This document proposes a research project to analyze student log files from an algebra learning environment called Aplusix. The project would involve researchers from universities in the Philippines, Vietnam, France, and the UK. They would collect and analyze data from Aplusix sessions with students in those countries. The analyses would develop models of student knowledge, emotional states, and behaviors using data mining and machine learning techniques. The models could help improve intelligent tutoring systems by more accurately representing students and adapting to their emotions.

1. 1
Regional programme ICT-Asia
Project proposal
(research – innovation)
Project proposal application form
A. General presentation
A1 Lead institution and project leader
Leader:
Ma. Mercedes T. Rodrigo
Lead institution:
Ateneo de Manila University
French Co-pilot (especially, for financial part)
Jean-François Nicaud
Lead institution:
Université Joseph Fourier, Grenoble, France
A2 Project title
A multi-dimension analysis of students’ log files in algebra
A3 Theme
Data-mining, machine learning, affective computing
A4 Project objective
1. Conduct several experiments with classes in Philippines, Vietnam and France of different
grades, using the Aplusix learning environment (http://aplusix.imag.fr), which allows students to
freely make calculation steps, as they do in paper-based environments, and which records all the
students’ actions in log files.
2. Gather and share the data on a website.
3. Analyse data on three levels:
a. Modeling the relationships between emotional states and usage choices, and predicting
each student’s emotional state and usage choices
b. Modeling Student’s knowledge, by automatic construction or by a combination of hand
construction and automatic analyses.
c. Comparing models derived from data sets from different countries to detect
cultural differences in usage or knowledge.
A5 Project summary
The project proponents propose to develop new models of student knowledge, emotional states,
and usage choices. Using the software Aplusix as a platform, researchers in the Philippines,
Vietnam and France will gather data from algebra students, share the data, and then analyze it
collaboratively using data mining and machine learning methods. This project contributes the
areas of cognitive science and affective computing. The models produced in this project may be
integrated and used together to extend intelligent interactive learning environments, increasing the
accuracy of their representations of student knowledge and enabling them to recognizing and
responding to student emotions and behaviors.
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2. A6 Information on the project partners
Asian partner A : Asian partner B :
Organization Department of Information Organization Ho Chi Minh City University of
Systems and Computer Science Pedagogy, Viet Nam
Ateneo de Manila University
Name of the Ma. Mercedes T. Rodrigo Name of the Mr. Nguyen Thai Son
lead lead
researcher researcher
Address Loyola Heights, Quezon City, Address 280 An Duong Vuong street,
Metro Manila, district 5, Ho Chi Minh City,
Philippines Vietnam
Tel/Fax +63 (2) 426-6071 Tel/Fax Phone: (848)8330124
Fax: (848)8398946
Email mrodrigo@ateneo.edu Email nthaison@gmail.com
France partner A : France partner B :
Organization Université Joseph Fourier, Organization Université Joseph Fourier,
Grenoble, France Grenoble, France
Name of the Jean-François Nicaud Name of the Mirta B. Gordon
lead MeTAH team lead AMA team
researcher researcher
Address Laboratoire LIG Address Laboratoire TIMC-IMAG
46, Avenue Félix Viallet (UMR 5525)
38031 Grenoble Cedex, France Domaine de La Merci - Jean
Roget
38706 La Tronche, France
Tel/Fax Tel +33 (0)476574775 Tel/Fax phone : (33) (0)4 76 63 71 53
Fax +33(0)476574602 fax : (33) (0)4 76 51 86 67
Email Jean-Francois.Nicaud@imag.fr Email mirta.gordon@imag.fr
A7 Other associated partners
Other country
Organization
Name of the Ryan Shaun Joazeiro de Baker
lead (no funding requested for this
researcher partner)
Address Learning Sciences Research
Institute
University of Nottingham
Jubilee Campus
Wollaton Road
Nottingham NG8 1BB, UK
Tel/Fax phone : (44) 115 9222 576
Email ryan@educationaldatamining.org
Asian Partner
Organization
Name of the Ma. Celeste T. Gonzales
lead
researcher
Address Education Department
Ateneo de Manila University
Loyola Heights, Quezon City,
Metro Manila,
Philippines
Tel/Fax +63 (2) 426 6001 loc. 5230
Email mctgonzalez@ateneo.edu
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3. B. Detailed project presentation
B1. General orientation of the project (basic research – applied research with or without
the participation of a company)
This project uses data mining and machine learning techniques to develop models that will
contribute to what is known in the area of cognitive science and affective computing. The
project aims to discover patterns in student behavior and relate these behaviors with the student’s
emotional state. This combination of information should improve the student model. When
developed, this model can extend the capabilities of intelligent interactive learning environments,
improving student learning and learning experiences.
To explain the major portions of this research project more extensively, this section’s discussion
is divided into two parts: algebra transformation computing and affective computing. The
algebra transformation computing section gives some background on the difficulties and
challenges of modeling student knowledge in algebra. The affective computing section surveys
recent research that shows that student emotions have an impact on their behavior and hence on
their learning. For an intelligent interactive learning environment to be fully responsive to
students, they must take into consideration both cognition and affect.
Algebra transformation computing
Student modelling in arithmetic and algebra is a well-established research field of the AI-ED
community (Wenger, 1987). Several frameworks have been adopted, based on elementary items
like procedures or rules: issues-and-examples (Burton & Brown, 1979), procedural networks
(Brown & Burton, 1978), mal-rules (Sleeman, 1983), intermediate representations (Twidale,
1991), ACT-R modeling (Anderson, Conrad, & Corbett, 1993), and Bayes Nets (Martin &
vanLehn, 1995). Automatic diagnoses of the students actions have been realised in these research
works. Researchers in mathematic education often model students with non elementary items
like conceptions or competences (Artigue, 1994; Balacheff & Gaudin, 2002). Diagnoses have
often been realised manually, but a few ones have been obtained automatically, in collaboration
with computer science teams (Jean et al., 1999).
