Talk: ISP Seminar

Turn-Taking Behavior in a Human Tutoring Corpus by Zahra Rahimi

In their research, Zahra and Homa, analyze turn-taking behavior between students in a human-human spoken tutoring system. This analysis could be helpful in understanding how users from different demographics interact with a tutor. In this study, they use sequences of speech and silence over time to mark ‘Active’ and ‘In-active’ states the dialogues between the tutor and the student. Considering both the tutor and student together we have four different combinations of these states, in which each one of them being active or inactive. The next step is to learn (using a semi-Markov process) a model from the dialogues. Using this model, they are able to measure the association of these models with features such as gender, scores obtained in the pre-test etc. The experiments provide some interesting results such as female students speak simultaneously longer with the tutor than male students; while their activities are less than their male counterparts. Also, for the students with a lower pre-test scores, the tutor tended to speak for longer time.

Content-Based Cross-Domain Recommendations Using Segmented Models by Shaghayegh Sahebi

Sherry presented her work on the job recommendation systems in her talk. This was done as part of her internship at LinkedIn last summer. The site originally used a single model to make job recommendations to the users by selecting features from their profiles. But, these profiles tend to vary a lot according to the job function the users play and the industry they are in. Professionals in academia, for example, may put a very different set of information on their resume as opposed to a banking executive. With this new study, they wish to segment users using these very features (current job function and industry etc.) before sending them to the recommender systems. This allows them to develop an efficient method of feature augmentation and adapt their algorithms.

The model was built and evaluated based on some pre-collected data. They evaluated the accuracy of the system in recommending the jobs that the users applied to. This, however, restricted them to a certain extent and an online A/B testing is still under process. We’ll have to wait and watch for the results to find out if they do better than the one-size-fits-all model that is currently in place.

Further Reading

  1. Z. Rahimi, Homa B. Hashemi “Turn-Taking Behavior in Human Tutoring Corpus.” AIED 2013. Available: http://link.springer.com/chapter/10.1007%2F978-3-642-39112-5_111

Talk: Socially Embedded Search

This week I attended a full house talk by Dr. Meredith Ringel Morris on Socially Embedded Search Engines. Dr. Morris put together a lot of material in her presentation and we (audience) could appreciate how she presented all of it, with great clarity, in just one hour. But I think it would tricky for me to summarize everything in a short post. Do check out Dr. Morris’ website to find out more information on the subject.

Social Search is term for when you pose a question to your friends by using one of the social networking tools (like Facebook, Twitter). There is good chance that you might have already been using “Social Search” without knowing the term for it. So, why would you want to do that instead of using regular search engines that you have access to? It may be simpler to ask your friends at times and they could also provide direct, reliable and personalized answers. Moreover, this is something that could work along with the traditional search engines. Dr. Morris’ work gives some insight into the areas where the search engineers have opportunities in combining traditional algorithmic approaches with social search. She tells us about what kind of questions are asked more in a social search and which types of them are more likely to succeed in getting a useful answer. She goes on further into how the topics for these questions vary with people from different cultures.

I really liked the part about “Search buddies” during the talk. In their paper, Dr. Morris and her colleagues have proposed implanting automated agents that post relevant replies to your social search queries. One type of such an agent tries to figure out the topic for the question and recommends friends who seem to be interested in that area by looking at their profiles. While another one would try to use an algorithmic approach and post a link to a web-page that is likely to contain an answer to the question. It was interesting to know more about how other people reacted to the involvement of these automated agents. While some of the people in the experiment appreciated being referred to for an answer, a lot of them found them obnoxious when they didn’t perform well in identifying the contexts. In her more recent work, Dr. Morris has tried to solve these problems by recruiting real people from Mechanical Turk to answer questions on Twitter. Such an approach could respond to people’s questions in a smarter way by collecting information from a several people. It could then respond to these questions in the form of a polling result and quote the number of people recommending a particular answer. It can also work by taking into account any other replies that the participant would have already received from one of his followers. The automated agent would then present that answer for a opinion poll from the Turkers. Although such a system could provide more intelligent replies than ‘dumb’ algorithms but it may still fail in comparison to responses from your friends which would certainly be more personalized and placed better contextually. During the QnA session, one of audience members raised a question (with a follow-up question by Prof. Kraut)  about comparing these methods with question-and-answer websites such as Quora. While these sites may not provide as personalized results but will certainly do better in drawing the attention of people interested in similar topics. It may not be always possible to find somebody amongst your friends, to answer question on a specialized  topic.

