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Intelligent LMS with an Agent that Learns from Log Data in a Virtual Community
Abstract
This study describes an agent that acquires domain knowledge related to the content from a learning history log database in a learning community and automatically generates motivational messages for the learner. The unique features of this system are as follows: The agent builds a learner model automatically by applying the decision tree model. The agent predicts a learner’s final status (Failed; Abandon; Successful; or Excellent) using the learner model and his/her current learning history log data. The constructed learner model becomes more exact as the amount of data accumulated in the database increases. Furthermore, the agent compares a learner’s learning processes with “Excellent” status learners’ learning processes stored in the database, diagnoses the learner’s learning processes, and generates adaptive instructional messages for the learner. A comparison between a class of students that used the system and one that did not demonstrates the effectiveness of the system.
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