Current Research into Student Interaction Networks, Part 2 – (the second of a series)

Previously on…

For the past four years, I have been experimenting with a social learning platform, which I call Pace Commons, which is based on Elgg social networking software.

Pedagogically, my major goal was to flatten the typical instructor centered hierarchies endemic to most learning management systems (such as Blackboard). As a teacher educator, it seemed philosophically inconsistent to be working with teachers and teacher candidates to promote student-centered, autonomy supportive classrooms while using a tool that reinforced traditional power dynamics. This project was greatly influenced by the work of Dron & Anderson in Teaching Crowds.

I also developed Pace Commons to be a research platform. I have been investigating these research questions:

eWhat types of patterns of interactions can we see in teachers and teacher candidates using a social learning platform?
What types of learning networks emerge in teachers and teacher candidates using a social learning platform?

In the first post in this series, I described the project and the social learning platform I designed, along with some preliminary discussion of methods. In this post, I will be looking at data from one course as a proof of concept of the analytic tools I am using.

Case Study: Looking at Teacher and Student Interactions in one course

In order to get started looking at the patterns of interactions between teachers and students on this social learning platform, I will focus on one course. This graduate level course was called “Computer Science for Teachers,” and was designed to train teachers and teacher candidates to design learning experiences to engage their students powerfully in computationality, which is a set of skills increasingly valued in K-12 schools. The course was offered online had three major components:

1. Exposing these teachers and teacher candidates to some of the key history involved in Computing and Education. This section featured the work of Seymour Papert, Alan Kay, Dartmouth College’s development of BASIC and time sharing, and PLATO (Programmed Logic for Automated Teaching Operations), among other things.
2. Immersing these teachers and teacher candidates into various types of computing programming – block based coding (using Turtle Blocks and Scratch), text based coding using Python’s Turtle module, and robotics coding using mBots and Edison robots.
3. Training these educators to develop powerful learning experiences for their K-12 students which incorporated computationality.

Throughout the course, these graduate students shared their work and their own learning process through making blog posts on Pace Commons and responding to the work of their colleagues via making commments on these others’ blog posts.

Interaction Frequencies

This first set of diagrams depict the frequencies of these types of interactions on Pace Commons.

Figure 1 illustrates the numbers of blog posts made by each course participant (the teacher – me – is represented by a purple bar in each of these diagrams). These graduate students were asked to share their progress and process via blog posts throughout the course. We can see that most of the students (11 out of 17) posted an above average number of blog posts; the remaining students (6 out of 17) shared a lower than average number of blog posts.

Figure 1: Blog post count for teachers and students

Figure 2 illustrates the number of comments left by each course participant (teacher and students). As might make sense (I am not entirely sure if it does or should), the teacher left the lion’s share of comments for the students, with almost 100 more comments than the most active student commenter.

Figure 2: Comment count – teachers and students

Figure 3 also depicts the number of comments left by course participants, but isolates the student interactions. We can see that 7 of the students (41%) generated an above average number of comments, with the remaining students (59%) sharing a below average number of comments with their colleagues. The effects of the presence or absence of the instructor becomes something much more relevant below, as we examine the social networks emerged from this course.

Figure 3 – Comments counts – students only

Learner/Learning Networks

The interactions from this Computer Science for Teachers course were then examined through the development of a set of social network graphs. The first interaction network graph is depicted in Figure 4. Several kinds of analyses can be performed through these types of graphs. For this analysis, I focused on power centrality (which is also called Eigen Centrality). This parameter works like this: “By calculating the extended connections of a node, …can identify nodes with influence over the whole network, not just those directly connected to it.” (https://cambridge-intelligence.com/keylines-faqs-social-network-analysis/). This figure, also utilizes a Force-directed graph drawing algorithm, specifically the Fruchterman–Reingold algorithm, which “assign(s) forces among the set of edges and the set of nodes of a graph drawing” (https://en.wikipedia.org/wiki/Force-directed_graph_drawing). I used the social network visualization tool SocNetV to do this work.

Figure 4 depicts the power centrality in the entire network of course participants – instructor and students. In this graph, the teacher and students in this course are each represented as nodes in the network and their relative influence is indicated by the size and color of the circles which depict them. Larger circles indicate more influence within this network, and nodes with the same degree of influence share the same size and color. These power relationships are a direct reflection of the interactions between each node in the network.