Our work is devoted to a double problem: didactic modelling and computational modelling of
students’ knowledge. We are interesting in automatic diagnosis of students knowledge in the
field of algebra. We are especially concerned by the ‘object’ aspect of algebra (Douady, 1984):
students have to deal with algebraic expressions in the symbolic register, where the semantic-
syntax duality is involved. It refers to the transformational activities of (Kieran, 2001), including
solving equations, factoring, expanding expressions and collecting like terms. They involve
transforming an expression or equation by maintaining equivalence. These activities require the
use of techniques that students must adapt of the type of the activity itself. We model the use of
these techniques by rules of action because it reflects observable student’s behaviour during the
schema course.
Interactive learning environments are viewed as interesting solutions to overcome the limits of
classical one-to-many teaching methods. However, these environments should incorporate
accurate representations of student knowledge in order to provide relevant guidance. In a
problem-solving environment, one way to build and update this student model is to precisely
follow what the student is doing, by means of a detailed representation of cognitive skills. This
approach is called model-tracing (Anderson, Boyle, Corbett, Lewis, 1990). Some model-tracing
tutors like the PAT Algebra Tutor (Koedinger, Anderson, Hadley, & Mark, 1997) contain rules
that can be used to solve the problem itself and assess the student's solution. ANDES (Conati,
Gertner, & VanLehn, 2002) is another model-tracing tutor which follow the student stages of
resolution and updates a Bayesian network after each student action to predict what the student is
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4. actually doing. Our system belongs to this model-tracing approach but there are some differences
with classical systems. In particular, our aim is not to describe the student's production in terms
of resolution rules. These may provide accurate student models although biased by the particular
expert-based set of rules considered. Instead we rely on low-level descriptions of the student
actions, like the sign of the term modified by the student or whether this term is a monomial, in
order to build high-level representations of the students' knowledge. For instance, in an
environment where students were asked to troubleshoot an electronic circuit, a system based on
our method would not know how to solve the problem, but would be able to identify the basic
features of the student's actions to discover main high-level behaviors.
Currently, many learning environments are able to store very detailed traces of students'
activities thus producing huge sets of low-level data. However, identifying high-level behaviors
from these data is not straightforward, especially if the concepts of the domain knowledge are
not explicitly encoded together with the corresponding traces. Since 2003 the AMA and the
METAH teams have investigated approaches to student modeling within the paradigm of
Algebra learning, using the Aplusix environment (Renaudie, 2005).
Our general approach aims at discovering patterns of student behaviors. Its principles are
applicable whenever the information carried by the traces may be split as finite sequences of
{previous state, following state} pairs, where each following state is the result of basic student
transformations performed on the corresponding previous state. We encode this information
using domain-dependent attributes, and apply machine learning techniques to discover high-level
behaviors.
This approach is interesting in that it allows incorporating other types of information, besides the
students’ actions, in a modular way. In particular, we expect that including information related to
the emotional state of the student when solving the exercices should improve our student’s
model.
Affective computing
Affective computing is defined as computing that relates to, arises from, or deliberately
influences emotions. It attempts to give computers the ability to recognize, express, and respond
to emotions intelligently (Picard, 1997).
A number of studies have attempted to arrive at models for judging student affective states.
Because human beings are capable of hiding their emotions, some studies conducted at the
University of British Columbia and the Massachusetts Institute of Technology have used
biometric sensors to monitor students’ physiological responses. Data from skin conductance,
heart rate, and electromyogram sensors synchronized with log files have been used to record
student reactions to events in an educational game (Amershi, Conati, & Maclaren, 2006). These
studies established threshold values above which readings are considered indicative of an
emotional response and correlated these with game events. Among the findings were that time
between student actions is an important determinant of student affect. For example, short time
intervals may imply impatience.
Mota and Picard’s (2003) study used a chair outfitted with sensors to record subjects’ posture
while using an educational game. An adult observer labeled behaviors and affective states
(high, medium, and low interest; taking a break; bored). The data from the sensors synchronized
with the observations were then used to train a neural network. The trained neural network was
subsequently able to recognize student emotions with an accuracy of 82.3%. If these
assessments were then fed back into the educational game, the game could intervene when
student interest begins to wane.
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5. Other studies detected student emotions without using biometrics. De Vicente and Pain (2002)
used expert opinion to arrive at rules to diagnose student motivation. They showed a series of
experts videos of individual children using a tutoring system. Using a specially-designed
software package, the experts could play back recordings of student interactions and then rate the
student’s emotional state. From the exercise, the researchers arrived at a set of 85 motivational
diagnosis rules, e.g. mouse movement was not random and the student performed the task
quickly therefore the student was confident and highly satisfied. Based on these rules, the
researchers concluded that it is possible to make diagnose a student’s motivational state based
solely on the information provided by the student’s interaction with the tutoring system.
Information on student achievement and log files of student behavior with an exploratory
learning environment enabled researchers to associate behaviors with low learning outcomes and
high learning outcomes (Amershi & Conati, 2006). After training, the resulting detector was able
to identify behaviors that were detrimental to learning, even after seeing only 10% of a student’s
actions. If incorporated in an ITS, the model could enable the ITS to choose the most
appropriate interface or intervention based on a learner’s classification.
One previous study has also investigated the relationships between affect and other categories of
behavior, such as whether students are off-task or “gaming the system”, attempting to succeed in
a learning environment by exploiting properties of the system rather than by learning the
material (cf. Baker, Corbett, Koedinger & Wagner 2004). Gaming the system and off-task
behavior have both been found to be amenable to automatic detection in learning systems
(Baker, Corbett, & Koedinger 2004); (Baker, in press).
