The purpose of this special issue is to promote the understanding of Web Mining as a novel and useful research methodology for investigating aspects related to the various usages of the Internet in higher education. Web Mining (or Web Data Mining) is the application of Data Mining tools and techniques to discover novel and potentially useful information from data drawn from the Web.
Traditionally, Web Mining consists of three types: Usage Mining, Content Mining, and Structure Mining, each of which uses different data: Log files describing activity within Web pages, text from Web pages, and information about connectivity of Web pages, accordingly. As a result, many different levels of e-learning might be researched, allowing diverse points of view for instructors, researchers, curriculum developers, learning environment developers, and policy makers.
We encourage submissions of empirical and conceptual articles which address (but not limited to) the following topics:
Assessing online students’ behavior throughout the learning process
Well, it’s been a while since the last post… So, this is just a life signal. There are a lot of things to tell, and only little time to tell them. Meanwhile, the to-post list is getting longer and longer. And, meanwhile, EDM’09 (The Second International Conference on Educational Data Mining) submission deadline is almost behind us already, and plans for visiting Cordoba are starting to be concrete. And since this is only a life signal, it can and should be short…
(It’s been a busy-busy period, I really hope to write more frequently…)
JEDM - Journal of Educational Data Mining - is on air. The first contect should be published on April 2009, but the excitement is already all over (or is it just me?! ;-).
The journal will be published online and free of charge, and its Website is already waiting for the new contents. I was honored to be offered a role in the creation of this new “baby”, and I truly hope I’ll have a lot of stuff to take care of…
Here is the Call for Papers for the new journal. I see its great potential in becoming the main stage for presenting EDM studies and EDM-related discussions.
Less than two weeks have passed since the official launching of the new Chrome browser by Google, but it seems like it was ages ago. The three-color circle-shaped icon is all over. Technorati.com is currently giving more than 53,600 results when seraching “Chrome”; “google chrome” is returning more than 9 million results when searching in Google.com; and even this blog’s statistics already shows the new colored icon.
Google, however, tries not only to improve the surfing-the-Internet experience, but also to enhance the company’s learning of surfing the Internet. Chrome will give Google the chance to understand not only what people search, but also how they search it. This is done via logging keystrokes from the Omnibox (the URL box, which is not only for URLs…). According to Google, about 2% of these keystrokes will be stored along with the IP of the Chrome-installed computer. Just to be clear: These are not only the finalized search strings, but every keywtroke on the way, including those which finally are not being sent to the search engine.
Two major questions arise:
What will the good people of Google do with this huge amount of data? Research-wise, I can instantly think of at least 10 ways of analyzing this data for getting insights about the users; plenty of data mining techniques might be applied for that matter. However, I have no idea as for Google’s intentions.
Is it legal? Well, the answer here is quite simple: Yup! Every single user who’d installed Chrome, had accepted the End User License Agrement, in which all kinds of strange “agreemnents” between the user and the company may be found.
So, Google simply has the authority to do whatever it wants with the data of the company’s products, and the users accept it. Sounds like the wet dream of every data miner, doesn’t it?
A short article in last weekend’s edition of Haaretz’ daily Israeli newspaper was entitled “The Big Sisters”. The article mentions two complaints of an Israeli customer against two telephone companies (in Hebrew, the word “company” is feminine, and this is why they were referred as “sisters” and not “brothers”); the two cases have nothing to do with each other, and only by chance they happenned to the same woman:
An Israeli student got a few calls to her cellular from a Jordan-based colleague, while the latter was travelling to Jerusalem through the West Bank; a few hours later, the cellular company representative called the Israeli’s mother (who’s the registrant of the phone), trying to understand her relation to the Occupied Territories.
A representative of an Israeli International Calls Provider called that very same mother and suggested her a special discount plan for Vienna and New York; the reason for this special offer (as the representative told her): that lady had increasingly dialed numbers in these destinations during the weeks beforehand.
We might say that these two companies had used the technology in a wise way, and for the benefits of their customers (and this is clear from their response to the journalist): In the first case, the company just made sure the phone had not been stolen from its customer; in the second case, the customer had been offered with a plan to reduce the amount of money she pays to the company.
Well, what’s wrong with that? It is the customer’s reaction to the two companies-initiated calls: I’ve been tracked!
