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Navigating Ethical Al: Empowering Educators with Tools, Frameworks, and Critical Perspectives

Photo of Ethical AI card deck with example prompts, images, and QR codes
Photo of CRAFT Ethical Engine card game designed by Marlon Matilla
by Marlon Matilla

The Navigating Ethical Al: Interactive Lessons and Equitable Practices for Educators webinar serves as a microcosm of the broader challenges and opportunities that artificial intelligence (AI) presents in the educational landscape. The session brought together educators to explore the ethical implications of integrating AI into classrooms, highlighting the intersection between technological innovation and pedagogical responsibility.

The Ethical Imperative in AI Education

Central to the discussion was the need for educators to critically engage with AI, not just as a tool but as a complex system with far-reaching implications. Dr. Kip Glazer, principal at Mountain View High School, emphasized that understanding the technical distinctions between different types of AI—such as generative and supervised AI—is crucial for educators (see Ethical Use of AI – Privileging measured and deliberate thinking, for further thoughts from Dr. Glazer). This technical literacy forms the foundation for ethical decision-making, as educators must navigate the biases inherent in AI systems and their potential impact on students and teaching practices. The dialogue in the session reflects a growing recognition that AI’s role in education is not neutral; it is laden with ethical considerations that educators must address proactively.

Practical Engagement with AI Ethics

Assistant professor Dr. Victoria Delaney introduced the Stanford Classroom-Ready Resources About AI for Teaching (CRAFT) project, which exemplifies how these ethical considerations can be translated into classroom practice. By developing adaptable AI literacy resources, the CRAFT initiative seeks to empower teachers to integrate AI education in a way that is both practical and responsive to the needs of diverse student populations. The project underscores the importance of flexibility and customization in educational resources, recognizing that teachers must be able to tailor AI lessons to their specific classroom contexts.

This approach is further exemplified by my CRAFT Ethical Engine card game, a tool I designed to foster critical thinking and ethical reasoning among students. This game moves beyond theoretical discussions, offering a hands-on way for students to grapple with the real-world implications of AI. Through scenarios like AI in law enforcement or AI-controlled military drones, the game prompts students to consider both the benefits and risks of AI technologies, thereby cultivating a more nuanced understanding of AI ethics.

Collective Responsibility and Advocacy

The session also highlighted the collective responsibility of educators to advocate for ethical AI practices. The Educator Bill of Rights, discussed by Dr. Kip Glazer, is a testament to this advocacy. It asserts the rights of educators to have a say in the AI tools introduced into their work environments and emphasizes the need for transparency and equity in AI implementation. This document not only empowers educators to protect their professional autonomy but also ensures that AI adoption in schools does not exacerbate existing inequalities or undermine educational goals.

The session’s exploration of these themes reflects a broader narrative within education: the need for a critical, reflective approach to technology. As AI becomes increasingly integrated into classrooms, educators are not just passive recipients of these tools; they are active participants in shaping how AI is used and understood in educational settings. This requires a deep engagement with the ethical dimensions of AI, as well as a commitment to advocating for practices that are fair, transparent, and aligned with educational values.

Engaging Educators in Discussion

The CRAFT Ethical Engine card game resource presented in the session and the Educator Bill of Rights can serve as starting points for bringing educators and students into conversations about ethical issues. As the presenters emphasized in this webinar, it is important to empower educators to think critically about how to safeguard against the ethical pitfalls that these technologies can produce and bring awareness to students about potential issues.

A Unified Perspective on AI in Education

Synthesizing the insights from the session reveals a unified perspective on the role of AI in education: It is a powerful tool that holds both promise and peril. The session participants collectively underscore that the successful integration of AI into education hinges on the ability of educators to critically assess and ethically navigate these technologies. Furthermore, our conversations with educators illustrate the necessity of an ethical framework for AI in education, one that is informed by a deep understanding of the technology and a commitment to equity and fairness. It is my hope that this synthesis of ideas and the resources shared can provide guidance for educators who are navigating the complex landscape of AI. Educators need more resources to ensure they are equipped to make informed, ethical decisions that benefit both their students and the broader educational community.


About the Author

Marlon Matilla is an educator dedicated to advancing data-driven and technology-focused learning in K-12 STEM education. Since 2015, he has taught mathematics, computer science, and cybersecurity with a strong emphasis on hands-on learning. As a CIRCLS Educator Fellow, he has contributed to AI education initiatives, including the co-design of ethical AI resources through Stanford’s CRAFT Fellowship. His recent publication, Optimizing Breakfast Choices: Leveraging Data Analytics in Packaged Foods for Informed Student Nutrition Decisions, supported by the University of Arkansas’ NSF-funded Data Analytics Teacher Alliance RET program, is published in the ASEE Professional Engineering Education Repository. Committed to merging research with practice, Marlon (aka Matt) aims to continue as a researcher-educator, fostering data literacy and ethical AI technology use in education.

Abstract Binary Chip

Introduction to Artificial Intelligence in Education

By Sarah Hampton

As an avid fan of CIRCL and the wife of a programmer, it’s safe to say I’m somewhat of a technophile. I’m typically and happily an early adopter of ed tech. Even so, my initial reaction to artificial intelligence (AI) in education was somewhere between skeptical and antagonistic. Like many teachers I’ve talked with, I was concerned that using AI would weaken the human connection that’s so important for a healthy school environment. I was and remain concerned about equity and access issues around technology. I also have serious questions about my students’ privacy. However, as I started digging into what AI actually is (and isn’t), I realized that I should learn more about it so I can offer my voice as a teacher to the communities developing the tools they want us to use. Over the summer, with the CIRCL Educator team, I’ll be digging into AI. In a series of posts, I will share the most important, perspective changing, and exciting things I’ve learned about artificial intelligence and what it might mean for education. I hope you’ll join me and let me know your questions and concerns.

First, let’s clarify artificial intelligence. What is and isn’t AI?

