All posts by Judi Fusco

Invention Coach: A Cyberlearning Project

By Judi Fusco

Cyberlearning projects are quite varied as we often discuss on this blog. Cyberlearning emphasizes the integration of what we know from research about how people learn with new and yet to be invented technologies to create new learning experiences that were not possible or practical before. You can learn more about Cyberlearning at the CIRCL website. In this post, we share an interview about a project that helps students invent their learning and it helps teachers support students during the process.

What is the big idea of your project?

How do we we help students transfer, in a flexible, adaptive way, what they are learning in school to novel contexts and situations? Our project focuses on transfer of concepts at the intersection of math and science, and one thing that we’ve found to be very successful at promoting this type of transfer is a method we call “invention” (Schwartz et al., 2011).

Invention is an exploratory task where students engage in inventing conceptual ideas through an exploration of data, often contrasting cases. Contrasting cases are examples that have many similarities but a few key differences that relate to deep principles and conceptual ideas. By contrasting the cases, students come to notice features that are important to understanding, but may not be obvious to novice learners.

As students explore the cases, they are asked to “invent” fundamental equations such as those for density or speed. The process of inventing or even attempting to invent equations on their own (even if they fail) prepares students for additional learning. After the invention process, we follow up with expository instruction (i.e., lecture) on a topic important to the concept, such as ratio.

We are currently building the Invention Coach — a system that guides and scaffolds students through the messy and iterative process of Invention. The picture below shows the Invention Coach’s main interface. In this screenshot, a student is working to invent an index of “clown crowdedness,” which is a proxy for density (mass/volume).

How do you use cyberlearning in your work?

Prior classroom studies with paper-based invention activities show that the process of invention is really successful in promoting transfer. The cyberlearning part comes in with the technology we are developing to support the learning. Invention works well for promoting understanding, but students often need one-on-one time with a teacher or facilitator to engage in productive invention. Unfortunately, it’s not possible to give a teacher to each student in a classroom to support the process. Our project is working to create a technology that can reduce the demands on the teacher by providing individualized and timely feedback to students throughout the invention process.

Before building our technology, we wanted to know what a human would naturally do to promote transfer. We started by observing the guidance a human invention coach (a teacher) naturally gives in one-on-one invention tasks with students as the students invent formulas for density and speed. Our initial research showed human invention coaches did help students learn (Chase et al., 2015) and that much of the work the coach did during the task involved asking questions and not giving answers. In fact, the more explanations a coach gave, the lower the transfer test score for the student. One might think that a human coach gave explanations because a student was struggling, and perhaps it was a poorer student to begin with and thus the lower scores aren’t surprising. However, our analysis showed that frequent explanations were not related to how a student was doing on the task, and that those explanations hurt the student’s transfer. It may be that the explanations “cut short” the time the student spends exploring and generating ideas, so the student doesn’t do as well on the transfer task.

Our initial work helped us understand the human expertise we needed to include in the technological Invention Coach. Now we are working to develop that Invention Coach to support all students in the classroom so they can engage in a productive exploration and invention experience.
One of the design challenges we face is that we are essentially developing an intelligent tutoring system to scaffold the solving of ill-defined problems whereas most intelligent tutoring systems focus on well-defined problems (often in algebra) with clear steps to get to the answer. In our ill-defined problems it’s not clear what the goal is nor is it clear exactly what the path is to the goal. Thinking about how technology can scaffold students through that process without overly guiding is one of the critical challenges of our work.

Tell me more about the Invention Coach and what it looks like.

The Invention Coach is an exploratory learning environment that follows a student’s trajectory through an invention task and provides adaptive feedback and scaffolding to help them engage in productive exploration to prepare them to learn from later expository instruction (Marks, Bernett, & Chase, 2016). We have designed the initial prototype (see figure above). The software gives students an invention activity and the students can ask for help, submit their solutions, and get feedback along the way. We’ve created a few different kinds of interactive modules that focus learners on diagnosing their own errors and thinking deeply about these concepts. Some modules focus learners on comparing specially designed contrasting cases. In the literature, there is a lot of research showing the benefits of compare and contrast activities for helping learners focus on the deeper features that novices often overlook.
The image below shows the “feature contrast module” where students are asked to compare the highlighted green “Crazy Clowns Company” bus with the highlighted blue “Bargain Basement Clowns” bus, to help learners realize that space (the bus size) is an important feature of density (e.g. “clown crowdedness”).

If we walked into a classroom, what would it look like to have students using the Invention Coach?

We’re gearing up to do our first classroom study with the Invention Coach in the Fall, but right now, students work individually with the computer. As discussed above, there is a mentor character on screen that guides them through the process and provides hints and feedback (see first figure).

Invention can be pretty frustrating, because it is a very novel task for kids and it’s also an iterative task, where students frequently fail to come up with the right solution. However, in our studies, we often see kids having “Aha!” moments when they come to discover critical pieces of a sensible solution. Or more often, this happens during later expository instruction when they realize the sophistication of the canonical solution. For example, after attempting to invent a ratio-based equation (Density = mass/volume), one student said during the post-lecture on ratio “Oh! Now I finally understand division!”

In the future, we are open to the possibility of students working in collaborative pairs and are currently toying around with ideas for how to do that. David Sears has done some work with Invention and found that it’s much more productive when students work in pairs. In our work, they are paired with a computer-based coach. Supporting students working together may lead to more discussions, argumentation, explanation with a live partner and could be future work.

I’m also interested in developing more teacher-focused technologies that would engender classroom-based discussion around the kinds of mathematical models that kids are inventing and building. I want to understand when those models are effective or ineffective.

Is there more about the project you would like people to know?

