Read AI BASED TECHNIQUE FOR UNDERSTANDING `LEARNING' text version

AI BASED TECHNIQUE FOR UNDERSTANDING `LEARNING'

SUMMARY Our earlier study (1-5) indicates that the teaching and learning techniques are iterative and the agile technique should be considered for delivering the teaching materials for facilitating the learning process. We are using agile problem driven teaching techniques considering both intuitive mode and controlled mode for learning. Yet, the learning skill of a student depends on various factors including a student's state of mind. According to the experts, learning should occur in context, be active, social and reflective. Several experiments were conducted to find the right methodology for `improving' the learning skill. A recent study by the US Department of Education and the National Research Council (6) recognized that the proper use of computer technology helps the learning process. The application of computers to learning and teaching has evolved from simple text to audio and visual communication. While the information is visual and auditory, it can also utilize kinesthetic, as the user interacts with the system via the keyboard and mouse. This has been successfully applied while designing the learning materials through gaming techniques using joystick or/and keyboard for interaction with the software system. Computer based courses should utilize all three learning styles, namely, visual, auditory, and kinesthetic. Developing a tutorial system that incorporates the user's learning goals contextually and uses computer technology appropriately to meet these goals in an innovative learning environment is a challenging task. In our ongoing study in the field of teaching and learning techniques, we are developing web based software system that can improve the learning skill of a student. However, the term `learning' is a complex phenomenon involving certain parts of a human brain that is yet to be fully understood using indepth analysis of both experimental neurobiology and cognitive behavior. We are developing Artificial Intelligence based software tool to understand learning and modify the teaching methodology accordingly. This tool has two modules: the first module is delivering the course contents online to the students and the second module is web based testing of the acquired skills by those students. While creating the course contents, our attempt is to use all three styles, namely, visual, auditory and kinesthetic considering both intuitive mode and controlled mode for learning. The testing module utilizes Artificial Intelligence technique that challenges a student with more difficult questions once they acquire standard level of aptitude for learning. This software system can be customized to serve various purposes. One way is to `force' a student to learn a chapter before proceeding to another chapter for the same course. This system will not allow the students to proceed for the next chapter until s/he takes the exam online on the present chapter and gets >90% scores, as for example. We also modify the delivery method of the course contents until the average score of the test results achieved is 90% or more. This software system can also be used to determine the aptitude of a student before placing him/her for Advanced Program (AP).

REFERENCES: 1. Agile Problem Driven Teaching in Engineering, Science and Technology (2009). Pradip Peter Dey, Thomas M. Gatton, Mohammad N. Amin, Mudasser F. Wyne, Gordon W. Romney, Alireza Farahani, Arun Datta, Hassan Badkoobehi, Ralph Belcher, Ogun Tigli and Albert P. Cruz. Proceedings of the American Society for Engineering Education-Pacific Southwest, San Diego (CA), March 19-20, 2009. (See at http://www.asethome.org/asee/ASEE_PSW_2009_Proceedings.pdf). 2. Teaching Mathematical Reasoning in Science, Engineering, and Technology (2009). Dey, P., Uhlig, R., Amin, M., Datta, A., Romney, G., Gatton, T., Wyne, M. & Cruz, A. Journal of Research in Innovative Teaching , Volume 2, p286 - 302. (See at http://www.nu.edu/assets/resources/pageResources/7638_JournalofResearch09.pdf). 3. A Web based Intelligent Tutorial System (2008). Thomas Gatton, Arun Datta, Pradip Dey, Jose Jorge Martinez and Chaoting Ting. Journal of Research in Innovative Teaching, volume 1, p158-173. (See at http://www.nu.edu/assets/resources/pageResources/Journal_of_Research_March081.pdf). 4. Multi-model Multi-strategy Teaching/Learning in Science, Engineering and Technology (2008). Pradip Peter Dey, Mohammad Amin, Michelle Bright, Arun Datta, Shakil Akhtar, Albert Cruz, Mudasser F. Wyne, Patrick Kennedy, Patrick Olson, Rell Snyder, Alireza Farahani, Samuel Afuwape, Hassan Badkoobehi and Amber Lo. Proceedings at the International Computer Science and Technology Conference, p158-162, San Diego (CA), April 1-3, 2008. (See at http://www.asethome.org/icstc/). 5. Agile Teaching: Dynamically Adjusting Teaching Strategies for Improved Learning (2008). WASC ARC (Academic Resource Conference) "Illuminating Learning, Accrediting Quality", Sheraton San Diego Hotel and Marian, April 16-19, 2008. 6. How People Learn: Brain, Mind, Experience, and School (1999). Bransford, J.D., Brown, A. L., & Cocking, R. R. National Academy Press, Washington, D.C.

For DEMO of the system, see.

Information

AI BASED TECHNIQUE FOR UNDERSTANDING `LEARNING'

2 pages

Report File (DMCA)

Our content is added by our users. We aim to remove reported files within 1 working day. Please use this link to notify us:

Report this file as copyright or inappropriate

335404


You might also be interested in

BETA
Interest
Introduction.p65
Microsoft Word - 2-EDICT-2010-1103-CE.docx
Newsletter_Nov_FA2.indd