Let’s start with defining AI as a machine doing something we formerly thought only humans could do. More specifically, though, AI is just a specific type of computer software. The difference between AI and the software you’re already familiar with is that it doesn’t follow a linear set of simple instructions. Instead, AI uses algorithms or rules that are set initially by the developer (a human) and then the AI builds a model when it runs through data. The AI continually fine-tunes the model as it encounters more data. That’s why some people say AI “learns” or “teaches itself.” It’s not learning like a human would, it’s able to build models that optimize for given criteria set in the algorithm. (For my math colleagues, think regressions/curve fitting on steroids.) The names AI or machine learning, which is a specific approach used in AI, make it sound like the software takes on a life of its own. That’s not true. As our English Language Arts colleagues could tell us, it’s just an example of anthropomorphism–ascribing human characteristics to a nonhuman object.

We’ll consider different types of AI in a future post. For now, we will say look at AI in two ways; on one hand, compared to prior types of software, AI is extremely sophisticated and capable of things we thought were unique to humans twenty years ago.

Let’s take an example you might be familiar with–Grammarly. (Full disclosure: I don’t use Grammarly routinely, but I decided to investigate after seeing their YouTube ad about 500 times and am guessing you may have seen those ads, too.) AI, like the type Grammarly uses, can “learn” what good writing looks like. It was trained about features of good writing by being shown hundreds of thousands of sentence pairs. In the pairs, one of the sentences was written poorly and one was a well-written target sentence. From these pairs, Grammarly “gained insight” on elements of good writing. However, while the AI learns, it doesn’t understand why a sentence is good like a human can. It can only recognize multiple detailed features or patterns that are part of the examples. Then, when the AI receives a new writing sample uploaded by someone, it can compare the new writing sample to the patterns it detected in the training examples to determine how closely the new writing sample matches the features in the training sentences. The AI provides guidance to the human writer by offering suggestions that would help the writing sample match the exemplary types of writing from the training.

That’s one high-level example for today. I have other projects to go through in later posts, but I want to go back to thinking about how we define artificial intelligence. A recent EdSurge article brought up a great point, “when educators have different concepts of what makes a piece of technology or tool ‘intelligent,’ it means that a variety of tools get lumped into the AI category—even if they aren’t technically ‘artificially intelligent.’” Let’s think about what’s typically considered artificial intelligence to start to define it. I say start to define it as the field of AI is rapidly changing, and folks in the field are still working on a more precise definition. I’m making a checklist to help us differentiate AI from other kinds of technology.

Checklist: Is it AI?

TechnologyIs it AI?Why?
Projector or Document CamNoThese are useful tools, and let us do smart things, but they’re more hardware than software.
Smart BoardNoThis is a mix of hardware and software, but the software doesn’t improve as you use it.
Basic robot like Ozobot or Dash and DotNoCool robots, but the software doesn’t “learn” over time. Other robots may learn, but not these.
LMS (e.g., Google Classroom, Schoology, Canvas)NoLMSs could support the use of AI software and present information adaptively or use it for grading assignments, but these do not currently implement AI.
IXLNoThis software does some interesting things that seem like they might be AI, but the software doesn’t improve as it interacts with more users.
Siri, Alexa, Ok Google, etc.YesThis software has been trained with lots and lots of voices so it can recognize yours. It also learns to recognize yours better over time.
Facial recognitionYesFace recognition technology is AI, but it is not considered to be very robust meaning that it can easily misidentify people.
Self-driving carYesAs the self-driving car takes more and more test drives, it gets better at driving.
Carnegie Learning’s MATHiaYesMATHia is Carnegie Learning’s online software program that deploys artificial intelligence to actually teach math. By providing targeted coaching and adapting to student thinking, MATHia mirrors a human tutor with more complexity and precision than any other math software.
GrammarlyYesGrammarly’s products are powered by an advanced system that combines rules, patterns, and artificial intelligence techniques like machine learning, deep learning, and natural language processing to improve your writing.
Adaptive Computer-based TestingMaybeMight or might not depending on the software. Stay tuned for more on this in a future post!

What’s up next? We have lots more to share, including sharing AI projects from CIRCL during the CIRCL Educators Summer of AI!  We’ll also tackle some of the big questions educators have about AI like:

  • When will AI matter to me? How could AI make teacher learning more relevant, valuable, or effective?
  • Should I be worried that AI will replace me? What is the ideal balance between human and machine?
  • What needs to be considered so AI can help teachers support different races, cultures, genders, and students with different abilities in ways without bias (or with less bias)?

I want to give Pati Ruiz, Judi Fusco, and Patti Schank a thank you for their thinking and help with this post. An additional thank you goes 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.

Tweet @CIRCLEducators and let us know if you have questions or thoughts about AI.

2019 STEM for ALL Video Showcase with image of youth in the background

2019 STEM Video Showcase Review: Teaching Accessibility to Broaden Participation

By Pati Ruiz and Amar Abbott

When this year’s STEM for All Video Showcase came around, two of us (Pati and Amar) were drawn to a video presentation titled Teaching Accessibility to Broaden Participation. According to the National Center for Education Statistics, 15% of K–12 students, 11% of college students, and 5% of graduate students have a disability. While this video focused on raising awareness about accessibility needs in graduate computer science courses, we found the video helpful in thinking about leveraging technology tools in the equitable design of courses.

Meeting the accessibility needs of all students is a federal mandate, however as an accessibility expert, I (Amar) think that it is often a struggle to provide students with the right supports due to a range of barriers including the absence of professional development opportunities for instructors as well as a lack of  affordances* in technology tools.

*What are affordances? Researchers use the term affordances to talk about the opportunities that a technology makes possible. The affordances of learning technologies are described in How People Learn II: Learners, Contexts, and Cultures as “a feature or property of an object that makes possible a particular way of relating to the object for the person who uses it (Gibson, 1979; Norman, 2013).”

