Category Archives: Learning Technologies

Developing Sample Computational Thinking Lessons with ChatGPT

February 1, 2023AI in Education, Computational Thinking, EducatorCIRCLS, Guest Author, Learning Technologies, Written by: Merijke Coenraad, Written by: Pati RuizPati Ruiz

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by Pati Ruiz, Merijke Coenraad, and Judi Fusco with contributions from Julio Vazquez

What is ChatGPT?

Let’s start with some definitions, ChatGPT is commonly classified as a natural language processing model, meaning it deals with language and human speech patterns, and “generative artificial intelligence”, meaning that it is AI that creates new content — in this case, new text.

More specifically, ChatGPT is a chat-based generative pre-trained transformer. Meaning that the model: (1) can generate responses to questions (Generative); (2) was trained in advance on a large amount of the written material available on the web (Pre-trained); (3) and can process sentences differently than other types of models (Transformer). Basically, it’s a chatbot that allows a user to ask a question in plain language and get a response in a way similar to how a human would reply.

What does this mean for education?

“ChatGPT is a good prompt for conversation.
I see this tool as a starting point for teachers and students.”
-Julio Vazquez, North Salem Central School District

Despite the precedent of banning access to ChatGPT set by New York City Public Schools in January 2023, not all school districts are following suit. Some educators believe that these AI systems and tools are out in the world and the best thing educators can do is to teach students to partner with AI tools so they can be better prepared for a technological world. For example, English teacher Cherie Shields was recently interviewed by the New York Times where she shared that she assigned students in one of her classes to use Chat GPT to create outlines for a recent essay assignment. She shared that the process helped deepen students’ understanding of the stories while also teaching them to interact with an AI system by manipulating their inputs to get the responses they were looking for. In this case, ChatGPT became a tool that can support learning when we thoughtfully include it in our lessons and also guide students in using it well.

Dr. Julio Vazquez, Director of Instruction and Human Resources, and his team are encouraging experimentation and access to ChatGPT for all faculty and staff and are thinking about how to provide students with access in a manner that will not conflict with student privacy laws. Staff members are rolling their sleeves up and starting to explore and learn about how they can use it with their students productively. In fact, they are exploring the use of ChatGPT to develop sample Computational Thinking (CT) lesson plans that the team uses as a jumping off point in their CT Pathways development process.

ChatGPT for Developing Sample Computational Thinking Lesson Plans

compass pointing north — North Salem Central School District

In a recent conversation with Dr. Vazquez, we asked him more about how he and his teachers are incorporating ChatGPT in their computational thinking lesson planning process.

Dr. Vazquez and his colleague Cynthia Sandler, Library Media Specialist, started by entering prompts into ChatGPT and seeing what came out. The searches started with prompt terms that went something like “generate a 5th grade lesson for computational thinking focusing on science.”

As the team began to analyze the lesson plans that came out, they realized they needed to make adjustments. Julio shared that he and his team have become better at giving ChatGPT enough context so that the lessons that are developed are closer to what the team expects of a lesson plan and the content better aligns to both CT and content area standards. For example, a more recent lesson prompt terms included:

“write a science lesson that integrates
9-12.CT.1
Create a simple digital model that
makes predictions of outcomes. and HS-PS1-5. Apply scientific principles and evidence to explain how the rate of a physical or chemical change is
affected when conditions are varied.”

The prompt terms and outputs were documented and provided a good starting point for sparking conversation. On first pass, the team collectively agreed that they liked the structure of the generated lesson plans. Beyond format and in terms of the content of computational thinking and subject area standards, the prompt terms entered into ChatGPT also included Habits of Mind, thinking dispositions which are implemented in North Salem, as well as the use of Visible Thinking Routines.

Dr. Vazquez and his team have worked with ChatGPT to develop sample computational thinking lessons across all subject areas K-12. These lessons are not meant to be implemented in the classroom “as is,” but rather, these sample lessons are to be used as a first draft, a starting point for consideration and conversation in North Salem. Teachers will vet the lessons for accuracy and then iterate and improve them in order to meet the learning needs of their students. Given the need for high-quality, integrated computational thinking lessons we will continue to work with Dr. Vazquez and his team at North Salem to learn more about how they are integrating ChatGPT in their work and their vetting process. We look forward to sharing more! Until then, do you have questions for us? Are you integrating ChatGPT in your classroom, school, or district? Let us know @EducatorCIRCLS.