People can visit our website to learn more or see our publications or they read about our Cyberlearning award Developing a tutor to guide students as they invent deep principles with contrasting cases.

Further Reading and References
Marks, J., Bernett, D., & Chase, C.C. (2016). The Invention Coach: Integrating data and theory in the design of an exploratory learning environment. International Journal of Designs for Learning, 7(2), 74-92.

Chase, C. C., Marks, J., Bernett, D., Bradley, M., & Aleven, V. (2015, June). Towards the development of the invention coach: A naturalistic study of teacher guidance for an exploratory learning task. In International Conference on Artificial Intelligence in Education (pp. 558-561). Springer International Publishing.

Schwartz, D. L., Chase, C. C., Oppezzo, M. A., & Chin, D. B. (2011). Practicing versus inventing with contrasting cases: The effects of telling first on learning and transfer. Journal of Educational Psychology, 103(4), 759-775.

A Cyberlearning Project looking at Collaboration

July 7, 2016Collaboration, Teachers & Researchers, Video Showcase, Written by: Judi FuscoJudi Fusco

By Judi Fusco

Our last post discussed embodied learning and Cyberlearning. Cyberlearning is many different things; on the CIRCL site, we have an overview of Cyberlearning. In this post, we’ll look at another example: a new Cyberlearning project developing technology that may be able to help support teachers and the collaborative learning process.

It can be difficult to understand what is happening during collaborative work in a classroom when there are multiple groups of students and just one teacher. In a previous post we discussed how it’s hard for an administrator to walk into a classroom and figure out what is happening when students are collaborating because it’s hard to walk up to a group and understand instantly what they are doing. It’s also hard for teachers because they can’t be in all of the groups at the same time. Of course, teachers wish they could be a fly on the wall in each group so that they could ensure that each group is staying on-task and learning, but that’s impossible. Or is it?

At the end of that previous post, I asked if cyberlearning researchers could help create tools to better understand collaboration. When I did that, I was kind of setting myself up to introduce you to a Cyberlearning researcher, Cynthia D’Angelo. She has a project that may lead to the creation of a new Cyberlearning tool to address the problem that it is impossible for a teacher to be in more than one place at a time. Watch this 2-minute video about Speech-Based Learning Analytics for Collaboration (SBLAC) and see what you think.

Cynthia’s research is still in early stages, but all the practitioners I’ve told about it find it interesting and want it for their classroom. Here’s a little more about the project:

In this project, work is being done to determine if technology that examines certain aspects of speech — such as amount of overlapping speech or prosodic features (like pitch or energy) — can give real-time insights about a group’s collaborative activities. If this could happen, and SBLAC went into classrooms, then teachers could get instant information about certain things occurring in group collaboration even when they weren’t present in that group.

The proposed technology would require a “box” of some sort to sit with each group to analyze the speech features of the group in real time. One research question in the project is, “Are non-content based speech features (such as amount of overlapping speech or vocal pitch) reliable indicators for predicting how well a group is collaborating?” Initial results suggest this is promising. (Note, this technology doesn’t analyze the content of the speech from the students, just features of the speech. Hopefully, this helps to preserve student privacy.)

It’s important to support groups during collaboration because sometimes groups aren’t effective or an individual student gets left behind. This work, while it is still in early stages, could potentially help teachers identify groups having problems during collaboration. A teacher would no longer have to guess how a group was working when s/he wasn’t present and could target the groups having difficulties to help them improve.

If you want to learn more about the project, watch Cynthia’s 3-minute video shared at the NSF 2016 Video showcase: Advancing STEM Learning for All. Or you can read the NSF award abstract. Stay tuned, as we’ll have more about this project from two teachers who are working with Cynthia on SBLAC this summer.

SBLAC really requires teachers and researchers to work together on this hard problem about collaboration as it tries to create new tools to help in the classroom. What do you think of the idea? What do you think is hard or important about collaboration? What kind of feedback would you want on the groups in your classroom. Could SBLAC help administrators understand collaboration? Going forward, we’ll talk more about collaboration and collaborative learning, so feel free to leave questions or comments about collaboration, too.

NSF Video Showcase

May 18, 2016Video Showcase, Written by: Judi FuscoJudi Fusco

By Judi Fusco

Hey CIRCL Educators, this year and last year, NSF researchers have made short videos to share information about their projects. I think the videos are great, as they introduce people to different NSF projects, let you know how you can get involved with those projects, and provide inspiration.

Check out the Cyberlearning Project Videos, vote, and give feedback, if you have time! (I know it’s that time of the year with end of school and all that is crazy, but these videos are worth watching.) The opportunity to ask a question or give feedback on the videos is open for another few days; the videos will be available indefinitely. Please share with your colleagues! If you have a favorite project you’d like to see featured and discussed here on the CIRCL Educators’ blog, let us know!

P.S. Here’s what I’ve watched so far today…

STEM Learning through Infographics
Diverse Learning Technologies
Using Data Visualizations to Empower Informal STEM Educators
Speech-Based Learning Analytics for Collaboration
A cyber-ensemble of inversion, immersion, collaborative workspaces, query and media-making in learning
and
Project-Based Inquiry Science (PBIS) CyberPD System with 24/7 Online Resources and 3-D Learning Support

Perspective on learning from an administrator

April 14, 2016Constructivism, School Administrators, Student-driven Learning, Written by: Judi FuscoJudi Fusco

By Judi Fusco

Today, for something completely different, I include snippets from conversations with Katie Hong, an administrator in a large school district in a school-wide Title 1 middle school. Katie is also a doctoral student pursuing her Ed.D. in the Pepperdine EDLT program.

