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Intelligent Tutoring on Virtual Cell: A Whole New World

This article discusses how intelligent tutoring systems can increase student learning by providing directions to resources, recognizing and providing feedback on learning obstacles, encouraging investigation and group interactions, and more. It also explores the functional requirements of a tutor and the model of student understanding.

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Intelligent Tutoring on Virtual Cell: A Whole New World

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  1. Intelligent Tutoring on Virtual Cell: A Whole New World Part II Ganesh Padmanabhan

  2. Overview • Review Part I • What is needed to choose a remediation? • Student Understanding Model • Mapping Learning Paths

  3. How can we increase student learning by using tutors? • Providing directions to informational resources • Recognizing and providing feedback on what is obstructing the learning process • Encouraging investigation • Encouraging group interactions • Providing alternate means of approaching similar concepts. • Increasing student comfort by being a companion through the investigative process. • Questioning the student • Rewarding the student

  4. What are the functional requirements of a tutor? A tutor must be able to: • Detect the need for remediation • Choose a remediation • Choose a time to remediate • Remediate

  5. Detecting the Need Examples: • Student asks for help • Student makes an incorrect assertion • Student behavior is indicative of a problem in the learning process • Tutor assumes need for remediation (quizzing, orienting, rewarding, humor etc.)

  6. Choosing the Remediation Examples: • Knowledge-based approach • Case-based • Algorithm X based approach

  7. Choosing the Time Examples: • Immediately following detection of need • At the end of the student’s current task/goal • At the end of an animation/experiment • Only when entering a particular area again (questions, review, supplementary info etc.)

  8. Remediating Examples: • Open a window with information • Create a light-bulb VRML object to be clicked • Show an animation • Start a dialogue

  9. Client Server Interaction Client Client Tutor Events Remediation Directives MOO Tutors MOO Tutors

  10. Tutoring System Tutor A TutorEvent TutorController Tutor Events inactive Remediator RemediationDirectives RemediationDirectives TutorEvents Client Tutor B Tutor C Remediation 1 Remediation 2 inactive active MOO Server

  11. What is missing? Functional Requirement Supporting Structure

  12. What is missing? Functional Requirement Supporting Structure

  13. Model of Student Understanding • Basic assumptions about how a student’s interaction with the system results in learning. • Student Action is linked to  Response from client is linked to Knowledge assimilated by student

  14. Student Action • A Tutor Event signifying that a student has performed a particular user-defined task. • Open Menu A, Run experiment A, Starts animation, etc.

  15. Result • An abstraction representing the user-defined response to be linked to some user action. • Menu A presented, Experiment X started, Organelle flexes muscles ;-)

  16. Cognit • An abstraction representing the unit of knowledge potentially obtained when a student is exposed to a Result. • Mitochondria is the place where cellular respiration takes place, Centriole plays an important role in coordinating mitosis, etc. • Concise logical units of knowledge

  17. Supporting Abstractions • Action Groups • Result Groups • Cognit Groups

  18. Mappings  {R1, R2, R3, . . .} A1 R1  {K1, K2, K3, . . .} {K1 + K2 + K3 + . . .}  {K4, K5, K6, ….}

  19. Why go through the trouble? • Gauge student learning by linking to student actions • Clarity in seeing “exactly” what paths a student may take in assimilating knowledge • Mechanism to allow tutoring system to act “intelligently” when selecting a remediation. • Standardizes the “currency” with which these tutors work. • More…

  20. Part III • Application of the ActionResultCognit scheme to an assertion tutor. • A look at the development process from finished module to finished tutors.

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