After watching the video, we wanted to learn more about the work that still needs to be done to bring an awareness of accessibility needs to those who design technology tools. Co-PI of AccessComputing, Sheryl Burgstahler shares that a major barrier to information technology (IT) that her Accessible Technology Services office works on is non-accessible PDFs; scanned-in images that screen readers can’t access. Another major barrier is videos that don’t have captions or that have unedited computer-created captions. Here’s a great example of a video of computer-created captions going wrong and more information about creating accurate captioning. Sheryl encourages faculty members to use accessible IT when delivering online content instead of just teaching about it. In the showcase video comments, lead Presenter, Richard Ladner described a “chicken and egg problem” in graduate computer science (CS) programs that don’t teach accessibility topics and textbooks that don’t cover these topics. The lack of education about accessibility perpetuates the lack of accessibility content in courses.

There are a few points to underscore:

  • It is essential for educators to be aware of the ways in which software is disabling to their students and other stakeholders.
  • We need to ensure that our video content is captioned and that the documents we share with students, like PDFs, are machine readable.
  • We need to understand that there is a lack of education in CS programs about accessibility and that we should be asking questions about the IT that’s being developed and used in our schools and students from learning management systems to  websites and videos.
  • When we make tools more accessible, the benefits are often ones that help everyone!

Through this video, we learned that the technologies like speech recognition, captioned videos, texting, and video chats that were designed to solve accessibility problems, often become mainstream because they make using technology easier for everyone. An example highlighted by the presenters is the use of video subtitles when we find ourselves watching a video in a noisy setting like a bus or a train. I (Pati) often use  the screen reader on my phone, voice recognition, audiobooks, and captions in videos. I (Amar), use many of the same accessibility features that Pati does. As a person with a learning disability, I also use accessibility technologies to function in my daily professional life. Those technologies include Kurzweil, Dragon naturally speaking, and Mindview mind mapping software. I also teach my students to use assistive technologies to manage barriers in their academic and personal pursuits.

We find that assistive technology tools can change a person’s life and hope that projects like Access Computing can continue to raise awareness – in technical fields – about the accessibility needs of all people. This is essential as we work towards the equitable design of courses. We encourage other educators to explore Teach Access, The DO-IT (Disabilities, Opportunities, Internetworking, and Technology) Center, and CAST to learn more about removing barriers to participation in the resources we prepare for our students, our colleagues, and their parents. As always, please share your thoughts with us on Twitter @CIRCLedu.

Citations

National Academies of Sciences, Engineering, and Medicine. 2018. How People Learn II: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press. https://doi.org/10.17226/24783.

STEM Video Showcase

Career Connections: Bridging the gaps through STEM Explorations and Community Partnerships

By Angie Kalthoff

I’ll be doing a series of blog posts around videos I from the NSF 2019 Video Showcase. The Bridging the gaps video caught my attention because I worked with underserved and underrepresented students for many years. As a technology integrationist, I was constantly thinking about how I could connect students and their families with careers they may not know about–maybe even careers in our own community. Based on our goal of connecting with the community, during the Hour of Code celebration in December, we created Community Code. Community Code was a way to bring people from our community into our classrooms to share their jobs and how technology is used in their workplace.

In addition, some local businesses and universities hosted family nights and offered a variety of activities for families to engage with. The goal was to connect our community with our classrooms from kindergarten through senior high school. As I watched the video, I became interested in learning more about this project because of the segment where students and community members shared their experiences.

Overview of the Program
i3STEM is a project focusing on “inquiry based extended learning opportunities for underserved and under-represented middle school student populations.” This program includes hands on STEM explorations and collaborative events with community partners for college and career connections. Researchers on this project are working to increase an interest in STEM through the events that are offered to the underserved and under-represented students in Metropolitan Nashville Public Schools. Their goal is to grow academic achievement in science and math and increase STEM awareness with the outcome of more students going into STEM careers. “STEMgineers”, students in the program, shared experiences about field trips, activities, and how they are now inspired to be future computer scientists, geologists, and science teachers! Kids who have faced challenges in the traditional classroom have now been able to participate in an experience that made them feel empowered and successful. Students were excited about the experiences they had with their community partners. The video shows how connections that were once difficult to make were turned into real life learning opportunities. Researchers close the video with a statement sharing how the experiences the students had are helpful for the whole child and not just for academics.

The Goal of the Program
Career Connections: Bridging the gaps through STEM Explorations and Community Partnerships offers extended learning opportunities to underrepresented schools. Their goal is to improve academic achievement in science in math, increase awareness and interest in stem in hopes that their students will pursue STEM related careers.

Outcome of the Program
A few of the main points researched include:

  • Attendance and participation in learning opportunities
  • Student views and interests in STEM or pursuing a STEM career
  • Student scores in math and science
  • Academic/observation scores for teachers in the program

They shared positive results in the Stem For All Showcase discussion; those included:

  • 73% of the students entering high school chosea STEM academy
  • 75% of students expressed an interest in pursuing a STEM related career
  • Academic gains for students in both math and science doubled from one year to the next
  • 73% of the teachers in the program have maintained or increased state standardized testing scores based on their students’ performance.

My thoughts on how this could be used in practice
After viewing their i3STEM website, I was able to see numerous activities that were implemented in their classes. Some of the activities were similar to what I’ve done in classrooms while some were brand new to me. For example, the Mystery Bag STEM pdf, includes cards that you can print off and add to a bag with resources for students to complete a project. Other projects include:

  • “As part of the Homestead Act, you are required to cultivate your many acres of land. Using only the items in your bag, engineer a technology to help with that task.”
  • “Production costs for your “Fancy Fidget” have gone up. Using the items in your bag, engineer an interesting fidget toy that costs less than .75 to build.”

The way the website is organized makes it easy for viewers to find what they are looking for based on the following categories:

  • Teacher Resources
  • – Here you can find lesson plans, links to helpful videos, and a teacher guide.

