relevant computational substrates:

1. Microsoft Word and Office
2. Did you know (e.g., Tip of the Day)
3. Office Assistant and other assistance programs

relevant theoretical dimensions and background information:

1. papers:
   1. Knowledge-Based Help Systems ---> see: G. Fischer, A.C. Lemke, and T. Schwab, Knowledge-Based Help Systems, in Human Factors in Computing Systems, CHI'85 Conference Proceedings (San Francisco, CA), New York, pp. 161-167
   2. ICM ----> see: G. Fischer, Enhancing Incremental Learning Processes with Knowledge-Based Systems, in H. Mandl and A. Lesgold (eds.), Learning Issues for Intelligent Tutoring Systems, Springer-Verlag, New York, pp. 138-163.
   3. Making Information Relevant to the Task at Hand ---> see: G. Fischer, and K. Nakakoji, Making Design Objects Relevant to the Task at Hand, in Proceedings of AAAI-91, Ninth National Conference on Artificial Intelligence, AAAI Press/The MIT Press, Cambridge, MA, pp. 67-73.
   4. Co-adaptivity ---> W.E. Mackay, Co-adaptive Systems: Users as Innovators, in CHI'92 Basic Research Symposium and G. Fischer, Shared Knowledge in Cooperative Problem-Solving Systems - Integrating Adaptive and Adaptable Components, in M. Schneider-Hufschmidt, T. Kuehme and U. Malinowski (eds.), Adaptive User Interfaces - Principles and Practice, Elsevier Science Publishers, Amsterdam, pp. 49-68.

2. brief analysis of an example: Office Assistant 1.0 (integrated with Microsoft Office 97). The system selects potientially relevant help pages based on the user's input words as well as an analysis of the user's recent actions. Action analysis is performed by a Baysian Network whose nodes either represent user actions, user plans or user needs. User needs are then linked with relevant help pages. Office Assistant 1.0 is not the first commercial software with user-adaptive technology. It is however the first commercial software that gives user monitoring and user-adaptation a very prominent position. The fact that Microsoft bundled this technology with one of its most visible products is both a good sign for Microsoft's trust in user adaptation as well as a good omen for the impending adoption of this technology by other software producers. The usage of Bayesian networks for user modeling purposes has been extensively discussed in UMUAI, as early as 1992 and very recently in several articles in the special issue on numerical uncertainty management.
   

relevant previous work:

1. analysis of Word (Jim Sullivan) ---> see: J. Sullivan, A Proactive Computational Approach for Learning While Working, Ph.D. Thesis, Department of Computer Science, University of Colorado
2. Bob Gatewood's project in Information Society class
3. analysis of cognitive tools (Norman, Landauer)
4. McGuckin study ---> see: G. Fischer, and B.N. Reeves, Beyond Intelligent Interfaces: Exploring, Analyzing and Creating Success Models of Cooperative Problem Solving, in E. Rich and D. Wroblewski (eds.), Applied Intelligence, Special Issue Intelligent Interfaces, Kluwer Academic Publishers, pp. 311-332.

interesting embedded topics:

Motivation

1. engagement in self-directed, authentic problems
2. make information relevant to the task at hand
3. create interesting and exciting products
4. provide multiple learning opportunities
5. provide challenges matched to skill levels
6. create communities (among peers, over the net)
7. provide access to real practitioners and experts
8. challenge: reinterpreting motivation at an organizational level
   1. who is the beneficiary and who has to do the work?
   2. memories: what will make employees want to share?
   3. people need to make explicit what they know and take the trouble to enter it into the system
      

Revisiting Critiquing

1. theoretical grounding: reflection-in-action, breakdowns, making argumentation serve design, information retrieval by context rather than query (making the context of the query known to the system)
2. tool (LC, technical editing) versus domain (kitchen design, LAN design)
3. embedded critiquing
4. conversation with the materials (see Winograd, p 206) ---> our claim: the back-talk of the materials is often not good enough
5. Winograd, p 44: "the difficulties with these taxonomies and rules is that a design that serves well both the particular material and the particular audience cannot be adduced from principles alone: it requires a leap of invention"
6. see story of tutoring versus simulation in Schoen/Winograd, p 180 (and Schank in Aspen article)
7. critiquing = "making the good better and the bad more difficult"
8. "once you know what you don't know: that is the opening of all learning to occur"
   

Design Frameworks addressed:

production paradox

1. people are not interested in learning per se, but in
2. ---> see: M. Carroll, and M.B. Rosson, Paradox of the Active User, in J.M. Carroll (ed.), Interfacing Thought: Cognitive Aspects of Human-Computer Interaction, The MIT Press, Cambridge, MA, pp. 80-111
   

Designing for Letting "People Get By"

1. main street and side street metaphor ---> see: G. Fischer, and H. Nieper, Personalized Intelligent Information Systems, Workshop Report (Breckenridge, CO), Institute of Cognitive Science, University of Colorado, Boulder, CO
2. suboptimal plateaus
3. too much information in the abstract (e.g., WORD), but not enough information in specific situations (e.g., eliminating an erroneously included word from the spelling corrector) ---> address this problem with "programmable / scriptable /end-user modifiable environments"; but this again adds additional complexity
4. examples: users do not switch from
   1. Word 5.1 ----> Word 6.0
   2. Word ----> Powerpoint
   3. Word ----> Canvas
5. family of systems emerging to address this problem:
   1. Word ----> generate HTML code
   2. Pagespinner ----> Pagespinner Assistant
   3. Office'97 ----> user modelling to provide contextualized help

Claims to be evaluated:

1. claim: "for most important mental tasks, acquiring the knowledge needed a the time of is not feasible"

2. claim: "why learn on demand, if use on demand is possible?"