USING AI PLANNING TO ENHANCE E-LEARNING PROCESSES

Antonio Garrido

Universitat Politecnica de Valencia

Lluvia Morales

Universidad Tecnologica de la Mixteca

Ivan Serina

Free University of Bozen-Bolzano

Outline

• Problem and motivation

• The myPtutor approach

– Overall architecture

– Definition of the course / Compilation of PDDL files / Solving the planning problem / Execution and monitoring / Adaptation

• Putting all together. Integration with Moodle

• Evaluation

• Conclusions

Education is changing and now includes all forms of supported e-learning and e-teaching

Web full of interoperable digital resources (LOs)

– Insufficient to accommodate different styles and preferences – profile dependent

– Right selection & combination of LOs is essential to facilitate and enhance learning

We need a student-centered learning route (plan) tailored to the students’ needs

And we need to execute it!

Problem & Motivation

Why Planning?

Generating a learning route resembles planning closely

E-learning AI planning

Students’ background/prefs Initial state

Learning goals to attain Top level goals

Profile-adapted LOs with prerequisites and outcomes

Actions with preconditions and effects

Ordering relations Causal link relations

Tailored learning route Solution plan

Manual and decision-making techniques

CBP that memorizes and adapts pre-stored plans

Mixed-initiative architecture for teachers and students

Steps:

• Define the course

• Compile the PDDL files

• Solve the planning problem

• Execute & monitor

• Adapt the plan

The myPTutor approach

Definition of the course

Planning-oriented graphical tool with drag&drop of visual components

With precs/effects and extended with conj. + disj. + recom. requirements, cost and multi-objective metric

Compilation of PDDL files

Use a knowledge engineering method that extracts metadata information (LOM) in an automated polynomial process

The PDDL problem is extracted from the students’ e-portfolio (profile+background+goal)

Solving the planning problem

Any PDDL planner can be used, but CBP seems more reasonable as the world is regular and problems tend to recur

CBP requirements:

• Plan library with sufficiently similar reuse candidates (high number of common init/goals) – mapping objects of the reuse vs. new instance

• Plan merging techniques – plan decomposition per (interrelated) goals and reuse of parts of the retrieved plans to complete the new one

• Used as a starting point for local search

• Built on top of OAKPLAN

Solving the planning problem

After creating the plan (learning route), the teacher validates it and decides whether to introduce it in the case base

• The learning route stability is important (inertia is very appreciated)

The learning route is uploaded to the LMS (Moodle in our case) as a plan manifest

Execution and monitoring

During execution two situations can happen:

• Flawed execution of an activity (effects don’t happen as expected – failed evaluation task)

• The student’s profile changes

New planning scenario is created

• Init state is now the current state

• Learning goals remain the same

• A validator (VAL) is used with the original domain, the new planning problem and the remaining part of the plan

• In case of flaws, the CBP is invoked to fix them!

Adaptation - fixing the plan

Our plan adaptation consists in adapting the plan rather than planning from scratch

• Better to keep stability

• We use LPG-ADAPT, a local-search-based planner that incrementally modifies plan candidates

• But other dynamic adaptation systems can be used

• CBP is valid for plan revision: – Evaluation, which verifies the presence of failures

– Repair, when the failure is discovered; looking for a repair by using the plan library, or aborting the plan

Integration with Moodle

Our approach is compatible with any LMS, but we use Moodle as a validation framework

We have implemented several extensions to allow a mixed-initiative mechanism for users and planning services

Teachers’ and students’ forms Gantt chart with the activities for students

Evaluation From a qualitative point of view

• Teachers agree with the plans in terms of their form, size and adaptation to the students

• For students the experience was highly positive – feeling the course is specifically designed for them

From a quantitative point of view

• Test the effectiveness of our approach with merging techniques (OAKPLAN-merge) vs. plan generation

• Experiments: 100 problems; 9 configurations (with 10, 20...90 fictitious students) simulating changes in 10 variants per configuration (plus one extra variant for the 90-th configuration)

Evaluation OAKPLAN (with and without merging techniques) vs. LPG and SGPLAN6

The case-base contains all the base problems and their solutions

Plan retrieval techniques are less useful when the changes are significant; but the benefits for this pay off in terms of stability

OAKPLAN (with and without merging techniques) with different case-bases

The case-base is initialised with the case-base problem (with 10 students) and updated after the corresponding variants are evaluated

Merging may increase the time where the case-base is not too informed

Conclusions

Planning technology must be introduced transparently to the user

Metadata extraction is very useful for knowledge engineering compilation, but it is not always fully specified

• Allow to map from e-learning models to PDDL models

Generating PDDL files is good for using standard planners, but CBP techniques show more appealing

• Better for adaptation, keeping good values of stability

• Better for teachers, who want to keep a plan library with their previous plans (and modifications)

THANK YOU!

QUESTIONS?