  • Student Resources (which were used for their project, probably less helpful for you)
  • – Student survey links, links to resources for local issues, help for choosing topics, and resources for projects.

  • About Us
  • – Shares contact information for the project, information about the schools and sponsors, and an over of the project evaluation.

I found their resources and thinking really helpful. I think this is a great way to create a bridge between community and education.

We’d love to hear from you — Tweet to @CIRCLEducators or use #CIRCLEdu.

Connected Code book bover and CIRCL Educators Book Club twitter handle @CIRCLEducators and chat #CIRCLEdu

Connected Code Book Club #SlowChat Questions!

Welcome to our Book Club discussion of Connected Code! next book club will be a Twitter Slowchat! If you have not participated in a Twitter Chat before, please follow @CIRCLEducators and the authors of Connected Code Yasmin Kafai @katyaskit and Quinn Burke @QuinnBurke174.

Here are our questions for this book! Please use A.# and #CIRCLEdu when answering questions. For example, to answer Q1:

A1. #CIRCLEdu  [the text of your answer]

Please also send all of your own questions to @CIRCLEducators ! And contact us if you need help!

Q1. In Chapter 1, p.9 the authors write “Programming is a form of expressing oneself and of participating in social networks and communities.” How do you and your students use tech tools for computational participation? #CIRCLEdu

Q2. In Chapter 2, p.20 the authors describe how Papert thought of learning: “as building knowledge structures” through the use of artifacts. How do you use technology tools to help students (or you) think about concepts? #CIRCLEdu What tools are you using?

Q3. In Chapter 2, p.23 the authors describe the personal dimensions – the set of informal ideas and theories that are connected to personal experiences – that learners carry with them. How do you help your students make connections between the ideas that already exist and what they are learning in your classroom? #CIRCLEdu

Q4. In Chapter 3, p.36 the authors describe how Debbie was able to apply ideas from programming in Logo to making fraction representations more visually interesting. This shift is described as on from programming code to thinking computationally in terms of the code. Have you been able to see the development of computational thinking skills in your students? How? #CIRCLEdu

Q5. In Chapter 3, p.41 The author’s share Donald Murray’s perspective about writing not being “magical.” They go on to say: “Much like writing three decades ago, computer programming still faces this myth of the ‘magical.’” How are you or others you know making programming a process that students can understand? #CIRCLEdu

Q6. In Chapter 4, p.56 – The authors say that “Motivation to program and persist at troubleshooting their own code increases significantly when they work in pairs.” How do you encourage collaboration in your class? #CIRCLEdu

Q7. What was your favorite quote? Please include the page number! #CIRCLEdu

Q8. In Chapter 6 the authors discuss how to incorporate tangibles and simulations in the classroom. What are some tangible computing projects that you have (or have seen) incorporated in classrooms that really work? Why did it work? #CIRCLEdu

Q9. In Chapter 7, Connected Teaching, the authors discuss supporting learner’s agency (pp. 120-122) as well as Dewey’s idea that “learning activities must be applicable and testable in the worlds that children inhabit outside of the classroom” (p.121). How do you hope that your students might apply what they learn in your classroom/school when they are outside of the classroom? #CIRCLEdu

Q10. In Chapter 8, the authors discuss the reframing of computational thinking to computational participation. What do you think? #CIRCLEdu

Woman types on laptop code books surround her photo by #WOCinTech Chat

How to Encourage Young Women and Marginalized People to Participate in CS and Engineering (part two)

by Pati Ruiz

This is the second post in a two part series based on my dissertation which focused on encouraging the participation of women and African Americans/Blacks, Hispanic/Latinx, and Native Americans/Alaskan Natives in computing. The first post focused on modeling an interest and passion for CS and creating safe spaces for students. This post focuses on building community, introducing students to careers, and making interdisciplinary connections.

Build Community and Connect Students with Mentors

Family support is important! Young adults encouraged and exposed to CS by their parent(s) are more likely to persist in related careers (Wang et al., 2015). And did you know that women are more likely than men to mention a parent as an influencer in their developing a positive perception of a CS-related field, more often citing fathers than mothers as the influencers (Sonnert, 2009)? Unfortunately, parents’ evaluation of their children’s abilities to pursue CS-related fields differs by gender; parents of boys believe that their children like science more than parents of girls (Bhanot & Jovanovic, 2009). Nevertheless, family support is crucial for young women and supportive family members — whether or not they are connected to the tech world — play a critical role in the encouragement and exposure that young women get to the field.

Helping parents understand the role that they can play is important. As educators, we can model for them how to encourage their children as well as how to dispel misconceptions and harmful stereotypes that their children might have heard. Sometimes parents and family members themselves might unknowingly be perpetuating harmful computer science world misconceptions with the comments they make to their children. As teachers, we can provide parents with training that might help them understand how to encourage and expose their children to the field in positive ways. After all, the research shows that this support can be provided by anyone – not just educators.

All of the young women in my study described the value of mentors. Even seeing representations of female role models in the media can encourage a young woman to pursue a CS-related degree. It’s important for young women to see representations of people who look like them in the field and to have real-life female mentors and peers who can support them in their pursuit of CS-related degrees and careers. As a result of the low number of women in the field, mentors and role models for women are primarily men. While this can be problematic, it does not have to be. Cheryan et al. (2011) found that female and male mentors or role models in computing can help boost women’s perceived ability to be successful if those role models are not perceived to conform to male-centered CS stereotypes. The gender of the role model, then, is less important than the extent to which that role model embodies current STEM stereotypes.

The actionability of some of the factors described above, then, allows educators and others to positively influence and encourage young women in high school to pursue CS degrees in college (Wang et al., 2015).