One of the first things Katie told me was how Keith Sawyer got it right when he said, “Many teachers spend their entire careers mastering the skills required to manage an instructionist classroom, and they understandably have trouble envisioning a different kind of school” (Sawyer, 2014 p. 3). Teachers are told to implement Common Core Standards with student-driven learning, emphasizing collaboration, but they have not been equipped to implement or facilitate constructivist methods in their classroom. Another issue compounding the problem is administrators. Administrators often evaluate teachers based on the instructionist view. As they evaluate, they convey to the teacher how they want to see traditional classroom practices. When Katie was a young teacher, she did student-driven, collaborative lessons; she had one on Mesopotamia where the students were working together exploring the role of irrigation and how it impacted the growth of civilization. Her principal walked in to evaluate her and was a little miffed because the class wasn’t quiet. He told her he’d come back when she was “teaching,” as he couldn’t do an evaluation on her with her students so off-task.

Administrators have huge power over teachers, and teachers often continue to focus on the traditional classroom practices because they want to please their administrator, receive an effective evaluation, and be viewed as an effective teacher by their colleagues. Administrators aren’t completely to blame. as there aren’t good evaluation instruments or tools to help them evaluate constructivist methods or classes doing cooperative learning. Also, many administrators lack sufficient knowledge about student-driven methods and collaboration.

As Katie and I have continued talking, she has made many observations that have stayed with me. She spoke about how an ideal teacher evaluation should involve much more time than it’s given. Often there’s only time for one classroom visit with a pre- and post- meeting, but it would be better to have visits on a continuous basis throughout the year. She told me that she, as an administrator, would like to observe teachers facilitating student-driven lessons, but teachers often don’t use student-driven lessons on days she’s evaluating them unless she specifically asks them to in their pre-meeting. She also wishes she could have tools to help her understand what is happening more quickly when she walks into a classroom where students are collaborating. When there are a lot of groups, it can be hard to understand and evaluate what is occurring. And the forms she has to use for evaluation often involve a lot of answering of questions that may not capture the most important details. For her own research, she’s interested in thinking about how to help administrators evaluate a constructivist classroom effectively. She said, “I want to see the interaction with the students and teacher and how the teacher facilitates–that would be my ideal observation. I learn so much more when I talk to the students. I want to see if they can synthesize material and apply it. I know the teacher knows the material. I don’t need to see them lecture. I want to observe what the students have learned and understand.”

Thanks for the important perspective, Katie. We’ll have more of your thoughts on student-driven learning in another post, soon. Administrators and teachers, what are your thoughts about teacher evaluations and student-driven learning? What do you need to be successful? If you teach teachers, do you talk with them about the topics covered in this blog post? Cyberlearning researchers, can we help Katie with some new tools for evaluation of student-driven collaboration?

Sawyer, R.K. (2014) Introduction: The new science of learning. In: Sawyer R. K. (ed.) Cambridge handbook of the learning sciences. Second edition. Cambridge University Press, New York: 1-18.

Learning Scientists and Classroom Practice

April 5, 2016Constructivism, Mathematics, Written by: Judi FuscoJudi Fusco

By Judi Fusco

As I promised in the previous post, here’s a look at Tesha Sengupta-Irving and Noel Enyedy’s 2015 article. In this post, I want to take a closer look at one study that shows the kind of work learning scientists do in classrooms with teachers.

Some teachers (and principals, parents, and others) question whether student-driven (open) pedagogies work for students; they worry if students are on their own, they might waste valuable instructional minutes, especially in math classes. However, by exploring data, discussing and debating, and constructing their own understanding, students in an student-driven, open instructional approach achieve the instructional goals of the course as well as students in a teacher-led (guided or instructivist) approach. In addition, and importantly, students seem to enjoy learning mathematics more when taught with an open or constructivist approach versus a guided approach. In their article, Sengupta-Irving and Enyedy (2015) discuss how important enjoyment is in learning, and why and how a student-driven instructional approach helps them learn.

In the study, students’ test performance was the same for both the teacher-led and student-driven approaches. So why don’t we just stick with teacher-led techniques? Why do we want to switch to more student-driven approaches? Sengupta-Irving and Enyedy, and many other learning scientists, don’t think it’s enough to create mathematically proficient students without helping them develop an interest (or even love) for the subject that the student-driven approach helps create. Learning without enjoyment seems like a lost opportunity that may prevent students from doing well in the future. The authors think if students learn and enjoy subjects, those students might want to go further in the subject and take more classes.

Using Learning Science as the Foundation to Build Practical Classroom Practices
So what did the students in the student-driven condition do while learning? On their own, the students started with a discussion to explore the data, tried to understand the problem, and debated the approach or solution with peers. They also experimented and during their discussion “invented” an understanding, in this case, of statistics. They (hopefully) invent what the teacher would have told them during a lecture. While it may seem inefficient to let students invent, because, after all, we could just tell them what they need to know, but the discussion and inventing engages them, helps them enjoy the subject, and strengthens their learning.

After they have gained some understanding on their own in their discussion, the teacher has a discussion with the students and helps them learn formal terms. Exploring first contrasts to what students do in the the instructivist or guided condition where the teacher tells them the formal terms, a great deal of information about the problem, what the important concepts are, and the approaches they should take in solving the problem. In the guided condition, students are not given an opportunity to explore informally.

For a long time, learning scientists have known that “telling” students after they have the opportunity to explore and develop their own understanding is more effective than telling them before they have had that opportunity (Schwartz & Bransford, 1998). Sengupta-Irving and Enyedy employ this learning science principle and find that students do well and seem to enjoy the lesson more.

