experiences, and bolster student engagement?

Use microlearning to Active Student Prior Knowledge and Generate Excitement for New Learning

Assign microlearning, such as a self-paced learning game, to assess and activate prior knowledge around a topic. Or place a few bite-sized learning opportunities about an upcoming lesson in your Learning Management System (LMS) for learners to preview beforehand to generate interest and excitement for new learning.

Use microlearning to Personalize Learning Experiences

Creating microlearning in various formats covering multiple topics gives learners the agency to make meaningful choices about their learning paths. For example, to learn a new concept or build new skills, learners can choose to engage with an interactive image, listen to a short audio guide, participate in a learning game, or watch an explainer video or animation. Additionally, learners who need remediation or want to extend their learning can quickly access content to review a topic again or complete additional microlearning lessons.

Use microlearning to Encourage Communication and Collaboration

Create different microlearning bites, each covering a specific objective or portion of a learning goal. Assign each student to engage with one microlearning bite and then use the Jigsaw method to have learners learn about a new topic in a cooperative style. Similarly, you can assign microlearning that includes thought-provoking, probing questions and have learners discuss on a discussion forum or by recording and responding to each other’s short video or audio responses.

Use microlearning to Engage Families and Caretakers

Distribute microlearning to learners’ families and caretakers to help them quickly learn content learners are learning in class to support them in taking an active role in their child’s learning at home.

Use microlearning to Reduce Time Spent Grading

Create microgames and assessments using tools that automatically grade and provide learner analytics to reduce the time spent grading. For example, create an interactive video with embedded questions, a short quiz on your LMS, or a learning game that automatically grades learners’ responses and provides you with learner analytics you can use right away to inform just-in-time teaching.

Use microlearning to Build Classroom Community

Have learners create microlearning lessons to teach each other about themselves, topics that interest them, or around specific learning objectives that they have mastered. Use these bite-sized pieces of learning to expand your microlearning repository, give learners ownership of their learning, and foster a sense of classroom community.

Use microlearning to Promote Learning Outside of School

Over time, create and curate a repository of microlearning assets, such as explainer videos, audio recordings, infographics, learning games, trivia quizzes, flashcards, etc., on your Learning Management System (LMS). Then, learners can easily access and continue their learning outside of school, cultivating a life-long learning mindset.

How to assess microlearning?
The flexibility of microlearning allows for an abundance of possibilities in how it is assessed. For example, if your goal is simply to educate people about a new process using a video, then you don’t have to assess, you can simply measure the reach by the number of views and effectiveness by the level of adherence to the new process by a specific date. If your goal is to educate people about the available services, then your performance indicator might be the use of those services. In other words, you have a license to be creative and to assess learning effectiveness in many different ways.

More formally, the evaluation of learning can be categorized into two types: assessments and indicators (Fennelly-Atkinson & Dyer, 2021). Assessments include most formal and informal methods of evaluating learning, which include surveys, check-ins (i.e. verbal, data, progress, etc), completion rates, knowledge checks, skill demonstration observations, self-evaluations, and performance evaluations. Meanwhile, indicators include indirect measures such as performance, productivity, and success benchmarks. Which type you use is largely dependent on the learning context and need. The key questions to consider are the following:

What measurable change is the microlearning impacting?
Do you need individual, organizational, or both types of data?
What is the ease of collecting and analyzing the data?
Can existing evaluations or indicators be used to measure the impact of learning?

