1. Applying learner modelling for user interface assistance in simulative training systems Alexander Hörnlein, Frank Puppe Dept. for Artificial Intelligence and Applied Computer Science, JMU Würzburg {hoernlein,
Gliederung:
Motivation
Task domain
System states and actions
Overlay model
Intervention system
Discussion and future work
Applying learner modelling for user interface assistance in simulative training systems

2. Motivation Simulative training systems have complex interfaces
Students want to learn the content NOT the UI
No one reads manuals
Hard/Impossible to teach UI ex-cathedra with distributed groups/individuals with asynchronous access
Learners of different competence need individual help
Applying learner modelling for user interface assistance in simulative training systems

3. Comprehensive system metaphor Static online courses with basic help
Applied kinds of help
Comprehensive system metaphor
Static online courses with basic help
(Dynamic & context-sensitive) built-in help
Additionally
Active help-system
Learner modelling
Intervening wrong user actions
Providing help for recent error(s)
Applying learner modelling for user interface assistance in simulative training systems

4. Learner can freely switch between main session tasks
Task domain
Learner can freely switch between main session tasks
Tasks consist of (sub-)tasks or atomic actions
diagnose
switch to
diagnose mode
delete wrong
diagnoses
navigate
diagnoses tree
add diagnosis
rate diagnosis
click on
„Diagnose“
click on
„delete“
scroll to
sub-tree
open sub-tree
click on
diagnosis
click on
„established“
or „suspected“
click on „+“
Applying learner modelling for user interface assistance in simulative training systems

5. System states and actions
System state is described with partial states
Actions change the system state Transition function
System states influence available actions Availability function
Task objectives result from system states Objective function
Applying learner modelling for user interface assistance in simulative training systems

6. Sequences of actions to reach the/a final state for a given task
Action lists
Sequences of actions to reach the/a final state for a given task
click on
„established“
or „suspected“
click on
diagnosis
click on
diagnosis
click on
„delete“
click on
„established“
or „suspected“
click on
„delete“
click on
diagnosis
click on
diagnosis
enter
search text
select diagnosis
click on
„hinzufügen“
click on
„delete“
click on
„Textsuche“
click on
„Textsuche“
click on
„established“
or „suspected“
select diagnosis
enter
search text
click on
„hinzufügen“
click on
„reset“
Applying learner modelling for user interface assistance in simulative training systems

7. Task decomposition to sub-tasks Knowledge about task domain
Concepts
Structural concepts
Task decomposition to sub-tasks
Knowledge about task domain
High level order concepts
When to start a task
Knowledge about objective function
Action concepts
Action lists for a given task
Knowledge about task domain (leaves), transition function and availability function
Applying learner modelling for user interface assistance in simulative training systems

8. Set of ordered symbolic values Interval Numerical score function
Overlay model
Set of concepts
Set of ordered symbolic values
Interval
Numerical score function
Symbolic score function
Inverted symbolic score function
 An overlay model is the 6-Tupel
Applying learner modelling for user interface assistance in simulative training systems

9. set of all change functions
Changes of the model
set of all functions
set of all change functions
Applying learner modelling for user interface assistance in simulative training systems

10. Example
Set of concepts C = {„to diagnose switch to diagnose, change …“, …}
Set of symbolic values S = {N3, N2, N1, P0, P1, P2, P3}
Interval I = [-25,25]
m1 initially set to: m1(c)=0 m2
m3(n)= N3, if N2, if N1, if P0, if P1, if P2, if P3, if N3 N2 N1 P0 P1 P2 P3
-15
-10 -5 5 10 15
Applying learner modelling for user interface assistance in simulative training systems

11. Rule sets Two rule sets to change overlay model
State rules IF „expected diagnoses changed“ AND NOT action = „click on ‚diagnose‘“ THEN DECREASE VALUE OF „if expected diagnoses change then one should diagnose“ BY
Dependency rules IF VALUE OF „to open diagnose subtree click on ‚+‘“ BELOW THEN DECREASE VALUE OF „to open therapie subtree click on ‚+‘“ BY
Applying learner modelling for user interface assistance in simulative training systems

12. Intervention system: requirements
Intervention system must
Prevent the learner from doing wrong actions
otherwise the learner has to manually undo the last action, which is sometimes not possible
Provide help if the learner seems to be stuck
Be unobtrusive
otherwise the learner can‘t focus on learning subject
Applying learner modelling for user interface assistance in simulative training systems

13. Intervention system: workflow
on learner action: system state and learner action (history) are gathered
state rules and dependeny rules are executed
if a concept has a bad rating (overlay model) an appropriate prepared intervention is returned (with a weight) (modelintervention rules)
the intervention gets a score based on
its weight
the kind and number of interventions returned after recent learner actions
the kind and number of interventions recently returned
all interventions with a score below a certain threshold are held back
if there are interventions left, then
the intervention with the highest score is returned
the rating (overlay model) of the intervention‘s concept is increased (interventionmodel rules)
the learner action is cancelled
the intervention content is displayed (by the feedback agent)
Applying learner modelling for user interface assistance in simulative training systems

14. Intervention system
Applying learner modelling for user interface assistance in simulative training systems

15. Discussion and future work
Done
Implementation nearly complete
Rule sets for most concepts of all-but-one main task
ToDo
Complete implementation and rule sets
Fine-tuning of rules and intervention score function
Future
Evaluation
Preset with stereotypes
Enable the learner to modify model
Different agent
Theoretical: Define different types of task domain relations, conditions
Applying learner modelling for user interface assistance in simulative training systems

16. Questions?
Applying learner modelling for user interface assistance in simulative training systems