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A multi-dimension analysis of students' log files in algebra

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  • 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. 4th call for proposals – Regional Programme ICT Asia
  • 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 4th call for proposals – Regional Programme ICT Asia 2
  • 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 4th call for proposals – Regional Programme ICT Asia 3
  • 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. 4th call for proposals – Regional Programme ICT Asia 4
  • 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) 4th call for proposals – Regional Programme ICT Asia 5
  • 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 4th call for proposals – Regional Programme ICT Asia 6
  • 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 4th call for proposals – Regional Programme ICT Asia 7
  • 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. 4th call for proposals – Regional Programme ICT Asia 8
  • 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 4th call for proposals – Regional Programme ICT Asia 9
  • 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 4th call for proposals – Regional Programme ICT Asia 10
  • 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 4th call for proposals – Regional Programme ICT Asia 11
  • 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