7 Things You Should Know About Analytics | EDUCAUSE

7 Things You Should Know About Analytics | EDUCAUSE) is a simple introduction to the concept of analytics. In just two-pages, it gives a simple overview to the concept, with a focus on the role that analytics play – in an academic context.

Highly recommended, if you are new to the concept of Analytics, and are looking for a very basic introduction.

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Week 1: Activity – Explore Hunch

One of the activities assigned to us for the first week was to explore Hunch – a recommendation engine of sorts.

It works in a simple way – it asks you twenty questions, and based on your responses, recommends items from various categories (magazines, games, food, etc) that are suited for you. Amazon has been doing this for some time now, and Facebook and Twitter attempt to do something similar. They recommends friends.

I chose not to get very deep in the understanding of how recommendation engines work. At least not till I wrote this post. We are in Tuesday already; nearly half the week is over. So it is a good idea for me to finish this post, so that I can go back and study if my initial thoughts on this have any basis at all.

First few thoughts on Hunch, per se (I’ll follow this up with a few thoughts on general recommendation engines):

  • Privacy: As some of my classmates have noted, requiring a Twitter or a Facebook OAuth is a put off. I think the primary reason for this is that you are not sure before you “get into Hunch” what it is about – and given that you possibly have a lot of personal information (at least in Facebook), you’d think twice before using FB to sign in. For the common user, who is concerned about privacy, it s not a very useful method – a direct signup feature would be useful.
  • Cultural/Regional: Hunch is very American. So if you are not an American, it doesn’t work very well for you. These are cultural aspects which Hunch probably did not consider. If I state that I am a non-vegetarian, I get recommendations of steak-houses, but I do not eat red meat, for example. Further, when you look at the recommendations, even if I want to act on them, chances are – they are not available in my country. As a Mac user, Hunch made some very interesting recommendations regarding Mac products, however, music via iTunes is not sold in India. Apples knows why, we don’t. There are no Apples stores in India, either.
  • Commercial: Next, the commercial angle; to an extent related to the regional and cultural aspect. The eCommerce sites are all US-specific – even for books. The most popular online bookstore, Flipkart.com, doesn’t find a mention.
  • Accuracy: Finally, the quality of the recommendations – they are far from relevant. Now, when I look at the results, I might say, hey, I find that interesting, but when I see a majority of the recommendations far from what I might really like, the algorithm seems to fail.

More often than not, recommendation engines have two primary shortcomings (a) they are based on a very narrow mapping (simplified relationships), and (b) they refer to a very limited data-set (limitation of available data).

Personally, if I were Amazon, I’d rather allow folks to link my Goodreads site and extract information from there, than just map to who bought other books when they bought the book that I am viewing right now. Goodreads seems to work on a data set that has been created by the user, and the system. When I do a book-compatibility check with friends on Goodreads, I’d like to believe I have a more closer understanding of recommendations from friends: I’d trust Goodreads more than I’d trust Amazon. The fact that someone bought some books is less valuable to me, than the fact that someone I know, liked (or disliked) the books – explained with a reason.

Recommendations as they exist today, I conclude (as of this post, I hope my opinion will be enriched as I go through this course), are slaves to the limitation of databases and the relationships that software architects can define, within the construct of a (software) program. Which means that soft-attributes that are more human and analogue, so to speak, aren’t a component of the mapping exercise.

In education, this takes on a different dimension. When learning online, It is quite possible (and easy) to recommend what content other students have liked, rated, which you might consider for your own consumption. The inherent problem in this is similar to (what I think) is called (Iterative) Economic Inequality: The rich getting richer. Allow me to explain this through an example:

iTunes has a built-in smart-playlist called “Top 25 Most Played.” It is a simple algorithm: it takes the 25 songs with the most play-counts. If you use this play-list often enough, the playlist does not undergo any significant change. Simply because the playcounts of these 25 songs increase (because you keep listening to these songs). The only way to break the authority of this play list is to choose songs manually, or select a custom smart playlist. Over a period, the “Top 25 Most Played smart playlist” changes. I refer back to my earlier statement, that this kind of recommendation is slavish to the limitation of the imagination and the construct of the program (or the programmer.)

When you add the variables of: learning styles (for those of us who still believe in it), variable pace of learning, environments in which we learn, cultural contexts in which we learn, and the economic situations which we prepare for, the recommendations and similar gimmickry are fairly redundant.

In (final) conclusion, I think that recommendations, used the way they are today serve little (and often, negative) benefits to learning. There needs to be either a radical shift in the way we think about this model, or it needs to encompass several more variables (which sounds infeasible to me) for it to make any sense in learning and teaching.

It may be worthwhile for you to see the model with Livemocha and Getglue.

Learning & Knowledge Analytics

I wasn’t very successful the last time around when George Siemens and his team has announced their (first?) MOOC (Massive Open Online Course) on Learning Futures. Hopefully, I will get back to it soon. For some reason, however, this current MOOC on Learning & Knowledge Analytics seems to be more promising (from the point of view that I may actually complete it).

