Learning Analytics MOOC – Week 3 – SNA

My goal with these blog posts is to summarize and reflect a little bit about things/content I’ve learned – my blog seems to be a good way to keep this for later on after the MOOC.

Week 3 is about an introduction to Social Network Analysis (SNA) and insights how social processes unfold. „SNA aims to understand the determinants, structure, and consequences of relationships between actors“ (Source http://www.lifescied.org/content/13/2/167.full.pdf+html) SNA is multidisciplinary (not only sociology and statistics) and main analysis methods are density, centrality and modularity types of analysis. We’ll do some analysis with test data and again visualization, this time with Gephi. The interesting thing will be what’s the use of SNA for learning (I’m not there yet).

Networks consist of actors (=nodes) and relationships/connections which can represent friendship, advice, hindrance, communication. In a spreadsheet nodes and relationships would be represented in rows (and weight via adding as many rows). Data can be collected by self-reports, interviews, collection from social networks (who is following whom on Twitter etc.) and special tools which collect data from LMS (activity in online discussion boards,..) and later on be analyzed in tools like Gephi. As these networks are seldom static, you have to decide on a time frame when collecting data. Also important: Anonymizing data, obtaining consent (which may lead to incomplete networks), ethics

 

Network Measures

In my understanding this very informative YouTube video from Dragan Gasevic http://www.youtube.com/watch?v=Gq-4ErYLuLA  lists network measures as follows:

a) Measures which are measuring the entire network:

* Diameter = „a measure which is determining the longest distance between any pair of two nodes in the network“

* Density = „is determining the potential of the entire network to talk to each other“ (how many connections of all the possible connections are actually happening)

b) Measures which are measuring the potential of individual nodes in a network:

* Concept of Centrality: (The meaning of centrality is dependent on the kind of different metric which is used)

** Degree centrality =  A very often used measure which „indicates the total number of connections for each actor in a network“

*** In-Degree centrality = Pointing to an actor / „how many other nodes are directly trying to establish communication or are talking  to a particular node“  (popularity, prestige)

*** Out-Degree centrality = Pointing away from an actor / „outgoing connections, may mean how many emails someone sent, generosity in conversations with others“  (gregariousness)

** Betweenness centrality = „measure which indicates the ease of connection with anybody else in the network but in particular to try to connect all these potentially small subclusters of the nodes“ (network broker)

** Closeness centrality = „used to measure the ease or the shortest distance of a node to anybody else in the network (indicates how quickly you can get to anybody else in the network, not useful for networks with many actors with no ties or groups with no connection to other groups)“

It also can be interesting to think about network modularity, e.g. smaller subgroups that are closely connected to each other (modules=communities). It is relevant for later use of modularity algorithms to identify the „giant component“ and use it as a filter.

 

About Gephi
Installation was easy but when I played around with the test data we were given, I even didn’t find the function to zoom in so I watched this YouTube video which was very helpful to get an overview of how to use Gephi (17 min very well spent): http://youtu.be/L0C_D68E1Q0

As I haven’t done anything like this before, I reduced my tests with the example blog dataset of week 6 to the „Average Degree“ and tried to find something useful. My results are in the attached pdf-file: w3-gephi-2

I’m looking forward to week 4 – maybe the Hangout times of day will be a little bit more convenient for Middle Europe again. And I still have to try Bazaar (I really want to do that), but again, this week, I had no time for that.

Learning Analytics MOOC – Week 2

Topic of this week is the „Learning Analytics Cycle“ and conducting some basic analytics with our test access to the Tableau software. The YouTube video from George Siemens about the Data/Analytics Cycle was very helpful, also the Google Hangout with Tony Hirst about „Data Wrangling“. I also attended the  Google Hangout on Wednesday and I am very impressed  by the commitment of the course facilitators – thank you!

