Re: SNAPP look-alikes | ||
Hi Bert - in your request for SNAPP-look alikes, I assume you are referring to tools that map social networks. If so, there are numerous options available (some free, some not): Netdraw: http://www.analytictech.com/netdraw/netdraw.htm Pajek: http://vlado.fmf.uni-lj.si/pub/networks/pajek/ NodeXL: http://nodexl.codeplex.com/ Netminer: http://www.netminer.com/NetMiner/home_01.jsp Touchgraph: http://www.touchgraph.com/navigator Gephi: http://gephi.org/ UCINet: http://www.analytictech.com/ucinet/ etc. The reason we went with SNAPP for the week 2 activity is that it's a simple browser plugin and doesn't require manually loading data. It demonstrates social network analysis without needing the techical skills of the tools listed above. | ||
I totally agree with you, especially the last part. I myself thought that I can use my old method of strategising my way in accessing the information from the MOOC, so I (by habit) created a blog for this, even followed George in creating the NetVibes, and etc.
But I still find that it’s too wide and diverse, that I still have to depend on my skimming skill (if I have the time), my time (to go through the email notifications) and the facilitator’s review (and from there I would rather read the reviews posted by other learners instead of recommended readings by the facilitator!).
As you said, I least worry about all these, because as long as we know where they are and how to ‘search’ for it later on after the course, I think we’re quite save to catch up at later stage. The important thing is to contribute via reviews, because that would ensure our understanding, at least for our own relief.
- Shazz, Kuala Lumpur
I agree with Mark. It's quite tough in the beginning, because of the 'panic' of not really knowing what we need to know in order to follow the topics... But thanks to previous experience in following George's teaching in 2008, I'm more confident in 'leaving behind' some of the texts/articles recommended to read in the study weeks.
In fact, I find it more comfortable reading others' reviews on the texts, and then go into the texts to 'understand the gist' myself and compare it with the reviews. At least some of the main ideas are already covered by the reviews, so we don't really miss much if we skim through and spot other points that interests us.
One thing for sure, what our eyes spot as interest might be a different angle of understanding compared to others. Interesting! ;D (even though it sounds like we tend to get lost too. LOL)
- Shazz, Kuala Lumpur
AK vs EDM vs Educational Research | ||
Today at the very interesting talk of Ryan Baker a question arise about the differences (and similarities) between Educational Data Mining, Learning and Knowledge Analytics and the traditional field of Educational Research. I think that this question deserves further exploration in this course. The definitions that those terms have in Wikipedia are very similar: Educational Research: Educational research refers to a variety of methods in which individuals evaluate different aspects of education including but not limited to: “student learning, teaching methods, teacher training, and classroom dynamics”. Educational Data Mining: Educational Data Mining (called EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in. Learning (and Knowledge) Analytics: Learning analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections for predicting and advising people's learning. Is there a difference? Do you think that EDM and LAK are just an evolution of Educational Research, but with better tools and data, or there is something radically different and unique in the new approaches? Do you think that EDM and LAK are synonymous or there is a meaningful difference between the two fields? Should we merge or we should keep them separated? Let's discuss. | ||
Re: What about learning analytics in the corporate sector? | ||
One of the areas that greatly interest me is simulations for corporate training needs. I have done work in the IT and Financial sectors that show the power of simulations to bring together some complex information about how a learner navigated a simulated job situation. Scores are too simplistic to do justice to such complex tracking of learner progress and competence. Consequently, learning & knowledge analytics become more complex as well. For example, let us consider a scenario that has multiple decision points (connected like in a graph) and multiple paths to the correct outcome. Let us assume that there is an ideal path (not hard to imagine in a highly disciplined process training). A learner's decision making trail or actions trail could be compared to the ideal path/trail and analytics could be programmed to infer from deviations to get a better and more comprehensive picture of learner performance. (also not unlike the notion of knowledge analytics being used to compare competency levels in a discipline). If you know cricket, you would be familiar with a graph that shows runs scored vs overs for both teams with circles denoting fall of wickets, resulting in what are popularly called "worms" that deviate from each other on the graph as the match progresses. Point is, these analytics move from being comparisions between numbers (Peter spent 5 more minutes than Pan on the google group), to being comparisons and analytics based on patterns and paths. Viplav | ||
Critiques of learning analytics? | ||
What are your concerns with analytics when applied to learning and knowledge? What types of critiques and concepts should we explore/consider? I've started with a few quick thoughts on the topic here: http://www.learninganalytics.net/?p=101 | ||
"If we do not re-present actions to the crowd through an interface that affects similaractions, it is just data mining for some other purpose. This is not a knowledgediscovery cycle."