Figure 4: Social network graph showing power centrality for all participants

As might be expected (for better or worse), the instructor is the most influential node in the network. Connected to the instructor, and at the next level of influence are a small set students (in various shades of green). The less influential students are depicted as being further from the center of the graph and have far fewer connections to one another. They are literally further from the action of these interactions, so to speak.

Figure 5 depicts the same type of network graph (power centrality with a Fruchterman-Reingold algorithm). However, for this graph, the instructor (and the related interactions with students) have been removed.

Figure 5: Social network graph showing power centrality for student participants only

This network graph of only the students that reveals itself is very interesting. There is still a cluster of student nodes in the center of the diagram and several students depicted as outside of this cluster. However, the nodes/students representing the highest degree of power centrality have shifted with the instructor’s absence. This likely means that many if not most of the interactions with the formerly most influential students were with the instructor and less so with their peers. If this is really the case, then the next question is why would this be. Why don’t the students who has been the most influential when the teacher was in the network not just move up a rung, so to speak?

In order to address this question, I am in the midst of coding the content of these interactions to see what, if any, patterns emerge. That analysis will be the focus of the next post.

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Current Research into Student Interaction Networks (the first of a series)

Introduction

For the past four years, I have been experimenting with a social learning platform, which I call Pace Commons, which is based on Elgg social networking software.

Pedagogically, my major goal was to flatten the typical instructor centered hierarchies endemic to most learning management systems (such as Blackboard). As a teacher educator, it seemed philosophically inconsistent to be working with teachers and teacher candidates to promote student-centered, autonomy supportive classrooms while using a tool that reinforced traditional power dynamics. This project was greatly influenced by the work of Dron & Anderson in Teaching Crowds.

I also developed Pace Commons to be a research platform. I have been investigating these research questions:

1. What types of patterns of interactions can we see in teachers and teacher candidates using a social learning platform?
2. What types of learning networks emerge in teachers and teacher candidates using a social learning platform?

Typical Pace Commons page

What was supposed to happen, of course, was that my students would be so empowered by this type of learning environment that they would throw off their shackles, so to speak, and embrace the opportunity of autonomy supportive learning. After all, that is exactly what happened to two cohorts of eighth graders when I did something similar in their biology class.

At you might guess, for these teachers and teacher candidates, not so much. Instead some other interesting findings emerged.

This is the first of a series (of currently undetermined length) where I will lay out my methods and the types of data I am looking at.

What Does This Type of Learning Environment Look Like?

Pace Commons works like other social networks (that means you, Facebook) where users can make create pages (think wikis), blog posts, comment on the postings of others, share photos and files, etc. There is even a Pinterest-y type feature. For my classes, I assign students to groups (one group per class typically), and delineate the work of the course via pages. Here is an example of one such course. In these two images, you can see how the work is laid out into units. This online course is called “Computer Science for Teachers,” and gives teachers and teacher candidates a solid background in the history of CS in Education, along with experiences in using block-based coding, text-based coding, and robotics in K-12 schools. Each of the units listed links to sub-pages with the details and requirements for each project and learning activity. Throughout the course, students share their work via blog posts, pages, and/or photos.

Coursework page on Pace Commons

What Kind of Data Have I Been Collecting

The system provides all the typical data collected for each post — type of user activity (e.g., blog post, comment, photo, etc.), date and time stamps, the actual posts themselves, and system-generated identifiers for each post.

How Am I Analyzing These Data

For the time being, I am working to do two things with these data. First, I want to see the patterns of interactions and the networks these interactions form. The image below illustrates this type of analysis. In it, we can see the clusters of interactions between students in a particular course. Then, I am investigating a small set of questions. What kind of interactions and learning networks are emerging? What are the types of activities that seem to generate larger or smaller networks? What is the role of influencers within this type of learning network?

The second thing I am working on is to using textual analysis tools to explore the content of these interactions as well. For example, what are the types of things around which the students are interacting? Are there some topics and/or students who are more or less “attractive” to the others?

Let’s look at some preliminary data

Here is a screenshot from a typical blog post. In it, we can see the end of the original blog itself, allow with a small set of responses. I responded to the blog post, and the blog author responded to my comment. Also, one other student responded to the original post.

Sample interaction set

This is what a network graph of that set of interactions would look like. The original author is indicated as a blue circle, and the two respondents are indicated as red circles. The double arrows between nodes 1 and 2 indicate the interaction between me and the author. The single arrow from node 3 indicates that student’s response to the original post. Not too complex, but it makes the point.

