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