What are the drawbacks of microlearning & how to mitigate them?
Microlearning does have some potential drawbacks. For one thing, it can be easy for learners to become overwhelmed by the sheer volume of micro-lessons that they are expected to complete. Additionally, microlearning can sometimes result in a fragmented understanding of a topic, as learners are only exposed to small pieces of information at a time. Microlearning often does not provide an opportunity for learners to practice and apply what they have learned. However, these potential drawbacks can be avoided or mitigated when microlearning is designed into learning activities. Another potential drawback of microlearning is that it can be difficult to maintain a consistent level of quality control. With so much content being produced by so many different people, it can be hard to ensure that all of the material is accurate and up to date. This problem can be mitigated by careful selection of materials and regular quality checks. Because of this, microlearning can create a significant amount of work for teachers. In order to properly incorporate microlearning into their classrooms, teachers need to have a good understanding of the material and be able to effectively facilitate discussion and debate. While it may require some additional effort on the part of teachers to do microlearning, it feels worth it as it has the potential to significantly improve student engagement and learning outcomes.

Which tools can you use to create microlearning?
While microlearning does not necessarily require the use of digital tools, the reach and potential of these types of learning experiences is magnified by technology. Because microlearning is so short and usually discrete, there are many types of tools and methods of delivery that can be used. Formal authoring tools such as LMSs and Articulate can be used, but are not required. Any type of tool that can create a static or dynamic piece of content can be used. Further, any type of delivery system can be used to disseminate the learning. Making microlearning relevant and specific to the learning context, environment, and audience are key to selecting a content creation tool and delivery systems (Fennelly-Atkinson & Dyer, 2021).

Summary
To wrap it up, microlearning is breaking down and chunking learning into bite-sized pieces. Microlearning might be small but can have a big impact on powerful teaching and learning. It can take many different forms, which means that there are just as many content-creation tools and delivery platforms. Likewise, there are a variety of ways to assess microlearning depending on the goal and purpose for its use. There is no one correct way of creating microlearning. Microlearning can be as simple as listening to the pronunciation of words on an audible dictionary online application. Teachers can use this flexible method of microlearning to support research-based instructional practices and personalize learning experiences.

So how might you use this approach to meet the modern learner’s needs? Tweet @EducatorCIRCLS and be part of the conversation.

References

Fennelly-Atkinson, R., & Dyer, R. (2021). Assessing the Learning in microlearning. In Microlearning in the Digital Age (pp. 95-107). Routledge.

Teague, C. (2021, January 11). It’s All About microlearning. https://community.simplek12.com/webinar/5673

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Apprentice Learner: Artificial Intelligence (AI) in the Classroom

June 7, 2022AI in Education, EducatorCIRCLS, STEM, Teachers & Researchers, Written by: Sarah HamptonPati Ruiz

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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. "839 + 445 = 1284" Space below the problem displays "hint," "done," "previous," and "next" buttons. — 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.”

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.

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 such as demonstrating the next step, specifying if the highlighted input is correct, etc. — 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:

I’m a Teacher, Will Artificial Intelligence Help Me?

March 15, 2022AI in Education, EducatorCIRCLS, Learning Technologies, Orchestration, Teachers & Researchers, Written by: Judi Fusco, Written by: Pati RuizPati Ruiz

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by Judi Fusco and Pati Ruiz

Artificial Intelligence (AI) systems are becoming more prevalent everywhere including education. Educators often seem to wonder, “What is it?” and, “What can it do?” Let’s address these questions and then discuss why and how YOU should be involved!

What is it and what can it do for teachers?

Artificial intelligence (AI) is a field of computer science that lets machines make decisions and predictions. The goal of AI is to create machines that can mimic human capabilities. To do this, AI systems use many different techniques. You are probably using AI systems every day because they are embedded in our mobile phones and cars and include things like face recognition to unlock your phone, digital voice assistants, and mapping/route recommendations. We’re not going to go into the details of how AI works in this post, but you can read a prior post on AI and check out this glossary of AI terms that might be helpful if you want more background on the topic. In this post, we will focus on examples of AI systems that can help teachers.

Teachers have to do countless tasks, such as lesson planning, teaching, grading mentoring, classroom management, keeping up with technology in the classroom and new pedagogical practices, monitoring progress, and administrative work, all while keeping students’ social and emotional needs in mind. While AI has come a long way since the 1950s when the term was coined and work on Intelligent Tutoring Systems began, it cannot replace a teacher in the classroom. We will share examples of how existing AI systems have successfully helped teachers and reduced their load.