Network graph of these interactions

And this is what a textual analysis of this set of interactions look like. I created a corpus of the original blog post and its responses, looking at both word frequencies (depicted as a word cloud) and networks of words (depicts as links between words). This word was done in Voyant-Tools. You can play with an interactive version here.

Word cloud generated from iteractions

Collocation links generated from student interactions

Conclusions (for now)

Obviously, none of this is very Earth shattering for now. People interact, their interactions can be graphed, and their interactions demonstrate sets of keywords.

For now, this is nothing more than a proof of concept.

Next steps: to do this type of analysis across an entire course. Stay tuned.

Classroom Discussions in Science, Part 4

This is the third of a four part series. Here is part 1. Here is part 2. Here is part 3.

I have been working to delineate and describe a typology of four types of conversations that could be powerful and effective in secondary science classrooms. They are:

1. Conversations that gather, focus student experiences or insights
2. Conversations that make real the processes of science and reflect science as a human (as opposed to received) activity.
3. Conversations that deepen and expand observations made during classroom demonstrations or laboratory activities.
4. Conversations that make visible the processing of learning itself.

This post is the fourth part of a series discussing each of these types of conversations.

Part 4: Conversations that make visible the processing of learning itself.

In many traditional science classrooms, the acquisition of science knowledge is assumed to be an additive function. First, I learn concept 1, then concept 2, then concept 3, on down to concept n, and at the end I know that topic.

And it’s certainly true that learning is cumulative, but there is little evidence from our daily lives to indicate that it is additive. Instead, learning happens iteratively, with multiple exposures in multiple types of representation until understanding is acquired.

We also know that the addition (all puns intended) of metacognition can support the acquisition and retention of knowledge as well as the development of understanding. And classroom discussions are ideal places to make present and deepen an understanding and familiarity with the process of learning itself.

These discussions can be guided by at first broad and then more narrow questions, like: “How do we know that?” “What evidence makes us feel comfortable that x is the case?” “Is that observation true for everyone or just some people (of a specific age or gender or cultural background or educational level)?”

If the teacher is really willing to have these kinds of discussions, not once, but throughout the school year, s/he can powerfully train their students to think about learning as a process, not as something given or transparent. And when that happens, students can start to think for themselves in very meaningful ways, especially in science classrooms.

This post brings to a close this series on discussions in the science classroom. I would love for you to join to the discussion by leaving a comment or reaching out on social media.

Classroom Discussions in Science, Part 3

This is the third of a four part series. Here is part 1. Here is part 2.

I have been working to delineate and describe a typology of four types of conversations that could be powerful and effective in secondary science classrooms. They are:

1. Conversations that gather, focus student experiences or insights
2. Conversations that make real the processes of science and reflect science as a human (as opposed to received) activity.
3. Conversations that deepen and expand observations made during classroom demonstrations or laboratory activities.
4. Conversations that make visible the processing of learning itself.

This post is the third part of a series discussing each of these types of conversations.

Part 3: Conversations that deepen and expand observations made during classroom demonstrations or laboratory activities.

Traditionally, science education has privileged the laboratory as the center of learning. And, since inquiry is central to the scientific enterprise, this makes sense. However, in a K-12 school setting, the pressures of time and resources narrow the potentially wide experience in the laboratory to something far more scripted and standardized. The broader implications of this for science education are a topic for another post on another day. Today’s goal is to discuss how classroom discussion can open up and focus these laboratory based investigations.

So, let’s imagine a laboratory experience in which students look at the effect of temperature and substrate on the action of enzymes. This could take place in a biology, chemistry, or even physics classrooms. In this thought experiment, students are looking at the time it takes for enzymes to work on their substrates under different temperature conditions. It doesn’t take much to imagine the data table which would be central to this activity.

Data table from our thought experiment

In a typical classroom, the students would collect the data, and then work (together or separately) to answer questions in their lab packets. Then, the labs are turned in, graded, and life moves on to the next topic.

But there is an opportunity between the time the data are collected and the questions are answered to have a class discussion about these data. This discussion could start out simply and concretely: at which temperature did the enzyme work the fastest or the slowest? Were there any enzymes that worked better being colder than warmer? What else surprised you.

The discussion can then follow the students’ interests. “But what about …?” “Wait, I didn’t get those results. Why not?” or even “Could enzymes work on the Moon or in space?”