One other issue that is sometimes discussed about student-driven approaches is whether students are off-task when on their own. It is true, student-driven classrooms are usually noisier than instructivist ones, but that’s because there is learning occurring—in my experience, I have found that learning is a slightly noisy phenomenon. The researchers looked at off-task behavior in the two instructional approaches in the study and there wasn’t a difference. They found more instances of off-task behavior in the teacher-led condition than in the student-driven condition and approximately the same number of minutes of off-task behaviors in the two conditions. I think it’s important to note that the teacher in this research reported that she was more comfortable with the teacher-led approach. Because of that, the teacher may not have used an open approach very often, and her students may not have been as familiar with an open approach–yet there was no extra off-task behavior. To alleviate concerns that student-driven approaches require more time to work, both instructional approaches used the same amount of time for the lesson.

I want to go back to the issue of enjoyment. If, after a lesson, students don’t want to think about it any more—because it’s boring, one of the terms the students in the teacher-led condition used to describe the lesson—then we probably have not done the best we can for the students. Sure, if we tell students about something, we’ve gotten through the lesson and are able to cross that topic off the list. But shouldn’t learning be something more than just an item on a checklist? What if learning was enjoyable and students left wanting more? Learn the same amount, in the same amount of time, with very little off-task behavior, and enjoy it = win-win-win-win. And, add the bonus that enjoyment can potentially help students in their future work and motivate them to continue their studies. I’d make time for that in my classroom.

I’d love to know what you think about the article and their findings. In future posts, we’ll talk about how to o student-driven approaches and hear from teachers who have some good tips. I’d also love to hear how you teach and what you’ve seen or experienced in your classroom. Below you can read more details of the study.

Sengupta-Irving, T., & Enyedy, N. (2015). Why engaging in mathematical practices may explain stronger outcomes in affect and engagement: Comparing student-driven with highly guided inquiry. Journal of the Learning Sciences, 24(4), 550-592, DOI: 10.1080/10508406.2014.928214.

Schwartz, D. L., & Bransford, J. D. (1998). A time for telling. Cognition and instruction, 16(4), 475-5223.

Details of the study
In the study, one 5th grade classroom teacher taught two sets of students the same mathematics topic, for the same amount of time, using two different approaches: open (student-driven; 27 students) and guided (instructivist; 25 students). The teacher was more comfortable with the guided approach, but had learned how to facilitate the open method and taught one class of students that way. The data collected included written assessments of the student’s work (a test), a survey inquiring about the students’ affect during the lessons, and video of the 5 hours of class time devoted to the topic for each instructional approach. The researchers report three main findings based on the analysis of this data:

Assessment data showed that when students were given the opportunity to explore and solve problems in an open way working with their peers, they performed just as well as students who were in the guided (instructivist) situation.
Survey responses indicated that students in the open condition enjoyed the lesson significantly more, compared to guided students. Also, students in the open condition did not express any negative affect statements, but guided students did. (“Bored” was one of the negative affect statements used by the guided students.)
Video analysis showed that in the two conditions, the amount of time spent in interactions between teacher and students, and students working together, were very similar. For example, for both conditions, there was a little over 3 hours spent in whole class activity and about 2 hours spent in small group work; during the small group work, adults spent about 1.5 hours helping the students with the lesson or managing behavior. Off-task time was roughly equal in the two conditions: there were 18 off-task instances (involving approximately 11 minutes (out of 300 minutes) of adult intervention) for off-task behavior in the guided condition, and 14 off-task instances (involving approximately 13 minutes (out of 300) of adult intervention) in the open condition.

Learning, teachers, and learning scientists…oh my!

March 15, 2016Written by: Judi FuscoJudi Fusco

By Judi Fusco

I’m thrilled to be writing in the Educators’ Corner. I’m Judi Fusco and I have been working with teachers (K-12 and higher education instructors and professors) for almost 20 years. I currently work at the Center for Innovative Research in Cyberlearning (CIRCL) and teach at Pepperdine University. If you want to learn more about me and my work, you can see my bio for SRI and visit the archive of Tapped In, the online community for education professionals that I helped co-found in 1997 (links to many of my publications).

I love to think about learning, and there are many directions we can go, but today, I’m going to give background about the learning sciences. Why? For one reason, because I believe that with better knowledge of learning sciences, practitioners can help researchers do a better job designing Cyberlearning tools and environments. A second reason is that researchers can help practitioners in understanding when learning is occurring or why it isn’t occurring, and even how to help make it occur. I think that as partners, we can do far more than we can alone. To become better partners, we need to speak the same language. This post is a start; I hope that you will join in the conversation about learning and learning science.

In the class I teach for first year doctoral students at Pepperdine, many of whom are (fabulous) K-12 teachers, my students and I think deeply about how people learn. Our conversation starts by asking, “What are learning sciences?” We use this book, The Cambridge Handbook of the Learning Sciences (2nd Edition), to guide much of our thinking. It’s a big book, and some students were dubious, but after reading it, they told me they enjoyed reading it. Below I share some of the topics we discuss as we read the first chapter in the book. If you like, the first (introductory) chapter is available as a free sample from Amazon so you can read it. If you want a quick summary, here are some of the things my students and I typically discuss when we read:

What are learning scientists? Learning scientists are people from diverse backgrounds who care about how people learn in schools, in museums, after school organizations, on the job, or anywhere. They have a deep understanding of cognitive processes (what happens in learning in the minds of the learner), and social processes (what happens in situations and interactions between students with other students, students and teachers) and use their knowledge of learning to design and improve the settings that they study. Their research and studies are often done in partnership with practitioners and students to see learning theories at work in the real world, not just as theory. Some learning scientists started their careers as teachers or other practitioners and so they have a very good understanding what the real world is like.