Introduce Careers

In their recent report titled Altering the Vision of Who Can Succeed in Computing, Couragion and Oracle Academy described the importance of introducing youth to careers in technology. They find that:

“It is critical to improve the awareness and perception of a breadth of careers in computing to meet the demands of our workforce and the desires of our students. We need to elevate high demand and high growth computing fields such as user experience (UX) and data science – that when understood, appeal to and attract underrepresented populations.“

What this report found is what I found in my research; many African Americans/Blacks, Hispanic/Latinx, and Native Americans/Alaskan Natives students don’t know people working in the computing field and don’t know what career options can look like. Couragion is working to change this by providing inclusive, work-based learning experiences that prepare students for jobs of the future. What I like about Couragion’s approach is that students are able to use an app to explore careers and engage with role models through text, activities, and videos. As they work their way through different career options, students take notes and reflect using a digital portfolio. I think this is a great way for students to develop career consciousness, something I wish I had when I was in school (as a student and teacher)!

As a teacher, the way I would connect my students with industry careers was to connect with local groups like GirlDevelopIt and invite speakers to my classroom. I also had college students visit my classroom – it usually works well to have recent graduates come back to talk to students because students relate well to recent high school graduates. I also introduced computer scientists in the news. If I were teaching right now, I would highlight 2018 MacArthur Fellow Deborah Estrin. In her Small Data Lab at Cornell, Dr. Estrin and her team are designing open-source applications and platforms that leverage mobile devices to address socio-technological challenges in the healthcare field. Or, I might direct them to this recent article written by Clive Thompson titled The Secret History of Women in Coding.

Some participants in my study mentioned that they ended up majoring in CS because of a mentor. One participant talked about how one of her high school teachers “dragged her to” a Technovation event. There, she ended up seeing a young woman who she “saw herself” in so she decided to apply to the same college that the mentor attended, got in, and went. This participant envisioned herself there because of this near-peer. She said that she didn’t connect with her mentor once she got to the university that they both attended for a year together, but just seeing her ahead of her in the program was motivating.

Again, the idea here is to create opportunities for students to connect with people in the field – to see themselves and to see the possibilities. Some groups that my students have worked with include Girls Who Code, Black Girls Code and Technolochicas – there are many others. Which ones do your students work with?

Make Interdisciplinary Connections

Finally, we have the idea of making interdisciplinary connections. CIRCL Educator Angie Kalthoff wrote a post for EdSurge discussing this very topic. Angie encourages teachers to ask their students: What are you doing outside of school that you want to tell other students about? She and a group of Minnesota educators organize student-powered conferences where middle schoolers showcase what they’re really interested in learning about. Check out her post because getting together with other educators to organize your own student-powered conference might be an excellent way you support and recruit young women and African Americans/Blacks, Hispanic/Latinx, and Native Americans/Alaskan Natives!

Interdisciplinary connections can be facilitated by teachers and it’s important to note that all of my study participants were very thankful to their K-12 teachers for having encouraged their pursuit of a technical field – even if they didn’t know they had. As one participant described, “a teacher who’s clearly passionate” is particularly encouraging.

One resource that can help you make interdisciplinary connections with students iss Connected Code: Why Children Need to Learn Programming by Yasmin B. Kafai and Quinn Burke. Join the CIRCL Educators book club to discuss this book starting in April!

Please note that the featured image for this post was created by #WOCinTech Chat, check them out! We’d love to hear from you — Tweet to @CIRCLEducators or use #CIRCLEdu.

Learning Something New

By Judi Fusco

TLDR: When you really don’t want to do something, having a friend help you learn can make all the difference. Emotions and learning drive the learning process.

Today’s post, is a reflection piece. I usually ground everything I write in research, but I have so much swirling in my head based on a lot of reading, and on an experience I had while learning something new.

Have you ever said something like, “Nope, no way am I ever going to do _______.”?

I said it repeatedly, and over the course of many months, to a person. She was persistent and kept asking me to learn this new thing for at least 6 months. I said no a lot, however, wait for it…. now I am doing it.

Why am I doing it?  Because the person who asked me (repeatedly) to do it means a lot to me. She wanted me to learn something new–that new something was out of my usual area of interest and something I never saw myself doing. Two strikes against her and the new thing, but somehow she got me to try it, helped me until I felt comfortable on my own, and now, almost 6 months later she is still my mentor and helps me understand more. She comes and talks to me about it and we work on it together so that I can learn more. We also have a lot of fun doing it. This post is a reflection on how this person got me to do _______. (I will discuss what I learned a little later in this post. First, I want to discuss the learning process.)

I see similarities in what my “coach” did and what instructional coaches do to motivate teachers to incorporate new technologies and pedagogies into their classrooms. When I talk with teachers who are being coached as they integrate technology or some new teaching method into their classroom, I hear many things about relationships between them and their coaches. Where we see coaches having success in getting teachers to try new things and make changes in their classroom, we usually also see strong relationships between the coach and the teacher with trust, respect, and sometimes friendships (not required). We see the coach supporting the person until their own interest develops and their identity changes. This is similar to what I experienced. Honestly, my relationship is the only reason why I was willing to even think about learning the new thing. My coach was patient and persistent when I was resistant. She encouraged and motivated me.

Relationships and emotions are an important part of learning, and for the past couple of years, I’ve been thinking about them more. I recently read the new brief on how emotions and relationships drive learning by Mary Helen Immordino-Yang and Linda Darling-Hammond. While the brief is written about children, emotions and relationships are very important in the learning process for adults, too.

Relationships bring so many things to the learning process. A relationship that helps support the learning process includes at least trust, respect, and motivation. For me, I definitely needed that motivational piece as I learned. Trust and respect are essential, but not enough when learning something new takes a lot of time and effort. A trusted coach helping with every step of the journey and giving that motivation can really make a difference. (Note, the journey can be longer or shorter depending on the person being helped, their interest, and the new thing being learned.)