This study (reported in Rodrigo et al, in press), conducted in a collaboration between the Ateneo
and Nottingham partners of this research proposal, combined observations of student behavior,
as in Baker et al 2004, with observations of student affect, using in both cases Baker et al’s
“quantitative field observation” methodology. In this methodology, multiple observers observed
each student sequentially, for 20 seconds, coding each student’s behavior and affect. The
researchers studied an educational game, The Incredible Machine, and determined that in this
game, gaming the system was in many cases preceded by the student displaying boredom in an
observation 60 seconds earlier.
This study made important advances in understanding the relationship between affect and
behavior, but was conducted in a system without full logging; hence, it was not possible to use
this study to directly produce an affectively and behaviourally appropriate learning environment.
Here, we propose extending upon this previous work by conducting affective and behavioural
observations in a system with rich and detailed log files, in order to develop concrete models of
student affect and behavior which can be used to develop an affective learning environment. By
combining these models with the partner teams’ models of student knowledge, we hope to
collaboratively contribute to the building of more effective intelligent interactive learning
environments. Data from several different environments (the Philippines, France, and Vietnam)
may also help determine whether the models are generalizable across different cultures or
whether some mental models, behaviors, or affective responses are culture-specific.
B2. Project description
The project involves:
- Two researchers and two students from Ateneo team, (Manila, Philippines)
- Three researchers from Hochiminh team, (Hochiminh, Vietnam)
- Two researchers and one student from MeTAH team (Grenoble, France)
- Three researchers and one student from AMA team (Grenoble, France)
- One researcher from Nottingham team (Nottingham, Great Britain)
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6. Objectives, origin, implementation plan including the contribution of each organization involved
1. Experiments with Aplusix.
2. Data collection.
3. Data analyses and model building.
1. Data collection
Teams involved:
The Ateneo / University of Nottingham team
The MeTAH team
The Hochiminh team
For gathering the data, we will use the Aplusix learning environment (Nicaud et al., 2004) for
Algebra that allows students to freely make calculation steps and records all the students’ actions
(Figure 1).
Figure 1. Aplusix problem solving interface
The data collection methodology will depend of how each team wants to analyze it.
Nevertheless, here is the principal methodology:
Data will be collected from first to second year high school students in the Philippines, from
grades 8 & 9 students (in Grenoble), and from classes of different grades (8, 9 and 10) at 2 or 3
high schools (in Vietnam). This entails making arrangements with the high schools, installing the
software, preparing data collection instruments, collecting the data, and encoding it.
Some experiments will mix the training and test modes to help students learn algebra; other will
be limited to the test mode to study the students’ behavior in a more stable context were learning
is not supposed to occur, because of the absence of feedback. Experiments will be conducted by
teachers in the usual functioning of the class. For the experiments in the Philippines, researchers
will manually log student behaviors and emotions.
2. Pooling of the log files
Teams involved:
The MeTAH team
This team would have to create a website to gather and share data from the different
experimentations. Each team member will be able to pick up what he/she want and need for
his/her analyse.
3. Data analyses and Model Building
Teams involved:
The Ateneo / University of Nottingham team
The MeTAH team
The Hochiminh team
The AMA team
Aplusix records all the students’ action in log files and contains a replay system allowing to
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7. watch the students’ behaviors afterwards, with all the details. This allows for thorough posterior
analysis.
Data analyses will be conducted following three main directions:
- Modeling the relationships between emotional states and usage choices.
- Student’s knowledge modeling, by automatic construction using clustering
techniques.
- Student’s knowledge conceptions by a combination of hand construction and
automatic analyses.
- Cultural differences between data sets.
For the Ateneo / University of Nottingham team:
The team’s major objective is to derive computationally tractable student models that reflect
student behavior and affective states that co-occur or precede one another using data mining
techniques
Once all the data has been gathered, the Aplusix log files will be distilled into a flat database, as
in (Baker, Corbett, & Koedinger, 2004). Then, the observation data will then be synchronized
with the Aplusix log files, using Walonoski & Heffernan’s (2006) time-window synchronization
method.
Once the data is in this form, detectors of the relevant behavioural and affect categories will be
developed, using Latent Response Models (cf. Maris, 1995), and models of the transitions
between behavioural and affective states will be conducted using Hidden Markov Models. The
behavioural models developed will be compared mathematically to previous models of gaming
the system and off-task behavior, developed by the Nottingham partner, and the affective state
models will be compared at a conceptual level to the previous models developed by de Vicente
and Pain.
Data analysis methods may include but is not limited to correlation, principle component
analysis, and time series.
For the MeTAH team:
Since 2003, we are engaged, in collaboration with AMA team, in a research work devoted to
automatic student’s modeling in algebra. Our general model is based on a three-steps procedure:
- A local interpretation of student’s behavior. We interpret each student’s transformation
as a sequence of correct and incorrect rules. Watching log files since the replay system
allow us to build a rules library.
- A software, named ANAÏS, has been developed to analyse students productions. For that
purpose, it uses established rules from our didactical analysis, while trying to find the
best sequence of these rules (correct or incorrect) between two expressions. The actual
process is based on heuristic research.We want to improve it by a probabilistic approach.
- Mistakes, and more globally behaviours, which interest us, are those which are
“persistent and reproducible” (Brousseau, 1983): they are representative of the student
knowledge. For that, we want to seek the stability of actions: a same rule used by a same
student in different algebraic context. The couple (context, rule) will be a good student
knowledge representation if the rule appears frequently in student’s production in the
context. We will use and compare different techniques as bayesian network and factorial
analyses.
