Four groups from ALLS present at PCSC 2012 in DLSU Canlubang, March 1-3, 2012

Four groups from ALLS presented posters at the Philippine Computer Science Congress 2012 held in De La Salle Canlubang from March 1 to 3.

The posters presented were:

Design and Development of an Affect-Sensitive Horror Game
Tiff Kang, Izza Perez, Gabriel Matias

Extension of an Intelligent Tutoring System for Debugging
Joyce Ann Rada, Javelin Karl Chi Magtalas, Carlo Martinez

Implementation of an Affective Agent for Aplusix
Thor Collin Andallaza, Rina Joy Jimenez

Development of an Affect-Sensitive Game Agent
Jason King Li, Tricia Monsod

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Call for Applications – The 8th Annual LearnLab Summer School

Call for Applications

8th Annual PSLC LearnLab Summer School
to be held at
Carnegie Mellon University, Pittsburgh, PA, USA
August 6-10, 2012

Application Process Open

Monday, August 6, 2012 – Friday, August 10, 2012

  • An intensive five-day course that focuses on a wide range of advanced learning technologies for course development and scientific research. The summer school covers the design and implementation of course materials with advanced learning technologies. It also focuses on designing, running, and analysis of in vivo experiments that
  • The course is half lecture and half hands-on activities.
  • The course will provide both conceptual background knowledge on advanced technology for learning and hands-on experience with state-of-the-art development tools.
  • Applicants choose one of four parallel tracks: Intelligent Tutor Systems development (ITS), In Vivo experimentation (IV), Computer Supported Collaborative Learning (CSCL), and Educational Data Mining (EDM).
  • Application deadline: Midnight April 8, 2012. Notification of acceptance April 30, 2012.

Summer School Content

We invite applications for participation in an intensive 1-week summer school on advanced learning technologies and technology-enhanced learning experiments. The summer school will provide a conceptual background and considerable hands-on experience in developing, running and analyzing technology-enhanced learning experiments.

Tracks

The summer school is organized into four parallel tracks: Intelligent Tutor Systems development (ITS), In Vivo experimentation (IV), 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. The goal for each track is described below.

  • ITS track: in the intelligent tutor system development track, you will learn to implement a prototype computer-based tutor, using authoring tools developed by LearnLab researchers, such as CTAT (the Cognitive Tutor Authoring Tools) or TuTalk. CTAT supports the creation of intelligent tutoring systems. TuTalk is used to develop tutorial dialogue systems that interact with students in natural language.
  • EDM track: if you are in the educational data mining track, you will learn to analyze an educational data set using data mining tools and methods. The data set used in hands-on activities could be one of the data sets currently in LearnLab’s Data Shop or you could bring your own.
  • IV track: if you are in the “in vivo” track, you will learn to design in vivo experiments. In the hands-on portion, you will create a prototype of an in vivo experiment for one of the LearnLab courses.
  • CSCL Track: if you are in the Computer Supported Collaborative Learning track, you will learn 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.

The summer school involves intensive mentoring by LearnLab researchers. The mentoring starts by e-mail before the summer school, in order to select a subject domain and task for the project, where appropriate. It 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.

Format

The summer school will last five days. Each day will include lectures, discussion sessions, and laboratory sessions where the participants will work on developing a small prototype system or a small prototype experiment in an area of math, science, or language learning. The participants will use state-of-the-art tools including the Cognitive Tutor Authoring Tools and other tools for course development, environments for Computer Supported Collaborative Learning, natural language dialog, semi-automated coding of verbal data, and DataShop for storage of student interaction data analysis of student knowledge and performance.

On the last day, student teams will present their accomplishments to the rest of the participants, followed by a “graduation” party. Participants will be expected to do some preparation before the summer school starts.

Background Reading

For those who would like to get more information prior to submitting an application, papers available provide background about the topics, technology, and tools that will be discussed during the summer school.

Course Instructors

The primary course instructors will include:

Dr. Kenneth R. Koedinger
Human-Computer Interaction Institute
Carnegie Mellon University

Dr. Vincent Aleven
Human-Computer Interaction Institute
Carnegie Mellon University

Dr. Carolyn Penstein Rosé
Language Technologies Institute
Human-Computer Interaction Institute
Carnegie Mellon University

Dr. Geoff Gordon
Machine Learning
Carnegie Mellon University

Dr. Noboru Matsuda
Human-Computer Interaction Institute
Carnegie Mellon University

Dr. John Stamper
Human-Computer Interaction Institute
Carnegie Mellon University

Other instructors may include:

Dr. Tim Nokes
Learning Research and Development Center
University of Pittsburgh

All instructors have considerable experience in research and development in technology-based learning experiments, computer-supported collaborative learning, intelligent tutoring systems and tutorial dialogue systems. Members of the team have taught summer schools for the past four years. All have taught similar material as semester-long courses.

