 |
Questions? Call 888-624-8373 |
|
|
|
|
Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 19
GOMS can be viewed as a simplified and more parameterized,
compiled COG. Moran (1981), however, stresses two contrasts.
First, where COG incorporates a limited mental mode! of the
system in its semantic level, GOMS incorporates no mental mode]
whatsoever. GOMS incorporates only the knowledge required to
perform a task. Second, where the focus of COG is the functional
description of various levels of user knowledge and the mappings
between these levels, the focus of GOMS is the sequencing of
operators and the time requirements for each. The bottom line for
GOMS is predicting performance times.
Kieras and Poison (1983) simulate users' behavior on partic-
ular computer systems. They have two representations in their
simulations, which with an additional twist can be viewed in much
the same spirit as Moran's (1981) view of the relation between
COG and GOMS. In the Kieras and Polson (1983) model, a job
task representation describes the person's understanding of when
and how to carry out tasks (very much like GOMS). The simulated
user's behavior is responded to by a simulation of the system, a
device representation, which is a GTN of the states and transi-
tions between them in the system. Some knowledge of this sys-
tem behavior, a mental GTN, can represent what the user knows
about the system- a thin, surrogate mental model. The former
GOMS-like representation is the user's knowledge that produces
performance, while the latter, the mental GTN, could be the user's
theory during learning, problem solving, and explaining how the
system works.
HOW USERS' 1iNOW[EDGE AFFECTS
THEIR PERFORMANCE
The discussion up to this point has treated what the user
knows as a static structure. While we have alluded to its un-
derlying role in behavior (learning, problem solving, explanation,
skill), we have not focused on these behavioral processes per se.
Nevertheless, this aspect is critical both to assessing the empirical
content of current analyses and to determining how these analy-
ses might be applied to practical problems like the design of user
interfaces and training materials.
19
OCR for page 20
Chaos and Misconception m Both Navices and Exerts
Learning involves internalizing, constructing, or otherwise at-
taining a representation of the system being learned. How does
this process proceed and what are its early results? The summary
picture is of a halting and often somewhat nonconvergent pro-
cess of problem solving and invention (e.g., Bott, 1979; Mack et
al., 1983; Rumelhart and Norman, 1981~. Indeed, the models that
learners spontaneously form are incomplete, inconsistent, unstable
in time, overly simple, and often rife with superstition.
A person may develop an understanding that is adequate for
simple cases but that does not extend to more complex cases. For
example, Mayer and Bayman (1981) found that users of calculators
often believed that evaluation only occurs when the equals key is
pressed. Scandura et al. (1976) describe a student who concluded
that the equals key and plus keys on a calculator had no function
because they caused no visible change in the display. Norman
(1983) describes learners who superstitiously pressed the clear key
on calculators several times, when a single key press would do.
People learning to use a simple programmable robot developed
wrong analogical models of its behavior that they accepted without
-testing unfit the models failed to predict the actions the robot
took (Shrager and Klahr, 1983~. Mantel (1982) found that users
performing a task in a menu-based retrieval system developed and
maintained simplistic sequences of actions that were eventually
ineffective in accomplishing their search goals.
Chaotic and misconceived conceptual models are not merely
an issue of early learning and something that users outgrow. Expe-
rienced users hold them as well. For example, Mayer and Bayman
(1981) asked students to predict the outcomes of key press se-
quences on a calculator. Even though all of the students were
experienced in the use of calculators, their predictions varied con-
siderably. For example, some predicted that an evaluation occurs
immediately after a number key is pressed, some predicted that
evaluation occurs immediately after an operation (e.g., plus) key
is pressed, and some predicted that an evaluation occurs immedi-
ately after equals is pressed.
Rosson (1983) found that even experienced users of a text
editing system often had rather limited command repertoires, rou-
tinely employing nonoptimal methods (such as making repeated
20
OCR for page 21
local changes instead of a single global change). Even in large pow-
erful systems, most of the activity involves the use of only a very
small portion of the system. In the case of UNIX, for example, 20
of the available 400 commands accounted for about 70 percent of
the usage (Kraut et al., 1983~. Like the Mayer and Bayman work
(1981), this suggests that even an extensive amount of experience
does not necessarily lead the user to a complete, consistent, or
even correct conceptual model. There are some things about a
system that most users never learn.