3x – 2 + 3x  – 2 Context description Rule
2x – 5x + 2x  – 5x
(Minus sign, a=b; ax + bx  (a-b)x
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8. For the AMA team:
Our student modeling approach will be based on a two-steps procedure, that requires a
representation of the student’s production into sequences of {previous state, following state}
pairs. The two steps are:
• a domain-dependent encoding of each {previous state, following state} pair produced
by the student, as a context-action-outcome (CAO) triplet, each item (C or A or O) of
the triplet being a set of attribute-value pairs.
• a domain-independent machine-learning procedure, based on a clustering technique
generating high-level patterns, that we call hereafter high-level abilities (HLA).
The students' HLAs are generalizations of the CAO triplets. They are represented within the
same formalism as the CAO triplets, i.e. {context, action, outcome}. Preliminary tests of this
approach on a subset of the Algebra domain transformations (namely, the movements of
expressions from one to the other side of equations and inequations) have given promising
results. Furthermore, HLAs may be translated by our system into natural language expressions
understandable by teachers as well as by the students themselves. HLAs might be used as inputs
to a tutoring system, for instance for generating or selecting a new set of exercises, which may be
eventually coupled with the Aplusix learning environment.
Figure 1 displays the general architecture, composed of the learning environment (1), the
encoder (2) and the machine learning construction of high-level abilities (3).
Fig. 1. General architecture of the AMA team approach
Within the present project, we plan to extend our (CAO) description to include emotional
information provided by the Manila University team.
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9. Partnerships and collaborations
Completing this project requires collaboration among partners from France, the Philippines,
Vietnam and the United Kingdom.
Within the Philippines, partners include faculty members and students of the Ateneo de Manila’s
Department of Information Systems and Computer Science and the Education Department and
the participation of four high schools.
Within the MeTAH team, partners include researchers and doctoral student of the Computer
Science and the Education Department and the participation of two Middle schools.
Within the Hochiminh team, partners include professors, students of Department of Math and
Computer science of Hochiminh City University of Pedagogy, teachers of four high
school of HCM ville.
Within the AMA team partners include researchers and doctoral students of the Computer
Science Department of Université Joseph Fourier, and also the MeTAH team.
Within the Nottingham team, the primary partner will be a researcher in the University of
Nottingham’s Learning Sciences Research Institute.
Contributions to affective computing
This research undertaking contributes to the field of affective computing by providing a student
model that satisfies the following design criteria for emotion recognition:
1. Input: The system must accept a variety of input signals related to emotion.
2. Pattern recognition: The system must extract features from the data gathered and classify
them into to significant categories, e.g. a smile versus a frown.
3. Reasoning: The system predicts the underlying emotions based on rules about how
emotions are generated and expressed.
4. Learning: The system tunes the rules, based on individual nuances.
5. Bias: The system must have its own emotional state that influences recognition of
ambiguous emotions.
6. Output: The system names or describes the emotion.
The limitation of the output is that the student model will at first only apply to the Aplusix
environment and may not apply to other tutors. However, by comparing this model to models of
student behaviors previously developed for Cognitive Tutors, and currently under development
for constraint-based tutors and educational action games by our Nottingham partner, this work
will in the long-term contribute to the development of student models which are broadly
generalizable.
Future applications in Intelligent Tutoring Systems
The models built from the analysis of the data may be used in the construction of an emotionally-
intelligent embodied conversational agent (ECA). This ECA will be integrated with Aplusix to
enable the software to respond to the students both cognitively and affectively. An ECA is
defined as a computer interface that has the same properties as humans in a face-to-face
conversation. Its abilities include but are not limited to the recognition of nonverbal input, the
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10. generation of nonverbal output, and the ability to give signals that indicate the state of the
conversation (Cassell, Sullivan, Prevost & Churchill, 2000).
One example of an ECA is Scooter the Tutor, an animated dog that monitors, detects, and
responds to students’ gaming behavior. When a student is determined to be gaming, Scooter
responds by turning red and growling, and giving the student supplementary exercises on the
items in which he or she gamed. If a student is not gaming, Scooter wags his tail and gives a
thumbs up. Results showed that about half as many students chose to game the ITS with Scooter
than the same ITS without Scooter, and Scooter significantly improved gaming students’
learning (Baker, Corbett, et. al, 2006).
Expected outcomes
The expected outcomes are:
• A corpus of data that will be available for multi-dimensional analysis
• A student model that reflects relationships between student affect and behavior
B3. Timeline and main implementation phases
We will communicate by emails principally during the data collection phases. After that, we will
expect to meet at a workshop (of a duration of about 6 days) in Manila by July 2008. We plan to begin
analyses and model building during the first semester of 2008. First results will be discussed in the
Manila workshop. Model testing and validation should follow the Manila workshop and last for about one
year. At the end of the project we plan to meet again at a workshop (assuming 6 days) in Grenoble, where
the main conclusions of the collaboration will be drawn.
NB: Ryan Baker will attend remotely, via videoconferencing.
Tasks 10-07- 12/07- 07/08 08/08- 10/08- 06/09
12/07 06/08 09/08 06/09
Preparation of Data Collection
Instruments
Scheduling of School Visits
Data Collection
Data Analysis
Joint Workshop in Manila
Model Testing and Validation with
more experiments if necesary
Joint Workshop in Grenoble
B4. Contributions
Each team will work on the data gathered by all the teams. It is an opportunity to have a large set
of data from different countries: data comparison can be made. Complementary approaches will
be involved to obtain models. We will consider interactions between the models during two
workshops: one in Manila and an other, in Grenoble. The results will be published in conferences
and journals.