I imagine that most people don’t really invest in learning something that they don’t like at all. For me, I didn’t want to do _______.  What is ___________ you may ask? It’s Pokémon GO. There’s a lot to learn as you play. There are strategies involved. There are lots of rules and tricks. Also, remember, I didn’t want to play it at all. I didn’t know anything about it and I wanted to keep it that way. I thought it was a useless waste of time. But I liked the person who asked me and she took the time to show me what I needed to know.  “She” was my 12 (now 13) year old daughter. She was asking me to hang out, and if a tweenager wants to hang, you should do it! However, despite the fact that I wanted the opportunity to spend time with her, I still didn’t want to learn the game.

As I mentioned before, playing this game was big identity change for me. Part of my identity is that I don’t play games. I imagine that teachers who aren’t interested in using technology in their classes or don’t see themselves as technology people also go through a similar identity change as they start using technology. (I’ll explore more about interest, identity, and learning in the near future, in another post.) Here, I really want to stress that without my relationship, I wouldn’t have learned. My coach took the time to show me what I needed to know. She answered all my questions and she never made me feel bad for my questions. I wouldn’t have ever been interested, much less chosen to learn on my own. I needed her to provide external motivation for me. In fact, in the beginning, I needed so much that she was kind of dragging me along in the learning process.

As teachers, we try to help students with this kind of support, but we may not get it ourselves. With a coach, the odds increase for this kind of support. Making changes in a teaching practice is difficult because a teaching practice affects other people, students and future students. Most teachers are cautious about wanting to make a change to something that generally works in practice because of all of the people who depend on it. That’s where a colleague or a coach can really help. There are times when a teacher is so interested in making the change that they can do it on their own, but most of the time, it’s so much easier to make a change with the help of someone else.

In this post, I’ve been thinking a lot about teachers and changes in practices, but I think we could insert any age learner in a scenario of making a change that they aren’t interested in making. Relationships strengthen the learning process at any age and are something we should think more about in the learning process. What do you think about the importance of relationships in learning? I’d also love to hear about changes you’ve made to your practice. How’d you do it? Did you have support or did you do it on your own? Have you ever had an instructional coach? Would you want an instructional coach?  Are you an instructional coach? We’d love to hear from you — Tweet to @CIRCLEducators or use #CIRCLEdu.

When to Collaborate

By Sarah Hampton

With all the benefits of collaboration I’ve shared with you in the past two posts, should we drop direct instruction altogether and completely restructure our classrooms around collaboration? As it turns out, there are better times to collaborate than others, so don’t throw away your podium just yet. Even proponents of collaborative learning have said “We do not see any reason to develop pedagogical methods which exclusively rely on group activities. Individual reflection is required in order to transform experience into learning and class-wide activities are especially valuable when it comes to structuring the informal knowledge that [previously] emerged.” (from The Mechanics of Computer-Supported Collaborative Learning Macroscripts.) So when should we design collaborative activities for our classrooms?

Have you ever been asked to collaborate on a task that was so simple that figuring out how to involve another person took more work than just doing it yourself? Collaboration is a very powerful activity, but it works best when you need the power of another person’s brain to help you. Otherwise, the overhead of working with another person is not worth it. So it only makes sense to make the effort of figuring out how to work with another person when the instructional task is very large or complex. (To learn more about why and how this works, check out the collective working-memory effect. It is so incredibly cool! Basically, you can leverage others’ working memories to extend your capacity as a group in a synergistic way so the total really is more than the sum of its parts!)

Let’s take a look at three sample classroom activities from A framework for analyzing cognitive demand and content-practices integration: Task analysis guide in science and decide whether or not collaboration is beneficial for the task based on its complexity.

Task 1 Low Cognitive Demand Task

crossword

Sample classroom activity from A framework for analyzing cognitive demand and content-practices integration

This is a classic example of a low cognitive demand activity–memorization. Students only need to acquire and recall factual knowledge to succeed in the task. In an article titled Collaborative Learning Enhances Critical Thinking, Gokhale demonstrated that lecture followed by individual “drill and practice” was equally as effective as lecture followed by collaborative learning groups in gaining factual knowledge. Because this is a low cognitive demand task, taking the time and effort to collaborate isn’t worthwhile. You could argue that it would be more efficient to team up with other students and “divide and conquer” the clues. While I don’t think that would be as effective if the learning goal is for each student to be accountable for the information, I agree that it would complete the task more quickly. Even so, remember that grouping up only to divide responsibilities is an example of cooperative learning, not collaborative learning.

Task 2 Low Cognitive Demand Task

Sample classroom activity from A framework for analyzing cognitive demand and content-practices integration

This is another example of a low cognitive demand activity. The student can follow the steps from the example to compute the speeds without having to make any sense of the underlying math or science ideas. In fact, there might as well be no context about the dog or runner or baseball because it’s all irrelevant to the task. (Side note for math teachers: Robert Kaplinsky has an article about imposter “real-world” problems called Beware of Fake Math Modeling Problems that’s worth a read!) You could still make a case that this kind of task is worthwhile for your classroom if your instructional goal is to develop a sort of automaticity in your students, “when I see a speed problem, I know I need to divide the distance by the time.” If you want students to memorize and apply the formula, then this might be your ticket. (Please note, though, that students might know what to do, but they probably won’t understand why they need to do it unless they’ve also done some higher level thinking about it.) Because Task 2 is another low cognitive demand task, it can be completed more efficiently individually.

Task 3 High Demand Cognitive Task = Good Collaborative Task

Guided Content

Sample classroom activity from A framework for analyzing cognitive demand and content-practices integration

This task goes well beyond acquiring or recalling factual knowledge and asks students to think critically about their comprehensive knowledge of a topic while they evaluate a new source of information. This is an example of a high demand cognitive task and therefore is a good candidate for collaborative activity. Students will almost certainly benefit from discussing their thoughts with others as they clarify and strengthen their arguments. According to Less is more: Teachers’ influence during peer collaboration, “When a student models a strategy that makes an argument more convincing or makes the discussion run more smoothly, other group members are stimulated to appropriate the strategy.” In fact, in their study and several others like it, students were more likely to benefit from hearing other students think aloud and seeing other students model strategies than anything the teacher said or did! That makes tasks like this the sweet spot for collaboration.

