In Uncategorized


Christine L. Borgman
Graduate School of Library and Information Science
University of California, Los Angeles


An empirical study was performed to train naive subjects in the use of
a prototype Boolean logic-based n­ formation retrieval system on a b b­ liographic database. Subjects were undergraduates with. little o no prior computing exper ence. SubJects trained with a conceptual model of the system performed better t an subjects trained with procedural n­ structions, but only on complex, problem-solving tasks. Perfo7mance was equal on simple asks. .D ff:r­ ences in patterns of nteract on
the system (based on a stochastic
process model) showed parallel re­
sults. Most subjects were able to
articulate some description of the
system's operation, but few articu­
lated a model similar to the card
catalog analogy provided in training. Eleven of 43 subjects were unable to
achieve minimal competency in system use. The failure rate was equal between training conditions and gen­ ders; the only differenc7s. found between those passing and fa l ng benchmark test were academic major and in frequency of library use.

Permission to copy without fee all or part of this material is granted provided that the copies are not mador dist buted direct commercial advantage, the ACM copynght notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission.

© 1985 ACM 0-89791-159-8/85/006/0268 $00.75


In the search to understand how a naive user learns to comprehend, reason about, and utilize an interac­ tive computer system, a number of researchers have begun to explore the nature of the user's mental model of a system. Among the claims are that a mental model is useful for deter­ mining methods of interaction [1,2], problem solving [2,3], and debugging
errors [4]; that model-based tr ining is superior to procedural training [2,5,6]; that users build models spontaneously, in spite of training [1,7]; that incorrect models lead to problems in interaction [4,7]; and that interface design should be based on a mental model [8,9]. Not sur­ prisingly, these authors use a varie­ ty of definitions for "mental model" and the term "conceptual model" is often used with the same meaning. Young [10] was able to identify eight different uses of the term "concep­ tual model" in the recent literature, for example. This author prefers the distinction made by Norman [7] that a conceptual model is a model pre ented to the user, usually by a des gner, researcher, or trainer, which is intended to convey the workings of the system in a manner that the user can understand. A mental model is a model of the system thatuser
builds in his or her mind. The user's mental model may be based on the conceptual model provided, but is probably not identical to it.

The first research comparin conceptual models to procedural in­ structions for training sought only to show that the conceptual training was superior [6,11]. Other recent research [1,2] has studied the inter­ action between training conditions and tasks, finding that model-based


training is more beneficial for complex or problem solving tasks.

Th research on mental models and training has been concentrated in the domains of text editing [11,12] and calculato s [1,2,4,10] no such research has yet been done in inform­ ation retrieval. Information re­ trieval is an interesting domain, as it is now undergoing a shift in user population. In the last ten years, a significant population of highly­ trained searchers who act as inter-· mediaries for end users on commercial systems has developed. Although end users have been reluctant to use tne commercial systems, libraries are rapidly replacing their card catalogs with online catalogs intended for direct patron use. The online cata­ logs are typically simpler to use and have a more familiar record struc­ ture, but still have many of the difficulties associated with the use of a complex interactive system. The result is a population of naive, minimally-trained, and infrequent users of information retrieval sys­ tems [13]. The need for an efficient form of training for this population is very great and we chose it as a domain to test the advantages of model-based training.


The experiment was structured as a two-by-two design, with two train­ ing conditions (model and procedural) and two genders. All subjects were undergraduates at Stanford University with two or fewer programming courses and minimal, if any, additional com­ puter experience.

We performed the experiment on a prototype Boolean logic-based online catalog mounted on a microcomputer with online monitoring capabilities. TWo bibliographic databases were mounted: a training database con­ sisting of 50 hand-selected records on the topic of "animals" and a larg­ er database of about 6,000 records systematically sampled from the 10- million record database of the OCLC Online Computer Library Center.

Subjects in each training condi­ tion received three training docu­ ments: an introductory narrative, a set of annotated examples of system operation, and a table of searchable fields.

The introductory narrative pro­ vided to the model group described the system using an analogical model of the card catalog. The instruc­ tions first explained the structure of a divided (author/title/subject)
card catalog and then explained the system structure in terms of the ways it was similar to a card catalog and the ways in which it was different. Boolean logic was described in terms of sets of catalog cards, showing sample sets and the resulting sets after specified Boolean combinations.

The narrative introduction for the procedural group consisted of background information on information retrieval that is commonly given in system manuals. The Boolean opera­ tors were defined only by single­ sentence statements.