These discussions are by definition communal, which can add a dimension and sense of inquiry that the individual or even small group work cannot or does not (usually). Also, as stated in the earlier parts of this series, these discussions remind the students (and the teacher) that science is a human activity.

And speaking of discussion, be sure to leave a comment or otherwise interact with these posts.

Classroom Discussions in Science, Part 2

This is the second of four part series. Here is part 1.

I have been working to delineate and describe a typology of four types of conversations that could be powerful and effective in secondary science classrooms. They are:

1. Conversations that gather, focus student experiences or insights
2. Conversations that make real the processes of science and reflect science as a human (as opposed to received) activity.
3. Conversations that deepen and expand observations made during classroom demonstrations or laboratory activities.
4. Conversations that make visible the processing of learning itself.

This post is the second part of a series discussing each of these types of conversations.

Part 2: Conversations that make real the processes of science and reflect science as a human (as opposed to received) activity.

One of the limits to true inquiry in the science classroom is the perception by the students that science is comprised of a set of received ideas and not the product of human endeavor.

For example, while we work with elementary and secondary students to make inferences and predictions from observations, they do not naturally make the connection that people who lived before they did made observations about things like gravity or the rotation of the Earth around the sun, or the spread of diseases or the erosion of rocks or the properties of various substances. They do not intuitively understand that the knowledge that they take for granted was developed over time by people just like them (excluding patriarchal and dominant societal structures, of course).

Therefore, another type of conversations in a science classroom is that which directly addresses the process of inquiry in the gathering of information.

Experiential learning (in a variety of strategies — inquiry-based learning, project-based learning, and problem-based learning) all contribute to an environment supportive of these discussions.

Here’s an example. A classic classroom scientific investigation involves three containers of water at different temperatures. One is hot (like bath water), one is cold (ice water), and the third is at room temperature. Students are instructed to place one hand in the hot water and the other in the ice water and to leave them them for about a minute (the teacher keeps time). Then, they are asked to move both hands into the room temperature water at the same time and to notice what they are experiencing. When they do, the hand that had been hot feels cold, and the hand that has been cold feels hot. This is as fun as science gets (without fire or explosions anyway).

Once each student has had this experience, the teacher can facilitate a class discussion around what they felt and what it might mean in terms of how our nervous system works. When handled well, the students will come to realize that their nervous system is only reporting on the relative differences in sensation instead of reporting an actual value so to speak.

This can lead to all kinds of other area of inquiry and their related conversations. And through them all, the students can come to understand that this is how knowledge is built. By regular people, just like them.

The next two parts of this series will deal with the other types of classroom discussions. Speaking of discussions, be sure to participate by leaving a comment. Here are links to all four parts:

 

Part 1

 

Part 2

 

Part 3

 

Part 4

#EL30 MOOC, Take 2

Back in October, I began participating a the E-Learning 3.0 MOOC organized by Stephen Downes. While I got a lot out of participating in the MOOC, I never did all the work I wanted to do.

But this is why I am so interested in this work:

“Connectivism is based on the idea that knowledge is essentially the set of connections in a network, and that learning therefore is the process of creating and shaping those networks.” [This last piece has shaped a good deal of my own research over the past few years.]

Going back to the beginning and Stephen’s brief introduction to the MOOC, I am struck by this:

“The learning in a connectivist course is emergent; it is not defined and transferred or transmitted; rather it is created through the process of individual experiences and interactions. It is something new, different for each person in the course, and in a broader, more social sense, an outcome of the course as a whole.”

So, I have decided to start again and get back into the course. My plan is to do one topic per week. Anyone want to join me?

Classroom Discussions in Science, Part 1

I was asked last week, preparation for professional development work I had been asked to do with some high school science teachers, to describe ways that classroom discussion could be used in secondary science classrooms.

To my surprise, I was able to delineate and describe a typology of four types of conversations that could be powerful and effective in secondary science classrooms. They are:

1. Conversations that gather, focus student experiences or insights
2. Conversations that make real the processes of science and reflect science as a human (as opposed to received) activity.
3. Conversations that deepen and expand observations made during classroom demonstrations or laboratory activities.
4. Conversations that make visible the processing of learning itself.

This post is the first part of a series discussing each of these types of conversations.

Part 1: Conversations that gather and focus student experiences or insights

For the sake of discussion (all puns intended), we are going to imagine a life science classroom that is working to understand homeostasis.

As a warm up or “Do Now” activity, the students are asked to imagine that they are members of a music group or band. Or that they were members of a sports team. While they are in the middle of performing or playing, they need to to communicate with their band mates or teammates while they are giving a performance or playing a game.