In summary, students need to need the power of each other’s mental capacities to collaborate, and this usually happens during high cognitive demand tasks. Collaboration is a powerful activity, so give your students opportunities to work on large and/or complex tasks that will make it worth their while.

When have you found collaborative activities to be effective in your classrooms? Do you agree or disagree with my assessments above? We would love to start a collaborative reasoning discussion so we can all learn better together. Tweet @circleducators to join the conversation!

Learn more:

Task Complexity as a Driver for Collaborative Learning Efficiency: The Collective Working-Memory Effect

The Mechanics of Computer-Supported Collaborative Learning Macroscripts

A framework for analyzing cognitive demand and content-practices integration: Task analysis guide in science

Collaborative Learning Enhances Critical Thinking

Beware of Fake Math Modeling Problems

Less is more: Teachers’ influence during peer collaboration

Mathematical Tasks Framework – Task Analysis Guide

Three women in a meeting image by rawpixel

How to Encourage Young Women and Marginalized People to Participate in CS and Engineering (part one)

by Pati Ruiz

This past July, I had the opportunity to present my dissertation research at the Computer Science Teachers Association conference in Omaha, Nebraska! My presentation was titled 5 Ways to Encourage Young Women & AHN to Participate in CS and Engineering. In this series of two posts I will summarize the highlights and share resources that I found incredibly helpful as I conducted my research.

In a recent Medium post, Code.org reported that in 2018, young women still only account for 28% of all students participating in AP Computer Science exams and only 21% of African Americans/Blacks, Hispanic/Latinx, and Native Americans/Alaskan Natives (AHN) youth participate. This is a problem that researchers like Jane Margolis have been working on for years. Dr. Margolis describes this structural inequality in computer science (CS) participation as an issue of empowerment and preparatory privilege. Addressing and dismantling the systems that perpetuate the underrepresentation of women and other groups in CS is important for the sake of equity and would also offer economic benefits (Beyer, 2014). With technology ubiquitous and mediating much of our daily lives, access to CS has become a civil rights issue. It is essential that those who sit at the design tables and those who lead technology projects represent diverse perspectives and the needs of our population as a whole. Unfortunately, there is a deep-seated lack of representation of women and AHNs in the computing field. This problem is the one I set out to study. My research focused on:

  • The intersectional identities of young women,
  • The distribution of power in computing, and
  • The elements that support, promote, and sustain the participation of women and underrepresented minorities in technical fields.

While I did study participants who identify as female, when I use the term “underrepresented minorities” I am including a range of identifiers that are considered marginalized in tech and computing, including the gender spectrum, age, race, socioeconomic status, and ability. Through my study, I wanted to gain a better understanding of the lived experiences of underrepresented women in undergraduate computer science and engineering programs. Among my primary findings is that more work needs to be done for positive advances to be made in the field.

This problem is particularly relevant to me. When I was in college, I studied CS in the school of business. That meant learning fundamental methodologies and approaches to computer programming with an emphasis on examining the complex relationships among science, information technology, business, and society. I did not go into the technology field immediately after graduation, though. The tech bubble had just burst, and I kept hearing about how hard it would be for me to find a job in tech. That — mixed with traditional CS world stereotypes (male, antisocial, etc.), stereotype threat, and not knowing anyone in the field or having a helpful advisor or any friends in my major who could help me — led me to pursue another passion: teaching. While I am so thankful to have gotten to teach Spanish (my first language) and Computer Science in grade 6-12 settings for over 15 years, I often wondered what would have happened if I had persisted in the tech world upon graduating. Where would I be now? Furthermore, as an educator interested in diversity and inclusion efforts, and someone who identifies as Latina, I have always been interested in the work being done to increase young women’s and AHN’s participation in computing from elementary school through industry. So, how can educators (specifically K-12 educators) encourage the participation of young women and AHNs in this field? Here are five ways:

  • Model an interest and passion for CS
  • Create safe spaces for making mistakes
  • Build community and connect youth with mentors
  • Introduce youth to careers in the field
  • Make interdisciplinary connections

You are probably familiar with these methods, and you are probably integrating many of these elements in your classrooms already! I will discuss the first two here and in my next post, I will provide some resources you might find helpful and that you can share with others as you continue to support all learners in your classroom.

Model an Interest and Passion for CS

My research and that of others shows that there are several ways that teachers can share their passion for the subject with students. Participants in my study identified teachers who modeled an interest and passion for CS and Engineering as creating opportunities for their students to engage with design, personalize their learning, share it with friends and family, and reflect on it. What my study participants were describing as supporting them in the CS classroom is a constructionist learning environment. Constructionist learning environments give students the opportunity to engage with design, personalize their learning, share, and reflect on their work.

As I conducted my research, I drew from two main frameworks when I looked to design engaging learning environments. First were the engagements practices found on the NCWIT EngageCSEdu platform and the repository of course materials centered around this research-based framework.

Three elements: Grow inclusive community, make it matter, build student confidence and professional identity.
NCWIT Engagement Practices

In my research, I found that the integration of these practices–growing an inclusive community, making it matter, and building confidence and a professional identity–engage diverse learners. Supporting these goals, the materials that are shared on the website can be sorted by engagement practice, course level, and programming language.

The second, very helpful resource that I use as an educator is the Universal Design for Learning (UDL) guidelines. This framework, described in more detail in this CIRCL Primer, is designed to improve and optimize teaching and learning for all people based on learning science research. The goal of UDL is to support learner variability by providing options to develop self-regulated learners who comprehend content and have high executive functioning skills.

UDL image that shows three parts of UDL: Providing multiple means of engagement, representation and action and expression to support learners who are purposeful and motivated, resourceful and knowledgeable, strategic and goal-directed

So, as CS teachers, you can model your interest and passion for CS by designing and delivering meaningful and interesting curriculum!

