FOR SPECIAL NEEDS EDUCATION. Jaakko Kurhila. 1 and Erkki Sutinen. 1. Abstract. In a learning situation, varying needs of disabled children can be met in a ...
AGENTS IN AN ADAPTIVE LEARNING ENVIRONMENT FOR SPECIAL NEEDS EDUCATION Jaakko Kurhila1 and Erkki Sutinen1
Abstract In a learning situation, varying needs of disabled children can be met in a personal and adaptive learning environment. We give a description of an agent-based learning environment for special needs education. The learning environment ensures individualized learning processes for every learner. We have implemented a functioning prototype targeted to children suffering from frontal lobe lesion. Although comprehensive tests with the prototype are lacking, initial reactions from the special educators have indicated that the concept is appropriate and worth developing. Modular and open design of the prototype provides a basis for further research.
1. Introduction The concept learning environment can be viewed as a physical space where learning occurs. In our framework, the learning environment consists of networked computers with accompanying software and their users. Users can be learners, teachers, special educators, occupational therapists, neuropsychologists etc. The basis for this framework is to tailor an agent for each participant in the learning environment. The definition of an agent is somewhat obscure [2, 3]. In our framework, an agent can be defined as a part of a program that acts on the behalf of the user or, as Russell and Norvig state it [4, p. 31], ”an agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.” The framework is to be used in a normal classroom setting with one or more teachers and several learners. The framework is designed to support the learning and teaching processes of each individual learner and teacher. One of the goals is to divide the responsibility of the teaching between a computer and a human teacher. 1 Department
of Computer Science, P.O. Box 26, FIN-00014 University of Helsinki, Finland
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Figure 1: Distributed learning environment, where every participant has his/her own agent.
There are customized agents for each learning environment participant. The agents belong to various classes according to environment users. Every learner has his/her own learner agent, teacher has his/her teacher agent etc. The agents communicate with other agents and modify the teaching and learning processes. Learner agents can, for example, provide exercises and additional material to learners when needed. Teacher agent observes the learners and provides information from the learners’ progress to the teacher. Other agents, e.g. neuropsychologist agent, can gather information from the learners’ actions to the neuropsychologists and help to diagnose the user. Figure 1 shows a simple example of a possible learning situation. The teacher agent has found learning difficulties with the learner in location 3, so he/she is instructed directly by the teacher, while learners in locations 1 and 2 are kept activated by their learner agents. Neuropsychologist agent gathers data from the actions of the learner in location 1. The first implementation of the learning environment is ready. It is a prototype of the learner agent for one learner, and it is to be used for practicing elementary mathematics with individualized and adaptive learning path. Because of the open design, the subject can be changed from mathematics to any other desired topic. As stated in [1], there are no sophisticated computer science solutions to special needs education, but the need for proper software is obvious. Practical reasons include the lack of human teachers. With software agents, not only the quantity of the teaching can be enhanced, but also the quality and the depth. In the following section, we outline the intended users of the prototype learner agent. In chapter 3 we describe the prototype in detail. In the concluding section we give an early evaluation of the learning environment.
2. Intended users Special needs education differs radically from ordinary education because of the heterogeneity. For the prototype implementation, we have limited the targeted users to children with mental programming disorders [5] caused by frontal lobe lesion, but the schema is most likely to be useful with other types of disabilities even without alterations. During a learning situation, mental programming disorders appear in various forms. The learners have difficulties in concentrating during a long-lasting task and upholding their motivation. They must be lead to the final goal by dividing the problem into smaller and shorter subtasks. The lost motivation can be brought back by personal, emotionally influencing feedback. Activeness can be regained by multi-sensory feedback. Computers are able to convey visual, aural and possibly tactile feedback. The line between essential and non-essential is vague for frontal lobe lesion patients. They cannot find essential points in a complex problem. Therefore, presentation must be eliminated to the simplest form. Even a slightest misfit distracts or interrupts the execution of the task.
3. Prototype implementation: the learner agent In this chapter, we describe the preliminary implementation of the learning environment for one computer. The objectives for the prototype and its architectural solutions are presented in detail. Visuality and user interface issues are examined briefly. 3.1. Main objectives The main objective in the prototype is to practice elementary mathematics, i.e. addition, subtraction, multiplication and division. It is presumed that the learner is already familiar with the presented arithmetical operations, or is taught by a human teacher while using the program. In other words, the learner knows how to add to numbers together when practicing with the addition calculations. The agent offers a practice partner to rehearse mathematical problem solving. If the learner finds trouble during practice session, he/she should be helped by the computer. In this framework, the learner agent is a program coordinating the practice session. The agent decides which exercise is presented to the learner by analysing the learner’s behaviour. To make a decision and to fetch an exercise the agent uses external changeable databases.
