Model-Driven Instructional Engineering to Generate Adaptable Learning Materials

Juan Manuel DoderoUniversidad Carlos III de Madrid

[ICALT 2006, ADALE Workshop]

Introduction

• Objective– Analyze model-driven software engineering approach for…

• Customizing high-level generation of LDs• Adapting LDs

• Agenda1. Related work

– Systematic Instructional Design– Instructional Engineering

2. Issues3. Exploring Model-Driven approach4. Conclusions

Systematic Instructional Design

• Instructional Design and Technology (IDT)– “IDT encloses the analysis of learning problems, as well as design,

development, assessment and management of processes and resources intended to improve learning. IDT professionals often use systematic ID procedures and employ a variety of instructional media to accomplish their goals” [Reiser & Dempsey, 2002]

• Related work on systematic IDT– R.D. Tennyson, A. E. Barro (1995): Automating instructional design:

Computer-based development and delivery tools, Springer, 1995.– Paquette et al. (1999): MISA: A knowledge-based method for the

engineering of learning systems. Journal of Courseware Engineering, 2.

– R. A. Reiser, J. V. Dempsey (2002): Trends and issues in instructional design and technology, Merrill/Prentice Hall.

– Sloep, Hummel & Manderveld (2005): Basic Design Procedures for E-learning Courses, in Koper & Tattersall (Eds.) Learning Design.

Instructional Engineering (IE)

• Definition of IE:– “A method that supports the analysis, the design and the

delivery of a learning system, integrating the concepts, the processes and the principles of instructional design, software engineering and cognitive engineering” [Paquette, 2002]

• Related work– CEM method L. Uden: “An engineering approach for online

learning”, Journal of Distance Engineering Education, 1(1)– MISA Method Paquette et al. (2005): An Instructional

Engineering Method and Tool for the Design of Units of Learning, in Koper & Tattersall (Eds.) Learning Design

– CPM profile Nodenot et al. (2003): A UML profile incorporating separate viewpoints when modeling co-operative learning situations, Int. Conf. on Information Technology: Research and Education.

Some IE Issues

• Issue 1: Different models– Different roles provide design specifications pertaining to

different models

• Issue 2: Different levels of abstraction– Any role can provide design specifications with different

levels of abstraction

• Issue 3: Different contexts– Learning objects and services are useful for specific

learning contexts (they are not instructionally universal)

• Issue 4: Different concerns– Pedagogy is the same, but learning topic is different– Learning topic is the same, but pedagogy is different

mappings andtransforms

Exploring MDA…

CIM:Computation-Independent

ModelPIM:

Platform-Independent Model

PSM:Platform-Specific Model

Model mappings and transforms

Meta Model C

Mapping

MetaModel A

MetaModel B

Model A

Model B

instanceOf

instanceOf

TransformationsModel C

instanceOf

Model-Driven IE

CIM:Computation-Independent

Model

PIM:Platform-Independent Model

PSM:Platform-Specific Model

E-learning model

LMS-specific

SCORM IMS LD Etc.

Security

Navigation

Etc.

Pedagogical model

MDIE: current approaches

• Adaptive hypermedia (applied to e-learning scenario)– S. Ceri, P. Dolog, M. Matera & W. Nejdl (2004): Model-driven design

of Web Applications with Client-Side Adaptation, ICWE’04.

• PSM level– Grob, Bensberg, & Dewanto (2005): Model Driven Architecture

(MDA): Integration and Model Reuse for Open Source eLearning Platforms, e-Learning and Education Journal, 1, feb 2005.

– Wang & Zhang (2003), “MDA-based Development of E-Learning System”, ICSA’03

• PIM level– Nodenot et al. (2004): Model-based Engineering of Learning

Situations for Adaptive Web Based Educational Systems, Proc. of WWW’04 Conference, New York, USA.

• Upper levels…– Díez et al. (2006): Towards An Effective Instructional Engineering

Analysis Method, Proc. of EC-TEL’06, Crete, Greece.

MDD Generative approach

+

FEATURE SELECTION

LOFEATURE

MODEL

Feature

Feature

Feature

Feature

Feature

Feature

Definition & Relations

Composition Rules

Design Rationales

CONFIGURATION KNOWLEDGE

Expert

Feedback

COMPOSITELEARNING OBJECT

Model merge

Expert

provides

Model: X0

Model Merge Stage

Expert

provides

Model: Xi

MUX

Pattern-Based Mapping

Metadata-Based Transformations

input models

output model

selection control

Model: ai

Conclusion

“The machine has been delegated a problem which is and remains primarily a teaching problem“, interview with Prof. R. Maragliano, elearningeuropa.info, 23 Aug 2004.

• MD instructional engineering– It is not about (fully) automated instructional design– It deals with different levels of automation

• Model-driven considers…– different levels of abstraction of specifications– different models/domains– separate context-specific issues– separate pedagogical issues