Example: Personalized online math learning software for middle and high school students

Mathia provides coaching to students as they solve math problems and gives teachers a detailed picture of where each student is, as well as suggestions for conversation starters to talk about each student’s understanding. This support allows teachers to spend more time with students focused on learning, while also directly giving the students additional, useful feedback as they solve math problems.

Example: A platform that provides immediate feedback to students and assessment data to teachers

Another AI system that supports both teachers and students is ASSISTments. It is also currently focused on math. For students, it gives assistance in the form of hints and instant feedback while they do math homework. For teachers, it gives information about which homework problems were difficult and what the most common wrong answers were. This can prompt teachers to spend time discussing the problems that students need the most help on, and teachers can be sure to re-teach concepts based on common wrong answers.

In addition to teaching content, when you think about all the things a teacher does in managing their classroom and all the “plates” they must juggle to keep 25, 30, or more students on task, engaged, and learning, you can imagine they could use some support. These next three systems described primarily support teachers.

Example: A digital assistant for teachers

One AI system that helps with classroom management tasks is a multimodal digital assistant specifically developed for teachers with privacy in mind, called Merlyn. Merlyn looks like a small speaker, but does so much more. It allows teachers to use voice and a remote control to control content from a distance. For example, with Merlyn teachers can set timers and switch displays between their laptop, document camera, and interactive whiteboard. Teachers can control a web browser on their laptop and do things like share a presentation, go to a specific point in a video, show a website, or search. This frees them up to walk around the classroom and interact with students more easily.

Other ways AI systems can support teaching and learning

The examples above show three categories of how AI systems have helped teachers and their students. Three more examples include, an AI system that can analyze the conversation from a classroom session and identify the amount that a teacher talked versus a student (i.e. TeachFX). This tool also identifies whether teachers let students build on each other’s thoughts leading to discussions. With the help of this AI system, teachers can work to engage their students in discussions and reflect on their practice.

Grading is another task that is very important but very time consuming. Gradescope, for example, supports instructors in grading their existing paper-based and digital assignments in less time than it normally takes them. It does this by scanning text and sorting similar responses together for the teacher to grade some of each type, the system then “learns” from the teacher, automatically grades the rest, and sends the grading to the teacher for review.

Finally, AI systems that are specialized within a subject matter can allow teachers to set up content-specific learning experiences. For example in the domain of science, Inq-ITS, allows teachers to select digital labs for their middle school students. When completing the assigned digital labs, students learn by doing. Inq-ITS autoscores the labs in real-time and shows the teacher performance updates for each student. A teacher can use the reports to provide the appropriate support to students who need additional help. Inq-ITS also supports students with hints while performing the labs.

Educators Must be Involved in the Design of AI Systems

The AI systems described above, support or augment, but never replace a teacher. We believe that AI systems can help by doing things that machines are good at while having teachers do the things that humans do best.

The AI systems above are also designed by teams that have made education and learning environments the main audience for their systems. They have also included teachers in their design process. There are other AI tools that exist and even more that are being developed to support teachers and students on other activities and tasks, but some don’t have the same focus on education. We think that it’s important that in the design of AI systems for classrooms, educators – the end-users – need to be involved in the design.

Some of the teams that design AI systems for education haven’t been in a classroom recently and when they were they probably weren’t the teacher. To make a technology that works in classrooms requires classroom experts (the main users) to be part of the design process and not an afterthought. When teachers give feedback, they help ensure 1) that systems work in ways that make sense for classrooms in general, and 2) that systems would work well in their specific classroom situations. (We’ll discuss why this is the case in another future blog post.)

A final, yet very important reason for educators to be involved, is that while AI systems can bring opportunities to support teaching and learning, there are also privacy, ethics, equity, and bias issues to be aware of. We don’t want to add anything to your already full plate, but as technologies come into your classroom, you should ask questions about how the system supports students, if the systems were designed for students like your students, what the privacy policies are, and any implications that might affect your students.