My interpretation of the Learning Analytics Cycle consists of these steps:
1. Data collection, Data Acquisition and Storage (Data is generated by or about the learners: Sources can be LMS, Student information systems, Social Media… any interaction between Learner & Institution)
2. Dataset Cleaning (missing data, different spelling of names,…)
3. Analysis & Visualization
4. Action (Intervention, Optimization,… and back to the learners)
That means the process starts with the learners and ideally the cycle / loop closes with feedback of the intervention to the learners.
When we look at data, we can do counting, sorting and therefore get different sort of charts with the same data. As to interpretation, theses aspects are relevant: looking for outliers, looking for similarities and differences, looking for trends, looking for patterns & structure.
Certainly, you have to think about what you would like to know when you do the data collection and not only when you do the analytics & visualization.

My tests with the Tableau Software:
After having registered with Tableau a lot of times –  at first to get a test version of the software (thankfully in this MOOC we get an extended test period), then to actually start it after installation and then even to get the video tutorials – I watched the „Getting started“ video (20 minutes) and was really impressed with the variety of functions.

As I don’t have a set of educational data which I could use for testing, I had to be somewhat creative to use a different kind of dataset for testing. At our University (and in Germany) we have very strict regulations for the use of user data and logfiles and so my example won’t have anything to do with educational data but with recreational data… But my goal at the moment was to play around with the Tableau Software in order to get used to working with table cells and rows and visualizations and I am satsified with my results:

I spent more time with the MOOC this week than I planned because it was fun and creating artifacts is really very time-consuming. I am looking forward to week 3 and hopefully, I’ll find the time to try Bazaar meetings.

 

Learning Analytics MOOC – Week 1

The Horizon Report Higher Education 2014 sees Learning Analytics in the Time-to-Adoption Horizon as „One Year or Less“ (in the Report 2013 it was „Two to Three Years“), so the topic LA is quite an interesting one for Educators.

However, Learning Analytics (LA) is a term which needs definition – The Society for Learning Analytics Research defines it as „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“.

The slide at 3:00 min shows that it’s a good idea to know more about LA: „Data trails reveals our sentiments, our attitudes, our social connections, our intentions, what we know, how we learn and what we might do next“.

I really liked the link to the fulltext article „Educational Data Mining and Learning Analytics“ (Baker & Siemens 2014) as it gives an introduction and overview of the field (graduate programs, journals, conferences, methods & tools, differences between Educational Data Mining, EDM, and LA research communities). The reasons of the growing use of LA are cited as „a substantial increase in data quantity, improved data formats, advances in computing, and increased sophistication of tools available for analytics“.

What about software (analytics / research tools)? I try to remember that for single functionality there is NodeXL and Gephi whereas integrated suites would be SAS, IBM BI Analytics suite and Pentaho. Open Socurce tools are R and Weka and in our course we will focus on Tableau, Gephi, RapidMiner and LightSide. I intentionally skipped doing a tool matrix because at the moment I don’t feel like being competent enough and other things were more important to me (I decided that it’s  the kind of MOOC where I choose my learning goals).

In week 1, I spent about 5 hours with the MOOC: at first looking at the course / resources / activities in edX (plus joining one of the live hangouts until midnight local time on Tuesday) and then signing in ProSolo. My first impression of ProSolo was that is wasn’t very intuitive, so in week 2 I’ll  have a closer look at what the menus „plan, learn“, „goals, competences, activities“ mean.

I look forward to week 2 of DALMOOC and I’m curious what we will do with the Tableau software.

DALMOOC – My first steps

The official start of this MOOC on „Data, Analytics and Learning“ (DALMOOC) is on Monday 20th, but a lot of activity has already taken place, e.g. two Google hangouts – at not such perfect times for Middle Europe… Therefore I watched the archive of the Course Design Explanation session (October 17th) on YouTube http://youtu.be/b2gSd6oxEBM which was quite intereresting.

