Intelligent Tutoring on Virtual Cell: A Whole New World

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

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

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

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

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.)

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

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.)

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

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

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

What is missing? Functional Requirement Supporting Structure

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

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.

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

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

Supporting Abstractions • Action Groups • Result Groups • Cognit Groups

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

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…

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.

Intelligent Tutoring on Virtual Cell: A Whole New World