Traditional approach of schooling versus new thinking. Instructionism, or the teacher as the expert telling students what they need to know and the students accepting it without questioning it, is the traditional model of schooling (Papert, 1993). Many of us have experiences in being lectured to by teachers who subscribe to instructionism. Instructionism makes the assumption that we can fill empty minds with knowledge and that presenting the same materials to different learners will have the same results. Instructionism is contrasted to constructivism where we assume learners are different and need to construct their own understanding based on what they already know through interacting with new information and others. I have heard from teachers that as Common Core and Next Generation Science Standards (NGSS) become more widely implemented in schools, different methods for helping learners–not just telling students what they should know–are needed. In informal settings and in some schools, hands-on, active learning with inquiry- or production-centered methods are used to help learners.

Learning scientists (Sawyer, 2006) take the view that in learning situations, knowledge needs to be generated, constructed, and practiced, and that learning with others in collaborative situations works well. (That’s not to say that there’s not a place for lecture, because there sometimes is, but lecture shouldn’t be the only mode.) Learning scientists use what they know from the research in the learning sciences to design good learning situations and environments.

What is Deep Learning? There’s a lovely table on page 5 of the book, or at 33% of the sample (if you downloaded it), that discusses deep learning versus traditional classroom practices.

According to the book, deep learning is about making new knowledge interrelated and interconnected into the other knowledge a person already has. It is about helping learners see patterns and understand the underlying similarities, differences, or principles. It is often done in collaboration with others through discussion of a topic. After a discussion, you’ll also have a better understanding of what you know and don’t know (even if you feel as though you have more questions than answers). Discussions allow participants to reflect to see what they understand and that others see things differently. Unfortunately, in many schools, because learners aren’t really exposed to methods that make learning relevant to them, they often focus on grades or certification. So they decide to cram knowledge into their heads just in time for a test.

Sengupta-Irving and Enyedy, N. (2015) show that when teachers and researchers think about learning goals and implement pedagogical strategies that focus on deep learning, learning becomes more relevant and enjoyable (as it should). I’ll share more about their paper in the next post and talk more about specific work learning scientists do.

This post has gotten long, so I’ll end here. In future posts, I plan to provide you with examples from Cyberlearning research projects that have taken technologies and designed new approaches, based on learning science research, to help people learn. In this blog, I want to start a conversation because conversations with diverse groups help advance thinking. Please post comments and questions. I look forward to thinking about learning with you.

You can read more about the learning sciences and learning scientists here at the CIRCL site.

References
Papert, S. (1993). The children’s machine: Rethinking school in the age of the computer. Basic books.

Sawyer, R.K. (2014) Introduction: The new science of learning. In: Sawyer R. K. (ed.) Cambridge handbook of the learning sciences. Second edition. Cambridge University Press, New York: 1-18.

From playpen to playground

August 10, 2014Cyberlearning, Written by: Natalie HarrJudi Fusco

By Natalie Harr

(Blog Post #6)

Digital Playgrounds vs. Virtual Playpens
Marina Umaschi Bers and her students in the DevTech Research Group at Tufts University are examining how technologies might be used to help our youngest learners to learn. The research team uses the analogy of “playgrounds vs. playpens” to help us understand how technology can help engage children in imaginative or exploratory play and the kinds of developmentally appropriate and playful learning opportunities that may not be possible without technology.

Playgrounds are places where children go to play and learn. Children can choose to play tag, climb a slide, rest under a tree, or imagine new games. When you think about the physical design of these spaces, playgrounds naturally support a child’s imagination, playful exploration, social interaction, and motor coordination all within a safe structured environment.

Now, think about how a playground compares to a playpen. In a playpen, the walls limit a child’s movement, exploration, socialization, and ultimately their playful curiosity. Bers and her students are developing technologies that allow learners to imagine, explore, and interact together as they would in a playground setting.

Meet KIWI: Kids Invent With Imagination

KIWI Prototype: Courtesy of DevTech Research Group

As an early childhood educator, I am JUMPING UP and DOWN about KIWI (now commercially known as KIBO)! This simple, easy-to-use robotics kit is purposefully designed for young children (4-7 year olds/preschool-grade 2) and can be seamlessly integrated into early learning environments.

With the pressures for more academic rigor in our schools today, the beauty of KIWI is that it engages children in meaningful, cross-curricular projects that support the development and application of fundamental academic skills that are most critical in the early childhood years — at the same time nurturing their developmental needs for creative play and exploration. By programming the KIWI robot, children playfully learn the logic of sequencing (how order matters), mathematical one-to-one correspondence concepts, and a wealth of pre-literacy skills that are at the core foundation of all early learning.

Check out this video to learn what KIWI is and how it can support
digital “playground” learning in early childhood settings.

Video: Courtesy of the DevTech Research Group

In this video, Marina Umaschi Bers explains why she is interested in creating developmentally appropriate technologies for early learners. She also addresses the purposeful design of KIWI (a KIBO prototype) and how it fosters meaningful learning opportunities for our youngest and most impressionable learners.

Video: By Natalie Harr

KIWI: A “Developmentally Appropriate” Learning Technology

“Developmentally appropriate practice, often shortened to DAP, is an approach to teaching grounded in the research on how young children develop and learn and in what is known about effective early education. Its framework is designed to promote young children’s optimal learning and development.”

-The National Association for the Education of Young Children (NAEYC)

KIWI consists of intuitive, easy-to-connect construction materials that are developmentally appropriate for early learners. Rather than “writing code” or arranging icons on a computer screen, young children physically connect tangible, wooden blocks that represent different computer commands (e.g., go left, shake, turn). Children “read” or make meaning of the words, icons, and colors located on the programmable bricks to decide what behaviors KIWI should do.