Required Background

The course is intended for anyone with the educational zeal who would like to learn how to create technology enhanced learning experiments or with the appropriate computational background to actually build an intelligent tutoring system. This could include seasoned edutech researchers, advanced graduate students, computationally sophisticated teachers and commercial or military instructional developers. The level of technical expertise require. Please contact us when in doubt. In the past, people with a variety of backgrounds have attended the summer school, including psychology, education, human-computer interaction, computer science, as well as instructors in a wide range of domains.

Applications

Please visit our online application page

Important Dates

  • The deadline for applications is April 8, 2012.
  • Admission decisions will be made by April 30, 2012.

Costs

The fee for attending the summer school is $950.00. The fee for Graduate Students is $500.00; proof of current enrollment is required for this rate. A limited number of graduate students scholarships are available. See the application for information about how to request a scholarship. The fee includes a continental breakfast and lunch, but not lodging or travel. Please make checks payable to Carnegie Mellon University.

Participants will be responsible for paying for their own travel, additional meals and lodging. Dorm rooms at the Carnegie Mellon University campus are available for a low rate (typically around $70/night for a single room). Rooms may be shared further reducing this cost.

Academic credit is not available, although participants will receive a certificate verifying their participation. 30 hours of Act 48 credit is available for K12 teachers.

For More Information

Please address inquiries to Michael Bett, LearnLab Managing Director, email.

 

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Program: Integrating Computer Science and the Learning Sciences, Ateneo de Naga

Integrating Computer Science and the Learning Sciences

11 February 2012, 8:00-17:00

Ateneo de Naga University

Time Presentation Title Speaker
08:00 Registration
9:00 Integrating Computer Science and the Learning Sciences [PPT] Ma. Mercedes T. Rodrigo
9:45 Data Collection Methods [PPT] Jessica O. Sugay
10:30 Break
11:00 Mining Student Help-Seeking Behaviors [PPT] Didith Rodrigo for Jose Carlo Soriano
11:30 Building an Affect-Sensitive Pedagogical Agent [PDF] Rina Joy Jimenez
12:30 Lunch
13:30 Mining novice programmer behaviors, affective states and errors [PDF] [PPT] Jen Agapito, Didith Rodrigo for Thomas Dy and Diane Marie C. Lee
15:00 Break
15:30 Studying Driver Anxiety [PDF] Jessica O. Sugay
16:00 Design of an Affect-Sensitive Game [PPT] Tricia Monsod
17:00 Calls for Collaboration and Closing
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Program: Integrating Computer Science and the Learning Sciences, Ateneo de Davao

Integrating Computer Science and the Learning Sciences

21 January 2012, 08:00-12:00

Ateneo de Davao University

Presentation Title Speaker
Registration
Integrating Computer Science and the Learning Sciences [PPT] Ma. Mercedes T. Rodrigo
Data Collection Methods [PPT] Jessica O. Sugay
Helping the Compiler Help You [PPT] Thomas T. Dy
Exploring the Relationship Between Novice Programmer Confusion and Achievement [PPT] Diane Marie Lee
Mining Novice Programmer Error Logs [PPT] Emily Tabanao
Design of an Affective Agent for Aplusix [PPT] Thor Collin Andalaza, Rina Joy Jimenez
Effective Help-seeking Behavior Among Students Using an Intelligent Tutoring System for Math: a Cross-Cultural Comparison [PPT] Jose Carlo Soriano
Development of an Affect-Sensitive Game [PPT] Jonathan Jason King Li, Tricia Angela Monsod

Workshop Pictures

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ALLS is Calling for Applicants for Summer Internships

The Ateneo Laboratory for the Learning Sciences is calling for applicants for summer internships for the following projects:

1. Development of an Educational Data Mining Workbench
Machine-learning software and techniques have made it easier for educational software and intelligent tutoring researchers to create real-time detectors of cognition as well as of metacognitive and motivational behavior that can be used to improve student science, technology, engineering and mathematics (STEM) learning.  However, there are still critical problems to overcome. First and foremost of these is: How do we get the data into the machine learning software for learning in the first place? The Educational Data Mining (EDM) Workbench is designed to address this “data labeling bottleneck” and the difficulty in distilling relevant features for machine learning.  Now on version 2, interns can expect to contribute to the development and testing of the EDM Workbench using Java and NetBeans. Continue reading

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