Now, let’s think about a totally different scenario, in which a student is using the imaginary ICanLearnAnyWhere Ubiquitous GPS-enabled Learning System. During his ubiquitous History class while travelling over the world, this student gets a pop-up message saying: “Welcome to Paris! Do you want to read about the history of Eiffel Tower?” What will this student feel at that very moment?
And, let’s think about a second imaginary scenario: After extensively using a High-School Mathematics VITS (Very Intelligent Tutoring System) for two weeks, one student gets the following message from the system: “Hey, we see that you are very good in Fractions, however you probably need some more practice in finding LCM; do you want to review the basics of finding LCM?”.
What will those students feel? Will they feel like they’re truly being helped by the advanced technology, or will they feel as being tracked? Will the answer to this question be different if a short notice had been presented at the beginning of the ubiquitous/VITS course, saying: “Your actions are being tracked for the benefit of helping you utilizing the system and earning higher score”? Maybe, on the other hand, such a notice will do more harm and will increase the dropping rate?
Above all, and since we know that these scenarios - both in commerce and learning - are everyday practice already, the big question is this: What is the golden path between privacy and data mining?
The heavy white book is already on my shelf, and now citations are possible. However, EDM’08 is, of course, much more than “(Baker, Barnes, & Beck, 2008)”. The wonderful two days of EDM’08 are behind us, and this is the time to do the aftermath.
First of all, cheers to the initiators and the organizers for a very-well-done conference (for the fillet we had in the first night’s banquet, “medium” was the perfect measure, however for a conference - ”very-well-done” is indeed a compliment :-). Interesting studies, good presentations, nice people, well-served food, beautiful location, and above all - the atmosphere, ho the atmosphere. Something new is being formed, and we are there, at the very first moments of this creation.
For now, I’d like to focus on what I may title: Bridges Over Interdisciplinarity. As far as I see it, this (i.e., the lack of them) is the main problem of our emerging domain.
The very nature of EDM is to be interdisciplinar. Just like “Bioinformatics”, “Computational Statistics”, “Music Information Retrieval”, or “Econophysics” - the name of our research area defines the two extremely-different fields that (together with some others) should synergically form a new one. However, it seems that “Education” and “Data Mining” are not yet equal in this merging equation, and that the latter is much more dominant than expected.
If EDM’08 was a reflection of the current world-wide EDM research (and I believe it was indeed), than it seems that it may be metaphorised as being built of two isolated islands: The “Education Island” (EI) and the “Data Mining Island” (DMI), and that some tour-ferries often depart from DMI towards EI, but always - as ferries do - get back home before sunset. Having two distinct islands is not the problem; the transportation between them is the main issue. Instead of the current situation, I’d prefer to see a wide, steady bridge being built, enabling a full-speed drive road between the islands.
Most of the questions asked in the CFP were of that highway-between-islands type, e.g.:
Can we use our discoveries to improve the software’s effectiveness?
Student learning data provides a powerful mechanism for determining which teaching actions are successful. How can we best use such data?
Can we use existing educational and psychological knowledge to better focus our search?
However, to my own opinion, many of the conference articles do not discuss such in-between questions, but rather taking only one side, choosing to settle only on one of the islands.
If EDM’08 is the mirror (and, as I mentioned before, I think it is indeed) - we are the ones who should carefully look at it. We should understand the current situation and ask ourselves if this is the desired one. Possible answers are mainly: “Sure!”, “Nay!”, “Don’t know”; and no matter what your own answer is, talking about it might help in laying down the constructions of the desired bridge. Furthermore, it might help in understanding if such a bridge is needed needed.
* * *
EDM’08 is behind us. EDM’09 is already “in the oven” (July 1-3, Cordoba, Spain). It took them only about 2 years to build the largest bridge in the world (Burapha Withi Expressway, Bangkok, Thailand). Will we finish our until EDM’10?
In a few hours from now I’l leave Israel for a family trip before EDM’08. My poster is ready and will be carried over miles and miles of New England region beautiful roads.
Training Magazine had recently published a list of 40 of the training industry’s rising young stars (age 40 and younger). This is the “first annual list”, i.e. the beginning of a tradition. Nominated by industry peers or self-nominated, the decision about the finalists was determined by a panel comprising members of Training’s Editorial Advisory Board.