Dr. Morris’ talk provided some really good backing for some of the recent steps taken by search engines like Bing (having ties with both Twitter and Facebook), Google (and the Google plus shebang) and also Facebook (with Graph Search) in this direction. It would be interesting to see how social computing research shapes the future of internet search.

Further Reading

You can find Dr. Morris’ publications on this topic here: http://research.microsoft.com/en-us/um/people/merrie/publications.html

Talk: Intelligent Tutoring Systems

Starting this week, I am adding a new feature on the blog. Every week I’ll be posting something about a talk or a colloquium that I attend. Serves as good talk notes, a writing practice and an assignment all in one full scoop? You bet it does!

The program that I am pursuing, Intelligent Systems Program provides a collaborative atmosphere for both students and faculty by giving them regular opportunities to present their research. It not only helps them gather feedback from others but also introduce their work to the new members of the program (like me!). As a part of these efforts, we have a series of talks called the ISP Colloquium Series.

For the first set of talks from the ISP Colloquium Series this semester, we had Mohammad Falakmasir and Roya Hosseini to present two of their award winning papers, both on Intelligent Tutoring Systems.

1. A Spectral Learning Approach to Knowledge Tracing by Mohammad Falakmasir

For developing intelligent tutoring systems that adapt to the student’s requirements, one would need a way to determine the student’s knowledge of skills being taught. This is commonly done by modeling it based on a couple of parameters. After learning from sequences of students’ responses to a quiz, one could predict the values of these parameters for future questions. This information could then be used to adapt the tutor to keep a pace that students are comfortable with. The paper proposes the use of a Spectral Learning [1] algorithm over other techniques such as Expectation Maximization (or EM) to estimate these parameters that model knowledge. EM is known to be a time consuming algorithm. The results of this paper show that similar or higher accuracy in prediction can be achieved while significantly improving the knowledge tracing time.

To design experiments with this new method, Mohammad and his co-authors analyzed data collected using a software-tutor. This tool was being used for an Introductory programming class at Pitt for over 9-semesters. They could then compare the performance of their new method over EM learning of parameters. They calculated both accuracy of prediction and root mean squared error as metrics for the comparison. Learning data was used from the first semester and tested against the second semester, and they could do this over and over again by learning data from the first-two semesters and predict the results from the third one and so on. This allowed them to back their results that show a time-improvement by a factor of 30(!), with a robust statistical analysis.

2. KnowledgeZoom for Java: A Concept-Based Exam Study Tool with a Zoomable Open Student Model by Roya Hosseini

Roya talks about open student modeling as opposed to a hidden one for modelling the students’ skills and knowledge. In her paper, she goes on to propose that a visual presentation of this model could be helpful during exam preparation. Using it one could quickly review the entire syllabus and identify the topics that need more work. I find it to be a very interesting concept and again something that I would personally like to use.

The authors designed a software tutor called Knowledge Zoom that could be used as an exam preparation tool for Java classes. It is based on a concept-level model of knowledge about Java and Object-oriented programming. Each question is associated with these concepts and specifies the pre-requisites that are needed to answer it. It also gives details on outcome concepts that could be mastered by working on a particular question. The students are provided with a zoom-able tree explorer that visually presents this information. Each node is represented using different sizes and colors that indicate the importance of the concept and the student’s knowledge in that area respectively. Another component of the tool provides students with a set of questions and adaptively recommends new questions. Based on the information from the ontology and indexing of the questions as discussed above, it can calculate how prepared a student is to attempt a particular question.

