姓名：謝宏偉學號：M99G0219班級：碩研資工一甲

201O 2nd International Conference on Education Technology and Computer (ICETC)

Neural Network Based Intelligent Analysis of Learners' Response for an e-Learning Environment

Outline1. INTRODUCTION 2. LEARNERS RESPONSE IN AN E-LEARNING

ENVIRONMENT3. INTELLIGENT RESPONSE ANALYSIS4. PROPOSED SCHEME5. EXPERIMENTAL RESULTS6. CONCLUSIONS

1. INTRODUCTIONThe scheme applies to typed-in single-word

textual response from the learners' end.

The proposed system is intelligently adaptive to inadvertent mistakes committed by the learner while responding to the system's queries.

2. LEARNERS RESPONSE IN AN E-LEARNING ENVIRONMENTTyped interactions may take place in an e-

Learning system under two circumstances, viz., dialog between the learner and the system, to simulate the real-life learning experience and assessment of learning achievement.

While objective type interaction is close ended and guided, based on the options available in the test item itself, dialog based test items need embedded intelligence to assess.

2. LEARNERS RESPONSE IN AN E-LEARNING ENVIRONMENTTo provide a more meaningful learning

experience, or assessment of learning achievement, under an E-learning environment, a learner must be offered the scope of interacting with the system through typed-in texts.

3. INTELLIGENT RESPONSE ANALYSISThe limitations of MCQ's are covered to

some extent by close ended questions having text based answers with single words or a few sentences in which case we get the best of both worlds.

3. INTELLIGENT RESPONSE ANALYSIS

3. INTELLIGENT RESPONSE ANALYSIS

3. INTELLIGENT RESPONSE ANALYSIS

4. PROPOSED SCHEMENeural Networks have been trained and

used as effective classification tools where the values in the output neurons are indicative of the class to which the input patterns culminate.

Viable and successful applications of ANN's as language classifiers are available both for text and phonetics based approaches .

4. PROPOSED SCHEME

4. PROPOSED SCHEME

5. EXPERIMENTAL RESULTS

5. EXPERIMENTAL RESULTS

5. EXPERIMENTAL RESULTS

5. EXPERIMENTAL RESULTS

6. CONCLUSIONSThe proposed scheme successfully

simulates a human instructor communicating with the learner through single-word typed in text.

Experimental results show that the system is intelligent enough to simulate the interaction between a human instructor and a learner when the learner's response is restricted to single word.