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Figure 2: The logical structure of the prototype.
There can be several learners and one teacher, but they do not interact with each other by a computer. The teacher’s task is to monitor the learners and their actions, and provide help when needed. In the future, message passing and other kinds of cooperation can be included in the environment to enable collaborative problem solving. Multi-sensory feedback is achieved by presenting the feedback visually on the screen and aurally by loudspeaking the exercises and feedback. To avoid distracting other learners in the same room, headphones can be used. Tactile feedback is not implemented in the prototype. The program keeps a score from problem-solving so that the learners can observe their personal progress. Points are given according to the level of difficulty. The points are not stored in a publicly available high-score list so that a learner can compare him/herself only with his/her previous accomplishments. The prototype is coded with Java, and it is usable in common windowing environments such as MSWindows or UNIX X-Windows. 3.2. Functionality of the learner agent The logical action in the prototype learner agent goes as follows (Figure 2). When a practice session is started, the first exercise is fetched from the database called the exercise cube (1) and provided by the learner agent through the user interface to the learner (2). As the learner uses the software, he/she generates data which is gathered by the learner agent (3). The data the learner agent gathers can be response times, correctiveness of the answers, number of attempts, and so on. The learner agent
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Figure 3: A visualization of a learner’s trail through the exercise cube.
pushes the data through a decision tree to find out how the learner should be helped or which exercise is fetched next from the exercise cube and presented to the learner (4 and 5). This procedure is repeated as long as the execution of the program is terminated or the learner agent runs out exercises. Detailed descriptions of the exercise cube and the decision tree are given in the following sections. The program can store each learner’s progress and individual achievements, so that the learner may continue from the level he/she has achieved. The learner identification is done with a typical login procedure. 3.3. Exercise cube The cube is a term for the set of exercises in the prototype implementation. In this prototype implementation, the exercises are mathematical problems. The term cube is used because the set has three different dimensions. The first dimension is the level of difficulty (x-axis in Figure 3). The proper level of difficulty is inferred from the amount of terms in the problem, the magnitude of terms, the magnitude of answers, the amount and closeness of multiple-choice answers, etc. The second dimension in the cube is the set of exercises of equal difficulty (y-axis in Figure 3). A large number of exercises and random picking method within that level ensure that the session is not similar to previous sessions, e.g. when the learner has reached his/her level and is continuously trying to advance to the next level. The exercises can often be partitioned to smaller problems or subtasks. For example, if the learner
cannot solve a problem 3+2+4, the problem can be partitioned to 3+2. If the learner can answer that, the original problem can then be presented as 5+4. This is the third dimension in the cube, and it is called the exercise helping path (z-axis in Figure 3). The helping path is not necessarily a partitioning of the problem. It can also be some other way to help the learner to achieve the goal. For example, when the learner is not able to calculate 1+5, the possible way to help is to present the problem as 5+1. Every topic has its own exercise cube. The prototype contains cubes for addition, subtraction, multiplication and division. Also combinated cubes for addition/subtraction and multiplication/division exist. The exercises and their multiple-choice answers must be coded manually to the program beforehand. The reason for this procedure is that problems with a sound pedagogical background are almost impossible to create by a computer. In addition, it is not enough that only problems have a sound pedagogical basis. Also the multiple-choice answers and the amount of them must be thought thoroughly. Creating problems manually forces to think the pedagogical basis for each problem and transfers the responsibility to the educationist. However, certain tools to aid the design of the problems to the cube can be done in the future. The contents of the cube can be changed to a different set of exercises, since the cube is an ASCIItext file consisting of the actual problem with a proper difficulty ranking, the solving path and the answers. The exercises can include non-numerical terms or even be non-mathematical as long as they are multiple choice questions. In the latter case, the prototype contains no support for speech output. 3.4. Decision tree The decision tree contains branches of questions which affect the decisions and actions the learner agent makes. For example, if the learner has answered the last five times correct and has not consumed too much time, the decision could be to raise the level difficulty. If the learner does not know the right answer, the decision is to present the next step from the exercise helping path. Therefore, each learner is likely to go a different trail through the cube. Figure 4 shows an example of a decision tree. The decisions are based on the answers (arcs) to the questions in the branches (nodes). The tree in Figure 4 is a simplified version of the decision tree in
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Figure 4: A simplified example of a decision tree used in the prototype implementation.