Here’s an extract from the Course Description:

The growth of data surpasses the ability of organizations or individuals to make sense of it. This concern is particularly pronounced in relation to knowledge, collaboration within an organization, teaching, and learning. Learning institutions and corporations make little use of the data learners “throw off” in the process of accessing learning materials, interacting with educators and peers, and creating new content. In an age where educational institutions are under growing pressure to reduce costs and increase efficiency, analytics promises to be an important lens through which to view and plan for change at course and institutions levels. […] Learning and Knowledge Analytics 2011 is a conceptual and exploratory introduction to the role of analytics in learning and knowledge development. Most of the topics do not require advanced statistical methods or technical skills. Topics covered during the six-week course will introduce participants to a systemic and integrated view of analytics in the following settings:

* K-12
* Higher Education
* Corporate
* Government
* Organizational

Feel free to join; it’s free. The full syllabus is at the LAK11 Blog

I’m choosing to post all about LAK11 on this blog because, well, it’s a journey in learning and somehow is related to my Masters, for which I started this blog.

 

MA (Education) Workshop Schedule (Mumbai)

The schedule for the MA (Education) – Course Code MES – for Mumbai has been announced, to be held at the K. J. Somaiya Comprehensive College of Education (KJS), Training and Research, Vidyavihar, Mumbai. KJS is Programme STUDY CENTER – 1688P for the MA (Education) programme.

This is available on the KJS Website. Link.

KJS also has a dedicated section for the Somaiya Center for Indira Gandhi National Open University (IGNOU)

The programme will be held between 8-14 November 2010, everyday from 9:30AM to 5:15pm.

I have created a download-able resource for this workshop. I have taken the liberty of adding the professors and lecturers names, based on the initials and the list of faculty at the KJS site. There maybe errors, so you are better off using the resource on the KJS site.

Download PDF of Workshop Schedule

New research on Study Habits

New research on study habits breaks a few long-standing myths.

“Yet there are effective approaches to learning, at least for those who are motivated. In recent years, cognitive scientists have shown that a few simple techniques can reliably improve what matters most: how much a student learns from studying.

The findings can help anyone, from a fourth grader doing long division to a retiree taking on a new language. But they directly contradict much of the common wisdom about good study habits, and they have not caught on.

For instance, instead of sticking to one study location, simply alternating the room where a person studies improves retention. So does studying distinct but related skills or concepts in one sitting, rather than focusing intensely on a single thing.

‘We have known these principles for some time, and it’s intriguing that schools don’t pick them up, or that people don’t learn them by trial and error,’ said Robert A. Bjork, a psychologist at the University of California, Los Angeles. ‘Instead, we walk around with all sorts of unexamined beliefs about what works that are mistaken.’

Take the notion that children have specific learning styles, that some are ‘visual learners’ and others are auditory; some are ‘left-brain’ students, others ‘right-brain.’ In a recent review of the relevant research, published in the journal Psychological Science in the Public Interest, a team of psychologists found almost zero support for such ideas. ‘The contrast between the enormous popularity of the learning-styles approach within education and the lack of credible evidence for its utility is, in our opinion, striking and disturbing,’ the researchers concluded. ” (Via Mind – Research Upends Traditional Thinking on Study Habits – NYTimes.com)

Via Guy Kawasaki

NUEPA

National University of Educational Planning & Administration

Link

The National University of Educational Planning and Administration (NUEPA), established by the Ministry of Human Resource Development, Government of India, is a premier organization dealing with capacity building and research in planning and management of education not only in India but also in South Asia. In recognition of the pioneering work done by the organization in the field of educational planning and administration, the Government of India have empowered it to award its own degrees by way of conferring it the status of Deemed to be University in August, 2006. Like any Central University, NUEPA is fully maintained by the Government of India.

The National University has its origin dating back to 1962 when the UNESCO established the Asian Regional Centre for Educational Planners and Administrators which later became the Asian Institute of Educational Planning and Administration in 1965. After 4 years of its existence, it was taken over by the Government of India and renamed as the National Staff College for Educational Planners and Administrators. Subsequently, with the increased roles and functions of the National Staff College, particularly in capacity building, research and professional support services to governments, it was again renamed as the National Institute of Educational Planning and Administration (NIEPA) in 1979.

Hechinger Report | Math education at home and abroad

An interesting article about teaching Maths around the world. The Indian example is missing (for obvious reasons), but it has a few interesting facts on how Maths is taught in the US.

“But if the key to high math achievement isn’t a particular pedagogical approach, what is the answer? William Schmidt, an expert on math education at Michigan State University, has identified three issues: the coherence and depth of the curriculum, the quality of assessments and the content knowledge of teachers.” (Via Hechinger Report | Math education at home and abroad)

It is interesting to note that many in India are advocating against the rigour of teaching that has pervaded the Indian education system for a while now. Perhaps it is worthwhile to see, if we are trying to fix something that has worked for us.