Create Safe Spaces for Making Mistakes

Learning environments that support metacognitive acts and encourage collaboration can support the persistence of girls in CS courses and careers as they learn to be resilient when faced with CS problems and challenges (Werner & Denning, 2009). Participants in my study described the importance of engaging in exploratory talk – or metacognitive monitoring of themselves and their partners. They described feeling very comfortable making mistakes with partners in pair programming activities because the stakes were not that high and they were able to talk through their work with someone else; it didn’t fall on them alone.

Modeling making mistakes is important. Let your students hear your problem-solving process and encourage them to share their own processes. But also make mistakes and talk about those mistakes. When I’m programming along with students (code along) and projecting my work on a screen, I make lots of mistakes and talk through those mistakes with my students. “My code didn’t run —  oh, I forgot to change directories in terminal and the file was not found, or I forgot a semicolon.” This modeling of mistakes is so important for students to see and hear.

One important note is that when grouping students, it is best to put those students with similar experience levels together and to avoid isolating women and underrepresented students – put young women and AHNs together so they can support one another, if you can. While some teachers may want to put an advanced student with a less advanced one, this is not always good. In Strategies for Educators to Support Females in STEM, Dr. Wiest (2014, p. 1) reminds educators to:

“Use varied, student-centered teaching methods within a ‘safe’ classroom climate. In particular, use mixed-ability, collaborative (rather than competitive) group work, hands-on methods, and meaningful (such as real-world and interdisciplinary) contexts. Use mixed-gender groups, but avoid placing only one girl in a small group, even if that results in having one or more all-male groups. Monitor and rotate these groups regularly.”

Read part two of this post here.

How do you model an interest and passion for CS? And, how do you create safe spaces for your students? Tweet @CIRCLEducators and tell us!

Unpacking Collaboration

By Sarah Hampton

Collaboration. We all know that means working together, and we all know it’s an educational buzzword with a positive connotation. It’s one of those words that I kind of gloss over when I see it in a paper or blog. My brain kind of does this disengage thing like, “I get that concept. It’s old news. Moving on.” Well, you know when you ask your students if they fully understand a concept, the infamous question, “Does that make sense?” and they answer, “yes,” and you know they don’t?  I didn’t fully get the concept of collaboration; I was doing exactly what my students do, saying “yes” and moving on. As I read this summer, I learned that collaboration may be something we talk about often, but there’s a lot more there than I thought. I know I’m definitely not ready to move on!

What is collaboration, exactly?

In the book, What do you mean by collaborative learning?, Pierre Dillenbourg humorously points out that, “When a word becomes fashionable – as it is the case with “collaboration” – it is often used abusively for more or less anything.” So what is it, exactly? At its core, collaboration is two or more people working together, but this can be deceptively simple. For example, collaborative learning shouldn’t be confused with cooperative learning in which students work together by dividing up tasks between team members and working independently. In collaborative learning, students must be mutually engaged in a “coordinated effort to solve the problem together.” Furthermore, merely asking students to “work together” is not enough to lead to positive learning outcomes, so teachers must be intentional about identifying and facilitating effective collaboration. (Tips for that in a minute!) On the other hand, students who are effectively collaborating may not even be in the same room together thanks to modern technology. I like how Mary Burns says it in Edutopia’s blog, 5 Strategies to Deepen Student Collaboration:

“In collaborative activities, we want to ensure that students don’t just occupy the same physical space but that they share an intellectual space—that they learn more, do more, and experience more together than they would alone.”

Why collaborate?

I knew collaboration was supposed to be good for learning, but I was surprised to see the number of documented benefits. In the Benefits of Collaboration, Laal and Ghodsi (2012) discuss collaborative learning (CL) and organize the results from multiple studies into social, psychological, and academic categories:

Quoted from pages 487-488 of Laal and Ghodsi (2012): Social benefits CL helps to develop a social support system for learners CL leads to build diversity understanding among students and staff CL establishes a positive atmosphere for modelling and practicing cooperation CL develops learning communities. Psychological benefits Student-centered instruction can increases students' self esteem Cooperation often reduces anxiety--everything is easier with a friend! CL can develops positive attitudes towards teachers Academic benefits CL Promotes critical thinking skills Involves students actively in the learning process Classroom results are improved Models appropriate student problem solving techniques Large lectures can be personalized

How can cyberlearning help?

I suspect what teachers (including me) have often called collaboration didn’t really hit the mark, and maybe we haven’t recognized collaboration when it was happening in other situations. Let’s take another look at a cyberlearning project we’ve talked about before to learn what’s going on during effective collaboration. Check out this post on Speech-Based Learning Analytics for Collaboration (SBLAC) to learn more about the project. In this video, the leader of SBLAC, Cynthia D’Angelo, talks about things teachers can look for during collaboration.

In your own classroom, you can look to see if everyone in a group is contributing to a new understanding or if one person (or a small number of the group) is doing the work. Good indicators include seeing group members verbalizing about what is confusing or talking through what makes sense. Making thoughts visible to others (e.g., saying what you are thinking or sharing in writing, a sketch, or a model) is a very important indicator that collaboration is occurring. You could even make your own rubric or checklist for what you are looking for as you walk around when groups are working together. Sharing this rubric or checklist with your students might help them collaborate better.

In the near future, I hope to see more projects like SBLAC that automatically code these indicators. It would be much more efficient to allow technology to streamline that process so we could focus on giving our students targeted interventions at optimal times.

What do you think? Did anything about the specifics or benefits of collaboration surprise you? Would you say you frequently use true CL activities or are you hoping to facilitate more for your students? Would you be excited or intimidated to use a tool like SBLAC in your classroom? How do you know if students are working well together? Leave us a comment–we would love to collaborate with you as we come to a better understanding of CL together!

I would like to give a special thank you to Judi Fusco for her time and endless patience as she recommended readings and discussed collaboration with me. Because of her, I experience the value of collaboration firsthand.