Evaluation of this method is done using a class-room study where students could use multiple tools (including KZ) to answer Java questions. They do a statistical analysis in comparison to the other tools that the features that KZ introduces. The results demonstrated that KZ helped students to reach their goals faster in moving from easy to harder questions. I was impressed by the fact that on top of these results, the authors decided to back it up with a subjective analysis by the students. Students preferred KZ over others by a great margin. They also received valuable feedback from them during this analysis.

While these tutors can currently support only concept-based subjects like programming and math where one could do by testing with objective-styled questions, the fact that we can intelligently adapt to a student’s pace of learning, is something that is really promising. I wish I could use some of these tools for learning my courses!

Footnotes

  1. You can find out more about spectral learning algorithms here: http://www.cs.cmu.edu/~ggordon/spectral-learning/. ^

Futher Reading

  1. M. H. Falakmasir, Z. A. Pardos, G. J. Gordon, P. Brusilovsky, A Spectral Learning Approach to Knowledge Tracing, In Proceedings of the 6th International Conference on Educational Data Mining. Memphis, TN, July 2013. Available: http://people.cs.pitt.edu/~falakmasir/images/EDMPaper2013.pdf
  2. Brusilovsky, P., Baishya, D., Hosseini, R., Guerra, J., & Liang, M.,“KnowledgeZoom for Java: A Concept-Based Exam Study Tool with a Zoomable Open Student Model”, ICALT 2013, Beijing, China. Available: http://people.cs.pitt.edu/~hosseini/papers/kz.pdf

How about collaboration?

My previous post on Computers and Chess, serves as a good prologue to this.

watson
That’s me geeking out at the Jeopardy stage setup.

A little more than two years ago, the IBM Watson played against and defeated the previous champions of Jeopardy!, the TV game show in which the contestants are tested on their general knowledge with quiz-style questions.[1] I remember being so excited while watching this episode that I ended up playing it over and over again, only to have the Jeopardy jingle loop in my head for a couple of days! Now, this is a much harder challenge for the computer scientists to solve than making a machine play chess.

Computers have accomplished so many things that we thought that only humans could do (play chess and jeopardy, drive a car all by itself …). While these examples are by no means small problems that we have solved, we still have a long way to go. While it can solve problems that we as humans often find difficult (such as playing chess, calculating 1234567890 raised to the power 42 etc.), it cannot* do a lot of things that you and I take for granted. For example, it can’t comprehend this post as well as you do (Watson may not be able to answer everything), read it out naturally & fluently (Siri still sounds robotic) and make sense of the visuals on this page (and so on). *At least not yet.

Computers were designed as tools to help us with calculations or computations. By this very definition, are computers are inherently better at handling certain types of problems while in others they fail? Well, we have no answer [2] to this question now and I at least hope that it isn’t in affirmative so that someday we can replicate human intelligence. As we have seen in the past, we certainly can not say that “X” is something that computers will never be able to do. But we can sure point out the areas in which the researchers are working hard and hoping to improve.

Here’s a video that talks about the topic that I am hinting at. While I promise not to post many TED talks in future, you can be sure of finding this central idea (the first half of the talk) as a common theme on this blog. Also, I prefer the word “Collaboration” over “Cooperation” [3] :

TLDR Let’s not try to solve big problems solely with computers. Make computers do the boring repetitive work and involve humans for providing creative inputs or heuristics for the machines. Try to improve interfaces that make this possible.

Although this was an idea envisioned in "Man-Computer Symbiosis" (Licklider J. C. R., 1960) more than half-a-century ago, researchers seem to have not given due importance to it when [4] the computers failed to perform as well as expected. Of course, more the number of “X”s that the computers are able to do by themselves, the more it frees us to do whatever we do best. When we do look around and observe the devices that we use and how we interact with the machines everyday, we seem to have knowingly or unknowingly progressed in the direction shown by Licklider. With the furthering of research in areas such as Human Computing, Social Computing, and (the new buzzword) Crowd-sourcing, the interest shown in such ideas has never been greater.

