ALLS Presents at EDM and AIED 2017

Posted on July 9, 2017 by alexis

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

 

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ALL Lecture Series

Posted on June 29, 2017 by alexis

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.

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Dr Gloria Washington on empathetic fitness trackers

Posted on June 5, 2017 by alexis

Dr. Gloria Washington is an Assistant Professor at Howard University in the Computer Science Department. At Howard, she runs the Affective Biometrics Lab and performs research with her students on affective computing, biometrics, and computer science education. Her research is supported by the Department of Homeland Security, Leidos, and the TIDES Foundation. Before coming to Howard University she was an Intelligence Community Postdoctoral Research Fellow in the Department of Computing Science at Clemson University. She performed research on identifying individuals based solely from pictures of their ears. Dr. Washington has more than fifteen years in Government service and has presented on her research throughout industry. Ms. Washington holds M.S. and Ph.D. in Computer Science from The George Washington University, and a B.S. in Computer Information Systems from Lincoln University of Missouri.

Dr. Washington’s talk had highlighted that the incidence of children with chronic disease is growing in the U.S. and these children have special educational needs that relate to the way they learn how to care for themselves. Children with chronic disease learn positive health behaviors taught through self-management education taught by patient advocates, nurses, and their families. Unfortunately, this education usually begins around age 10 or 12; leading some to develop unhealthy habits and lack self-efficacy in improving their health. Fitness trackers were first created to help adults keep abreast of their fitness goals. However, these devices are slowly being introduced to children. There are no health and wellness technologies that are designed for children and exploit human physiological information to interpret and empathize with a child’s mental and/or physical health. Additionally, social cognitive models/theories were developed to help educational professionals identify the factors that influence how a person learns positive and negative health behaviors. These models include factors related to ethnicity, age, and socioeconomic status. Although these factors have proved significant in helping to design educational interventions for health psychologists; these theories have not been adapted for creation of educational materials relevant to children with chronic disease. There exists an opportunity for a new genre of fitness trackers that empathizes with the user, teaches positive health behaviors, contributes to a child’s self-efficacy and emphasizes the scientific underpinnings of a disease. This tool should also allow children the ability to teach themselves, their peers, and their caregivers through show and tell, positive reinforcement, and fun game-based activities. This talk focuses on introduction of a new empathetic fitness tracker that is used for instructional teaching of young children with chronic disease.

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Private Penny: The Resistance Privacy Policy and End-User License Agreement

Posted on April 7, 2017 by alexis

END-USER LICENSE AGREEMENT

By downloading, installing, or using Private Penny: The Resistance (referred to herein as the “Game”), you, the end-user (referred to herein by the words “you”, “your”, “yours”, and their derivatives), agree to be bound by this End-User License Agreement. This EULA establishes a legal agreement between you and the creators of the Game— Raphael Angelo Reventar, Melvin Luis Mendoza, and Therese Beatriz Pedro (referred to herein as “Authors” or with words such as “we”, “us”, “our”, and their derivatives)— with regard to the use of the Game and its Content.

“Content” means the text, script, 2D assets, and other multimedia elements you can view or access through the use of the Game.

If you do not agree with the terms and conditions listed hereof, do not download the Game.

1. Description and Use of the Game

Private Penny: The Resistance is a 2D action mobile platformer about antibiotic misuse and resistance developed by the Authors, which you may download and use through a compatible electronic device, such as a smartphone or tablet (“Device”).

At our discretion and without providing you prior notice, we reserve the right to modify the Game, including but not limited to its design, functionalities, and overall Content. Moreover, provide you with updates, upgrades, and/or support for the progression of the Game and its Content.

Additionally, you understand and agree that Content available in the Game is provided to you as is and is intended for entertainment and game play. Hence, it is not guaranteed that they are accurate. You, as a concurring user, should exercise judgment in your use of the Game and its Content.

2. License Grant

You are granted a personal, non-commercial license to download, install, and use the Game and to access the Content within for your use.

3. Restrictions and Ownership

Using the Game does not grant you ownership of any intellectual property rights in the Content that you access. Any form of the utilization of Content from the Game is not permitted unless requisite consent is duly obtained from the Authors. These terms do not accord you the right to own or use any distinguishing branding, trademarks or logos used exclusively within and for the Game.

Unless awarded with a written authorization from the Authors, the provider of the Game’s Content, you shall not:

1. copy, translate, alter in any way, or create any derivative work of the Game, its Content, or any part thereof;

2. redistribute, publish, sell, or in any other way make the Game available to third parties;

3. use the Game and access its Content through any technology or technique other than those provided by default (such as but not limited to bots, automation software, GPS-mocking, or any form of hacks); or

4. use the product for any purpose, commercial or otherwise.

Any rights not purposely granted to you herein are herewith reserved by the Authors as the sole and exclusive owner of all rights and titles.

PRIVACY POLICY

The nature of Private Penny: The Resistance does not require the developers to collect or share any personal information from the Game’s users. Personal information refers to any information referring to an identifiable or identified human person. No such information will thus be asked of the product’s users.