Once the blocks are connected in an appropriate sequence from left to right (just like reading), children use the robot’s scanner (similar to a handheld grocery store scanner) to program each command – sequentially one at a time (one-to-one correspondence) – into the CHERP (Creative Hybrid Environment for Robotic Programming) software.
By pressing KIWI’s start button, the robot comes to life and performs the sequence. Be sure to check out KIWI’s FREE curriculumhere.

Video: By Natalie Harr

Bers explains how computer programming is a natural fit in an early childhood curriculum.
Children learn sequencing skills in the context of making a robot!

Design Feature of KIWI

main body of robot

Robotic pieces

Photo: Courtesy of DevTech Research Group

programmable bricks

Photo: Courtesy of the DevTech Research Group

GREEN: “Begin” or start sequence block
ORANGE: “Sing“ or sound blocks (clap, stomp)
PURPLE: “Shake” or action blocks (shake, spin, turn)
RED: “End” or stop sequence block

How It Fosters “DAP”
Developmentally Appropriate Practice

KIWI’s main body is constructed of natural wood for longevity and durability in early childhood settings. Its “shoebox” size enables children to explore its features within small groups or as a whole class.

The body’s plain design encourages children to personalize it with their own artistic creations. Throughout this creative process, kids are experiencing balance and forces and making engineering design decisions as they balance and anchor their creations onto the mobile robot.

The robot’s underbelly is covered with a clear plastic layer revealing its inner mechanisms – provoking curiosity, conversation and wonderment among children and adults alike.

The robotic pieces (sensors, motors, wheels, interlocking blocks) are made of durable, natural wood for easy manipulation by small hands. Their unique sizes and shapes help students to correctly match them with their corresponding slots and prevent choking hazards.

The simple design of KIWI gives children a sense of choice and ownership of their construction, without feeling frustrated or emotionally overwhelmed by too many options.

The child-friendly pieces of KIWI can be compared to their own body parts to understand their functionality: we see with our eyes (light sensor), we hear sounds with our ears (clap sensor), we read words (just like the scanner “reads” the barcode) on each programmable block.

Just like reading words in a story, students see the importance
of left to right directionality to create a sequence of commands.

KIWI’s scanner reads a barcode just like a scanner at a grocery store. Children can easily understand how it works based on their prior knowledge and experiences.

Programmable blocks are vibrantly color-coded and labeled with recognizable words (using uppercase letters for preschoolers) and corresponding icons (supporting pre-literacy skills) to help children successfully build a programmable sequence of commands.

Children can sort (a mathematics skill) the programmable bricks by color to organize and understand the different robotic functions.

Their interlocking design scaffolds correct sequencing (e.g., the “green” begin block can only be attached at the start of the sequence).

The construction process enables learners to be active and develop their fine and gross motor skills.

The KIWI (prototype) in action in early childhood classrooms.

Video: Courtesy of DevTech Research Group

Meet KIBO

KIBO Robotic Kits are now commercially available. Check out KinderLab Robotics Store for more information.

Envisioning the future of education – CYBERLEARNING

July 28, 2014Cyberlearning, Written by: Natalie HarrJudi Fusco

By Natalie Harr

“Cyberlearning is about designing new kinds of applications and technology rich experiences, learning how to use them well to foster and assess learning, making the experiences work for particular disciplines and populations, and putting them in place in the world in ways that make a difference.”

-Center For Innovative Research in Cyberlearning (CIRCL)

(Blog Post #4)

Merge Ahead

CYBER is a generic prefix that means of, relating to, or characteristic of the culture of computers. A computer is any
programmable, electronic device, that can store, retrieve, and process data (including smartphones, G.P.S. devices, tablets, and laptops). -Merriam-Webster Dictionary

LEARNING is a relatively enduring change in behavior as a result of experience. People can learn alone or with others in collaboration. Learning can be facilitated by learning environments that incorporate
information and communication technologies.
–How People Learn: Brain, Mind, Experience, and School,The National Academies Press, 2000

The 21st century requires students to develop a
contemporary skill set for our global economy. Rich skills in computation, collaboration, communication, and creativity are highly valued in our modern society. As the world has evolved, so has our understanding of how people learn. In contrast to traditional teaching methods, in cyberlearning projects, students are designing, creating, solving problems, making mistakes, actively reflecting on their experiences, and gaining deeper understanding as they learn essential 21st century skills.

CYBERLEARNING is an exciting, new field of research that merges these two disciplines of study (learning & computing) to design learning technologies —technologies that can help people learn and assess learning. This innovative field uses what scientists have discovered about how people learn and how to foster learning to inform the design of these technologies. These new innovations can potentially transform who, what, when, where, and how we learn.

Learning Sciences

Study of how people learn

Computing

Study of computers & technology, including design and uses

New Field of Science!

How can technology be used to help people learn?

EcoMUVE: A Screenshot of the Pond Module. A Virtual Reality Cyberlearning Technology. Photo Courtesy of the EcoMUVE Development Team

Virtual reality (VR) technology can be used to create computer-simulated environments that can immerse learners into a virtual world. Using computer controls, learners can interact with a virtual environment as if it’s a real setting. Virtual worlds can mimic real places (e.g., a volcano, the digestive system) or imaginary settings (e.g., a planet from another galaxy) for deep exploration. Learners are then free to explore and investigate phenomena that are too big or small, too fast or slow, or too dangerous to otherwise experience in real life.

For Example…

EcoMUVE: Multi User Virtual Environment

EcoMUVE: A demo video of the Pond Module. Courtesy of the EcoMUVE Development Team

EcoMUVE, for example, is a 3-D virtual world designed to immerse middle school students in simulated habitats (a pond or forest module) as part of an inquiry-based ecosystems curriculum.This Multi User Virtual Environment, or MUVE, has the look and feel of a video game, but it is used instead to immerse learners within the complexity of a specific habitat.