We understand that most teachers don’t have a single extra minute but it is crucial to have current teachers in the design process. If you want to learn and think about AI systems, as they become more prevalent, you will become an even more invaluable teacher or technology leader in your school/district. Your voice is important and getting more educators involved makes a more powerful collective voice.

Looking ahead

If you’re still reading this blog, you probably have an interest in AI systems; below we suggest a few places to connect. Teachers are critical to the design of effective AI technologies for schools and classrooms. We hope this post has given you some insights into how AI systems might support you and your students. If you are interested in getting involved, we have some links for you below. Consider this blog post an invitation to you to connect with us and join the conversation; we hope you’ll join us in thinking about the future of AI in Education.

In our next post we will discuss how AI systems informed by learning science principles may help solve problems in learning environments.

Let us know your thoughts @educatorCIRCLS.

Ways to join:
Educator CIRCLS
AI CIRCLS
Join the ASSISTments Teacher Community
Leadership Programs — TeachFX

Book Review: Design Justice: Community-Led Practices to Build the Worlds We Need

June 25, 2021Culturally Responsive Teaching, EducatorCIRCLS, Teachers & Researchers, Written by: Pati RuizPati Ruiz

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by Pati Ruiz

Critical pedagogy seeks to transform consciousness, to provide students with ways of knowing that enable them to know themselves better and live in the world more fully.
—bell hooks, Teaching to Transgress

Written by Sasha Costanza-Chock, Design Justice: Community-Led Practices to Build the Worlds We Need, explores the relationships among design, power, and social justice. I was drawn to this book because it centers those who are intersectionally disadvantaged, this refers to individuals that might have multiple minoritized identities and originally refered to the oppression of African American women. It also shares the work done by Design Justice Network (DJN) to “build a better world, a world where many worlds fit.” The Design Justice Network is “an international community of people and organizations who are committed to rethinking design processes so that they center people who are too often marginalized by design.” This network is a community of practice that is guided by a set of 10 principles; I am a DJN signatory. I hope this short post prompts you to read the whole book that was made available for free on PubPub (the open-source, privacy-respecting, all-in-one collaborative publishing platform) or sign up for the Design Justice Network newsletter to learn more. This book has really inspired me to think differently about design and what it takes to truly make design accessible.

What is Design Justice?

The book begins with a definition, or “tentative description” of design justice:

Design justice is a framework for analysis of how the design of technologies, tools, and learning environments (to name a few) distributes benefits and burdens between various groups of people. Design justice focuses explicitly on the ways that design reproduces and/or challenges the matrix of domination (white supremacy, heteropatriarchy, capitalism, ableism, settler colonialism, and other forms of structural inequality). Design justice is also a growing community of practice that aims to ensure a more equitable distribution of design’s benefits and burdens; meaningful participation in design decisions; and recognition of community-based, Indigenous, and diaspora design traditions, knowledge, and practices (p. 23).

After a comprehensive overview of the values, practices, narratives, and site/locations of design, the book turns to the pedagogies of design in Chapter 5. In this chapter, the author focuses on answering the question: How do we teach and learn about design justice?

Costanza-Chock responds to this question by writing “I don’t believe there is only one way to answer this question, which is why I use “pedagogies” in the plural form.” Among the pedagogies described in the chapter are:

Paulo Freire’s educación popular or popular education (pop ed)
critical community technology pedagogy
participatory action design
data feminism
constructionism, and
digital media literacy

Exploring Design Justice Pedagogies

In our previous work, as CIRCL Educators, we wrote about constructionism. This pedagogy is one that teachers often turn to and as Costanza-Chock notes, it is not one that is “explicit about race, class, gender, or disability politics.” However, it should center the social and cultural aspects of learning, the construction of knowledge in the learner, and the learner’s contexts (e.g.a student’s racial/ethnic background, social class, and other social identities). Furthermore, Costanza-Chock writes that “in a constructionist pedagogy of design justice, learners should make knowledge about design justice for themselves and do so through working on meaningful projects. Ideally, these should be developed together with, rather than for, communities that are too often excluded from design processes.”