Why I joined this course

I already looked at the course in edx.org (whose user interface I already know very well) and was surprised that the first thing I saw in the Courseware section was a special DALMOOC Course agreement about participation in a study – I am still not sure what’s the difference if I say yes or just ignore it  (which I did for now). Afterwards I watched the „How DALMOOC works“ video which emphasized on „It’s about connections & creation not content“ and that it might be a little disorienting at the start for someone who has taken MOOCs that were more structured. For DALMOOC in edx.org, 4 points were mentioned to help orientation: the visual syllabus, a daily email, hangouts/recordings and edX forums.

Course Tools
In the course, many tools beyond edX will be used – for my orientation I add these URLs in the blog post:

I am looking forward to the official start of DALMOOC on Monday. I don’t know yet how much time I will spend, because the course is parallel to the busiest time of year with the beginning of the winter semester at our University. Besides the edx.org course portal https://www.edx.org/course/utarlingtonx/utarlingtonx-link5-10x-data-analytics-2186 I will have a look on what happens on Twitter (#dalmooc  https://twitter.com/dalmooc ) and maybe Google+ https://plus.google.com/106806974410074176435/posts

Justice MOOC – Conclusion

Today I finished the HarvardX Justice MOOC on edX with the final exam. There was no time limit for the test, however, after the first half, I got timed-out and had to log in again and continue, which was irritating as I didn’t know what would happen once I had left the web form. In the end, the result was very successful and I am looking forward to getting my certificate after July 15th.

The course felt like a long time period (12 weeks) – I actually prefer MOOCs with about 4-6 weeks. However, the incentive of the course certificate was very strong for me – and certificate in context of this course meant doing the final exam. In order to do that, it was necessary to engage with the content of 24 lectures (each about 25 minutes), at least that’s what I thought and prepared to do accordingly. The topic „What’s the right thing to do“ was very interesting up to the end of the course and the 24 self tests and 5 graded quizzes during the course were very helpful. I really liked the course structure with 2 poll questions every week and the subsequent challenges – you got a live feeling and it helped to trigger short forum entries discussing pros and cons. I’ve scrolled through many forum posts and was reminded once again, that the (cultural) diversity of the participants is always a big factor in MOOCs. You don’t get that in closed environments. And moral reasonings and questions about political theory naturally differ a lot between different countries and cultures.
In this course I got a better understanding of distributive justice and indeed, it influenced my way of thinking: „Once the familiar turns strange, once we begin to reflect our circumstances, it’s never quite the same again“. It would have been nice to have a live session with Prof. Sandel, but that was apparently limited to the first run of the MOOC last year.

It’s my second MOOC which I officially completed, and it was very different from the cMOOC „Moodle MOOC on WizIQ“ in June 2013. The Moodle MOOC focussed on letting the participants produce digital artifacts, discussing and sharing (accompanied by many live sessions with learning experts) whereas the Justice MOOC was about learning a lot of predefined content (video lectures) and reflecting about theories. In my opinion, both didactical concepts were very good and fitting for the particular purpose.

I have taken many, many MOOCs in the last years, obviously only a small part of them with the official stamp of completion. That doesn’t mean that I didn’t like some of the other ones or that I failed there. On the contrary, I learned a lot by attending live sessions, reading some of the literature and taking part in discussions. Certificates may be a nice incentive, but if you don’t need a certificate, why should you go the extra way? I think the aspect of the MOOC completion rate as a general success factor is heavily overrated – eventually MOOCs are open for everyone and shouldn’t come with too many restrictions (you have to do this and you have to do that until [date] otherwise you get drawbacks etc.). I also think we should appreciate the „quiet“ MOOC participants who learn their own way and don’t like to express themselves like some others do in many, many forum posts (for me, the term „lurkers“ is a very disrespectful one).
In conclusion, it is very important to define in advance what a MOOC is about, the workload, expectations and the paths (and level of participation) a learner might take. A short survey at the beginning (including the option „just checking out this MOOC“) and a short midterm survey won’t hurt.

(Update 21.7.14)

And that’s my certificate:
Justice Certificate Thumbnail