1. State of Art in Contextualised Learning Associate Professor Kinshuk
Director
Advanced Learning Technologies Research Centre
Information Systems Department
Massey University, Private Bag
Palmerston North, New Zealand
Tel: Ext 2090
Fax:
URL:

2. Directions in Contextualisation
Major theme: Adaptation for Individualization
Individualised learning in increasingly global educational environment
Bridging the gap among different types of learners
Human teacher model (designer teacher and local implementer teacher)
User exploration adaptation
Cognitive Trait Model

3. Overall Contextualisation Agenda

4. Context of Human Teachers

5. Supporting user exploration Exploration Space Control

6. Exploration Exploration is a self-initiated learning activity.
Learning by exploration is an effective technique for task oriented disciplines.
It provides not only skills of the domain but also the understanding of the embedded concepts.

7. Learning by exploration
Learning actually takes place by accessing various information resources such as hypertext, demonstrations, simulations, and so on.
Exploration activity: searching these information resources to comprehend the information and to acquire domain concepts/knowledge.
Exploration space 

8. Exploration space =
Extent of the information resources (including the domain concepts/knowledge) +
Exploration operations (such as search, selection, apply, integration etc.)

9. Problems in educational exploration
Learners should be free to explore to “construct” their learning (Carroll et al., 1985).
However, learners may not know what to and how to explore.
Excessive mental efforts to search and integrate the information from different resources may cause cognitive overload.
“Lost in Hyperspace” phenomenon!
Adaptation 

10. Examples in exploration adaptation 1/2
Adapt the navigation of the exploration paths that learners should follow
Tailoring the information to be presented to the learners
(making it easier for the learners to search and comprehend domain concepts and knowledge)

11. Examples in exploration adaptation 2/2
Simulation-based learning: Parameters restriction for easier interpretation of the results.
Problem sequencing: Focus learners’ attention on specific parts of the domain. This allows an easier understanding of the domain in gradual manner.

12. Supporting learning by exploration
The exploration space needs to be limited for the novice learner, and restrictions should gradually be removed as the learner progresses in the learning process.
Exploration Space Control (ESC) is the over arching phenomenon encompassing various adaptivity mechanisms.

13. Exploration Space Control (ESC)

14. Exploration space control (ESC)
ESC controls the extent of exploration space according to domain complexity and to the learners’ competence, understanding levels, experiences, characteristics, etc.
It integrates current technologies for exploration.

15. Purposes of ESC To facilitate active learning:
Suitable for learners with higher learning competence. Achieved by reducing cognitive load as less as possible.
To facilitate step-by-step learning:
Suitable for learners with lower learning competence. Achieved by reducing cognitive load as much as possible.
A combination of ‘active learning’ and ‘step-by-step learning’ covers whole learner spectrum, and therefore ESC is applicable for all kinds of learners.

16. ESC Control Levels
Embedding information: This facilitates the creation of information space and involves scaffolding.
Limiting information resources:
Limiting number of information resources
Selecting types of information resources appropriate for looking into current domain material

17. ESC Control Levels Limiting exploration paths:
Limiting the number of feasible exploration paths to be looked into
Limiting the exploration paths which are non-feasible or are unrelated to the current domain material
Limiting information to be presented:
Limiting the amount of information.
Adapting the contents of information to each learner

18. ESC and Current Technologies
Control Levels
Embedding information
Limiting information resources & exploration paths
Limiting exploration paths
Limiting presented information
Limiting exploration paths & presented information
Current Technologies
Scaffolding
Navigation
Problem ordering (courseware)
Information tailoring
Simulation setting

19. Designing systems with ESC
Identification of learning goals to be accomplished by the learners
2. Selection of scaffolding methods
Selecting various information resources to accomplish each learning goal (e.g. hypertext, simulation, demonstration)

20. Designing systems with ESC
2. Selection of scaffolding methods
Developing the information resources (decision about the information to be presented) based on:
Amount of the information
Contents of the information (such as abstract/concrete, detail, and theory/example)

21. Designing systems with ESC
2. Selection of scaffolding methods
Selecting various exploration operations to be used in and between each information resource (such as Select, Trace, Apply, Integrate, and Interpret).

22. Designing systems with ESC
3. Deciding levels of control to be applied to different information resources:
Deciding the major purpose of ESC (active or step-by-step learning)
This step helps designers to decide on the ways of how to control exploration space

23. Designing systems with ESC
b) Deciding control levels

24. Designing systems with ESC
Deciding application of control levels according to learner and domain models
Learner model factors:
Preferences
Knowledge Levels
Experiences
Competence
Exploration Process
Cognitive Load (Mental Efforts)
Domain model factors:
Type of knowledge (know-how, know-why …)
Degree of detail (Granularity)
Depth (Deep or Shallow)

25. Exploration Space Control Formalisation
Prescribes treatment for each student by mapping each particular student attribute to each control element.
Describe a defined set of steps for further formalization.
What are student attributes?

26. Student Attributes 1/2
Working Memory Capacity: cognitive system that allows us to keep active a limited amount of info (7+/-2 items) for short time, and has a central execution unit equipped with operational ability (Miller, 1956).
Inductive Reasoning Ability: is the ability to construct concepts from examples.

27. Student Attributes 2/2
Information Processing Speed: determines how fast the learners acquire the information correctly.
Associative Learning Skill: is the skill to link new knowledge to existing knowledge.
Domain Experience: is the familiarity of the domain concepts and skills.
Domain Complexity: is the student perception regarding difficulty of the concepts in the domain.