Hand in hand with the pedagogies described in this chapter is the decolonization of design practices, which refers to deconstructing Western privilege of thoughts and approaches. Those involved in the decolonizing design movement advocate for a global approach to design that rethink historical narratives and seek to center design practices erased or ignored in Eurocentric design practices. As Costanza-Chock describes “design justice pedagogies must support students to actively develop their own critical analysis of design, power, and liberation, in ways that connect with their own lived experience.” As teachers and educators, our role is to figure out a way to overcome existing design challenges so that our students can implement just design principles.

Principles of Design Justice

What are practical examples of what teaching about design justice looks like? Based on the author’s experiences in her own courses, 10 principlesillustrate what this movement envisions:

Principle 1: We use design to sustain, heal, and empower our communities, as well as to seek liberation from exploitative and oppressive systems.
Principle 2: We center the voices of those who are directly impacted by the outcomes of the design process.
Principle 3: We prioritize design’s impact on the community over the intentions of the designer.
Principle 4: We view change as emergent from an accountable, accessible, and collaborative process, rather than as a point at the end of a process.
Principle 5: We see the role of the designer as a facilitator rather than an expert.
Principle 6: We believe that everyone is an expert based on their own lived experience, and that we all have unique and brilliant contributions to bring to a design process.
Principle 7: We share design knowledge and tools with our communities.
Principle 8: We work towards sustainable, community-led and -controlled outcomes.
Principle 9: We work towards non-exploitative solutions that reconnect us to the earth and to each other.
Principle 10: Before seeking new design solutions, we look for what is already working at the community level. We honor and uplift traditional, indigenous, and local knowledge and practices.

At Educator CIRCLS we are at the beginning of our conversation around AI in Education. These design justice principles will be front of mind as we continue to consider and discuss the variety of ways AI technologies are currently being developed and employed. Please let us know your thoughts by tweeting @EducatorCIRCLS and sign up for the CIRCLS newsletter to stay updated on emerging technologies for teaching and learning.

Learning from Gaming

June 17, 2021EducatorCIRCLS, Teachers & Researchers, Written by: Sarah HamptonPati Ruiz

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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.

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):

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;
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!

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)

Exploring the 2021 STEM For All Video Showcase

June 14, 2021Computer Science, Culturally Responsive Teaching, EducatorCIRCLS, STEM, Teachers & ResearchersPati Ruiz

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Featuring 287 short videos of federally funded projects aimed at improving STEM and Computer Science education, the 2021 STEM For All Video Showcase highlighted strategies to engage students and address educational inequities. The array of 3-minute videos showed the depth of work going on in the field to think about equity and social justice in the wake of COVID-19. Below are some favorites of our CIRCLS team that we hope you enjoy as well!

Co-Creating Equitable STEM Research Led by Communities
Contributed by Leah Friedman
This video features a project partnership between the Cornell Lab of Ornithology and community organizations around the country that are historically excluded from science research. Centering community wisdom and leadership, the group investigates the impact of noise pollution on public health in order to co-create appropriate solutions. This project is an amazing model of upending typical hierarchies of knowledge creation or control in STEM research, provides a really concrete framework for conducting research with community members, and exemplifies ‘broadening’ in every sense of the word.

Interest Stereotypes Cause Gender Gaps in STEM Motivation
Contributed by Judi Fusco
Thinking about stereotypical gendered messages that young children, older children, teens, and even adults receive about whether they belong somewhere is so important. These messages may be subtle, nuanced, and not intended, but they happen; we need to make sure we aren’t excluding anyone, especially without realizing it.

