Thursday, November 20, 2014

Attack of the untext - my own stumbling blocks

It's been a while since Rhizo14 ended, but the community is going strong! Facebook may not be as active (or maybe facebook is  hiding most Rhizo posts from my timeline...that could be it...anyway), but we are still chugging along with the collaborative *graphy. I can't call it an ethnography, or autoethnography because variables have changed.  Some of us decided to get together and write an article for Hybrid Pedagogy on why the Collaborative *graphy article is taking so long (a meta-article if you will) but we got stuck there too (or it seems as though we are stuck).  I think others have written about their own personal views on this on their own blogs, so I've been working out what my own stumbling blocks are with this project. I think I have a way to explain things now!

So, when working collaboratively in previous collaborative work situations your final product feel unified.  The main analogy that I can give give is the main root of one plant which looks like this:

In this example (going with the rhizome metaphor) you have one main path, and all the side paths are footnotes, citations and references, and end-note commentary.  The coloring is all the same because regardless of where you have one author or many authors the final product sounds like a unified voice.  Many ideas have come into, and go out of, this main line (see expansion roots in the model), but at the end of the day those side roots don't overtake the main idea or argument.

The original project started as a collaborative autoethnography (CollabAE).  This eventually became an issue because some people stepped back from the  project and thus is was no longer an autoethnography for the entire MOOC, but rather an multi-author ethnography (MAE) of the MOOC. We could use other people's anonimized data, assuming that we had their permission. At that point it wasn't introspective (auto-ethnography) but rather analytic - but this seemed to lack the rhizomatic aspect (to some extent anyway) that made the CAE unique in this aspect, and there were issues of silencing voices (or inadvertantly silencing voices since some people didn't want to be authors, or weren't comfortable with their views being part of this analysis). Things got busy with school, work, and others pursuits that I lost track of the CAE.

The CAE, at least the way we collected data, looks like the image above.  Each color represents a different author, and each other has (probably) a main point and certain supporting literature, tangents, side-points and so on that they made in their original write up. Some authors connect to other author's writings, and this is visualized above as roots crossing through other root's paths.  As chaotic as this may look, it does make sense. I think the closest analogy for this would be George Veletsiano's Student Experiences of MOOCs eBook. To some extend (being a bunch of experimental academics ;-) ) we may have over-thought this CAE.  In hindsight, I would say that this should be a multi-step process.  Perhaps the first step in the process, with a deliverable, would be an eBook, similar to Veletsiano's style, of our Rhizo experiences.  Here people can write anonymously or  eponymously.  Submitted chapters could go through peer review, but not the traditional academic peer review - the peer review that aims to disambiguate and seeks to grow those side-roots a bit in case eventual readers want to do down the paths.  There could be a foreword (Dave Cormier perhaps?) but the reader would be left to read, process, and make sense of each individual story.  As such this could be not a collaborative AE  but a cooperative AE (CoopAE), people working concurrently, but not necessarily together to get this done.  One big, overall, document, but each chapter can stand on its own.

So since the CollabAE wasn't going far, a couple of people thought we could sit down and write an article about what's up with this process.  Why are things taking so long?   The visual for this untext looks something like this (according to me anyway).

Whereas the CollabAE has separate, but distinct, stories where others commented on, but didn't necessarily write-over the text, in our meta-analysis I am seeing both original (concurrent) threads emerging (two or more people writing at the same time but not about the same message). This is represented by different color main-roots.  Then I am also seeing people expanding on those main-roots (different color sub-roots) by either adding onto the document, or having side conversations.  I have to admit that this is fascinating as a brainstorming piece, and it could be considered by some as a performance piece or something alternative like #remixthediss.

That said, however, the problem is that we don't have an audience.  A document as chaotic as this one is helpful to us as authors to help us better understand our own positions on things, and to better help us understand or analyze our own lived experiences in the MOOC.  However, I am not convinced that this is geared toward a reading audience. It's not necessarily something that they expect, and I am not sure how a reader will process this chaos.  For me, at the end of the day, I go back to my goal.  What is the goal of the *graphy project (decided to change it's name since CollabAE and CoopAE seem to not describe it)?  What is the goal of the untext about the *graphy project? Is the goal something for the internal constituents? Something for the public?  If it's for both, what's the overlap space where the final published product would be useful (and comprehensible) to both?  Good questions.  I've got my own answers, but as a group...I don't know :)

As a side note, this seems like an interesting application of co-learning (see connected courses for more details)




Monday, November 17, 2014

DALMOOC episode 6: Armchair Analyst

Week 6 CCK11 blog connections
I was trying for a smarter title for this episode of #dalmooc thoughts, but I guess I have to go with Armchair Analyst since I ended up not spending a ton of time with either Gephi or Tableau last week. So, the reflection for week 4 is mostly on theoretical grounds; things I've been thinking about (with regard to learning analytics) and "a ha" moments from the videos posted.