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Teachers Partnering with Artificial Intelligence: Augmentation and Automation

July 7, 2022AI in Education, EducatorCIRCLS, Learning Technologies, STEM, Written by: Judi Fusco, Written by: Pati RuizPati Ruiz

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By Pati Ruiz and Judi Fusco

Artificial intelligence systems are increasingly being deployed in K-12 educational settings and we expect this trend to continue. Our starting point is that AI systems should support or augment, but never replace, a teacher. In order to ensure this, these systems should be developed with the input of teachers, students, and families.

So, what types of AI systems do teachers want to see developed? A group of teachers from the Merlyn Mind Practitioner Advisory Board shared ideas for how AI might help teachers better support their students. One scenario emerged around students who have Individualized Education Programs or Plans (IEPs)¹. In this post we will describe how an AI system might support teachers and students by automating:

Planning and Supporting Preferences
Monitoring
Documentation

Planning and Supporting Preferences

First, a teacher could input student instructional plans into the system. Then, the system can review the plans, make recommendations, and send alerts to the teacher when something may not work for a student. In the alert, the system could provide suggestions of adaptations on lessons or assignments based on the needs of each student. For example, an AI system can scan what’s coming up in an instructional unit and alert the teacher that the website they selected does not meet the accessibility standards required by the students in the classroom. A more advanced system could also suggest an alternative option, or even better, search for multiple resources that are appropriate and let the teacher decide what resources are best suited for their students’ instructional needs. In all cases, the AI system is only helping and making suggestions that the teacher may act on.

An AI system can also allow for a broader range of inputs from students for assignments based on their needs. For example, if a student accommodation includes submitting assignments as recorded audio, but the teacher prefers written assignments, an AI system can convert the student’s audio to text so the teacher can review or grade the text. The text-to-speech tool should also allow the teacher to hear the student’s voice for a particular sentence or phrase, for example, if the translation was not successful. Alternatively, if a student needs to hear the teacher’s comments on their assignments instead of reading them, the AI system can convert the text comments into spoken text for the student to hear. To additionally help the teacher, the system might suggest comments that they had written for another student so the teacher can reuse or repurpose them. The system might also remind the teacher of a student’s preference for feedback and if the student prefers verbal feedback, the teacher could read and record the comments for that more personal touch.

Monitoring

To support teachers in providing adequate accommodations for their students, an AI system can monitor student IEP information and make automated recommendations for needed support. For example, the system could identify students who require extended time and either share a list with the teacher or make appropriate adjustments to due dates for individual students in a learning management system. Here, we point out the need for AI systems to be able to interact with other systems or be embedded within them. Additionally, the system must do this in a way that does not expose sensitive information about students to the whole class.

Related to the text-to-speech and speech-to-text ideas discussed above, an AI system can also provide individualized read-aloud capabilities for students who need that support. The system could also remind the teacher to provide tools, like headphones or closed captioning for students who need to listen to content. We firmly believe that AI systems can help by doing things that machines are good at, while continuing to enable teachers to focus on what humans do best—like developing interpersonal relationships and identifying nuanced needs. With these types of automated supports, it is important to ensure that teachers have the ability to make the final decisions about students’ needs and that students have the agency to accept and decline supports as they go.

Documentation

Supporting a classroom with students who have varying needs—whether they are documented in an IEP or not—requires a significant amount of monitoring and reporting on the part of educators. An AI system could support teachers by not only monitoring the individual requirements of students, but also documenting the adjustments and accommodations that were made for each student. This documentation could then be shared with the students’ families to provide a summary of the work that students have accomplished and how they have been supported in completing that work. Of course, a teacher would review and verify that the summary produced by the AI system is accurate and flag any issues with the write-ups that would need to be addressed by the AI design team.

By the end of the instructional unit, teachers would be able to review reports of student progress, identify what worked and what didn’t, and ensure that all students are making meaningful progress. Automating, planning, tracking, and documentation can give a teacher more time to care for students; however, given the various risks AI systems bring, it is crucial that teachers also have the capability to override an AI system when needed.

