Abstract—In science education, it is believed that students should understand the qualitative principles that govern the subject including the cause-effect relationships in processes before they are immersed in complex problem solving. Traditional educational programs for teaching organic chemistry do not usually explain or justify an observed chemical phenomenon. These programs do not “explain” simply because the results are obtained through chaining the rules or by searching the reaction routes that have been pre-coded in software. This paper discusses the development techniques, simulation results, and student evaluation of a software tool that aimed to help chemistry students learn organic processes through the study of causal theories in a chemical system. Mastering the causal theories of physical phenomena can help students in answering fundamental questions in science education. The simulation technique used is qualitative reasoning that emphasizes the importance of conceptual knowledge and causal theories in education, particularly concerning predicting and reasoning about system behaviour. The results from a preliminary evaluation showed that the tool is effective in terms of its ability to promote students’ understanding of organic reactions through the inspection of the explanations generated by the software, where students are seen as the recipients of knowledge delivered via the “explanation” pedagogy. Keywords—Evaluation, explanation, learning, organic reaction, qualitative reasoning. I. INTRODUCTION RGANIC chemistry reaction is a difficult subject to learn. Many chemistry students learn organic reactions by memorizing the steps and formulas of each reaction which can easily be forgotten. They face difficulties in dealing with the principles governing the processes and the cause-effect interaction (causal theories) among these processes. If students learn the subject by memorizing the steps and patterns of each reaction, then they may not be able to answer simple questions such as: Why would this reaction go this way? What is favourable about this particular step? Why was the process stopped? This is the educational problem that is being solved, Y.C. Alicia Tang is with the University of Tenaga Nasional, Selangor, Malaysia (phone: 603-8921-2336; e-mail: aliciat@uniten.edu.my). S. M. Zain is with the Department of Chemistry, Malaya University, Kuala Lumpur, Malaysia (e-mail: smzain@um.edu.my). R. Abdullah is with the Department of Artificial Intelligence, Malaya University, Kuala Lumpur, Malaysia (email: rukaini@um.edu.my). as memorizing formulas is not a good method in any type of learning. In understanding organic reactions, one has to know the many cognitive steps from one chemical reaction to another until a stable product is formed. These cognitive steps (the “mechanisms”) are among the many difficulties chemistry students are facing; such as lacking the skills to analyze the various steps and translate the reactions into the forms that can be used to predict the final product in reasonable and justifiable ways. There has been many strives for innovation in teaching and learning chemistry using computer software. However, most of the chemistry educational software used traditional approach [1]. Traditional chemistry educational software is inadequate to promote understanding such as explaining why and how things happen. These programs do not “explain” simply because the results are obtained through chaining the rules or by searching the reaction routes that have been pre-coded in software. Existing knowledge-based systems for organic chemistry are not using qualitative reasoning as the problem solving technique. Examples of techniques used are Self-Organizing Map (SOM) and Neural Networks and Genetic Algorithms as described in [2]. Qualitative reasoning (QR) which makes causality explicit is of value in education. The potential of this new methodology for building science educational software has been demonstrated by several high cited works such as CyclePad [3], VisiGarp [4]-[6], ALI [7], and Betty’s Brain [8]. The common features of these systems are the ability to predict and explain the behaviour of physical systems in qualitative terms in educational and training setting. The success of the software to promote and induce learning and the birth of articulate software [9]-[10] marked another milestone for further investigation, application, and popularity of qualitative reasoning techniques. Other QR-based systems are: Intelligent tutoring systems for training [11] where the simulation is based on components ontology and QPT, qualitative models in ecology and their use in intelligent tutoring system by Salles & Bredeweg [12] and Salles et al. [13], Error-Based Simulation (EBS) to predict the qualitative behaviour of mechanics problems and to generate feedback for learning from mistake [14]-[16], and works on authoring Graph of Microworld (GMW) [17]-[19]. This paper discusses the results of the implementation of the conceptual framework described in previous works [20]-[21]. Development and Evaluation of a Chemistry Educational Software for Learning Organic Reactions Using Qualitative Reasoning Alicia Y. C. Tang, Sharifuddin M. Zain, and Rukaini Abdullah O INTERNATIONAL JOURNAL OF EDUCATION AND INFORMATION TECHNOLOGIES Issue 3, Volume 4, 2010 129