I think week 3 and week 4 blend together for me.  For example, in looking at analytics the advice, or recommendation, given is that an exploration of a chunk of data should be question driven rather than data-driven.  Just because you have the data it doesn't necessarily mean that you'll get something out of it.  I agree with this in principle, and many times I think that this is true.  For instance, looking back at one of our previous weeks, we saw the analytics cycle.  We see that questions we want to ask (and hopefully answer) inform what sort of data we collect and potentially how we go about collecting it.  Just having data doesn't mean that you have the data that you need in order to answer specific questions.

On the other hand, I do think that there are perfectly good use cases where you might be given a data-dump and not have any questions.  Granted, this makes analysis a bit hard, like it did for me the last couple of weeks.  This data (anonymized CCK11 data, and sample evaluation data for Tableau) didn't really mean much to me, so it was hard to come up with questions to ask.  On another level I've been disconnected from the data, so it's not personally meaningful as a learner (CCK11 data was anonymized), and since I didn't have a hand in organizing, offering, and running CCK11 it's not as useful for me as a designer.  However as a researchers, I could use this data dump to get some initial hypotheses going.  Why do things look the way they look?  What sort of additional, or different, data do I need to go out and test my hypothesis?  How might I analyze this new data?  As such, a data-driven approach might not be useful for answering specific questions, however it might be a spark to catalyze subsequent inquiry into something we think might be happening; Thus helping us formulate questions and go out and collect what we need to collect to do our work.

So, for example, I have just started my EdD program at Athabasca University.  I have a lot of ideas running through my head at the moment as to what I can research for a dissertation in 3 years†. As I keep reading, I keep changing and modifying my thoughts as to what do to.  I may be able to employ learning analytics as a tool in a case study research approach.  For instance, I teach the same course each spring and fall semester, an online graduate instructional design course on the design and instruction of online courses (very meta, I know). The current method of teaching is quite scaffolded, and as Dragan was describing last week (or this week?) I tend to be the central node in the first few weeks, but my aim is to just blend in as another node as the semester progresses. This process is facilitated through the use of the Conrad & Donaldson Phases of Engagement Model (PDF).

So, one semester I can use this model to teach the course and another semester I might create an Open Online Course based on the principles of connectivism and run the course like that. I'd have to make some changes to the content to make sure that most course content be Open Access content, that way I would be eliminating some variables, but let's assume I've done this and I'm just testing Connectivism vs "regular" graduate teaching (whatever that is).  I can then use SNA, as one of my tools, to see what's happening in these two course iterations. I can see how people are clustering together, forming communities (or not), how they are engaging with one another and so on. This analysis could be an element of the study of efficacy of connectivism as employed in a graduate course‡.

On the other side of things, if I were to just stick with my traditional online course, I could still use SNA to improve my course.  One of the things that I notice is that some groups tend to form and stay together early on in the semester.  These seem to be informal groups (person X commenting on person Y's posts throughout the semester more than they do for person Z). Since the semester is 13 weeks long, a JIT dashboard of course connections would be useful to both encourage people to find study groups, but also to engage more with people that they don't normally engage in.  People who usually post late in the forums (at least in my experience) don't often get many responses to their posts, which is a real pity since they often bring some interesting thoughts to the discussion.

A good example of this is the image above, the CCK11 blogs from Week 6.  I see a number of disconnected blogs.  Were these blogs never read (as measured by the click-through rate on the gRSShopper Daily)? Were they never commented on by anyone? Some of the blogs may not speak to anyone in the course, but in a course of 1131 participants (citation), assuming a 80% drop off by week 6, that's still around 200 people active in the MOOC, why is not one connecting with these posts, and can we do anything to spur participation?  Maybe an adopt a blog post campaign?  This is also where the quantitative aspects of SNA mesh with the qualitative aspects of research. Here we could also do an analysis of what gets picked up (those connected nodes) to what doesn't get picked up, and do an analysis of the text. This might help us see patterns that we can't see with SNA alone.

That's it for week 4.  And now I am all caught up.  Welcome to week 5!  Your thoughts on Week 4?