In the pond module, learners investigate a virtual pond and its surrounding environment during a two-week period to understand why the fish have died off. They begin by going underwater and examining the life below the pond’s surface. They take virtual measurements of such factors as water temperature, weather conditions, turbidity (water clarity) and pH levels on different virtual days, working together to understand the fundamental components of the virtual ecosystem and identify the causal relationships that influence them.

The EcoMUVE development team, composed of Chris Dede, Professor Tina Grotzer, Dr, Amy Kamarainen, Dr. Shari Metcalf as well as numerous master’s and doctoral students, explains their work below:

“The first module represents a pond ecosystem. Students explore the pond and the surrounding area, even under the water, see realistic organisms in their natural habitats, and collect water, weather, and population data. Students visit the pond over a number of virtual “days,” and eventually make the surprising discovery that, on a day in late summer, many fish in the pond have died. Students are challenged to figure out what happened – they work in teams to collect and analyze data, and gather information to solve the mystery and understand the complex causality of the pond ecosystem.” -The EcoMUVE Development Team

EcoMUVE is released under a FREE license from Harvard University. REGISTER HERE for access to EcoMUVE downloads and curriculum. EcoMUVE is funded by the Institute of Education Sciences of the U.S. Department of Education.

follow-up research: eco-mobile

Photo Courtesy of EcoMUVE Development Team

The EcoMUVE project team received funding from the National Science Foundation and Qualcomm’s Wireless Reach initiative, for a new follow-up research project called EcoMOBILE. (Ecosystems Mobile Outdoor Blended Immersive Learning Environment). Stay tuned to learn more about this augmented reality (AR) technology in a future post.

Common Misconceptions about Cyberlearning

Cyberlearning is often misunderstood by the general public as “online learning.” This confusion stems from the creation of cyber-related words to help describe our swift changing horizon of technology and its impact on our world. However, these words (eg., a cybercafe, cybersurfing, cyberbullying) often describe online or Internet-based environments, thus limiting our full understanding of “cyber” and its implications.

Another misconception is that using technology will automatically foster learning. As I’ll try to show you, fostering learning with technology is complex (as is fostering learning without technology); it requires not only good technology but also using the technology and facilitating discussion around its use in effective ways.

Cyberlearning has also been misinterpreted as a replacement of teachers within classrooms.

As demonstrated in the video below, cyberlearning requires the expertise of teachers to facilitate and contextualize the rich learning opportunities allotted by these educational technologies. These learning opportunities would otherwise be impossible or impractical without the combined power of teachers and next generation technology.

Produced by Kelly Whalen for KQED Education in conjunction with Northwestern University’s iLab, with support from the National Science Foundation.

technology tidbit #2

July 21, 2014Classroom Technology, Constructivism, Written by: Natalie HarrJudi Fusco

By Natalie Harr

“Would you rather that your children learn to play the piano, or learn to play the stereo?”
-Mitchel Resnick, Amy Bruckman, Fred Martin (1996)

(Blog Post #5)

In the article, Pianos Not Stereos: Creating Computational Construction Kits (1996), Mitchel Resnick and his colleagues from MIT (Massachusetts Institute of Technology) Media Lab pose the question,
“Would you rather that your children learn to play the piano, or learn to play the stereo?” Playing the stereo means choosing and listening to pre-recorded music. Playing the piano allows exploring and constructing sequences of sounds, rhythms, tempos, harmonies and styles of music. Stereo players are consumers; a piano player creates.

One can think about educational technologies the same way. Resnick and his colleagues point out that there is a lot of “emphasis on the equivalent of stereos and CDs” in our educational technologies “and not enough emphasis on computational pianos” in what we make available to learners.

Video: Courtesy of PhET Sims

For Example…

PhET Interactive Simulations (see above) are widely used in classrooms today to help learners visually comprehend physical phenomena (e.g., forces of motion, gene expression, molecular shapes) that cannot be seen with the naked eye. Through the use of graphics and click-and-drag manipulation tools, PhET simulations are interactive enough to help students explore cause-and-effect relationships, connect them with underlying scientific concepts or real-world scenarios, and envision what cannot be easily observed in the real world. Resnick would say that PhET is a consumer technology; learners can choose a pre-created simulation to work with and manipulate it.

Just Think About It

PhET is a highly valuable tool for exploring “what happens when” scenarios and to help learners construct mental images of invisible phenomena. But just imagine if learners could build their own computer simulations — trying things out and making decisions on how to best model the complexities of the physical world — then running their simulation to see what happens. With that said, let’s check out the technology below…

Scratch Jr: A Technology Toolbox for Young Creators!

Scratch Jr. Screenshot. Image Credit: Dev Tech Research Group

This new cyberlearning technology called Scratch Jr. supports young learners from ages 4-7 as producers of expressive media.

Using a touchscreen device, children can create their own interactive stories and games by dragging and connecting graphical programming blocks to make characters and stories come to life.

And, it’s a FREE app for Android and i Pad tablets!

Resnick (cited at the beginning of this post) would say Scratch Jr. is a “creator” technology; children can playfully design, build, model, and test their own ideas using this digital toolbox. This kind of technology provides opportunities for deep, multidimensional learning that could not be made possible with a consumer technology. Educational technologies, such as Scratch Jr. -developed by Marina Bers and the DevTech Research Group– are designed with a constructionist approach to learning. In this approach, educational technologies are allowing learners to be creators. Stay tuned for more posts regarding Scratch Jr.

How Do They Come Up with These Technologies??!!

