Multilitteratus Incognitus

Even though this semester is relatively calm, compared to last semester, I still find myself not writing as much as I think I would like.  I've set aside, temporarily, the book I was meant to have finished reviewing last October, on MOOCs, until the semester ends and I can focus on them a little more. One reason for the refocus of energies is EDDE 804. We are focusing on leadership in education, and I am finding myself spending a lot more time pondering the topic.  I was going to be "ruthlessly pragmatic" and just focus on the assessments, but the cohort members provide for some really interesting discussion and points to ponder.  Another thought that crossed my mind was this: am I over MOOCs?  There was a time when I used to check out coursera, edx, futurelearn, and the other not-so-usual suspects for new courses, however these days going to those sites seems more like a chore than anything else.  I've downloaded a whole bunch of videos from previous courses...

In my continuous quest to go to Pocket-Zero (may be a losing battle since I keep adding interesting stuff to read), I came across a post from a friend and colleague, Rebecca, who was discussing and brainstorming a bit about the ethics of research in twitter communities . As a quick synopsis, of the  hot button issue (at least from what I interpreted), was that in one instance (mature) researchers were researching a more general hashtag on twitter and this seemed to be OK, while in another instance a younger researcher (high school student) was researching a hashtag specific to breast cancer and social media, where a level of trust seemed to have been breached.  So, the question is: what is fair game in social media research? Specifically Rebecca asks What are the ethical obligations of anyone wishing to conduct research/analysis on a twitter community of care? Are the obligations different if the community is not care based? (e.g. #lrnchat) . My initial thinking is tha...

Hey!  We've made it! It's the final blog post about #dalmooc... well... the final blog post with regard to the paced course on Edx anyway :)  Since we're now in vacation territory, I've decided to combine Weeks 9 and 10 of DALMOOC into one week.   These last two weeks have been a little light on the DALMOOC side, at least for me.  Work, and other work-related pursuits, made my experimentation with LightSIDE a little light (no pun intended).  I did go through the videos for these two weeks and I did pick out some interesting things to keep in mind as I move through this field. First, the challenges with this sort of endeavor: First we have data preparation. This part is important since you can't just dump from a database into programs like LightSIDE. Data needs some massaging before we can do anything with it.  I think this was covered in a previous week, but I think it needs to be mentioned again since there is no magic involved, just hard work! T...

Hello to fellow #dalmooc participants, and those who are interested in my own explorations of #dalmooc and learning analytics in general.  It's been a crazy week at work with many things coming down all at the same time such as finishing advising, keeping an eye on student course registrations, and new student matriculations, making sure that our December graduates are ready to take the comprehensive exam...and many, many more things. This past week I really needed a clone of myself to keep up ;-)  As such, I am a week behind on dalmooc (so for those keeping score at home, these are my musings for Week 7). In week 7 we are tackling Text Mining, a combination of my two previous disciplines: computer science and linguistics (yay!). This module brought back some fond memories of corpus linguistics exploration that I had done a while while I was doing my MA in applied linguistics. This is something I want to get back to, at some point - perhaps when I am done with my doctorat...

I see a lot of WTF behavior from learners. This is bad... or is it? Oh hey!  It's week 6 in DALMOOC and I am actually "on time" this time!  Even if I weren't it's perfectly OK since there are cohorts starting all throughout the duration of the MOOC (or so I suspect), so whoever is reading this: Hello! This week the topic of DALMOOC is looking at behavior detectors (types of prediction models).  Behavior detection is a type of model (or types of models) that we can infer from the data collected in the system, or set of systems, that we discussed in previous weeks (like the LMS for example).  Some of these are behaviors like off-task behavior such as playing candy crush during class or doodling when you're supposed to be solving for x . Other behaviors are gaming the system, disengaged behaviors, careless errors, and WTF behaviors (without thinking fastidiously?  or...work time fun? you decide ;-) ). WTF behavior is working on the system but not the task ...

Whooooa! What is all this? Alright, we're in Week six of DALMOOC, but as usual I am posting a week behind.  In previous weeks I was having a top of fun playing with Gephi and Tableau. Even thought the source material wasn't that meaningful to me I was having fun exploring the potential of these tools for analytics. This week we got our hands on Rapidminer a free(mium) piece of software that provides an environment for machine learning, data mining and predictive analysis.  Sounds pretty cool, doesn't it?  I do have to say that the drag and drop aspect of the application does make it ridiculously easy quickly put together some blocks to analyze a chunk of data. The caveat is that you need to know what the heck you are doing (and obviously I didn't ;-) ).  I was having loads of issues navigating the application, and I somehow managed to not get some windows that I needed in order to input information to, and I couldn't find where to find the functions that I...