Thank you, Schawn, for the good review on Week 1 readings.
I would like to point out the 4 points you have summarised in here, especially item number 2 - "During the design of the learning experience how can the data be captured in a non-intrusive way. If you cannot design and develop the capturing this data in a non-intrusive way, how can you design it in a way where the student will participate in providing enough quality data."
It is true that it is not easy to get students to participate in the first place, even though they spend most time on the same tools for personal use. They somehow do not 'trust' the sharing of info through the tools, even if it benefits themselves. With that as a problem, another consequence is in getting 'quality data'. So far, I only manage to get less than 10% of the student number to participate actively and this also subsides in time. It's quite tricky to get good stats out of this kind of action research, without students' cooperation. They believe in 'open' source only for their personal benefit (taking) and they don't really bother or believe in giving back to the community (giving). There's always a lack of give-and-take in this situation. So how to go about it?
Defining goal is important, but then again, the action itself requires a lot of "promotion" and "negotiation with rewards" in order to get others participate and provide quality data. Not easy. If there's any way to sort out these 2 points, before getting reliable and valid results for the other 2 points, it would be wonderful...
- Shazz
Kuala Lumpur
Definition – This is a Personal (as in, me the person) learning environment
(as in, the ecology in which I learn)
More details are available at: http://davecormier.com/edblog/2010/10/10/disaggregate-people-not-power-part-two-now-with-more-manifesto/
Hoping my students can take up and learn from here too,
- Shazz @ LAK
14 Jan 2011
The following are the gist my eyes could catch while skimming through the journal by Baker, S.J.D., Yacef, K. (2009) The State of Educational Data Mining in 2009: A Review and Future Visions: http://www.educationaldatamining.org/JEDM/images/articles/vol1/issue1/JEDMVol1Issue1_BakerYacef.pdf
My short review is laid out at the end...
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The Educational Data Mining community website, http://www.educationaldatamining.org/ , defines educational data mining as follows: “Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in.
Another name for data mining is Knowledge Discovery in Databases (KDD) - it is the field of discovering novel and potentially useful information from large amounts of data [Witten and Frank 1999].
Baker [in press] classifies work in educational data mining as follows:
Student models represent information about a student’s characteristics or state, e.g. the student’s current knowledge, motivation, meta-cognition, and attitudes. Modeling student individual differences in these areas enables software to respond to those individual differences, significantly improving student learning [Corbett 2001].
As Bartneck and Hu [2009] have noted, Google Scholar is the most comprehensive source for citations – particularly for the conferences which are essential for understanding Computer Science research.
Recent years have also seen major changes in the types of EDM methods that are used, with prediction and discovery with models increasing while relationship mining becomes rarer. How would these trends shift in the years to come?
Educational data mining methods have had some level of impact on education and related interdisciplinary fields (e.g. artificial intelligence in education, intelligent tutoring systems, and user modeling).
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Basically this journal talks about the literatures written in the topic of Educational Data Mining (EDM), and how the trend in research has shifted from one aspect to another. Initially, the method used for EDM was more on relationship mining, but towards the later stage the researches are using more of prediction and discovery with models as methods.
One part that interests me in this article is the impact shown on EDM on related fields such as Artificial Intelligence (AI), which is what I'm into in this past 1 year. In some ways, I have a 'hunch' and prediction that I may be doing some research on this area with the connection to AI.
Start the ball rolling,
- Shazz @ LAK
11 Jan 2011
Digesting slowly...,“The measurement, collection, analysis and reporting of data about learners
and their contexts, for purposes of understanding and optimising learning and
the environments in which it occurs” - Learning Analytics 2011 Conference site: https://tekri.athabascau.ca/analytics/
Playing around with Hunch | ||
If you created a Hunch account (week 1 activities: http://learninganalytics.net/syllabus.html#Week_1 , share your reactions with others - were the Hunch recommendations accurate? What are the educational uses of a Hunch-like tool for learning? | ||
