Advanced Interactive Learning
Environments UG4/MSc 2011-2012
will be updated
throughout course
lecture Topics and Materials
Last updated: 19th January 2012
This course will consist of lectures and discussions.
Lecture slots are Tuesday and Friday 12.10 - 1pm in David Hume Tower,
room 7.01.
1. Syllabus
The issues addressed will include the following:
Previous development of teaching systems and limitations; adaptivity in
relation to the domain and the learner;
Methodology: empirically informed and user centred design; involving
students and teachers in the design process;
Modelling and simulating domain knowledge;
Modelling the user: diagnosis, errors and misconceptions; modelling
affect;
Pedagogical Issues: theoretical and educational basis of teaching
tools; using Pedagogical Agents;
Models of interaction and communication; Educational dialogue;
Evaluating the design and effectiveness of educational software.
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2. READINGS AND MATERIALS:
The main page for readings and
references for the course is: Readings
and Materials
Course material can basically be divided
into:
A. CORE ESSENTIAL material - these will
comprise:
- the content of the lectures
- the content of the seminars
- you will expected to know about a number of core systems - these
include those covered in the seminars and others as indicated
- Required Reading
that may be set each week
It will be assumed that you have read this material.
B. USEFUL BACKGROUND
material. This will be indicated as
Background Reading,
and should help improve your understanding overall, and will
provide additional examples. This will also include reference to other
systems in addition to those indicated as 'core'.
C. REFERENCES AND OTHER MATERIALS: this will be links to
various resources, references cited in lectures and other relevant
literature.
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3. WEEK BY WEEK GUIDE TO COURSE
CONTENT TO
BE
UPDATED
Week 1: Introduction and
examples (lecture1
and
2
as
pdf)
A. Required reading:
Joseph Beck, Mia Stern, and Erik
Haugsjaa (2001). Applications of AI in Education,
ACM
Crossroads Student
Magazine, Fall 1996, 3.1, Issue on Artificial Intelligence.
online at
http://portal.acm.org/citation.cfm?id=332148.332153&coll=portal&dl=ACM
This paper is a useful general overview.
B. Background Reading: TO BE UPDATED:
C. Other References and Resources: TO BE
UPDATED:
Example from CBBC Schools: Alien Cookbook - teaches numeracy (5 to 7
year olds)
http://www.bbc.co.uk/schools/starship/maths/aliencookbook.shtml
Adventure Author:
Goolnik, S., Robertson, J. and Good, J. (2006). Learner Centred
Design in the Adventure Author Project,
International Journal of Artificial
Intelligence in Education, 16, 381-413. available from
http://aied.inf.ed.ac.uk/abstract/Vol_16/Goolnik06.html
McFarlane, A., Sparrowhawk, A. and
Heald, Y. (2002) The role of games in education,
A research report to the DfES,
http://www.teem.org.uk
Robertson, J., & Good, J. (2003). Using a Collaborative
Virtual Role-Play Environment to Foster Characterisation in Stories.
Journal of Interactive
Learning Research,
14(1),
5-29.
Robertson, J., & Good, J. (2005b).
Story creation in virtual game worlds.
Communications of ACM, 48, 61-65.
Robertson, J., & Oberlander, J.
(2002). Ghostwriter: drama in a virtual environment.
Journal of Computer Mediated
Communication. 8(1).th Retrieved 14 February
2006
http://jcmc.indiana.edu/vol8/issue1/robertson/robertson.html
Crystal Island:
Alelo Tactical Language and Culture Project:
Standup Project: see http://www.csd.abdn.ac.uk/research/standup/
Vicarious Learning Project: see
http://www.tlrp.org/proj/phase111/cox.htm
________________________________________________
TO
BE UPDATED:
Weeks 2 and 3: Classic Intelligent Teaching Tools ( lecture3,
lecture4
and lecture 5
as pdf )
A.
Required reading:
Read
the relevant sections that refer to LOGO, Sophie, Guidon, Quest,
Envision, Qualitative Process Theory, Lisp Tutor,
BUGGY/DEBUGGY/IDEBUGGY, WEST, Steamer, Scholar, Sophie, WHY, MenoTutor
and Cognitive Apprenticeship in:
Sleeman, D. & Brown, J.S.
(eds.) (1982), Intelligent
Tutoring Systems, Academic Press.
Wenger, E. (1987) Artificial intelligence and tutoring
systems: computational and cognitive approaches to the communication of
knowledge. San Francisco: Morgan Kaufmann.
B. Background Reading: TO BE
UPDATED:
C. Other References and
Resources: TO BE UPDATED:
________________________________________________
Week 4: Approaches to design (slides6.pdf)
Refs to be added
________________________________________________
Week
5
and
6: Student presentations: (slides attached)
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Seminar 1:
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Seminar 2:
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Seminar 3:
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Week
7/8:
Formative and Summative Evaluation (slides7.pdf,
slides8.pdf)
see Shaaron Ainsworth 2003 tutorial slides: http://sydney.edu.au/engineering/it/~aied/Ainsworth_tutorial.pdf
Weeks 9/10:
Week 11: Conclusions (and possibly if time, Interaction
and
Dialogue,
slides9.pdf)
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References for Seminars: dATES AND SYSTEMS
STILL TO
BE
UPDATED
Readings recommended by the seminar presenters are marked with
**
___________________________________________
Other possibly useful
references: TO BE UPDATED:
___________________________________________
Some other useful links:
TO BE UPDATED:
The International Journal of
Artificial Intelligence in Education
(IJAIED) is the official journal of the International AIED Society.
IJAIED publishes papers and other items concerned with the application
of artificial intelligence techniques and concepts to the design of
systems to support learning.
LearnLab: Pittsburgh Science of
Learning Center (PSLC)
"Learnlab is a facility designed to dramatically increase the ease and
speed with which learning researchers can create the rigorous,
theory-based experiments that pave the way to an understanding of
robust learning. Run jointly by Carnegie Mellon University and the
University of Pittsburgh, LearnLab makes use of advanced technologies
to facilitate the design of experiments that combine the realism of
classroom field studies and the rigor of controlled theory-based
laboratory studies.
PSLC's
LearnLab is a national resource for learning research that includes:
- Authoring tools for online courses, experiments, and integrated
computational learner models
- Support for running in vivo learning experiments
- Longitudinal microgenetic data from entire courses
- Data analysis tools, including software for learning curve
analysis and semi-automated coding of verbal data."