The examples provided were the same in each conditioD, but the anno­ tations for each reflected the dif­ ferences in the introductory mater­ ials. The list of searchable fields (16 of 25 fields were searchable) was also identical and gave examples of the search elements for each field.

The training tasks used for the benchmark test were all classified as simple tasks, requiring the use of only one index and. no more than one Boolean operator. The experiment con­ sisted of five simple and ten complex tasks, the latter requiring two or more indexes and one or more Boolean operators. All tasks were presented as narrative library reference ques­ tions and were designed to be within the scope of questions that might be asked by undergraduates in performing course assignments.

Subjects were given the instruc­ tional materials to read and then performed the benchmark test, which consisted of completing 14 simple tasks on the small database in less than 30 minutes. The test was based on pilot test findings that those who took longest to complete the training tasks were least able to learn to use the system (r=-0.83, p< .OS). If the subject passed the benchmark test, he or she was interviewed briefly, given the experimental tasks to perform, and then asked to perform one addi­ tional search while talking aloud for the experimenter. Subjects were in­ terviewed again after completing the experiment.



Due to a high failure rate on the benchmark test (11 of 43, or
26%), we were able to gather a valid dataset of only 28 cases. The dif­ ference in time required to complete the benchmark test was significant (p<o.OOOl), with those failing aver­ aging 39.2 minutes and those passing averaged 18.2 minutes. Subjects failed equally in the two training conditions and by gender.

Subjects who passed the bench­ mark test tended to be from science and engineering majors rather than social science and humanities (p<o.OOO.l), and were less frequent visitors to the library (average 8.0 visits per month vs. 18.4 visits for those who passed). Major and library use were not correlated.

In task performance, we found no difference between training condi­ tions on number of simple tasks cor­ rect (p>0.05). The difference· on number of complex tasks correct was in the predicted direction (subjects in the model condition scored higher than those in the procedural condi­ tion) but was not significant (p=0.08).

The user actions and system responses captured in the monitoring data were reduced to 12 discrete states and treated as a stochastic process. The patterns of interaction were measured using the two-sample Kolomogorov-Smirnov (K-S) test. On simple tasks, we found no significant differences between training con­ ditions on any of zero-, first-, or second-order two-sample K-S tests (p>0.05 for each). On complex tasks, we found significant pattern differ­ e ces between training conditions on each level (p<o.Ol for zero-order; p<o.OOl for first- and second-order tests).

The analysis of model articula­ tion ability was based on four meas­ ures coded from the interview data: completeness of the model, accuracy of the model, level of abstraction, and use of a model in approaching the tasks. The first three variables were highly correlated, necessitating their combination into an index. We found no difference between condi­ tions on either the model index or on the task approach variable.

If the subjects were able to describe the system's operation at all, it was most likely in terms of an abstract model bearing little resemblance to a card catalog anal­ ogy. Of 28 subjects, l5 (5 model condition, 10 procedural) gave some form of abstract model, four (3 model, 1 procedural) articulated a card catalog-ba ed only one subject (procedural condition) artic­ ulated a model based on another meta­ phor (robots retrieving sheets of paper from bins), and eight subjects (6 model, 2 procedural) were unable to describe the system in any model­ based manner.

Only minor differences between genders were found. Men scored high­ er than women (p<0.05) on the index of describing the system, although gender explained only 14% of the variance in the model index on a linear regression. Men were found to make more errors on simple tasks than women (p<0.05), but the difference was not significant for errors on complex tasks. On simple tasks, men and women reflected different pat­ terns of use at all three levels of zero-, first-, and second-order tran­ sitions (p<o.Ol, 0.01, 0.001, respec­ tively). On complex tasks, men and women also reflected different pat­ terns of use at all three levels (p<o.Ol, 0.05, 0.01, respectively), although less strongly.

A more complete description of the results can be found in Borgman [14].


Perhaps the most surprising (and unpredicted) finding is the degree of difficulty encountered by some of the subjects in using the system. The system was similar to those in common use in libraries and the questions were similar to those an undergrad­ uate might ask in seeking information for a course assignment. Yet more than one-fourth of the subjects could not complete 14 simple tasks in less than 30 minutes. The tasks were not difficult; nine of them were merely replications of the examples (which included the search result).

The subjects who had the most difficulty were those majoring in the social sciences and humanities. It


has frequently been conjectured that this group might have more difficulty using computing technology, but hard evidence is difficult to establish [15]. The effect is not explained by measures commonly associated with major, such as number of math and science courses or number of comput­ ing co1!rses.

