Knowledge that Transforms

Lack of familiarity with database design methods could prevent many end users from effectively implementing their database management system packages. An inexpensive solution would be for end users to learn required database design skills from software tutors tailored to their needs. This research describes two tutors developed to teach these skills to end users. The tutors were based on a modified Entity-Relationship database design method. They improved an end user's natural learning process by incorporating design principles and facilitators. Empirical comparison of the tutors tested the teaching effectiveness of the facilitators. The results lead to recommendations for closing the gap between skills required and skills learned by end users in database design. Development of tutors that teach specific database design skills irrespective of the software package used in implementation has important implications for practitioners and researchers.

In a recent article in MIS Quarterly, Bostrom, et al. (1990) report that, A consistent pattern of findings emerges that indicates that learning modes is an important predictor of learning performance, both by itself and in interaction with training methods. The findings suggest that in the design of training, it is essential to match training methods to individual difference variables (p. 101). We do not agree that Bostrom, et al.'s results represent a consistent pattern of findings. Moreover, the results of this research should be discounted because the measures of learning styles were derived from an instrument with very poor psychometric properties. Thus, the conclusion that learning styles are important factors in end-user training (EUT) is unsupported at the present time.

The use of group decision support systems (GDSS) is rapidly growing. One key factor in the effectiveness of these systems may be the manner in which users are supported in their use of this technology This paper explores two types of GDSS facilitative support: chauffeur-driven and facilitator-driven. In the former case, a person is used to reduce the mystique of the GDSS technology for users. In the latter case, a person assists the group with its group process in addition to reducing the mystique of the technology. The work unfolds a research story in which the original thinking of the research team to the effect that facilitator-driven GDSS facilitative support is superior is proven incorrect. The results of a pilot study caused the research team to reverse its thinking and hypothesize that, given the nature of the facilitation used and the task faced by the group, chauffeur-driven facilitation would have an advantage. The results of the experiment reported in this paper support this hypothesis. Arguments are presented to the effect that, to be effective in a judgment task environment, facilitation must be open and adaptive rather than restrictive.

Database design is often described as an intuitive, even artistic, process. Many researchers, however, are currently working on applying techniques from artificial intelligence to provide effective automated assistance for this task. This article presents a summary of the current state of the art for the benefit of future researchers and users of this technology. Thirteen examples of knowledge-based tools for database design are briefly described and then compared in terms of the source, content, and structure of their knowledge bases; the amount of support they provide to the human designer; the data models and phases of the design process they support; and the capabilities they expect of their users. The findings show that there has apparently been very little empirical verification of the effectiveness of these systems. In addition, most rely exclusively on knowledge provided by the developers themselves and have little ability to expand their knowledge based on experience. Although such systems ideally would be used by application specialists rather than database professionals, most of these systems expect the user to have some knowledge of database technology.

Cognitive feedback in group decision making is information that provides decision makers with a better understanding of their own decision processes and that of the other group members. It appears to be an effective aid in group decision making. Although it has been suggested as a potential feature of group decision support systems (GDSS), little research has examined its use and impact. This article investigates the effect of computer generated cognitive feedback in computer- supported group decision processes. It views group decision making as a combination of individual and collective activity. The article tests whether cognitive feedback can enhance control over the individual and collective decision making processes and can facilitate the process of convergence among group members. In a laboratory experiment with groups of three decision makers, 15 groups received online cognitive feedback and 15 groups did not. Users receiving cognitive feedback maintained a higher level of control over the decision-making process as their decision strategies converged. This research indicates that (1) developers should include cognitive feedback as an integral part of the GDSS at every level, and (2) they should design the human-computer interaction so there is an intuitive and effective transition across the components of feedback at all levels. Researchers should extend the concepts explored here to other models of conflict that deal with ill-structured decisions, as well as study the impact of cognitive feedback over time. Finally, researchers trying to enhance the capabilities of GDSS should continue examining how to take advantage of the differences between individual, interpersonal, and collective decision making.