28. Exploration Space Control Elements
Navigational Path:
Number
Relevance
Content:
Amount (Detail)
Concreteness
Structureness
Information Resources:
Student attributes and ESCEs?
Example of Working Memory

29. Low Working Memory Capacity 1/3
Number of paths: “decrease” (protect learners from getting lost in too much information, from overloading the working memory with complex hyperspace structure)
Relevance of paths: “increase” (give important information directly without irrelevant info)
Amount of information: “decrease” (important information only, protect from information overload, give more time to review essential content if necessary)

30. Low Working Memory Capacity 2/3
Structure of information: “unchanged”
The increase of structure-ness could facilitate the building of mental model and thus assist future recall of the learned information. But versatile learners tend to have smaller short-term memory than serial learners, and the increase of structure-ness limits versatile learners’ navigational freedom, which is the primary way they learn. So the net effect cancels out.

31. Low Working Memory Capacity 3/3
Concreteness of information: “increase” (grasp the fundamental concepts first and use them to generate higher-order concepts)
Number of information resources: “increase” (choose the media resources that work best along their cognitive styles)

32. Working Memory Formalisation
Path
Content
Info
Resource
Level
Number
Relevance
Amount
Concrete-ness
Structure
Low - + \
High \+ \-
“+”  should increase
“-”  should decrease
“\+”  could increase
“\-”  could decrease
“\”  stay unchange

33. ESC Formalisation Matrix
ESC Formalisation Matrix
Path
Content
Info Res
Student Attributes
Level No
Rel
Amt
Con
Str
Work mem capacity
Low - + \
High \+ \-
Induct reason skill
Poor
Good
Info process speed
Slow
Fast
Assoc learn skill
Domain Experience
Little
Many
Domain Complexity

34. ESC and Student Model
ESC assumes that the system is able to correctly identify the information about a particular student.
Existing student models are good in inferring competency level and external preferences, but weak in modelling of individual differences in cognitive processing.
Cognitive Trait Model

35. Cognitive Trait Model 1/2
Goal is to have a student model that can be persistent over a long period of time and consistent across a variety of domains.
CTM need not be able to predict the student behaviour at the very first time but learns gradually.
Complimentary to existing student models.

36. Cognitive Trait Model 2/2
New learning environment can use it directly rather than re-learning about the student from scratch.
Can be saved on removable media and can be used in offline modes.
CTM considers those students’ traits that are (regarded) highly relevant to the domain (such as a combination of working memory capacity, inductive reasoning skills, associative learning skills and so on.

37. Incorporation of Cognitive Trait Model
Student Performance Model
Student
Behaviour
History
Cognitive
Trait
Model
Trait Analyser
Interface Module

38. ………… ………. Trait Model (TM) Trait Model Gateway (TMG)
Interface Listener Component (ILC)
Learner Interface (LI)
Manifest Detector Component (MDC)
……….
Manifest 1
Manifest 2
Manifest n
Trait Model (TM)
Trait Model Gateway (TMG)
Individualised Temperament Networks Component (ITNC)
…………
ITN 2
ITN 1
ITN n
Action History (AH)
Action History Component (AHC)
External Competence Based Model
Stores learner actions
Provides user interface of the virtual learning environment to the learners
Listens to learners actions and send them to AHC
Save learner actions to the AH, and provide means for the PDC to retrieve actions
Optionally provide learners’ competence states for the MDC.
Provide procedures to detect manifests from the history of learners’ actions. A manifest is a piece of interaction pattern that manifests a kind of learner’s characteristic (e.g. low working memory capacity)
Provides procedures to run the individualised temperament network (ITN) based on the input of the MDC. An ITN is a neural network-like structure representing a particular cognitive trait (e.g. working memory capacity) of the learner.
Provide an interface of the TM for updates.
Persistent storage of the values of the cognitive traits of the learners.

39. Example of manifests Low Working Memory Capacity
Example of manifests
Low Working Memory Capacity
High Working Memory Capacity
non-linear navigational pattern
linear navigational pattern
constantly reverse navigation
rare (or none) reverse navigation
frequently revisit learned materials
infrequent (or none) revisit learned material
unable to absorb side information while still remain progressing in the main tract manifests
able to learn side information while still remain progressing in the main tract
unable to perform tasks simultaneously
able to perform tasks simultaneously
low comparison speed
high comparison speed
unable to retrieve information effectively from long-term memory
able to retrieve information from long-term memory effectively
in long sequence of calculation or procedural, frequently missing steps or lost components
performing long sequence of calculation or procedural, without missing steps or lost components
older age
younger age
unable to comprehend highly demanding text or concepts
able to comprehend highly demanding text or concepts

40. Individualised Temperament Network
Trait Analyser
Neural network that adjusts CTM according to the results of pattern detector
Examines the records of student’s actions to find patterns that give signs about student’s cognitive ability
Trait Analyser
Pattern Detector
Individualised Temperament Network
CTM Updater
Student
Behaviour
History
Cognitive
Trait
Model
Pattern examples:
Navigational linearity
Reverse navigation
Excursions
Simultaneous tasks
Retrival of information from long-term memory
Long sequences of calculation or procedures

41. Synopsis
Individualisation of learning process and globalisation of resources demand much more contextualisation in the learning than ever before.
Adaptation in learning process is a major step towards contextualisation.
In exploration based learning, adaptation requires analysis of student’s competence, behaviour, and cognitive processing.
Exploration Space Control, existing student modelling techniques, and Cognitive Trait Model provides an integrative solution towards this requirement.