SIDENOTES:
† The more I think about this, the more I am learning toward a pragmatic dissertation rather than a "blow your mind" dissertation. I see it more as an exercise that will add some knowledge in this world, but given that doctoral dissertations are rarely cited, I am less interested in going all out, and more interested in demonstrating pragmatics of research through a topic of interest. Thoughts on this? I definitely don't want to stick around in dissertation purgatory.
‡ I'm pretty sure that someone (or quite a few) have written about this, especially with regard to CCK08, but let's just roll with this example.

Thursday, November 13, 2014

DALMOOC episode5: Fun with Gephi

CCK11 Tweet visualization
Alright, after a few days of being sidelined with a seasonal cold, I'm back on #dalmooc.  Still catching up, but I have a feeling I am getting closer to being at the same pace as the rest of the MOOC ;-)  In any case, this is a reflection on week 3 where we started messing around with social network analysis (SNA).  This is cool because it's something that I had started doing on another MOOC on coursera, with Gephi, so it was an opportunity to get back on and messing with the tool.

So, what is SNA?  SNA is the use of network theory to analyze social networks.  Each person in this network is represented by a node (or edge), and nodes can be connected to other nodes with a vertex (or many vertices). These connections can indicate a variety of things (depending on what you are examing), however for my usage in educational contexts I am thinking of vertices as indicators of message flow, who sends messages to whom in a network, and also who refers to whom in a network. I think this latter one is interesting from an academic citation point of view as well.

As was pointed out in week 3, SNA can help discover patterns of interaction in online learning environments. I think that it can also help up discover patterns in physical environments, however this is harder because we don't have big brother watching the physical environments as much as we can collect data about patterns of participation in virtual environments. It's much more labor intensive to keep accurate track in a physical environment.

An interesting application of SNA is its use in understanding learning design (Lockyer et al - in video). We can use SNA to analyze patterns of interaction in courses that we design an implement, thus we can (to some extent) see how our designs are affecting the learner's patterns of participation.  While this is feasible, I think that it's also hard to keep the variables pinned down so that there are no confounding errors. If you've designed an online course (that is NOT self-paced) you can see the same course design taught different ways if you put different faculty in the driver's seat.  As such I think that in studies using SNA to analyze course design (and/or teaching methods) it's important to account for all variables.

Other interesting things from Week 3:

An Instructor-centered network is one where the instructor is central node in network. These are recognized in literature as only leading to lower levels of knowledge construction (see Bloom's taxonomy). Related to this type of network is having one learner have a dominant role in a course, thus the instructor is replaced (or shares the spotlight) with a dominant learner.  This is also not desirable from a pedagogical point of view. One can start with an instructor-centered environment and facilitate the change to a P2P interaction. Students will need scaffolding in order to reach that P2P network.

Sense of community is predictor of success in educational endeavors. A common way of collecting this type of data is questionnaires, and I think that in education this can be both in-class as part of a mid-term temperature check in the course, but also in the final course evaluation.  I am wondering, however, how accurate this self-reporting is. Is this just an affective measure? Or can learners feel like they are lacking a sense of community but in reality have it but not get as much as they feel they need?

Network brokers are nodes that connect two or more communities in a network and have a high degree of centrality.  These network brokers can see information across many different communities, and as such can have access to many different ideas flow through them. Network brokers are associated with high levels of achievement and creativity. So, in an educational setting it's good to be a network broker.

Cross-Class networks are latent ties by attending the same events, so even though I am not connected with many people in #dalmooc (at least on twitter I don't retweet or write to many others in the MOOC - maybe I should...) I am connected to other people through the course hashtag and by attending the same event. In a traditional learning setting this could be likened to participating in a learner community such as UMassID.com (our instructional design community) or the Athabasca University Landing network.

CCK11 Blogs, week 6
Next up, the Gephi portion of this post.  I've been messing around with Gephi data from CCK11. I was quite excited to get my hands on the CCK11 data to mess around with in Gephi until I remembered that I didn't tweet all that much in CCK11...D'oh! I was curious to see where I was  in the network of connections.  Even if I were active I don't think I'd be able to see myself there because the data appears to be anonymized (and rightfully so).