 

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Analysis of Novice Programmer Tracing and Debugging Skills using Eye Tracking Data

Posted on February 15, 2017 by alexis

Description of the Project

Eye-tracking is an emerging field in which special video cameras are used to follow and record the movements of a test subject. Eye gaze fixations indicate the test subject’s focus, hence the behavior of the test subject’s eyes can be used as an empirical measure of user attention. Eye gaze data has been used for a variety of fields from autism research (Navab, et al., 2012) to sports (Barfoot, et al., 2012) to animal behavior (Kano & Tomonaga, 2009) to fashion design (Ho, 2014) to reading comprehension (Graesser, et al., 2005).

Because accurate eye tracking requires relatively new and expensive technology, research and publications that make use of eye tracking data is somewhat rare.  This implies that the field is open for contributions.

We propose to develop national capacity to conduct eye tracking related research.  This entails the hosting of eye tracking workshops and the execution of a common research project. Because the proponents are from computer science, the common research project will focus on novice programmer tracing and debugging skills.

Statement of the problem

During the 4th International Outsourcing Summit held in 2012, Silicon Valley-trained Filipino information technology experts noted that “only 10 percent of information technology (IT) or computer science graduates are hireable.”  Although the Philippines produced 70,000 IT graduates in 2011, big companies consider most of these graduates to be undereducated and even uneducated by their standards.  To blame, these experts say, are substandard training and education programs.  Many schools allow their graduates obtained degrees without knowing how to program (Cuevas-Miel, 2012).

Programming is a fundamental skill that all graduates of IT-related courses must possess, yet it is a difficult skill to learn.  Since 2006, Dr. Rodrigo of the Ateneo de Manila has fostered an active research group that has studied novice programmer education.  Funded by several Department of Science and Technology grants, the Ateneo has studied novice programmer errors (Rodrigo et al., 2013), novice programmer affective states such as confusion (Lee, et al, 2011) and frustration (Rodrigo & Baker, 2009), and has built systems to analyzing novice programmer compilation logs (e.g. Dy & Rodrigo, 2010).

Prior works’ data sources have been limited to logs of novice programmer compilations and human observations. Both of these tended to be coarse grained.  The logs were essentially snapshots of novice programmer activity that have no representation for interim thought processes.  The human observations of each student were recorded every three minutes or so. This research project collects finer-grained data that enables us to track what it is that the students are attending to.  This, in turn, helps us determine whether they are in fact tracing the logic of the code or if they are confused.

Purpose of the research

This research project studies the tracing and debugging strategies employed by novice programmers, both as individuals and as pairs. Subsequent analyses will attempt to draw differences between high- and low-performing students and then arrive at recommended debugging strategies that teachers can then convey to students. The research questions that the group hopes to answer include but are not limited to the following:

  • What problem solving strategies do novice programmers employ when visually parsing through a code fragment?

  • How do the strategies employed by high-performing and low-performing students differ?

How does collaboration between pairs of novices affect the speed and accuracy with which bugs are identified and resolved?

Theoretical Framework

Eye-tracking is a data collection technique in which an individual’s eye movements are measured (Poole & Ball, 2006).  These measurements enable the researcher to determine where a person is looking (point-of-regard) and the sequence in which the eyes shift from one object to another.  Eye-tracking research is founded on the “eye-mind hypothesis” that asserts that eye traces are an indicator of where an individual’s attention is focused.   Visual attention refers to focalization. Individuals withdraw from some environmental stimuli so that they can effectively deal with others (Duchowski, 2007). Hence, eye-tracking data can provide researchers with insight regarding individuals’ visual information processing.

There are typically two types of eye-trackers: table-mounted and head-mounted.  Table-mounted eye trackers, as the name implies, are positioned on a table and are focused on the participant’s eye.  Head-mounted eye-trackers are similar eye glasses (Duchowski, 2007).

Poole and Ball (2006) and (Duchowski, 2007) describe several features used to measure eye-gaze.  Fixations refer to positions of the eyes when the eyes are relatively stationary.  They are an indicator of the amount of attention that is being applied to the point-of-regard.  Saccades refer to movement of the gaze from one point to another. No encoding takes place during a saccade, but regressive saccades, i.e. backward movement across a region that has already been visited, can indicate that an individual is having difficulty processing the material. Scanpaths are sequences of saccades and fixations.  They can indicate compliance with or departure from what is regarded to be an optimal path through the material.  Blink rate and pupil size can both indicate cognitive effort.  A low blink rate indicates a high workload while large pupils imply greater processing.

Eye-tracking research has been used to study human attention in a variety of circumstances.  These include but are not limited to reading, driving, marketing and advertising, and computer science.

Expected Output

This research project should result in prescriptions for teaching code comprehension, tracing, and debugging, some of the hardest computer-programming skills to learn.  It should also lead to research capacity building and publications in highly reputable journals and conferences.

Justification

The project is significant in at least three ways:

  1. The addition of the eye-tracking component develops local expertise in the collection, analysis and interpretation of eye-tracking data. Because the technology is not yet widely used for this type of research, it has a high potential to lead to new contributions and collaborations in other areas of research.

  2. It will contribute to the body of literature about novice programmer cognition, learning strategies, and debugging strategies.

  3. It will contribute to novice programmer education by helping establish expert patterns of parsing code that can then inform instructors about how to teach novices how to read and trace through code fragments.  This can lead to a higher level of IT preparation and training.

 

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