clearly as we can the position for which we actually did and do argue and give examples of their misrepresentations. The underlying issue seems to concern the advantages of using technical psychological theories to identify underlying mechanisms in human-computer interaction. We argue that such theories are an important part of a science of human-computer interaction. We argue further that technical theories must be considered in the context of the uses to which they are put. Such considerations help the theorist to determine what is a good approximation, the degree of formalization that is justified, the appropriate commingling of qualitative and quantitative techniques, and encourages cumulative progress through the heuristic of divide and conquer. Volume 2, Number 4, 1986 Graphically Defining New Building Blocks in Thinglab Alan Borning ThingLab is a constraint-oriented, interactive graphical system for building simulations. A typical problem in ThingLab (and systems like it) is that, to define an object with a new kind of constraint, the user must leave the graphical domain and write code in the underlying implementation language. This makes it difficult for less experienced users to add new kinds of constraints or to modify existing ones. As a step toward solving this problem, the system described here allows the graphical definition of objects that include new kinds of constraints. This is supported by an interface in which a user can open two views on an object being defined, a use view and a construction view. The use view shows the object's normal appearance. The construction view contains additional objects and constraints, which serve to graphically specify the new constraints on the defined object. any given command. There were three forms of practice: (1) Pure Guided Practice, in which subjects were told exactly what keystrokes to type to solve the problems; (2) Pure Problem Solving Practice, in which subjects solved problems without guidance; and (3) Mixed Practice, in which the first problem for a command was presented in Guided Practice form and two others in Problem Solving form. The spacing of the training problems was also manipulated; the problems pertaining to a given command were either Massed (i.e., presented consecutively), o r Distributed (i.e., separated by other instructional material). After a 2-day delay, subjects solved new problems on the computer without referring to the instructional materials. The results indicate that problem solving was a more difficult form of training than guided practice, but it produced the best performance at test. Distributing the spacing of training problems during training also improved performance at test. The results have clear pragmatic implications for the design of interactive tutorial manuals as well as implications for cognitive models of skill acquisition. A Cognitive Model and Computer Tutor for Programming Recursion Peter Pirolli This paper discusses cognitive models of learning to program recursion and their relation to lessons on recursion in an intelligent computer tutor for LISP programming (the LISP Tutor). The cognitive models are implemented as production systems in which programming skill is characterized as the decomposition of programming goals into subgoals and elementary actions via the application of programming plans. Two sets of learning mechanisms are used in the cognitive models. Analogical problem-solving mechanisms use declarative knowledge of example program solutions to overcome problem solving impasses. Knowledge compilation mechanisms summarize problem solutions into efficient problem solving skill. Analyses and simulations of novice and expert programming were used to develop ideal models of the programming knowledge to confer upon students and bugs that characterize common misconceptions. The LISP Tutor uses the ideal models and bugs to guide its interactions with students. Experimental evaluations of the LISP Tutor indicate that it is more efficient and effective than classroom instruction. Designing Interactive Tutorials for Computer Users Davida H. Charney and Lynne M. Reder This paper aims at finding the optimal combination of written instruction and on-line practice for learning a new computer application. Experimental subjects learned commands for an electronic spreadsheet by reading brief user-manual descriptions and working training problems on-line. The form of the training problems was varied within subjects in order to control how much independent problem solving subjects engaged in while learning SIGCH1 Bulletin July 1987 Volume 19 Number I

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