I did run some analysis of the blog connections in CCK11 using Gephi again (part of the data dump available in #dalmooc) and here was a place where I expected to see myself and see who I was connecting to, however, again, the data was anonymized. My question entering into this analysis was more about analyzing my own patterns of interaction.  I was new to MOOCs back in 2011 and CCK11 was the MOOC where I really started learning about connecting with others for academic purposes. Thus, I wanted to see what my initial and developing connected literacies pointed to. Alas, this was not to be :-)


As Dragan mentioned in one of the videos of this week, analytics should be question-driven, not data-driven. Just because you have data, it doesn't mean that you should use it, or that you will find anything meaningful in it.  This was the case with me and this data. There were some interesting visualizations, but I wanted to have a look at the people involved, who connected to whom, and look more at the qualitative connections: what posts, what ideas, what types of know-how got distributed throughout the network and by whom. It's a little hard to do this with anonymized data, so you really need to abstract and think at higher levels when working with this.  If we had data from other MOOCs, this type of anonymized data could be useful to compare patterns of participation of one MOOC to another.

Thus concludes my week 3 experiences.  What were your thoughts?

Wednesday, November 12, 2014

Questions about Co-Learning

What do you get when you mix connected courses, thinking about academia, and cold medicine?  The answer is a blog post (which I hope makes sense) :-)

As I was jotting down my initial thoughts on co-learning in the previous post I completely forgot to address some of the initial thinking questions for this module.  Here are some initial thoughts on co-learning and how I would address these questions:

What is co-learning and why employ it?
For me co-learning is when two or more people are working together to solve a problem and learn something new.  As I wrote in my previous post, the individuals in this community do not all need to start from the same point. There can, and will, be learners that are more advanced in certain areas as compared to others.  This is perfectly fine, and it's realistic to expect this.  This can be a community of practice, it can be a broad network of learning, or a loosely connected network of learning that centers around a hashtag.  The reason to co-learn is, for me, three-fold.  First you have a variety of learners in the classroom their lived experiences, and previous knowledge, can be beneficial in this learning experience. Second, by having learners co-learn (and in my mind co-teach) they are learning not just the material but they are deconstructing it so that they can explain it to others. This act of deconstruction allows a closer analysis of the subject matter and, hopefully, a more critical view of it.  Finally, this is something that came to mind when engaging in #dalmooc this week - when looking at Social Network graphs of courses, in some cases we see the instructor as a central node, which is a quite privileged position. However this isn't good for learning, so having a course where there is a high degree of connections among many nodes, and the instructor becomes just another node in the network, this spells out good things for learning (or so research says - don't ask me to cite anything, I wasn't taking detailed notes when I was viewing Dragan's presentations)


How can teachers empower students as co-learners?
This, for me, has been the most difficult thing. I teach a course that is an upper level graduate course, which means that students come to my course late in their studies and thus their habits are formed.  Most expect weekly asynchronous discussions with the familiar 1-post, 2-reply scheme.  Many students seem to go beyond this (anecdotal evidence from teaching this course over the last 3 years), however some do not, and there are many reasons for that.  Having co-learning occur means that learners need to be more present, and to some extent their schedule isn't fully their own.  They need to see what their peers are doing so that they can bounce off those messages, riff off them, respond to them, and, when necessary, pertrube them (in educational ways).  I think teachers can empower students to be co-learners by slowly stepping back and scaffolding students to take on that role.  How quickly or slowly you step back depends on the group of learners that are in the classroom.  I don't think that there is a magic formula here, however we are all beholden to the academic calendar, so I would say that it happens somewhere between week 1 and 6 (for a 13-week semester). Even as instructors step-back, it's important to maintain a noticeable teaching presence, and a social presence.  Nothing annoys learners more (I find) than having an instructor that's not there.


How does this pedagogy differ from traditional methods of teaching and learning?  How does the instructor support a co-learning environment? What obstacles might educators encounter in this paradigm shift?  What obstacles might students encounter in this paradigm shift?
I guess here it depends on how one defined "traditional". If traditional means lecture then this approach of co-learning is like night and day compared to lectures.  However, if we encompass Vygotsky's social constructivism, or concepts like Wegner's Communities of Practice as "traditional" then I don't think that co-learning varies a ton from these.  I think that co-learning is a natural extension to constructivism, connectivism, and communities of practice.

I think the key thing, as I wrote above, for support is that sense of social and teacher presence going back to the community of inquiry model. The idea here is that an instructor is just a node  in this learning network.  Sure,the instructor by virtue of being older and having had more learning experiences (and time to read and digest more) is a more knowledgeable other in this aspect. However, his knowledge and voice isn't what drowns out the voices of the learners.  The instructor is there to help people navigate the network, wayfind, provide appropriate scaffolds, advise, and when necessary promote certain content. I don't think we can get away from content and certain "core" knowledge, so the instructor as an MKO in this area has a responsibility of sharing what they know with others, without being overbearing. 