Constructionism is an approach to learning “by doing.” It builds from the renowned work of Jean Piaget and his theory of constructivism (notice the subtle difference in spelling). Piaget said that people generate knowledge and meaning (build schema) based on interactions between their experiences and their ideas.

Seymour Papert, a protege of Piaget, took this theory several steps farther. He has argued for a constructionist approach to learning; people actively engaged in designing things and making them work. As a revolutionary thinker, he has envisioned the power of computers as a tool for learning, especially for children.

This video was made publicly available on YouTube by Seth Morabito.

Seymour Papert is the world’s foremost expert on how computers can foster learning. This video demonstrates his remarkable insights into technology and learning decades ago — far before computers were feasible or affordable.

Constructionism: A Brief Timeline

I. The Beginning (1967-1980)

Image Courtesy: Logo Foundation Website

Logo: Learning by Programming

In 1967 Seymour Papert and his colleagues at the Massachusetts Institute of Technology (MIT) developed the first version of Logo; a groundbreaking computer programming environment to support mathematical learning. Since then, Logo has undergone several iterations and became widespread with the dawn of personal computers in the 1970’s. It has been used by young learners, novices, and experienced learners alike as a tool to develop simulations, games, and multimedia presentations. The most popular LOGO environment has featured a turtle icon, whose actions are controlled by the input of computer commands. In 1980, Papert published his highly influential book (especially in education) called Mindstorms: Children, Computers, and Powerful Ideas.

II. Logo Legacy continues (1990’s)

A Programmable Brick

For the past twenty years, Mitchel Resnick (a protege of Papert) has been developing a new generation of educational technologies that draw on the work ofSeymour Papert. In the article Pianos Not Stereos: Creating Computational Construction Kits (1996), Resnick and his colleagues describe three technologies they developed at the MIT Media Lab that draw on the constructionist approach to learning:

StarLogo was designed to help students “construct worlds in the computer” to explore the behaviors and patterns of decentralized systems (e.g., ant colonies, traffic congestion).

MOOSE Crossing was an online community that provided students a way to collaboratively create and interact within virtual worlds.

The programmable brick, a computerized and programmable Lego (e.g., reactions to sound, light, motion) block, now serves as the basis
for Lego robotic kits today.

III. educational technology (today)

Lego MindStorms (based on the programmable brick shown above) andScratch are two widely used educational technologies from Resnick’s MIT Media Lab that aim to support “learners as creators” in their own design activities. These technologies have been implemented into schools and other learning environments across the globe.

Lego MindStorms

A YouTube video made publicly available by Camilla Bottke

Scratch

Video: Courtesy of Scratch Ed

IV. Educational Technology (of the future!)

In upcoming blogs posts, we will explore the “next generation” of learning technologies such as KIWI, Eco- MOBILE, Scratch Jr., InquirySpace, etc., that all have foundations in this constructionist approach to learning.

TECHNOLOGY AND EDUCATION

July 3, 2014Cyberlearning Report 2017, Written by: Natalie HarrJudi Fusco

By Natalie Harr

(Blog Post #3)

“Education is what remains after one has forgotten what one has learned in school.” -Albert Einstein

Technology has transformed nearly all aspects of society, and education needs to support this transformation. Schools and communities are faced with the challenges of preparing our youth for the societal challenges ahead of them. As society becomes more technological and globally connected, students must become more techno-
logically literate, flexible, creative, computational, and collaborative — in addition to the traditional knowledge and skills that have been valued in schools for over a century.

An Industrial Factory, Image Credit: Wikipedia

A Look at Workforce Challenges
As new technologies have emerged, demands of the workforce have drastically shifted. For example, manufacturing used to be manually repetitive and regimented, but technology has made modern manufacturing and factories far more automated. Human work in those environments is more creative nowadays and less repetitive. Workers also need to be able to communicate and collaborate well and make informed decisions. Since technology has been integrated into these environments, workers need to understand technology well enough to support it.

Early 20th Century
Industrial Age

Courtesy: Library of Congress

Mid 20th Century
Digital Revolution

Early 21st Century
Information Age

Courtesy: National Science Foundation

Technology has transformed other work environments as well. Advances in areas as diverse as medicine, travel, communication, entertainment, and even space exploration have continued to evolve as technology innovates. Communication, collaboration, technological literacy, and critical thinking are important in all these different fields.

In addition, there has been a fundamental shift within our nation’s economic structure. We now live in a global, knowledge-based, innovation-centered economy. This requires communication and collaboration across cultures and languages in addition to the other skills listed above. Current-day students will be able to thrive in such an economy only if they have the multidimensional skill set it requires.

Getting Schools out of the Industrial Age

American schools were originally designed to prepare students for a national industrial economy. As our world has evolved, the
foundational structure of American education, however, has remained largely unchanged.
But schools can take advantage of technology too, especially to help foster deep, multi- dimensional learning opportunities that will better prepare students for the challenges of the 21st century.

As we have come to better understand how people learn, inquiry and project-based
instructional pedagogy that encourage a collaborative learning environment have begun to supersede traditional styles of teaching. At the same time, traditional educational tools have been modernized by incorporating digital technology (e.g., interactive white boards, video projectors, digital microscopes), and this has allowed them to be used in more collaborative ways.

In parallel, the Internet has changed the ways we can access and share information, and computers and handheld devices have become more ubiquitous, portable, and versatile. Such technologies and others that are being developed will allow communication, sense-making, collaboration, and new kinds of learning experiences that will foster deep learning and critical and creative thinking needed to succeed in the modern world. The challenges are to imagine the roles technology might play in education, to continually design innovative learning technologies, and to understand how to use them well to support learning.

Changing Education Paradigms Video (Dec 2010)
This RSA Animate is created from a speech given by Sir Ken Robinson, a world-renowned education and creativity expert.