References

  1. Licklider J. C. R. (1960), Man-Computer Symbiosis. IEEE. Available: http://groups.csail.mit.edu/medg/people/psz/Licklider.html.

Footnotes

  1. More about Watson from IBM here. See also, Jeopardy vs. Chess. ^
  2. Amazon’s Mechanical Turk does talk about “HITs” or Human Intelligence Tasks ^
  3. In AI terms, it would indeed be multi-agent co-operation but then again we are not treating humans just as agents in this case. ^
  4. AI Winter: http://en.wikipedia.org/wiki/AI_winter ^

Computers and Chess

Deep Blue vs Kasparov '96 Game 1
Deep Blue vs. Kasparov: 1996 Game 1. Deep Blue won this game but Kasparov went on to win the match by 4-2. In the 1997 re-match, however, Deep Blue won 3½–2½.

To design an algorithm for playing the game of chess has been one of the challenges that has attracted the attention of many mathematicians and computer scientists. The sheer number of combinatorial possibilities make it hard to predict the result for both humans and computers alike. There have been many highly publicized games pitting humans against the (super) computers in the ’90s and ’00s, such as the Deep Blue vs. Kasparov one.

It was around the same time that I was starting out with chess and was interested in learning how to play better. My father had gifted me a copy of a computer game called Maurice Ashley Teaches Chess. It included playing strategies, past-game analysis and video coaching by the chess grandmaster Maurice Ashley. It also had a practice mode where you could compete and play against the computer. I didn’t end up being a good chess player but if my memory serves me right, it did not take me long to start beating the in-game AI. But things have changed a lot since then. Computers are not only faster and more powerful now (to explore more number of moves) but are also equipped with better algorithms to evaluate a decision. Let’s compare excerpts from the introductory chapters from two of my textbooks:

From "Cognitive Psychology" (Medin et.al., 2004):

The number of ways in which the first 10 moves can be played is on the order of billions and there are more possible sequences for the game than there are atoms in the universe! Obviously neither humans nor machines can determine the best moves by considering all the possibilities. In fact, grandmaster chess players typically report that they consider only a handful of the possible moves and “look ahead” for only a few moves. In contrast, chess computers are capable of examining more than 2,000,000 potential moves per second and can search quite a few moves ahead. The amazing thing is that the best grandmasters (as of this writing) are still competitive with the best computers.

Now consider, "Artificial Intelligence: A Modern Approach (3rd Edition)" (Russell et.al., 2010):

IBM’s DEEP BLUE became the first computer program to defeat the world champion in a chess match when it bested Garry Kasparov by a score of 3.5 to 2.5 in an exhibition match (Goodman and Keene, 1997). Kasparov said that he felt a “new kind of intelligence” across the board from him. Newsweek magazine described the match as “The brain’s last stand.” The value of IBM’s stock increased by $18 billion. Human champions studied Kasparov’s loss and were able to draw a few matches in subsequent years, but the most recent human-computer matches have been won convincingly by the computer.

So, what happened in the six year gap between the publishing of these books? It turns out that there has indeed been such a shift in the recent years. The computers’ superior performance stats can be seen on this Wikipedia entry. We have come a long way since the Kasparov vs. Deep Blue matches due the the advancements in both hardware and AI algorithms. Computers have now started not only wining but dominating in the human-computer chess matches so much so that even mobile phones running slower hardware are reaching Grandmaster levels. Guess, time’s right for switching to new board games! Btw, Checkers is a solved problem since 2007: http://www.sciencemag.org/content/317/5844/1518.full! It will end up in a draw (they have a computational proof of that) if both players use the perfect strategies, i.e. the one that never loses.

Image Credits: en:User:Cburnett / Wikimedia Commons / CC-BY-SA-3.0 / GFDL

References

  1. Russell et.al. (2010), Artificial Intelligence: A Modern Approach (3rd Edition), 49. Prentice Hall. Available: http://www.amazon.com/Artificial-Intelligence-Modern-Approach-Edition/dp/0136042597.
  2. Medin et.al. (2004), Cognitive Psychology. Wiley. Available: http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0471458201.