Among the Top Ten, one can find Gideon Zailer, CEO and founder of e-learning knowledge solutions of Israel. Besides being a very nice person, Gideon is very interested in data mining in the context of improving the software his company developes.
So, we can surely say that EDM is well established within the agenda of at least a tenth of the Top Young Trainers world-wide. If this is the case also for the education researchers, EDM2008 will be very crowdy…
One of the main challenges of my research is that of validation. While trying to understand online learner’s motivation through their log files, I’ve came to the stage where the automatic tool for doing that is almost ready. The mechanism is clear, the code is written, and only one tiny little screw is missing in this machine: validation…
The measuring tool we’ve been developing should give us the answer to the following question: What is the level of motivation of a student during a given learning period? And it seems that the suffix of that question is the core of the problem. Unlike other questions that might be answered aftera certain period of time is over (e.g., What is the student’s grade? What is the student’s attitudes towards a completed task?), the affective state should be measured continuously and constantly. Actually, this is why I was atracted to measure it through log files in the first place.
So, what is the best practice for collecting information from online learners during their learning sessions? More precisely, the questions is that: What is the best practice for collecting information from online learners during their learning sessions, without disturbing the learning and with the highest chances of getting an accurate and reliable feedback (as much as any human feedback is accurate and reliable)? Well, now that this question is loudly asked, it’s time for me to go looking for answers…
Data mining has many applications. Every data mining freshman must have heard the cool story of how Wal-Mart used association rules for increasing their selling rates. This story, which according to many is only an urban legend, demonstrates what we all now know: Data mining is good for e-commerce.
Take, for example, Amazon.com. In Web Mining background presentations I give to M.A. students in courses discussing (research of) Web-based Learning Environments, I usually demonstrate to them the “surfing experience” in this huge Website. First, I login with my real username/password combination, and go over the list of recommended items. Here is a glance to the first three items in it (click thumbnail to enlarge):
The list is built mostly of history novels, history of science non-fictions, and some networking gadgets. Then, after a few keystrokes during which I change the username/password, I reveal the Mr. Hyde in me, and although the system is still greeting me with my real name, a totally different list of recommendations appears (click thumbnail to enlarge):
Suddenly, Origami and napkin folding (that’s pretty cool!) populate the wish-list I presumably wish to list. Since both accounts were used by the same (real) person and from the same machine (i.e., same IP), it’s clear enough that Amazon has been using the usage data (i.e., log files) of its customers and surfers in order to “help customers find what they want and purchase it in a way that is simple and convenient” (i.e., increase the store’s selling numbers; quote by Amazon’s Senior Manager for Data Mining, taken from here).
Well, although it might be funny (or not), this little example demonstrates the well-known power of data mining. However, what I’m most curious about are the less-known-sometimes-strange potential implications of it.
Take, for example, the future! Moreover, think about how depressing it will be. Now, when you’re already bad-mooded, it’s the right time to cheer you up with some great news: Data mining has shown that although medium-term future will be quite confusing, long-term future will be good! This fascinating result was presented by Alberto Pepe of UCLA (paper was co-authored with Johan Bollen LANL) in AAAI 2008 Spring Symposium on Emotion, Personality, and Social Behavior (held three weeks ago at Stanford University, Palo Alto, CA). The full paper can be reached via arXiv.org.
Well, thinking about it, data mining has to do with forecasting, so even depression forecasting doesn’t seem too weird. But, what about politics? Well, as been reported a few weeks ago, there is a strong connection between those two, and it is basically data mining application which brought NY Governor Eliot Spitzer to resign. The first link in the chain which finally brought to the 1000$-an-hour-prostitution-affair revelation was, as it turns out, a warning produced by a fraud-detecting software which uses data mining techniques to point out irregular money transactions.
* * *
So, now I’m really encouraged. When I first told friends and peers that I’d like to use data mining methods for extracting motivation of online learners (and affective aspects of online learning in general), they looked at me in an odd way. “How do you think you might evaluate one’s motivation without seeing him, without hearing him, without talking to him?”
Well, now I’m sure I’m not the only dreamer. If politics and depression may benefit from data mining, why shouldn’t affective learning research? It seems that my feelings regarding it had been phrased already:
You may say I’m a dreamer, but I’m not the only one
(Imagine, John Lennon)
Well, this is a great excuse to meet again with this wonderful song…
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