Intelligent Learning System for Automata (ILSA) and the Learners’ Achievement Goal Orientations
Studying automata theory exposes the students to the theoretical foundation of Computer Science where they learn abstraction, generalization, and reasoning. However, teaching and learning automata is challenging because of the involved abstract notions and mathematical background. Many students experience difficulty in understanding the computability concepts. Recent advances in teaching the course focus on the development of different pedagogical tools that can be used to facilitate the learning of automata theory and formal languages. Developments of tutoring systems for automata, like simulators, are continuously advancing. Fundamental efforts on features of automata simulators, based on the open literature, are focused on the following: visual creation, animation, conversion (transformation), interaction, logs generation, and saving and exporting facility. They do not support customization based on learners’ performance in the tutor environment, like provision of individualized learning path and feedback. While these existing tutors facilitate teaching and understanding of the concepts, they do not focus on identifying whether learning is achieved.
Another factor that mediates student achievement is goal orientation. This theory suggests that students’ behavior and response to the learning environment are guided by goals. Some students are performance-oriented while others are mastery-oriented. These personal goals interact with the learning environment, sometimes referred to as classroom goals. How these classroom goals align with students’ individual goals can have an effect on both a student’s achievement and learning experience.
Hence, the first goal of this study is to augment the capabilities of an automata simulator to characterize Intelligent Tutoring System (ITS) that is driven by a learner model to support individualized learning path, feedback, and support. The second goal of this work is to include features in the ITS that are intended to cater to the different achievement goal orientations of learners. The last goal would be to determine relationships among learners’ intutor behavior, their goal orientations, and learning.
Tecson, C. (2018). Intelligent learning system for automata (ILSA) and the learners’ achievement goal orientations. In S. Murthy, H. Ogata, W. Chen, J. C. Yang, M. Chang, L.-H. Wong, & M. M. T. Rodrigo (Eds.), 26th International Conference on Computers in Education Doctoral Student Consortium Proceedings (pp. 21–24). Asia-Pacific Society for Computers in Education (APSCE). https://apsce.net/icce/icce2018/index.html@p=1026.html