Risks

The imagined AI system described helps teachers do what they do best by supporting them to ensure their students receive the accommodations they require and then documents those accommodations. Using such systems will come with risks, and AI systems that engage with student IEP data need to have the highest level of data privacy and oversight. As we discussed earlier, educators must be involved—for example, the teacher is in charge of giving feedback, but the system may make suggestions that help the teacher give better feedback. If educator experts are not in the loop, there could be harmful consequences for students. Educators must be diligent and not assume that every accommodation determined by an AI system is correct or the best decision. AI systems lack full context and the ability to make human decisions. Educators must have oversight and be able to verify and approve every decision made by the system.

Educator Voices

This blog post presents an imagined AI system based on conversations with a group of practitioners from the Merlyn Mind Practitioner Advisory Board. We need more teachers and educators involved in these conversations, so please consider this blog post as an invitation to you to connect with us and join the conversation on the future of AI in Education. In addition to Merlyn Mind, if you are interested in getting involved, please visit the links below.

¹ An IEP is a legal document in the United States that is developed for all public school children who need special education. It is created by district personnel with input from the child’s guardians and is reviewed every year. For more information see https://www2.ed.gov/about/offices/list/ocr/docs/edlite-FAPE504.html

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

Teacher Support Webinar Resources

March 15, 2021AI in Education, EducatorCIRCLS, Learning Technologies, OrchestrationPati Ruiz

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Educators, Artificial Intelligence, and the Future of Learning

Watch the recording.

Learn more about TeachFX:

How voice AI can help create more equitable classrooms by Jennifer Carolan
Every minute counts, especially for English learners by Kristin Pierce
Discourse in the Japanese vs. American math classroom by Daniel Lee

Visions for how AI can support teachers and classrooms:

Robots and Agents to Support Collaborative Learning by Sandra Okita and Sherice N. Clarke
Virtual Peers and Coaches: Social and Cognitive Support for Learning by Judith Fusco, Wendy Martin, H. Chad Lane, and Catherine Chase
Pedagogical Agents: Back to the Future by W. Lewis Johnson and James Lester

Meet the Practitioner Panelists:

Kip Glazer, Ed.D.
Principal, San Marcos High School, Santa Barbara, CA
Twitter: @kipglazer

Kip Glazer, Ed.D. is a High School Principal in Santa Barbara, CA. She had been a classroom teacher and technology coach prior to becoming a school administrator. She is a native of South Korea and moved to the United States as an adult. Her experience of being an immigrant who learned to speak English as an adult has shaped her personal and professional identity.
She has a doctorate in Learning Technologies from Pepperdine University. She is interested in all things technology and how they impact learning.

Kip’s Statement on AI:
As a literature lover, I am interested in the good, the bad, and the ugly side of technologies that have been extensively explored in science fiction. As much as I see the potential for incredible benefits of AI in the classrooms to support the teachers and students alike, I am equally concerned about its misuse by people whose intentions may not align with the mission of public education. I firmly believe that teachers and administrators must be provided with the right type of training to be able to support all our students, and we all have an important role to play when it comes to creating the future with AI.

Pati Ruiz, Ed.D.
Researcher, Digital Promise
Twitter: @pati_ru
Pati Ruiz is a researcher with the Learning Sciences Research team. Prior to joining Digital Promise, Pati spent 16 years as a high school computer science teacher, Spanish teacher, and K – 12 administrator. Pati has also taught public school leaders and pre-service teachers in both the U.S. and Mexico and helped design online learning experiences for computing and information technology faculty focused on diversifying undergraduate computing programs.

Pati’s Statement on AI:
As a former Spanish and computer science teacher, I think a lot about emerging technologies and how they apply in learning contexts. Recently, I have been focused on AI and how it affects students, their families, and communities. I am particularly interested in the consequences (intended and unanticipated) of these emerging AI technologies on historically excluded students, specifically Latinx, Black, Indigenous, and students with disabilities. Working with the Center for Integrative Research on Computing and Learning Sciences (CIRCLS), I have seen work ranging from Intelligent Tutors and systems designed to adapt and personalize learning, including some that are developing pedagogical agents and robots. I’ve also seen work that seeks to minimize bias and promote equity in AI, projects using computer vision, natural language processing and speech technologies. Of all of these efforts, I consider the work to promote equity and accountability in AI to be the most important and I created this list to focus on those issues.