SkiBed PerfoImance
Human performance analyses have been well developed in ve-
hicular control (e.g., aircraft, ship, automobile) and target pursuit
tasks. Many of these analyses explicitly hypothesize a mental
model of the system being operated (e.g., Baron and Levison,
1980; Jagacinski and Miller, 1978; Pew and Baron, 1983; Veld-
huyzen and Stassen, 1976~. In these cases, the mental model is
used to anticipate the response of a dynamic system and hence to
overcome the deleterious effects of time delays either from other
humans or hardware. These models have produced good descrip-
tions and predictions of human performance.
Because these-models deal with spatio-temporal trajectories,
their applicability is limited to continuous detection and movement
tasks. In contrast, episodic models of movement that incorporate
an additional, abstract level of description in terms of discrete
situation-action pairs have much in common with goal-action mod-
els in human-computer interaction. Discrete representational and
data reduction techniques developed for episodic skilled perfor-
mance (Jagacinski et al., in press; Miller, 1985) may prove useful
in the domain of human-computer interaction. Software user tasks
do, however, typically involve a larger set of situation-action pairs
than is covered in human performance analyses, and they proba-
bly involve more varied categorization and planning by the human
operator. Whether they can be generalized to the greater cogni-
tive complexity of human-computer interaction tasks is an open
question.
If we assume that knowledge of simple sequences is in the
form of goal-action pairs, then we should be able to apply what we
know from traditional verbal learning studies about the retention
of paired associates (e.g., Hilgard and Bower, 1975; Postman and
21
OCR for page 22
Stark, 1969) to predict which systems will be easy to learn and
what kinds of errors will occur. For example, presumably, those
systems that have few paired associates to be learned or those that
have distinct, nonconfusable goal-action pairs will be easy to learn
and remember.
T ~ . ~ , - A~` ~
~ ,'
Knauer et se. t.Y6~', Barnard et al. (1981), and others have
explored certain aspects of this issue with muted results. Lan-
dauer et al. (1984) cliscuss the difficulties of constructing command
names that are natural, that is, those that would have existing
goal-action paired associates in memory and ready to transfer eas-
ily to a new task. They argue that if one incorporates command
names generated by naive users, these names are natural but often
are not distinctive enough to allow users to keep from getting them
confused among each other. Preexisting paired associates can help
transfer, but if they are not distinct paired associates as a set (e.g.,
A-B may be good until it must be learned along with A-C), the
confusion can offset any positive effect from their naturalness.
Poison and Kieras (1984, 1985) embody the GOMS model in a
production system-based simulation of users' behavior while using
software. This is a very concrete representation of what the user
knows when performing well-learned tasks and has a number of
confirmed behavioral correlates. Their analyses postulated that
-the number of productions (the number of rules needed to decom-
pose goals into subgoals, to find methods to fit the subgoals, and to
execute the sequence of actions in a method) necessary to perform
a task is a good predictor of the time it takes to learn a system,
that the number of productions that two systems have in common
predicted the ease of learning the second after the first, that the
number of productions used in constructing the next overt action
predicted the delay from one overt action to the next, and that the
number of items held temporarily in a working memory predicted
the likelihood of errors or delays (Kieras and Bovair, 1985; Kieras
and Poison, 1985; Polson and Kieras, 1984, 1985; Poison et al.,
1986~. Some of the predictions afforded by this specific analysis
have been successfully tested; others are being tested now.
Though this approach is to be lauded for its specificity and
the accuracy of some of its predictions, its weakness lies in de-
termining how one counts the number of productions required for
a task. Since production rule formalisms are general program-
ming languages, a single function can be programmed in many
ways. Consequently, for purposes of replicability, it is important
22
Representative terms from entire chapter:
evaluation occurs
Items in cart [0]
