Plan recognition in virtual laboratories
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Plan Recognition in Virtual Laboratories. Ofra Amir and Ya’akov ( Kobi ) Gal Ben-Gurion University of The Negev Department of Information Systems Engineering. Background. Educational software in the sciences is including open-ended “construction” environments

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Plan recognition in virtual laboratories

Plan Recognition in Virtual Laboratories

Ofra Amir and Ya’akov (Kobi) Gal

Ben-Gurion University of The Negev

Department of Information Systems Engineering


Background

Background

  • Educational software in the sciences is including open-ended “construction” environments

  • Students build and analyze models using these software


Virtual labs

Virtual Labs

  • An interactive simulation of a chemistry lab.


Virtual labs dilution problem

Virtual Labs – Dilution Problem

“Your supervisor has just asked you to prepare 500mL of 3M HNO3 for tomorrow's experiment. In the stockroom explorer, you will find a cabinet called Stock Solutions. Open this cabinet to find a 2.5L bottle labeled 15.4M HNO3. ”


The problem

The Problem

During class:

Please prepare a flask containing 500 ml of a 3M solution…

I’m confused

Boring…

This is easy!


The problem1

The Problem

I wonder how each student performed…

After class:


The goal

The Goal

  • Develop automatic support for teachers in their analysis of student performance

    • Approach: use plan recognition to infer students’ problem solving plans


Related work

Related Work

  • Bayesian networks to model students’ interactions with intelligent tutors [Conati et al., 2002]

  • Complete algorithms for plan recognition (CSP)

    [Reddy et al, 2009; Quilici et al, 1998]

  • Heuristic algorithm for recognizing students’ activities in pedagogical software for statistics [Gal et al, 2008]


Solution building blocks

Solution Building blocks

  • User actions

  • Recipes

  • Plans


User actions log files

User Actions – Log Files


Recipes pollack 1990

Recipes [Pollack, 1990]

  • Basic actions are rudimentary (log actions)

  • Complex actions are abstract

  • A recipe for a complex action describes

    • A series of sub-actions for completing the action

    • Constraints on these actions


The recipe language

The Recipe Language

  • Representing actions:

  • Recipe structure:

Constraints on sub-actions parameters

D[dt:H20, sid_1,did_1][ 0 ]

Action Name

An action parameter with value constraint

Action Pre-conditions

Parameter without value constraint

D[sid_1,did_2] -> D[sid_1,did_1][ ] C[sid_2,did_2][ ]

The complex action

sub-actions

did_1=sid_2


Plans in virtual labs

Plans in Virtual Labs

  • A plan for a complex action is

    • A hierarchy of recipes towards completing the action

  • The plan represents students’ activity with the software

  • Plan recognition

    • Infer students’ activities based on their actions with the software, given a set of recipes


Plan recognition approach

Plan Recognition Approach

Plan Recognition

Algorithm

Student Plan


Dilution problem recipes

Dilution Problem Recipes

  • To solve the problem, the student should:

    • Pour H20 to the destination flask

    • Pour HNO3 to the destination flask

  • Students can solve this problem in many different ways


Dilution problem example solution 1

Dilution Problem - Example Solution 1

2

1


Dilution problem example solution 2

Dilution Problem - Example Solution 2


Challenges in the virtual labs domain

Challenges in The Virtual Labs Domain

  • Indefinite repetition of activities

  • Interleaving activities

  • Trial-and-error, mistakes

  • Conclusion:

    • Complete approach intractable for this domain


Build plan greedy algorithm

Build Plan Greedy Algorithm

  • Input:

    • A set of Virtual Labs basic actions, A set of recipes

  • Algorithm steps:

    • initialize open list with the actions from the log

    • for each recipe in order of increasing depth

      • find match (Recipe, open list)

      • while match exists

        • add complex action to the open list

        • create branches from the complex action to its sub-actions

        • remove sub-actions from open list

        • Call find match with the updated open list


Plan recognition in virtual laboratories

Recipe: C -> SM SM

Recipe: D -> C C

( SM , did=ID5 , sid=ID1 )

( SM , did=ID5 , sid=ID1 )

( MO , id=ID5 )

( MO , id=ID5 )

( SM , did=ID6 , sid=ID5 )

( MO , id=ID1 )

( AS , id=ID6 )

( MO , id=ID6 )

( MO , id=ID5 )

( FC , did=ID6 ,sid=ID5 )

( SM , did=ID6 , sid=ID5 )

( C , did=ID5 , sid=ID1 )

( MO , id=ID5 )

( MO , id=ID5 )

( AS , id=ID6 )

( MO , id=ID6 )

( MO , id=ID5 )

( FC , did=ID6 ,sid=ID5 )

( C , did=ID6 , sid=ID5 )

( C , did=ID5 , sid=ID1 )

( MO , id=ID5 )

( MO , id=ID5 )

( AS , id=ID6 )

( MO , id=ID6 )

( MO , id=ID5 )

( FC , did=ID6 ,sid=ID5 )

( C , did=ID6 , sid=ID5 )

( D , did=ID6 , sid=ID1 )

P3

P2

P1


Find match

Find Match

  • Find match searches for actions in the open list which fulfill the recipe for the complex action

  • Actions in the match can be free ordered as long as they satisfy the constraints in the recipe

  • Was implemented as depth first search, but can be implemented in other ways

  • Find Match is complete, given a recipe and an open list of actions


Dilution problem partial plan

Dilution Problem – Partial Plan

MSC – Mixing solution component

MSI – Mixing solution through intermediate flask

SDP – Solve Dilution Problem


Empirical evaluation

Empirical Evaluation

  • The algorithm was run on 20 log files taken from real student interactions

    • 6 different problems

    • logs ranged in size from 20 actions to 187 actions

    • Plans ranged in depth from 3 to 14 levels

  • The plans were validated by a domain expert


Contributions

Contributions

  • A new computationally efficient plan recognition algorithm that can cope with interleaving activities, mistakes, indefinite repetition.

  • The algorithm can be integrated with real pedagogical software

  • Shown to succeed on real-world data


Plan recognition in virtual laboratories

Questions


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