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Summer School

11th Annual LearnLab Summer School

2015 application process is open

  • When: Monday, July 13, 2015 - Friday, July 17, 2015
  • Where: Carnegie Mellon University, Pittsburgh, PA, USA
  • Cost: $950 General Fee / $500 for Graduate Students. Some graduate student scholarships available for full time graduate students. (see application).
  • Apply: Click Here To Apply
  • Contact: Michael Bett -
  • Important Dates:
    • The deadline for applications is Midnight May 14, 2015.
    • Admission decisions will be made by June 6, 2015.
    • Call For Applications
    The LearnLab Summer School is an intensive 1-week course focused on creating technology-enhanced learning experiments and building intelligent tutoring systems. The summer school will provide you with a conceptual background and considerable hands-on experience in designing, setting up, and running technology-enhanced learning experiments, as well as analyzing the data from those experiments in a technology supported manner.

    The summer school lasts five days evenly split between lectures and hands on activities. Each day includes lectures, discussion sessions, and laboratory sessions where the participants work on developing a small prototype experiment in an area of math, science, or language learning. The participants use state-of-the-art tools including but not limited to the Cognitive Tutor Authoring Tools and other tools for course development, TuTalk tools for authoring natural language dialog, TagHelper tools for semi-automated coding of verbal data, and DataShop for storage of student interaction data and analysis of student knowledge and performance.

    On the last day, student teams present their accomplishments to the rest of the participants, followed by a "graduation" party. Participants are expected to do some preparation before the summer schools starts.

    The summer school is organized into four parallel tracks: Intelligent Tutor Systems development (ITS), Computer Supported Collaborative Learning (CSCL), and Educational Data Mining (EDM).  The tracks will overlap somewhat but will differ significantly with respect to the hands-on activities, which make up about half the summer school. Although as a participant you will be assigned to one of the tracks, based on your preferences stated in the application, it will be possible to "shop around" - that is, participate in activities of tracks other than the one to which you have been "officially" assigned. Our primary concern is that the summer school will be a good learning experience for you.

    The summer schools involves intensive mentoring by LearnLab researchers, which starts by e-mail before the summer school (in order to select a subject domain and task for the project, where appropriate) and continues during the summer school with a good amount of one-on-one time during the hands-on sessions. The mentors are assigned based on your interests as stated in the application. (All participants will have the opportunity to interact with all course instructors, but will interact more frequently with their designated mentor.)

    The following researchers will function as mentors and instructors:

    Ken Koedinger
    Vincent Aleven
    Carolyn Rose
    Geoff Gordon
    Tim Nokes-Malach
    Noboru Matsuda
    John Stamper
    among others

    The Four Tracks


    *ITS track:* in the intelligent tutor system development track, your goal will be to implement a prototype computer-based tutor, using authoring tools developed by LearnLab reseachers, such as CTAT (the Cognitive Tutor Authoring Tools) which supports the creation of intelligent tutoring systems, or TuTalk, which is used to develop tutorial dialogue systems that interact with students in natural language. Both CTAT and TuTalk have been designed for non-programmers. You will be able to use these tools even if you have no programming experience. Depending on your interest, your tutor might be related to a planned or possible experiment (perhaps an /in vivo/ experiment), or it might be related to a tutor development project that you are involved in or are planning to start up, or a course that you are teaching. CTAT-built tutors typically focus on multi-step problem solving as is often found in math, physics, or chemistry, but they are also being applied with increasing success and frequency to language learning, where the exercises presented to students often have smaller granularity. Tutorial dialogue systems built with TuTalk focus on directed lines of reasoning, designed to help students to understand important concepts in the domain or to address common misconceptions. They can, but don't have to be, connected to a tutor that helps with problem solving. During the week, you will start out by doing some cognitive task analysis to understand the nature of the problems for which your tutor will provide tutoring. Then, depending on your interest, you will use one or more of the tools described above to implement a computer-based tutor. By the end of the week, you will have a prototype running. In fact, if you decide to focus on intelligent tutoring systems development, you will already have implemented some intelligent tutor behavior by the end of day 2 (an Example-Tracing Tutor).

    *EDM track:* if you are in the educational data mining track, your goal will be to analyze an educational data set using data mining tools and methods. The data set could be one of the data sets currently in LearnLab's Data Shop or you could bring your own. (Best to work this out beforehand with your mentor - see below.) A typical data set would be a detailed record of the interactions that students had with a computer tutor over a (possibly extended) period of time, but other data sets are welcome/interesting too. You will identify a driving question that guides your analysis (quite possibly, one that you were interested in already), such as to understand how students' strategy use evolves over time, or what aspects of students' meta-cognitive abilities are related to learning. You will need to familiarize with the data, for example, by playing as a student working through the activities that the data pertain to, operationalize your hypothesis into more detailed analysis questions, run the analysis with various tools (e.g., DataShop, TagHelper, R, SPSS, Weka, RapidMiner, or other relevant packages) and algorithms (e.g., Logistic Regression, exponential-family Principle Component Anlaysis), interpret the results, and prepare a summary poster presentation.

    *CSCL Track:* if you are in the Computer Supported Collaborative Learning track, your goal will be to implement automatic support for collaborative learning that could be integrated with an existing environment, such as the Virtual Math Teams on-line learning environment. You will do this using authoring tools developed by LearnLab reseachers, such as TuTalk, which is used to develop tutorial dialogue systems that interact with students in natural language, TagHelper, which allows automatic triggering of support through real time analysis of collaborative interactions, and SIDE, which is a prototyping environment for reporting interfaces for group learning facilitators. All of these tools have been designed for non-programmers. Depending on your interest, your collaborative learning support might be related to a planned or possible experiment (perhaps an /in vivo/ experiment), or it might be related to a tutor development project that you are involved in or are planning to start up, or a course that you are teaching. During the week, you will start out by doing some protocol analysis of logs from collaborative learning interactions to understand where these types of interactions go awry and to motivate the design of your automatic support prototype. You will also participate in discussions related to current research in computer supported collaborative learning. Then, depending on your interest, you will use one or more of the tools described above to implement your design.


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