B5. Organization & Credentials
Ateneo de Manila University
The Ateneo de Manila University is a 150-year old Jesuit institution in Quezon City, Philippines.
Its Department of Information Systems and Computer Science (DISCS) (discs.ateneo.edu) has
been in existence since 1984. DISCS offers undergraduate programs in Computer Science and
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11. Management Information Systems, masters programs in Computer Science and Information
Technology, and a doctorate in Computer Science.
DISCS is engaged in several research areas, among them elearning, wireless and mobile
applications, and biomedical informatics. In the area of elearning, DISCS recently completed a
two-year project to develop educational multimedia for grade school social studies and Filipino
(national language) classes.
The Ateneo Java Wireless Competency Center is the research center of DISCS. It develops
games and other applications for mobile devices as well as location-based services. Among its
clients are the Philippines’ major telecommunications and media corporations.
Finally, in the area of biomedical informatics, the DISCS faculty is involved in the ONCO-
MEDIA project (www.onco-media.com) also funded by ICT-Asia-France. The purpose of the
project is to develop a novel grid-distributed, contextual and semantic based, intelligent
information access framework for medical images and associated medical reports.
Ma. Mercedes T. Rodrigo
Ma. Mercedes T. Rodrigo has a Ph.D. in Computer Technology in Education from Nova
Southeastern University in Florida. She has participated and continues to participate in the
development of educational multimedia materials for Social Studies, Filipino, and Science
education for the use in Philippine public and private schools. She has published papers on the
current uses of ICTs in education in the Philippines.
She has worked as a consultant for the Philippines’ Department of Education. In 2005, she was
on the team that drafted the Philippines National Strategic Plan for ICTs in Education. She has
extensive experience in the training of public school teachers in the use of ICTs and of school
administrators in the creation of strategic ICT plans.
In 2006, she and her students collaborated with Dr. Ryan Baker of the University of Nottingham
on research to determine the relationship between affect and usage choices in a simulation
environment. This study resulted in a full conference paper that was accepted for publication in
the Artificial Intelligence in Education 2007 conference.
One MS Computer Science student under Dr. Rodrigo’s mentorship is also conducting a study
relating subject affect with usage choices while playing games developed by the AJWCC.
The MeTAH team
MeTAH (Modèles et Technologies pour l’Apprentissage Humain) is a pluridisciplinary team
(researchers in computer science and education) of the LIG laboratory (Laboratoire
d’Informatique de Grenoble). MeTAH has 50 members and LIG about 500 members. The
MeTAH team works for long in the field of ICT systems for education, both domain specific
systems (in algebra, chemistry, electricity, surgery), and generic systems for e-learning. The
researches includes on the one side elaboration of theoretical frameworks, and, on the other side,
the design, implementation, experimentation and evaluation of ICT-systems.
Jean-François Nicaud
Jean-François Nicaud is a Professor in computer science of the Joseph Fourier University
(Grenoble). He has been head of a computer science laboratory and of a teaching department.
His research work is for long in the field of algebra at many levels, from the building of
theoretical frameworks to the implementation of software. With a few researchers and students,
he has designed and implemented ICTs for algebra called Aplusix. The last version of Aplusix
combines a microworld, a cognitive solving process, and CAS like commands (CAS for
Computer Algebra System). The Aplusix software is currently commercialized in France, UK
and Italy. New modules concerning a tree representation of algebraic expressions and graphical
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12. representation is currently under development in the framework of the ReMath European project
(IST 4-26751). Jean-François Nicaud also works for long in the field of student modelling in
algebra.
The Hochiminh team
Department of Math and Computer science of Hochiminh City University of Pedagogy has had
many achievements in training and research. One of our objectives is applying informatics
technology in teaching mathematics. We got 58 professors and lecturers in mathematics and
informatics. Most of them are lead researchers in many fields: Analysis, Algebra, Geometry,
Methodology and Didactic of Mathematics, etc.
Nguyen Thai Son
Nguyen Thai Son has a PhD. in Complexe Analysis from University of Pedagogy of Hochiminh
city (2001). He is the leader of the Department of Maths and Computer science of Hochiminh
University of Pedagogy from 2001. He published 4 papers in international journals. He is
director of Center Application of Software in teaching of Mathematics in secondary school of
Hochiminh city.
The AMA team
The AMA team, formerly “Equipe Apprentissage”, is a team composed of three permanent
researchers and 5 doctoral students. The main area of research of our team is the models and
methods allowing a system to adapt to its environment and provide pertinent responses based on
available information. This domain covers machine learning (algorithms able to acquire
knowledge based on empirical data) and the modelization of human learning (both in teaching
situations and through social interactions). Our team has contributions in statistical learning
theory, in algorithmic developments for classification of numerical and symbolic data and in
cognitive modelling. Our approaches combine different and complementary representation
paradigms, which are seldom found in a same group, like Inductive Logic Programming, Neural
Networks, Latent Semantic Analysis, Reinforcement Learning, etc.
Mirta B. Gordon
Mirta B. Gordon has a PhD in theoretical physics from Université de Grenoble (1983). She is
Research Director in CNRS, and is the leader of a machine learning team (AMA) since 2001.
Since 1987 she is involved in pluridisciplinary research on the properties and applications of
neural networks and machine learning algorithms, as well as on the modelization of complex
systems. She gives a course on Models of Memory and Learning for graduate students in
Grenoble. She has supervised 10 PhD students.
She published 45 papers in international journals, 36 papers in international conferences, and
was invited speaker in 30 international conferences. She is co-author of a book on Neural
Networks [“Réseaux de neurones - Méthodologie et applications” G. Dreyfus, J-M. Martinez, M.