It is doubtful that academic major alone is the factor determining success or failure at the information retrieval task. It is more likely that academic major is a surrogate for some other measure. Related research in human factors of comput­ ing has begun to identify psychologi­ cal and skill factors that influence computing ability, such as cognitive style [15], spatial memory, and age [16]. The pattern differences be­ tween men and women also suggest that some individual differences may be operating. The individual differen­ ces issues are of particular concern for online catalogs in library envir­ onments, most of which serve a very heterogeneous- population. Given the minimal control that system admin­ istrators have over training · this class of users, it is important that the system be easily accessible by a broad population.

Another factor that distin­ guished those who passed the bench­ mark test from those who failed was frequency of library usage. The result is in the opposite direction of that which would be predicted: the frequent library users failed and the infrequent ones passed. If fre­ quency of library usage were corre­ lated with major, this result would be easier to explain. However, we can say that frequent visits to the library (for whatever purpose) offer no advantage in learning to use an online catalog.

The performance differences were in the predicted direction, but less strong than we had hoped. However, the performance results were bol­ stered by the stronger pattern dif­ ferences in the monitoring data: no significant differences on simple tasks but very significant differ­ ences on complex tasks. The pattern differences suggest at least a dif­ ference in method of interaction, if not a difference in cognitive proces­ sing. Given the nature of these results, the interaction effect, the small sample size, and the small

number of tasks, we consider the hypothesis to be supported. We would be reluctant to generalize the find­ ings beyond this sample, however.

The results of this research and that of Halasz & Moran [2] show that model-based training is superior only for complex or problem-solving tasks. Our next challenge is to delineate the distinction between simple and complex tasks and thereby isolate the factors that may cause such an inter­ action. These issues are left for future research.

The predicted differences in model articulation based on training condition were wholly unsupported. The problem may have been methdo­ logical; the questions to solicit the model appear to have been interpreted in a variety of ways. A more con­ structive explanation is that we may have captured the variance in who is able to articulate a model, rather than in who is able to build a model. It is possible that mental models were constructed in precisely the manner predicted, yet we were unable to capture this result. We can con­ sider the presence of a model de­ scription sufficient to indicate that the mental model exists, but not a necessary condition. This interpre­ tation is reinforced by the lack of correlation between task performance and model articulation.

Another interesting aspect of the model articulation results is the lack of correlation between ability to describe the approach to search tasks and ability to describe the system. Subjects were frequently able to describe their approach to performing searching tas s in terms of the system's operation, but were unable to describe the same opera­ tions when asked how the system worked. It is possible that the questions solicited two types of models. The model used in problem solving (which results in performance effects) may be different from the model used in describing the system. According to Halasz [3], these two types of models may occur in sequence: one first builds a model for problem solving and only after practice is able to explain how it works. This interpretation is rein­ forced bx the fact that no subject was able to describe the system but not able to describe his or her ap­ proach to the tasks.


One last possibility is that the amount of time spent in training and system use was insufficient to devel­ op the model. Models develop over time with exposure to the system. Given further practice, stronger re­ sults might have been seen.


The present study compared the use of conceptually-based training to that of procedurally-based training on a prototype online catalog. Al­ though the training effects were not as strong as predicted, wdid find the hypothesized interaction effect between training method and task complexity, indicating that concept­ ually-based training is not always superior. The challenge of delin­ eating whep it is superior remains.

As expected, we found that it is easier to measure differences in who is able to articulate a model than in who is able to build a model. Sub­ jects in both conditions were able to develop models to some degree, indi­ cating that people do build models even if not trained with them. The fact that no relationship was found between model articulation and per­ formance further suggests that the measures captured articulation abil­ ity only.

Perhaps the most important find­ ing from this experiment is not the mental models result but the likeli­ hood of individual differences in the ability to use this particular tech­ nology. Given an equal number of math, science, and computing courses, engineering and science majors still out-performed the sociascience and humanities majors. This finding sug­ gests that we may be build ng systems for which access is inequitable. We are particularly concerned about this result in library environments, where equal access to information for all is a primary goal of the institution. If the implementation of a new tech­ nology discriminates among our users, we must find a way to achieve equity through training, design, or addi­ tional assistance.

The research- reported here is the first in what is intended to be a continuing research program. The second phase, to study the individual differences correlates of technology use, is already in progress (17].

New results from the later research will be incorporated in the confer­ ence presentation. It is our hope that this research will contribute not only to our understanding of human-computer interaction, but also to improving equity in access to information technology.