the prototype. The prototype contains a decision tree with five levels of nodes and 72 leaves which lead to five different decisions. The five possible decisions in the prototype are
to get an exercise of same difficulty level to raise the difficulty level to help the learner (i.e. present a next step in the exercise helping path) to go down a level to send a message to the teacher2 . First four decisions steer the learner through the exercise cube. The fifth is used in a situation where helping the learner is beyond the capabilities of the learner agent, or the learner stops responding. The decision tree can be viewed responsible for the pedagogics in this learning environment. Depending on the questions in the nodes and the limits of the answers in the arcs, the learning environment can be altered to act according to various pedagogically justified learning strategies. As well as the exercise cube, the decision tree can easily be modified or even totally replaced, because the decision tree is a simple ASCII-text file. The tree in the prototype is static (i.e. constructed before the execution of the program), but a shift to a dynamic tree is one of the future research topics.
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the teacher agent is not yet implemented, this decision is actually useless.
Figure 5: The user interface of the Finnish prototype.
3.5. User interface and visual appearance The visual appearance of the program is as simple as possible to ensure that the attentiveness is not distracted by inappropriate or misleading visual cues and signals (Figure 5). There are no movement on the screen while presenting an exercise, and the colors and fonts are chosen to suit persons with slight visual impairments. The multiple-choice answers are coded with the exercises. The amount of the answers can vary from 1 to 10. The answers are always arranged in an ascending order to avoid the mental strain searching causes to frontal lobe lesion patients. The user interface is capable to utilize a variety of input devices. The program can be used with a mouse or some other pointing device. The learner can also control the program from the keyboard by just two buttons; one to scan the multiple choices and one to select the answer.
4. Concluding remarks We have designed a framework for an adaptive learning environment to special needs education. A prototype implementation for single learner is ready. New features in our approach include the usage of software agents monitoring and guiding the learner, thus providing the adaptivity needed to ensure meaningful individual learning process. The design has been an interdisciplinary effort. The prototype offers a practice partner to rehearse basic mathematics, but due to the modularity in
design, other subjects are possible. Also, the prototype can easily be extended to serve as a diagnostics tool for neuropsychologists and special educators. Our approach rooted in special needs education generates new perspectives to ”ordinary” education. The designed scenario is sensitive to differences between the learners. The presented framework is particularly fruitful subject to computer science, since there is an intrinsic correspondence between human problem solving and computer programming methods. This correspondence is even more distinct with the intended users of the prototype, the frontal lobe lesion patients. Even though actual usability and utility tests are lacking, feedback from the special teachers indicate that the concept is genuine and a need for this kind of a learning environment exists.
5. Acknowledgements The authors wish to thank Tuula Eriksson for her advice on neuropsychological aspects and Erkki Lamminranta for his valuable information on special education. We are also grateful to the software project team Sampo Jokinen, Mikko Koskenniemi, Sami Laasanen, Ran Nyman, Tuukka Vartiainen and Pasi V¨ais¨anen for the implementation of the prototype.
References [1] ERIKSSON, T., KURHILA, J. and SUTINEN. E.: ”An Agent-Based Framework for Special Needs Education”. In SCAI ’97 Sixth Scandinavian Conference on Artificial Intelligence: Research Announcements, Helsinki, Finland, August 18-20, 1997, pp. 3-12. University of Helsinki, Department of Computer Science, Report C-1997-49. [2] FRANKLIN, S. and GRAESSER, A.: Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents. In Proceedings of the ECAI’96 Workshop on Agent Theories, Architectures, and Languages: Intelligent Agents III, pp. 21-36, Springer, August 12-13 1997. [3] NWANA, H. S.: Software agents: an overview. The Knowledge Engineering Review 11, 3 (1996), 205– 244. [4] RUSSELL, S. and NORVIG, P.: Artificial Intelligence: a Modern Approach. Prentice Hall, 1995. [5] VILKKI, J.: Neuropsychology of mental programming: an approach for the evaluation of frontal lobe dysfunction. Applied Neuropsychology 2, 1995, 93–106.