Samuelides, M. B. Gordon, F. Badran, S. Thiria, L. Hérault. Collection Algorithmes, Eyrolles
(2002) Livre de 386 pages, ISBN 2-212-11019-7,(2nd edition (2004) ISBN 2-212-11464-8.
English version (2005) : “Neural Networks”, ISBN 3-540-22980-9, Springer.]
University of Nottingham
The University of Nottingham is one of the distinguished Russell Group of top British
universities. It was founded in 1798, and is routinely ranked within the top ten universities in
Great Britain. The Learning Sciences Research Institute was founded in 2006, and was the first
research institute devoted to Learning Sciences in Europe. The LSRI offers masters and doctoral
level programs in the Learning Sciences, and is composed of researchers with backgrounds in
Education, Psychology, and Computer Science/IT.
4th call for proposals – Regional Programme ICT Asia 12

13. The LSRI is engaged in research in several frontier areas within its field, including technology-
enhanced learning, mobile learning, educational data mining, and mathematics cognition.
Ryan Shaun Joazeiro de Baker
Ryan Baker has a PhD in Human-Computer Interaction from Carnegie Mellon University’s
School of Computer Science (2005). He is a Research Fellow at the University of Nottingham’s
Learning Sciences Research Institute. His research focuses on how students choose to use
interactive learning environments, and how learning environments can and should adapt to
differences in student choices. He uses and develops methods for live and replay-based
observation, and techniques for mining data from educational systems. He has published 25 peer-
reviewed conference, journal, and workshop papers, and has served as the co-chair of two
scientific workshops in educational data mining.
B6. Others
- Experience of the lead researchers in the implementation of similar programmes
Ma. Mercedes T. Rodrigo has experience in creating instruments for and collecting data from
public and private school system. She also has relationships with Philippine schools that enable
the research team to conduct studies with these schools’ students.
Dr. Rodrigo’s recent research has been concerned with the gathering of data on affect and usage
choices for games, simulation environments, and intelligent tutoring systems.
Jean-François Nicaud has been responsible of a French project of the ACI “Ecole et Sciences
Cognitives” devoted to the analysis of student behaviors in algebra and the production of
teaching strategies (a 2 years projects, 2003-2004). He is responsible (for the French side) of a
bilateral cooperation between Brazil and France in the framework of the CAPES-COFECUB
programme (2004-2005, renewed for 2006-2007). The subject is “Integration of ICT
technologies in the teaching of mathematics”. CAPES-COFECUB finances travels and stays of
researchers visiting each other.
Jean-François Nicaud is also responsible of the Grenoble partner of the ReMath European
project (IST 4-26751). The work consists of construction of theoretical frameworks,
development of new module of Interactive Learning Environments, elaboration of learning
scenarios, and cross-experimentation of the new software. This is a 3 years project (2006-2008).
- Presentation of existing activities correlated with the main objective of the project
In 2006, Drs. Rodrigo and Baker led a group of students in a research project to determine the
relationship between affect and usage choices in a simulation environment. They found that
boredom and confusion precede or co-occur with gaming. Delight and flow are negatively
correlated with gaming.
- Prospect for the sustainability of the collaboration after the end of the ICT-Asia project
financing
The proponents of this project believe that it will be possible to collaborate further once the
models have been created. The models can then be implemented as ECAs for Aplusix.
4th call for proposals – Regional Programme ICT Asia 13

14. C. Budget proposal
Income Expenditure
4th call for proposals – Regional Programme ICT Asia 14

15. 1st Asian partners: Ateneo Team :
year
Consolidated budget:
• Two students – for 10 months; 50% of their time will be
- Ateneo de Manila University spent on the project; cost to Ateneo is P160,000 (approx.
Loyola Heights, Quezon City, €2,500)
Metro Manila, Philippines • Dr. Rodrigo – 20% of her time for the project duration; cost
to Ateneo is P150,000 (approx. €2300)
- Universite de Pedagogie de • Use of facilities: P100,000 (€1,550)
Hochiminh ville
Hochiminh, Vietnam Organization of an workshop in Manila (assuming 6 days)
• Logistics
2000€
French partners: • Mobility
0€
- MAE • Board and Lodging
0€
- Université Joseph Fourier
Bâtiment Administratif,
621, avenue Centrale Hochiminh Team :
Domaine Universitaire,
Consolidated budget:
Saint Martin d'Hères – Gières
France • Dr Nguyen Ai Quoc 60% (approx. 4000€)
• Dr Nguyen Thai Son 10% (approx. 1500€)
-CNRS • Dr Le Thi Hoai Chau 10% (approx. 1500€)
25 rue des Martyrs
The workshop in Manila (assuming 6 days for 2 person)
BP 166
• Logistics
38042 Grenoble cedex 9
0€
• Mobility
- Project Cluster de recherche 2*500 €
ISLE Rhône-Alpes • Board and Lodging
“ Informatique, signal, logiciel 2*(6*120+4*120) €
embarqué” funded by Rhône-
Alpes area.
Leaders:
Nicolas Balacheff, laboratory LIG MeTAH Team :
(Grenoble)
Nicolas.Balacheff@imag.fr Consolidated budget:
Alain Mille, laboratory LIRIS (Lyon)
Alain.Mille@liris.cnrs.fr • A Ph-D student, Marie-Caroline Croset, 60% of her time
(approx. 20000€)
• Dr Jean-François Nicaud 10% of his time (approx. 11200€)
• Dr Hamid Chaachoua 10% of his time (approx. 7200€)
• Equipement (1000€)
• Operational cost (1000€)
• International seminars (mobility 2*1500€)
The workshop in Manila (assuming 6 days for 2 persons)
• Logistics
0€
• Mobility
2*2000 €
• Board and Lodging
2*(6*120+4*120) €
AMA Team :
Consolidated budget:
• Regional Programme ICT Asia
4th call for proposals – Ph-D student, Vivien Robinet, 60% of his time (approx. 15

16. Co-financement: 105550 euros
Total 1st year Request to MAE: 18200 euros
Total: 123750 euros
4th call for proposals – Regional Programme ICT Asia 16

17. 2nd Asian partners: Ateneo Team :
year
Consolidated budget:
- Ateneo de Manila University • Two students – for 10 months; 50% of their time will be
Loyola Heights, Quezon City, spent on the project; cost to Ateneo is P160,000 (approx.
