Applying Learning Principles to Development of Multimedia for Addressing Bias in Street-Level Public Decision-Making

JI. of Educational Multimedia and Hypermedia (2003) 12(2) 163-183 This article describes a specific effort to create a multimedia training technology (A Child Protective Service Investigation Simulator or CPS Simulator) for a representative group of street-level public servants--child protective service (CPS) workers. The article first provides some background on why bias in this area is a significant problem, then the basic features of the technology are outlined and a rationale for the development of the software within the current environment for developing educational technology is presented. The bulk of the article discusses the research on which the initial design of the CPS Simulator was based, and how the ultimate design of technology incorporates characteristics of situated learning environments and constructivist approaches to learning. ********** Street-level bureaucrats, such as police officers, welfare workers, and schoolteachers are employees of public agencies who have regular direct contact with members of the public. By definition, street-level bureaucrats are the ultimate implementers of public policy, and the outcome of their decisions often determines the distribution of public goods, benefits, sanctions, and services. While decision makers presume that their wishes are being precisely fulfilled by these public servants, in reality street-level workers typically possess a high level of decision-making discretion within which personal bias can play a major role (Lipsky, 1980). Unfortunately, many street-level bureaucrats are poorly trained and the training that does occur may be ineffective in addressing bias. However, opportunities exist to create training technologies specifically designed to help street-level workers learn to overcome bias. Bias is closely connected to learning. It is generally recognized that new information only becomes meaningful when it is related to knowledge already existing in the mind of the learner (Piaget, 1959; Ausubel, 1968; Anderson, 1983) and when the context for learning is highly personal and adaptive to the learner (Novak, 1985). Existing biases can form either bridges or obstacles to further learning depending on whether the bias in question helps or hinders the establishment of a link to new knowledge. Unfortunately, in many cases bias acts as a barrier to learning and good judgment. Overcoming bias represents a particularly higher order learning challenge. People form emotional attachments to their beliefs and will often regard ideas or experiences that challenge those beliefs as threats to the self. As a result, simply providing information that contradicts one's beliefs or biases is typically ineffective as a training strategy (Head & Sutton, 1985). More immersive learning experiences are needed. Similar to teachers whose beliefs about teaching are often closely linked to their experiences as students (Kagan, 1992), the decision-making of many street level bureaucrats is likely to be based on their personal experiences rather than on a rational decision-making process (Ross, 1977). Biases in decision-making also occur because mental schema and belief systems cause only certain environmental events or stimuli to be selected out for further processing. (Kahneman, 1973). This selective attention or encoding effect and its impact on cause-effect judgments appears to be particular salient in social situations (Holland, Holyoack, Nisbett, & Thagard, 1986). Essentially, trainees in these situations will often place too much emphasis on the personalities of those they are interacting with. These processes (e.g., attribution error, selective encoding, etc.) tend to be automatic, operating outside of conscious awareness and control (Schneider, Dumais, & Shiffrin, 1984). Hence biased decision-making can remain undetected. Because most training does not attempt to address the personal experience that underlies bias, normal training technologies are likely to leave these barriers to good decision-making intact. …