Activity for Stories of Algebra for the Workplace
Contributed by Jeremy Roschelle
What if every student could tell a story of how they’ll use math in the future career? Although this is just a beginning, it seems to me the technology for personalized AI-driven STEM storytelling will arise soon enough — and could help students create their own STEM identity.

You Deserve A Seat at The Table: The Data Economy Workforce
Contributed by Jonathan Pittman
This video features a project at Bethune Cookman University that uses an immersive game learning experience to help students gain 21st century digital workforce skills. Using gamified immersion is an excellent approach to build workforce skills and learn about the future of work.

Big Data from Small Groups: Learning Analytics and Adaptive Support in Game-based Collaborative Learning
Contributed by Dalila Dragnić-Cindrić
In this project, groups of up to four students work together in a 3D game-based environment called Crystal Island to solve complex eco-problems. A research team from Indiana University and North Carolina State University is investigating how students in small groups communicate and coordinate with each other when problem solving. Researchers used learning analytics to drive adaptive support.
The lead presenter is one of our Emerging Scholars, Asmalina Saleh. PIs are James Lester and Cindy Hmelo-Silver. CoPI is Krista Glasewski.

Activity for “WHIMC: Using Minecraft to Trigger Interest in STEM”
Contributed by Wendy Martin
If you are a fan of Minecraft or alternative histories you should check out H. Chad Lane’s video about his project: What-If Hypothetical Implementation in Minecraft (WHIMC). I enjoyed learning about how those researchers were encouraging students to create alternate worlds to help them better understand the phenomena that shape our own world.

To explore videos from past video showcases, visit the STEM For All Multiplex.

Reflections on Coded Bias

June 4, 2021AI in Education, EducatorCIRCLS, Teachers & Researchers, Written by: Pati RuizPati Ruiz

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“Algorithmic justice––making sure there’s oversight in the age of automation––is one of the largest civil rights concerns we have.”Joy Buolamwini

On May 3rd, 2021 Educator CIRCLS hosted a watch party for the film Coded Bias which highlights the incredible work being done by organizations, data scientists, and activists on an international scale. The film challenged our unconscious biases and encouraged us to listen to one another as we consider the ways that we interact with artificial intelligence (AI) on a daily basis. To begin with, the film made very clear the wide societal impacts, both positive and negative, of AI as well as the fact that AI algorithms can perpetuate biases. Given this, we believe it is essential to become more knowledgeable about AI so that we, as educators, can make informed decisions about AI. As we watched this film we considered and discussed the ethical implications that need to be fully investigated before new AI tools are adopted in our classrooms. This film also helped us see that we also need to investigate the people designing the AI and helped us arrive at some important questions that we need to be asking about AI.

Here are some questions:

How was the AI system designed, for classroom use or other situations? At what point are teachers brought in to make decisions about their students?
What data was used when the system was trained?
- What groups of people were included during the testing process?
What data will be collected by the system and what will happen to that data if the tool is sold? Will it only be used for only the purpose specified? Are there any potential dangers to the students? Are there any potential dangers to the teachers who use the systems with their students?
- Can students be identified from this data?
- Can teachers be identified from this data?
- Can this data be used to evaluate teachers’ performance (something that may not be specified by the system)?
How does the system interact with students, and can I give feedback to the system or override the decisions?

Another very important question but a difficult one to answer is: When this AI tool fails, how does it fail, and what are the consequences? While EdTech designers might not be able to accurately answer this question, you might be able to use it to start a conversation about the pitfalls of this particular piece of technology. It will also challenge EdTech designers to think about these difficult questions and engage the design process to adjust their product if needed. After all, starting these conversations about the ethics of AI and where its faults lie is our duty.

Sign up for the CIRCLS newsletter to stay updated on emerging technologies for teaching and learning and let us know what you think by tweeting @EducatorCIRCLS

Pedagogy Really Matters

January 19, 2021EducatorCIRCLS, Learning Technologies, Teachers & Researchers, Written by: Sarah HamptonPati Ruiz

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by 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!

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.