Kelly Thomas
Special Education Teacher, Newport News School District (VA)

Kelly Thomas is currently a Special Education teacher in Newport News, VA. Previously, she worked as a Customer Operations Manager for Sentara Health Plan for ten years. Kelly also owned and operated her own daycare home for seven years which sparked her love of teaching. She started in elementary education as an instructional assistant before obtaining her Bachelor’s degree and teacher certification. She has been in education for more than twelve years in one capacity or another.

Kelly’s Statement on AI:
Before TeachFX, my direct instruction approach could be described as traditional, as it pertained to the ratio of teacher talk to student talk. I taught lesson content material and my students responded when asked to do so. Students were and still are encouraged to demonstrate lesson mastery in a “I do, We do, You do” gradual release method. The goal was and is for students to skillfully move from dependence to interdependence to independence. Now, thanks to Teach FX, my students feel more empowered to create and drive their learning experience. Teach FX has helped me become a skilled practitioner and/or facilitator towards that end. I now am aware of the ration between my teacher talk time and my student’s talk time. As a special education teacher, I am very aware of the importance of wait time, however I hadn’t considered that wait time occurs both after I speak as well as after my students speak. I am becoming more comfortable with periods of silence which used to be very uncomfortable for me. I find now that my students are more engaged with each other as well as myself! I love teaching and my students grow and thrive daily as they demonstrate their love of learning!

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.

Introduction to Ambitious Mashups

December 9, 2020AI in Education, EducatorCIRCLS, Learning Technologies, Written by: Merijke CoenraadPati Ruiz

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by Merijke Coenraad

As an educator, you know better than anyone else how much educational technology is changing, particularly within the last year. The Center for Innovative Research in Cyberlearning (CIRCL) has worked with researchers for the last eight years as they have developed and investigated learning environments and technology that pushed the boundaries between technology and education. This community of researchers (and their partner teachers) has focused on how emerging technologies could be important for learners and adapted into the learning tools that would positively impact the education of students five or ten years later.

The recent Ambitious Mashups report examines the work researcher teams did. You might have also seen our previous post encouraging you to attend the Ambitious Mashups Webinar. In case you missed it live, you can watch it here. As we reviewed all of the projects, we discovered that they together researchers with computer science expertise, knowledge of learning sciences theories and methods, and a firm commitment to investigating equity. More than just focusing on emerging educational technologies, CIRCL projects had a strong focus on groups that are marginalized within society and underrepresented in STEM professions such as students from marginalized races, girls, low-performing schools, low-income settings, students with disabilities, and students who are learning English.

Looking across projects, the CIRCL team found that researchers weren’t concerned with just one technology, research method, or learning theory. These projects were ambitious, pushing the frontiers of research and technology and studying big learning goals, and they were interdisciplinary mashups, involving many elements together in novel integrations. Therefore, we have deemed the results of CIRCL to be ambitious mashups and worthy of review by not only researchers, but by educators as well. These ambitious mashups bring together a set of novel technologies in unimagined ways to tackle learning challenges. As educators who will soon be encountering these emerging technologies in the classroom, this report points to what you can expect from ed tech and questions to start asking yourself as the research ambitious mashups of the past eight years become the technologies of the next decade.

So, what did we learn from looking at all the cyberlearning research? Reviewing the research projects completed through CIRCL, the team identified five themes representing the elements of the cyberlearning research community:

In this series of posts, we are going to look across some of these themes because we at CIRCL Educators believe that there are many things to think about as the emerging technologies of cyberlearning begin to enter the classroom and there are already exciting findings that can influence your teaching!

After eight years of researching together, the CIRCL community has learned a lot about what it means to do innovative research at the forefront of educational technology. Being a CIRCL Educator, what have you learned? How can you create an ambitious mashup in your classroom? Tweet us @EducatorCIRCLS and tell us about your innovative technology use and stay tuned for future blogs in this series about CIRCL Ambitious Mashups.