Ateneo Laboratory for the Learning Sciences

The 25th International Conference on Computers in Education (ICCE 2017) is organized by the Asia-Pacific Society for Computers in Education (APSCE). ICCE 2017 will be held in the Rydges Latimer hotel, Christchurch, New Zealand, from December 4th to 8th 2017 (Mon to Fri).

Below is the complete ALLS ICCE 2017 lineup.

Full papers

• Jenilyn Agapito and Ma. Mercedes Rodrigo. Designing an Intervention for Novice Programmers Based on Meaningful Gamification: An Expert Evaluation
• Cristina Dumdumaya, Michelle Banawan, Ma. Mercedes T. Rodrigo, Noburo Matsuda, Evelyn Yarzebinski and Amy Ogan.   Investigating the effects of Cognitive and Metacognitive Scaffolding on Learners using a Learning by Teaching Environment

Short papers

• Michelle Banawan, Ma. Mercedes Rodrigo and Juan Miguel Andres.  Predicting Student Carefulness within an Educational Game for Physics using Support Vector Machines
• Ma. Mercedes T. Rodrigo. Exploratory Analysis of Discourses between Students Engaged in a Debugging Task
• Maureen Villamor and Ma. Mercedes Rodrigo.  Impact of Both Prior Knowledge and Acquaintanceship on Collaboration and Performance: A Pair Program Tracing and Debugging Eye-Tracking Experiment

Posters

• Maureen Villamor and Ma. Mercedes Rodrigo.  Exploring Lag Times in a Pair Tracing and Debugging Eye-Tracking Experiment
• Mikaela Malit and Ma. Mercedes Rodrigo.  Leveraging An Existing Learning Management System For Alternative Learning

The 10th International Conference on Computers on Educational Data Mining and the 18th International Conference on Artificial Intelligence in Education were held last June 25 to 28 and June 29 to July 1 in Wuhan, China. In attendance were six members of the Ateneo Laboratory for the Learning Sciences: Dr. Ma. Mercedes Rodrigo, May Marie P. Talandron, Cristina E. Dumdumaya, Maureen Mamilic-Villamor, Cristina Enriquez and Yancy Paredes.

The following conference papers and poster papers were accepted into the conference and presented that week:1. Characterizing Collaboration in the Pair Program Tracing and Debugging

1. Characterizing Collaboration in the Pair Program Tracing and Debugging
   Eye-Tracking Experiment by Maureen Villamor and Dr. Rodrigo — short paper,
   EDM
2. Assessing the Collaboration Quality in the Pair Program Tracing and
   Debugging Eye-Tracking Experiment by Maureen Villamor, Yancy Vance Paredes, Japeth Duane Samaco, Joanna Feliz Cortez, Joshua Martinez, and Ma. Mercedes Rodrigo – poster, AIED
3. Modeling the Incubation Effect among Students Playing an Educational Game
   for Physics by May Marie P. Talandron, Ma. Mercedes Rodrigo, and Joseph Beck
   – full paper, AIED
4. Regional Cultural Differences in How Students Customize Their Avatars in
   Technology-Enhanced Learning by Cristina E. Dumadaya, Evelyn Yarzebinski,
   Ma. Mercedes T. Rodrigo, Noboru Matsuda, and Amy Ogan – poster, AIED
5. Proficiency and Preference Using Local Language with a Teachable Agent
   by Cristina E. Dumadaya, Amy Ogan, Evelyn Yarzebinski, Roberto De Roock, Ma.
   Mercedes Rodrigo, and Michelle Banawan – poster, AIED
6. Constraint-Based Modelling as a Tutoring Framework for Japanese Honorifics by Zachary Chung, Takehito Utsuro, and Ma. Mercedes Rodrigo – poster, AIED

Monday, 10 July 2017, 9:00 to 4:00
Ateneo de Manila University (Exact room TBA)
ADMISSION IS FREE

Deep Learning with Educational Data
Joseph Beck, Ph.D.

This whole-day lecture focuses on applications of deep learning for educational data. Deep learning is a machine learning approach using neural networks with multiple levels of representational transformation (i.e., hidden layers). Deep learning has been used in a variety of domains over the past five years with impressive results. Recently, it has been used for educational data sets with mixed results when compared to traditional modeling methodologies.

In this lecture, Dr. Beck will provide an introduction to machine learning followed quickly by a discussion of deep learning. He will discuss applications as well as current work.

Joseph Beck, assistant professor of Computer Science, has been at Worcester Polytechnic Institute since 2007. His research focuses on educational data mining, a new discipline that develops techniques for analyzing large educational data sets to make discoveries that will improve teaching and learning. His work centers on estimating how computer tutors impact learning. He established the first workshop in the field and in 2008 was program co-chair of the first International Conference on Educational Data Mining. He holds a BS in mathematics, computer science, and cognitive science from Carnegie Mellon University, and a PhD in computer science from the University of Massachusetts, Amherst.

To register, please sign up here.