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...

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...

Continuing with my exploration of DALMOOC, we've reached the end of Week 2 (only a few days late ;-)  ).  I've been playing with Tableau, which I can describe as Pivot Tables on steroids.  I briefly explored the idea of getting some IPEDS data to mess around with, however that proved to be a bit more challenging than I had anticipated. So, I ended up using the sample data of course evaluations to figure out how to work Tableau.  The following are some interesting visualizations of the data that I had: The one thing I realized, as I was playing around with the data, is that it's really important to really know what your data means.  I thought I knew what the categories meant, because I thought that institutions of higher education used similar lingo.  The more I played with the data, the more I realized that some things weren't what I was expecting them to be.  Thus, in order to know what is being described and portrayed through the visualiza...

I've had this app on my iPad, by TechSmith, for the past few years, but I've never really used it.  The App is called ScreenChomp and it allows you to have a digital whiteboard that you can use to write and narrate.  I through that a plain text description of the learning analytics cycle (still catching up on week 2 of DALMOOC) would probably be confusing, and using PowerPoint and Adobe Presenter would be too static.  So, I applied the learning analytics cycle to a course I teach, and I decided to hand-write everything. Heck I attempted to draw as well, but my lack of artistic talent shows ;-) Direct link to the screenchomp (if the embed doesn't work):  http://www.screenchomp.com/t/qE1lplho DALMOOC Week 2, Description of the Data Analytics Cycle from Apostolos K. on Vimeo . How does this cycle apply to your courses?

My Twitter Analytics, 10/2014 Another day, another #dalmooc post :)  Don't worry, I won't spam my blog with DALMOOC posts (even if you want me to), I don't have that much time.  I think over the next few days I'll be posting more than usual in order to catch up a bit.   This post reflects a bit of the week 1 (last week's) course content and prodding questions. I am still exploring ProSolo, so no news there (except that I was surprised that my twitter feed comes into ProSolo.  I hope others don't mind seeing non-DALMOOC posts on my ProSolo profile. Week 1 seemed to be all about on-boarding, of tools and definitions.  So what is learning analytics?  According to the SOLAR definition, "Learning Analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs." It's a nice, succint, definition - which I had...

Value Map Badge It's Saturday, so it must be #OpenBadgesMOOC time :)  The thing that I just noticed about these badges on the #OpenBadgesMOOC is that if you look closely enough they look stitched.  Maybe there is an easter egg hidden somewhere, whereby if you earn all #OpenBadgeMOOC badges they send you a sash with all of them stitched on - LOL :) In any case, it's the end of Week 4 on the MOOC (2 more weeks to go) and this week we are talking about validation.  It's interesting.  One of the things that comes to mind as I progress through these is that the initial "levels" were a little easier to articulate, at least for my #ESLMOOC project, but as the weeks progress it's getting harder since I don't have all of the information. This also draws a parallel to Kirkpatrick's Level's of Evaluation where Levels 1 and 2 are easier to measure, at least in the short term, but Levels 3 and 4 (and if you look at Philip's 5th level ) it gets harder...

In my most recent blog post I wrote about the lecture and how it's not the most important part of the xMOOC. I have to say, that as far as I am concerned, I failed the Think Again course.  My 2 quiz score average was 65%.  OK, this may not be failing, after all there were 2 more quizzes left, where presumably I could have done better and raised my average to something I liked, and I did have another opportunity to raise my second quiz score for Quiz 2; so I guess, if I were my own professor I would say to myself "not all is lost," but I decided to drop the course anyway.  The next couple of months will be really crazy for me, between work (day job), teaching a course online (my adjunct job), and preparing a couple of papers for publication; so I know I don't have time to spend on this course :-) The other thing I realized was that I was a mildly hungry man at a free all you can eat buffet.  I came to coursera, and xMOOCs in general, with personal...

It's week 4 of #cfhe12 so it must be time for Big Data and Analytics as the topic of discussion. It's interesting coming back to this topic of discussion because it was the topic of the first MOOC I took part in, LAK11, and it's a topic I've been thinking (or at least keeping on the back burner) since I was in business school. On of th things to keep in mind when talking about Analytics is that there are quite a few definitions out there , so, when talking about learning Analytics it is important to define what we aim to get out of our discussion about Analytics and how we wish to employ the potential insight that we get from this data. There are two topics that have recently come up in my neck of the woods: knowing what sort of data one can get from the various campus systems, and knowing what it means (and accurately representing what the data tells us). First, it's important to know what sort of data you can get out of your systems, like the LMS. As I've...