Data mining: A view from the popular press

 Data Mining: How Companies Know Your Personal Information - TIME 

20th Century Classroom Techniques with 21st Century Students

From the perspective of a professor:

Teaching is the practice of conveying knowledge and information to students.

Learning is the process a student follows to acquire said knowledge imparted by the professor.

The art of teaching and learning has, and always will, come down to these definitions. They are independent of time. It might be a discussion of teaching and learning in the year 10,000 B.C., or those practices as they are conducted in the 21st century that we currently live in -- teaching and learning has always been.

For the time, I will define the degree of learning that a student achieves as a qualitative measure that is inversely proportional to rote, short-term memorization. We know that mere memorization does nothing to affect what the student has learned, and immediately departs the student's brain after an exam / quiz / course is finished. (Trying to assess learning in a way that yields an honest, objective, quantitative measure is an interesting topic as well -- one that I'll save for another time.)

Pedagogy - the methods and practices behind teaching - has substantially changed. (And if it hasn't for you, then it probably needs to.) This is true regardless of whether you are teaching computer science, biology, music, or Asian history. That is what this article in the Chronicle is focused on. Professors (including myself) are continually challenged every year to adapt our methods in a way that will maximize the degree of learning for the 21st century student.

Not surprising, this is not an easy task to accomplish. Even though I have never had a student ask me the question, "Uhhh, do I really need to show up?", I have had students consider not coming to class when they learn that I might be putting slides up online. In fact, students often expect their professors to use PowerPoint. And, though I don't put up all of my slides, student definitely expect it. (And, they seem to appreciate it... mostly.)  They certainly expect you to use technology. They appreciate good hi-tech demonstrations, gadgets, and discussions of topics that are relevant in their world, which is infiltrated with modern technology.  (Don't dare use an overhead projector!) With respect to course content management, they expect you to use the campus learning management system. (We use Blackboard and Moodle.) I have strived to learn how to use Blackboard effectively to maximize student learning... or is that student convenience? Students expect to have a central point of access for all class material available to them 24/7. Students expect your class to have an online presence. They appreciate good topic-related videos and other engaging exercises. One might even argue that students expect to be entertained to some degree. The course model of the 20th century is long gone!

I've both read and listened to talks from experts about the importance of active learning. I am a absolute believer in active learning in all disciplines, but I admit that this is something that I have a difficult time trying to incorporate in the classroom. I am still trying to figure out how to come up with engaging, active exercises in an introductory computer science lecture that focuses on learning to program in Java, or in teaching relational algebra in a database systems class. I can come up with several, but to set up the exercise / activity requires far more time than I have available. (The time management dilemma of the dedicated professor vs. the outsider's ignorant perspective that we have it easy - yet another topic for another day.) I try to abstain from pure preaching and talking to them, and repeatedly pause for exercises, have students come up to the board, and so on. I am quite certain that there is more I can do, but it goes back to that time resource!

I attend classes offered by the institutions I've taught at that discuss effective teaching strategies for the 21st century student.  It's interesting to hear the wide range of ideas out there. My colleague (Dr. Perrone) passed this article on to me, and I suppose it has caused me to reflect on my current practices, as well as consider how students learn today.  For me, this article has a few ideas that I have not seriously considered. First, have students take a quiz on their own learning and commitment levels to the course, and give the quiz in class. Sure, I have asked students to reflect on what they have learned, but to actually give them random "quizzes" is an interesting idea. It is a way of making the students actively think and realize that their commitment level to the class can affect their learning, even opening the floor for periodic discussion about the topic.

Another idea involves creating a central, course-wide blog and requiring students to participate in it. I have students keep individual journals online, but they are private, only viewable between myself and the student. A blog would be interesting, but would certainly require some moderation on my part, for sure. The article suggests that perhaps a course-focused Twitter account might be useful, inviting students to tweet about course topics, even during the lecture! This is an interesting way of openly recognizing that students are going to using their cell devices whether you like it or not. So, why not encourage them to do it in a productive way, discussing course topics? I'm not sure how I feel about that one though.

I think the big "take home" for me is a reminder of how students learn today.  It is something I need to strongly consider for a bit before I jump on any bandwagons here. This article is suggesting that most of learning is accomplished outside of the classroom. I don't disagree with that. I've always believed that. After all, we preach that students ought to be spending three hours for ever hour of lecture outside of class.  By no means should anyone read the article and think that the role of the lecture and classroom experience is irrelevant. I'm not sure the author is saying that. We just need to keep considering new pedagogy to keep our lecture relevant, modern, engaging, and active without robbing students of the opportunity to learn both inside and outside of the classroom.

It is not too hard for a professor to try to set aside time once in a while to assess the current tools and technologies out there, and reflect on ways that they can adapt new methods. My observations have been that students appreciate it.

It's certainly hard to try to figure out how to teach to different learning styles, especially today. I was a classically-trained student. I learned through lectures, I was expected to pay attention in classes during a time when students were kicked out of class for disruption. And, I was hardly entertained! I was expected to work outside of the classroom. In fact, I wanted to work outside of the classroom. But why? Why do so many students today expect so much more out of their professors? Why do many fail to recognize their important responsibility in their learning? (Why do I get such blank stares when I suggest that they work on additional exercises to increase their learning, even when I suggest that I will help them?) Alas, let's face it: professors are a very odd breed! We are just different. We went down the grueling path of obtaining a doctorate because we love learning, and find more enjoyment out of challenging ourselves with regard to what we have learned.

Our students are no better or worse than us. They, like us, are just different. They have their own learning styles, agenda, priorities, interest level in our subject, and so on.

Damn, why can't everyone just be like me? :-)

2011 KDD Cup - Predict Music Interests

Well, it's another year, and that means there is another opportunity to take part in one of the biggest data mining competitions held every year -- the KDD Cup, which is a part of the ACM SIGKDD conference. (FYI, KDD-2011 is in San Diego this year, August 21-24.)  This year, the competition is based on analyzing music ratings from users of Yahoo! Music. The challenge is to analyze the ratings and find information about how songs are grouped, how hidden patterns link various albums together, which artists complement each other, and so on.  The ultimate goal here is to develop a classifier that can analyze a user's music ratings, and come up with the best suggestions on which songs users would like to listen to next.

So, where does "data mining" come in?  The data consist of 300 million ratings performed by over 1 million anonymized users. The ratings are given to different types of items-songs, albums, artists, genres-all tied together within a known taxonomy.

The prize:
- 1st place: $5,000
- 2nd place: $2,000
- 3rd place: $1,000

(Yes, this is a bit less money than the Netflix prize a few years ago!)

Good luck!

Virtual Choir Joins Voices from 58 Countries

Virtual Choir Joins Voices from 58 Countries

Wow. I know this is not related to my usual topics, but I recommend that you pause for a moment and listen to these beautiful voices. The clip posted [click here for it on Youtube] is only a few minutes long. And for those audiophiles out there, yes, there is certainly a LOT of audio editing and additional effects applied to the raw vocal streams. This is a must in order to smooth out the noise and deal with enormous issues that must come with using using streaming audio over a webcam! I'm sure some of the volunteers perhaps have slightly better microphones, but from what I understand, if you listen to some of the individual recordings available via youtube, even with the very best singers, they are pretty rough sounding. Thus, audio processing is a must. And besides, who cares! This product is nothing short of wonderful. The music is peaceful, melancholy, and a bit haunting.... just wonderful. I'm looking forward to the final product soon.

Cutting commutes with data mining - bootstrappr - Blogs

There are enormous opportunities for data mining to help us better understand our data-centric world. Here's another example -- analyzing traffic data in order to predict commute times.