The trick here is having that sense of when to share something and when to let learners struggle a bit. Again research points to the fact that when learners struggle a bit they tend to learn better. I think this is also an area where the instructor might potentially face some obstacles by the learners themselves or their own superiors.  If the learners want content (or *gasp* lectures), then there might be a push from the learners to ask the instructor for nicely packaged answers to their questions. I have seen this in exit evaluations at my own department.  Since we are a department of applied linguistics we don't deal with classroom management (our students are, for the most part, teaching professionals or they go into teaching). We provide the applied linguistics theory, and a space to think about it, criticize it, deconstruct it, and utilize it. However our faculty don't provide cookie-cutter solutions to language learning problems because the answer (as usual) is "it depends".  However learners, in their previous learning experiences, are used to getting nicely packaged data bits, such as "World War I started on ____" or "The first president of the United States was _____" and so on.

This obstacle is something that also affects learners because they need to discover ways in which to not only take the knowledge that they gain in their courses now, but to be able to continuously go out, read the updated literature in the field, deconstruct it, analyze it, and put it back together in meaningful ways to solve their own problems.  The classroom environment provides a nice laboratory where co-learning can be practiced, however once students graduate they need to discover networks in which they can continue to actively co-learn.  This is a literacy that we, as educators, need to help our learners cultivate.

I think that's it for co-learning for now.  Thoughts?






Monday, November 10, 2014

Active Co-Learning

I took a small hiatus from Connected Courses in the last module because everything sort of piled on at the same time and  I had little space to breathe.  Yes, I've been dalmoocing, so I guess everything is a choice ;-).  I guess that was my jump-out week of connected courses, and now I am dipping in again. I love the language of cMOOCs ;-)  The truth is that I've felt a little fatigued with #ccourses.  I am not sure if it's the length, or the time I've been engaged with it (7 weeks if you consider the pre-course and that's before we got to Diversity, Equity, and Access), so I guess I needed a little mental break.  I don't think this is an issue unique to MOOCs because I've been feeling a mild case of senioritis in my first EdD course. Luckily I've done all of my deliverables, submitted them, and have gotten feedback, so now I am participating with my peers and engaging in the participation aspect of the course.

Anyway, these next two weeks are about Co-Learning in #ccourses and worlds have collided!  Connected courses has collided with my EdD course to produce a thinking storm (in my head). I am not going to talk a lot about the resources shared this week (oddly enough I have shared some of these with my own class in the past!), but I wanted to talk a bit about my little connected moment.

So, as we are discussing LMS mining and learning analytics in EDDE 801 one of my classmates mentions that he sees learning as something social. I don't know if he is also on #ccourses or if this is a happy coincidence, but this got me thinking.  I think that learning can be social, and many types of successful learning can be social, but learning is not exclusively social.  For instance, I can sit down with a book, or some MOOC videos, and read or view them.  If I am paying attention and the material is at my level then chances are that I will learn something.  That said, I don't think that all learning works this way.  I do think that in many cases learning is social.  The construct that comes to mind is Vygotsky's More Knowledgeable Other. 

If we are all in a group, let's say in #ccourses, and we are all tackling the topic of this module (co-learning), I would say that we don't all come to the learning environment with the same background, know-how, and knowledge.  We may have some similar experience and background, but the specifics matter.  Thus, as we are learning together I may be able to teach someone a small nugget of knowledge (or know-how) or vice versa. The teaching aspect may not be reciprocal between any two given interlocutors, but it doesn't have to be.  This is when the community comes in. If we are all members of a community and we get each other's daily posts, tweets, delicious links (that relate to this course), then we are partly learning from other's contributions, even if they don't directly learn something from our contributions.  Thus, the act of co-learning is also an act of teaching, at least as defined by Wiley (in the TEDx video this week) when he defines Education as a relationship of sharing. A successful educator, according to Wiley, is someone who shares fully with their students.  In a co-learning environment we are all learners and we are all educators. 

 So, here is a question that popped up while I was pondering this: what is the difference between an "aha" moment when you are by yourself (reading a book, or watching a MOOC video) and "learning" in a social environment?


SIDENOTES:
  • Even though I sat out the module on Diversity, Equity, and Access, I think that the videos on Feminism, Technology and Race; and wikistorming, are interesting to watch and think about. If you haven't watched them, I encourage you to do so :)
  • This week Alec Couros asked "what endures" when thinking about technologies.  The answer was that technologies come and go, but it is the social connections that endure (thus, I would paraphrase this as reach out and talk to someone in your social network, don't just consume).  This is quite true.  Remind me one of these days to expand upon this and Elliniko Kafeneio ;-)