Metro Manila, Philippines €2500)
• Dr. Rodrigo – 20% of her time for the project duration; cost
- Universite de Pedagogie de to Ateneo is P150,000 (approx. €2300)
Hochiminh ville • Use of facilities: P100,000 (€1,550)
Hochiminh, Vietnam
The workshop in Grenoble (assuming 6 days for 4 persons)
• Logistics
0€
• Mobility
4*2000 €
French partners: • Board and Lodging
4*(120 * 6 + 120 *4) €
- MAE
Hochiminh Team :
- Université Joseph Fourier
Bâtiment Administratif, Consolidated budget:
621, avenue Centrale • Dr Nguyen Ai Quoc 60% (approx. 4000€)
Domaine Universitaire, • Dr Nguyen Thai Son 10% (approx. 1500€)
Saint Martin d'Hères – Gières • Dr Le Thi Hoai Chau 10% (approx. 1500€)
France
The workshop in Grenoble (assuming 6 days for 2 person)
-CNRS • Logistics
25 rue des Martyrs 0€
BP 166 • Mobility
38042 Grenoble cedex 9 2*2000 €
France • Board and Lodging
2*(120 * 6 + 120 *4) €
- Project Cluster de recherche
ISLE Rhône-Alpes MeTAH Team :
“ Informatique, signal, logiciel
embarqué” funded by Rhône- Consolidated budget:
Alpes area. • A Ph-D student, Marie-Caroline Croset, 60% of their time
Leaders: (approx. 20000€)
Nicolas Balacheff, laboratory LIG
(Grenoble) • Dr Jean-François Nicaud 10% of their time (approx.
Nicolas.Balacheff@imag.fr 11200€)
Alain Mille, laboratory LIRIS (Lyon)
Alain.Mille@liris.cnrs.fr • Dr Hamid Chaachoua 10% of their time (approx. 7200€)
• Equipement: 1000€
• Operational cost: 1000€
• International seminars: 2*1500€
The workshop in Grenoble (assuming 6 days for 7 person)
• Logistics
2000 €
• Mobility
0€
• Board and Lodging
0€
AMA Team :
Consolidated budget:
• Ph-D student, Vivien Robinet, 60% of his time (approx.
20000€)
4th call for proposals – Regional Programme ICT Asia 17

18. Total 2nd year Co-financement: 105550 euros
Request to MAE: 21200 euros
Total: 126750 euros
Total for the two years 250500 euros
4th call for proposals – Regional Programme ICT Asia 18

19. Financial support request to the French Ministry of foreign affairs (MAE):
1st Researchers Doctoral students Organization of Total
year Mobility Board and Mobility Board and the workshop (€)
Lodging Lodging Manila
Ateneo 0 0 0 0 2000 2000
Hochiminh 2*500 2*120*10 0 0 0 3400
MeTAH 1*2000 1*120*10 1* 2000 1*120*10 0 6400
AMA 1*2000 1*120*10 1*2000 1*120*10 0 6400
Total 5000 4800 4000 2400 2000 18200
2nd Researchers Doctoral students Organization of Total
year Mobility Board and Mobility Board and the workshop (€)
Lodging Lodging Grenoble
Ateneo 2*2000 2*120*10 2*2000 2*120*10 0 12800
Hochiminh 2*2000 2*120*10 0 0 0 6400
MeTAH 0 0 0 0 2000 2000
AMA 0 0 0 0 0 0
Total 8000 4800 4000 2400 2000 21200
Total request to the MAE for the two years: 39400 €
NB: No funding requested for University of Nottingham partner
Some publications of the AMA team in the last years
1. Analyse statistique de comportements d’élèves en algèbre. Gilles Bisson, Alain Bronner,
Mirta B. Gordon, Jean-Francois Nicaud, David Renaudie. Communication orale. Actes
de la conférence Environnements Informatiques pour l’Apprentissage Humain - EIAH,
Strasbourg, avril 2003, pp. 67-78.
2. Automatic peculiarities detection in PhenoScreen information system .Wieczorek S.,
Bisson G., Trilling L., Lafanechère L., Maréchal E. et Roy S.; in « European Conference
on Computational Biology (ECCB) & Journées Ouvertes : Biologie, Informatique,
Mathématique (JOBIM), Paris (France) 2003.
3. Hierarchical learning in polynomial support vector machines. Sebastian Risau-Gusman et
Mirta B. Gordon; Machine Learning 46 (2002) 53-70.
4. A Computational Model of Children's Semantic Memory. Denhière G., Lemaire, B.; in
Proceedings of the 26th Annual Meeting of the Cognitive Science Society (CogSci'2004)
(2004) 297-302.
5. Seller's dilemma due to social interactions between customers. Mirta B. Gordon, Jean-
Pierre Nadal, Denis Phan, Jean Vannimenus. Physica A 356, Issues 2-4 (2005), pp.
628-640.