This thought experiment attempts to addressing the guiding questions: How could they do this? What challenges would they face?

Students would be given time to think about this and then make some notes about their thoughts. Once they have done so, they can be invited to share their thoughts and notes with a classmate or two. Lastly, the teacher can then facilitate a discussion that begins with the small group sharing.

The teacher’s work should be shaped by whatever he/she considers essential or enduring understandings (in the terms of McTighe and Wiggins) about homoestasis. If it were me, I would be listening for: a need to respond to changes in the internal and/or external environment; transmitting this change to the cells or organs that need to respond; and coordination of this response. (Note: this is what I consider to be essential. Another teacher would definitely state homeostasis in different terms).

So, this classroom discussion would serve multiple purposes:

1. It would challenge and validate the thinking performed by the students individually and with their peers.
2. It would raise additional questions or concerns, in order to deepen the students’ engagement with and understanding of the essential understandings around homeostasis.
3. it would serve as a transition to the next activity.

The set of strategies expressed in this scenario are shaped by the work of W. V.O. Quine’s philosophy of mathematics as synthesized through the work of Robert P. Moses in Radical Equations.

Moses talks about the importance to student understanding of a transition from what he calls “people talk” to “regimented language,” language that is abstract and consistent with a particular area of study. This type of classroom discussion sets the table in the students being able to make what looks like a natural or organic transition from the “people talk” of the students grappling with the practical problem of communication among group members to the “abstract talk” of homeostasis.

The set of strategies expressed in this scenario are shaped by the work of W. V.O. Quine’s philosophy of mathematics as synthesized through the work of Robert P. Moses in Radical Equations. Moses talks about the importance to student understanding of a transition from what he calls “people talk” to “regimented language,” language that is abstract and consistent with a particular area of study.

Types of language for learning from Moses, Radical Equations

This type of classroom discussion sets the table in the students being able to make what looks like a natural or organic transition from the “people talk” of the students grappling with the practical problem of communication among group members to the “abstract talk” of homeostasis.

The next three parts of this series will deal with the other types of classroom discussions. Speaking of discussions, be sure to participate by leaving a comment. Here are links to all four parts:

Part 1

Part 2

Part 3

Part 4

Microgrids and Education

I have been spending a lot of time recently thinking about and researching the role that networks and networking plays in learning environments.

Mostly, I have been working at ways to promote, support, and foster what Paul Baran called distributed networks in schools and classrooms.

This article talks about energy/power microgrids, which due to their decentralized nature, can survive the breakdowns associated with larger, more centralized power grids. It left me wondering about analogues in educational settings.

Your thoughts?

E-Learning 3.0 So Far

For the past few weeks, I have been participating in Stephen Downes’ cMOOC, E Learning 3.0.

So far, the course has addressed some really interesting and, I think, important topics, such as Data, Cloud, Graphs, and Identity. And Stephen has been addressing them in ways that are surprising and provocative. For instance, he discussed Identity as a philosophical issue. But it was embedded (all puns intended) in issues of trust and security from a technological perspective (encryption and block chain, for example). I had never made the connection between who I consider myself to be (my identity) and the expression of who I consider myself to be writ via the internet and the ramifications of security and trust. I hate to say it, but my mind was blown.

As an aside, I was engaged with these concepts and materials whilst stuck in my car in a snow storm for hours last week. I am sure my own sense of physical isolation really made these concepts come alive in so many ways. Serendipity indeed.

All of these ideas have been vitally important to me. As a teacher and education researcher, I am very interested in learning environments that can result in the emergence and evolution of personal learning networks. I know that self determination, authentic learning experiences, and openness are central to these networks. And this course is my way of deepening my understanding of all of this.

Despite being fully engaged by the course, I cannot shake the feeling that I have been doing it wrong. Because I have not watched every video, done every task, responded to every blog post. You get the picture.

I believe that this discomfort is somehow central to what I need to learn here. So, I will keep hanging in there.

 

 

 

Identity – Mine (at least partially)

This week in the #EL30 course with Stephen Downes, we are looking at identity.

Stephen really surprised me by connecting graphs (which I thought I understood) with trust (as in trusted networks and connections — and BitCoin) and identity.

This week’s task (really, it was the task was last week or the week before, but I am working on it), was to create a identity graph for ourselves.

Here is mine. Thanks to Mattias for his tool Thought Condensr.