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Intelligent Tutoring on Virtual Cell: A Whole New World

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gretchen-nichols
17 diapositives · 315 Views · Nov 28, 2014

This paper explores the functional requirements and implementation structures necessary for intelligent tutoring systems in virtual cell environments. It discusses client-server interactions, various tutor types, and effective ways to increase student learning. Key strategies include providing resources, recognizing learning obstacles, fostering group interactions, and utilizing feedback mechanisms. The importance of choosing appropriate remediation timing and methods is emphasized, alongside a proposed underlying structure to support these processes. Insights into tutor attributes and types, such as orientation and questioning, are also examined.

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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 I Ganesh Padmanabhan

  2. Overview • Tutor functional requirements. • Underlying Structure Needed for Implementation • Client and Server interactions • Tutoring System • Types of Tutors

  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. Proposed Underlying Structure Detecting need, choosing remediation, and choosing time call for: • Eventing Mechanism spanning client and server • Event abstraction • Event Sequence abstraction • Event Grouping abstraction Remediating calls for: • Remediation abstraction • Remediating Mechanism spanning client and server

  10. Client Server Interaction Client ITS Client ITS Tutor Events Remediation Directives MOO Tutors MOO Tutors

  11. Eventing Mechanism Client-Side • Tutor Event • NameSpaces MOO-Side • Event • Event Sequence • Event Group

  12. Remediating Mechanism Client-Side • Remediator object MOO-Side • Tutoring System • Remediation object

  13. 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

  14. Tutor Controller • Manages activation and deactivation of tutors • Handles incoming tutor event notifications from client • Handles and regulates outgoing tutor remediation directives to client

  15. Tutor • When activated, listens to tutor events, and takes remedial action. • Tutor Attributes • Activation Trigger Sequence • Activation Trigger Conditional • Log of pertinent information • Cycling Pattern • Remediation Logic

  16. Tutor Types • Orientation Tutor • Reward Tutor • Questioning Tutor • Assertion Tutor • Student Profiling Tutor • Collaboration Tutor

  17. What a strange trip its been! • Part II • Expand upon choosing a remediation • What constitutes an effective remediation? • What information does a tutor need to select appropriate remediations? • Underlying Structure Proposed for satisfying in part the informational requirements of the tutoring system. • Deductive Problem Diagnoser