6. Guiding the Search in the NO Region of the Phase Transition Problem with a Partial
Subsumption Test. S. Wieczorek, G. Bisson and MB. Gordon. In Proceeding of ECML
2006. LNCS 4212/2006. 18-22 september, Berlin. p 817-824.
7. Partial Subsumption Test and Phase Transition. S. Aci, G. Bisson, MB. Gordon, S. Roy,
S. Wieczorek. In proceedings of ILP conference, pp 222-225, Santiago de Compostela
August 24-27, 2006. Spain.
4th call for proposals – Regional Programme ICT Asia 19

20. Some publications of the MeTAH team in the last years
1. Bouhineau D., Bronner A., Chaachoua H., Huguet T., Analyse didactique de protocoles
obtenus dans un EIAH en algèbre., Actes de la conférence EIAH 2003. Environnements
Informatiques pour l'apprentissage Humain, Strasbourg, 2003.
2. Bouhineau D., Bronner A., Chaachoua H., Nicaud J.-F., Patrons d'exercices pour
Aplusix. Une étape du développement de l'EIAH occasion d'un travail entre didacticiens
et informaticiens, Actes de la conférence EIAH 2005, Environnements Informatiques
pour l'apprentissage humain, Montpellier, 2005.
3. Chaachoua H., Bittar M., Nicaud J.-F., Student’s modelling with a lattice of conceptions
in the domain of linear equations and inequations, Actes PME30, 2006.
4. Gordon M., Bisson G., Renaudie D., Sander E., Robinet V., Balacheff N., Croset M-C,
Nicaud J.-F., Chaachoua H., Bouhineau D., Bittar M., Modélisation cognitive d'élèves en
algèbre et construction de stratégies d'enseignement dans un contexte technologique,
Project report of the “Ecole et sciences cognitives” research programme. Cahier du
laboratoire Leibniz n°123, http://www-leibniz.imag.fr/, 2005.
5. Nicaud J.-F., Bouhineau D., Chaachoua H., Mixing microworld and CAS features in
building computer systems that help students learn algebra, International Journal of
Computers for Mathematical Learning, Vol. Vol. 9, Issue 2, 2004.
6. Nicaud J.-F., Chaachoua H., Bittar M., Automatic calculation of students’ conceptions in
elementary algebra from Aplusix log files, ITS, 2006.
7. Nicaud J.-F., Chaachoua H., Bittar M., Denis B., Student's modelling with a lattice of
conceptions in the domain of linear equations and inequations, AIED, Amsterdam, 2005.
8. Sander E., Nicaud J.-F., Chaachoua H., Croset M.-C., From usage analysis to automatic
diagnosis: the case of the learning of algebra, AIED, Amsterdam, 2005.
Some publications of the Ateneo and Nottingham teams in the last years
1. Baker, R.S. (in press) Modeling and Understanding Students' Off-Task Behavior in
Intelligent Tutoring Systems. To appear in: Proceedings of ACM CHI 2007: Computer-
Human Interaction.
2. Baker, R.S., Corbett, A.T., Koedinger, K.R. Detecting Student Misuse of Intelligent
Tutoring Systems. Proceedings of the 7th International Conference on Intelligent
Tutoring Systems, 531-540, 2004.
3. Baker, R.S.J.d., Corbett, A.T., Koedinger, K.R., Evenson, E., Roll, I., Wagner, A.Z.,
Naim, M., Raspat, J., Baker, D.J., Beck, J. Adapting to When Students Game an
Intelligent Tutoring System. Proceedings of the 8th International Conference on
Intelligent Tutoring Systems, 392-401, 2006.
4. Baker, R.S., Corbett, A.T., Koedinger, K.R., Wagner, A.Z. Off-Task Behavior in the
Cognitive Tutor Classroom: When Students "Game the System". Proceedings of ACM
CHI 2004: Computer-Human Interaction, 383-390.
5. Rodrigo, M. M. T. (2005). Quantifying the divide: A comparison of ICT usage of
schools in Metro Manila and IEA-surveyed countries. International Journal for
Educational Development, 25, 53-68.
6. Rodrigo, M. M. T. (2003). Tradition or transformation? An evaluation of ICTs in Metro
Manila schools. Information Technology for Development, 10(2), 95-122.
7. Rodrigo, M.M.T., Baker, R.S.J.d., Lagud, M.C.V., Lim, S.A.L., Macapanpan, A.F.,
Pascua, S.A.M.S., Santillano, J.Q., Sevilla, L.R.S., Sugay, J.O., Tep, S., Viehland, N.J.B.
(in press) Affect and Usage Choices in Simulation Problem Solving Environments. To
appear in Proceedings of Artificial Intelligence in Education 2007.
4th call for proposals – Regional Programme ICT Asia 20

21. References
Amershi, S. & Conati, C. Automatic recognition of learner groups in exploratory learning
environments. In Mitsuru Ikeda, Kevin D. Ashley, Tak-Wai Chan (Eds.), Intelligent Tutoring
Systems, 8th International Conference, ITS 2006. Jongli, Taiwan, June 2006, 463-472. Germany:
Springer-Verlag.
Amershi, S., Conati, C., & Maclaren, H. Using feature selection and unsupervised clustering to
identify affective expressions in educational games. In Proceedings of the Workshop
"Motivational and Affective Issues in ITS" In conjunction with ITS2006, 8th International
Conference on Intelligent Tutoring Systems, Jhongli, Taiwan.
Anderson, J.R., Boyle, C.F., Corbett, A., & Lewis, M. "Cognitive modeling and intelligent
tutoring", Artificial Intelligence, 42, 7-49, 1990.
Anderson, J.R., Conrad, F.G., & Corbett, A.T. The Lisp Tutor and Skill Acquisition. In:
Anderson, J.R. (Ed.) Rules of the Mind, 143-164, 1993. Hillsdale, NJ: Lawrence Erlbaum
Associates.
4th call for proposals – Regional Programme ICT Asia 21