The research reported here was funded by the OCLC Online Computer Library Center, Dublin, Ohio. The interface simulator was developed and implemented by Howard Turtle and Trong Do, under the direction of Neal Kaske and W. David Penniman. The author also is grateful for the assistance of her dissertation ad­ visor, William Paisley, and the other members of her committee, Everett M. Rogers, David A. Thompson, and Barbara Tversky, all of Stanford University.


[1] Bayman, Piraye; Mayer, Richard E. 1984. Instructional manipu­ lation of users' mental models for electronic calculators. International Journal of Man­ Machine Studies, 20, 189-199-.---

[2] Halasz, Frank G.;- Moran, Thomas P. 1983. Mental models and problem solving using a calcula­ tor. In Janda, Ann (ed.), Human factors in computing systemS: Proceedings of a conference sponsored by the Association for Computing Machinery Special In­ terest Group on Computer and Human Interaction and the Human Factors Society. 1983 December
12-15, Boston, MA. New York, NY: Association for Computing
Machinery, 212-216.

[3] Halasz, Frank G. 1984. Mental models and problem solving using a calculator. Ph.D. disserta­ tion. Stanford, CA: Stanford University.

(4] Young, Richard M. 1981. The machine inside the machine: Users' models of pocket calcula­ tors. International Journal of Man-Machine Studies, 15, 51-as:-


(5] Carroll, John M.; Thomas, John C. 1982. Metaphor and the cognitive representation of com­ puting systems. IEEE Transac­
tions QQ Systems, Man, k Cyber­
netics, SMC-12:2, 107-116.

(6] Foss, Donald J.; Rosson, Mary Beth; Smith, Penny L. 1982. Reducing manual labor: An ex­ perimental analysis of learning aids for a text editor. In Association for Computing Mach­ inery, Proceedings of the human factors in computer systems ference. 1982 March 15-17, Gaithersburg, MD. New York, NY: Association for Computing Mach­ inery, 332-336.

(7) Norman, Donald A. 1983. Some observations on mental models. In Gentner, Dedre; Stevens, Albert L. (eds;), Mental models. Hillsdale, NJ: Lawrence Erlbaum Assoc.

(8] Jagodzinski, A. P. 1983. A theoretical basis for the repre­ sentation of on-line computer systems to naive users. Inter­ national Journal of Man-Machine Studies, 18, 215-252.

(9] Moran, Thomas P. 1981. The command language grammar: A rep­ resentation for the user inter­ face of Interactive systems. International Journal of Man­ Machine Studies, 15, 3-5 ----

(10] Young, Richard M. 1983. Surro­ gates and mappings: Two kinds of conceptual models for inter­ active devices. In Gentner, Dedre; Stevens, Albert L. (eds.), Mental models. Hills­ dale, NJ: Lawrence Erlbaum Assoc.

[ll] Mack, Robert L.; Lewis, Clayton H.; Carroll, John M. 1983. Learning to use word processors: Problems and prospects. Assoc­ iation for Computing Machinery Transactions on Office Informa­ tion Systems,-r:3, 254-271..

[12] Douglas, Sarah A.; Moran, Thomas P. 1983. Learning text editor semantics by analogy. In Janda, Ann (ed.), Human factors in computing systems=- Proceedings of a conference sponsored by the Association for Computing Mach­ inery Special Interest Group on

Computer and Human Interaction and the Human Factors Society.
1983 December 12-15, Boston, MA. New York, NY: Association for Computing Machinery, 207-211.

(13]Matthews, Joseph R.; Lawrence, Gary L.; Ferguson, Douglas K.
1983. Using online catalogs: nationwide survey. New York, NY: Neal Schuman.

(14) Borgman, Christine L. 1984.
The user's mental model of an
information retrievar---systeffi:
Effects Q£ performance. Unpub­
lished PhD dissertation, Stan­
ford University.

(15] Coombs, J.J.; Gibson, R.; Alty, J.L. 1982. Learning a first computer language: Strategies for making sense. International Journal of Man-Machine Studies,
14:4, 449-486.

(16] Egan, Dennis E.; Gomez, Louis M.
1984. Assaying, isolating, and
accommodating individual differ­ ences in learning a complex skill. In Dillon, Ronna F. (ed.), Individual differences in cognition, Vol. 2. New York; NY: Academic Press.

(17] Borgman, Christine L. 1984.
Individual differences in learn­
ing to use a library online
catalog: --pilot Project. Re­
search project funded by the
Spencer Foundation.