Since many of the problems we face every day are ill-structured, educators and researchers agree that it is important to develop skills for solving ill-structured, everyday problems. In many such ill-structured and complex problem-solving tasks, a critical objective for the problem solver is to develop a useful mental model of the situation. To this end, model-centered Instruction (MCI) environments provide opportunities for students to construct mental models and then transfer their knowledge and skills into real contexts. However, very few empirical studies have demonstrated how model-centered instruction affects ill-structured problem solving.
Therefore, the purposes of this exploratory study were to investigate the effects of model-centered instruction, and to develop a basis on which one could suggest whether expert modeling or self-guided modeling is more appropriate for learning in a particular ill-structured problem-solving situation: ethical decision making in program evaluation. In addition, this study examined how the two levels of the learners’ initial status (inexperienced and experienced) interact with the two types of model-centered instruction, an issue that has not previously been addressed in this context. As a result, this study contributes to the knowledge of when and how expert modeling and self-guided modeling might be effective, efficient, and engaging, for learners with different levels of expertise.
Sixty two pre-service and in-service evaluators participated in this study. Those participants were classified as inexperienced or experienced learners, based on: their work experiences in evaluation and ethics; courses taken in evaluation and ethics; and pretest results. Participants were randomly assigned to each of the two types of model-centered instruction: expert modeling or self-guided modeling. In the expert modeling instruction, participants were provided with the conceptual models of experts on how to solve ethical conflicts within program evaluation. In the self-guided modeling instruction, participants received no guidance in developing their own mental models.
The results of this study indicated that, during instruction, inexperienced learners in the expert modeling group invested less mental effort and time than those in the self-guided modeling group. In addition, inexperienced learners in the expert modeling group also exhibited more engagement than those in the self-guided modeling group. Therefore, it seems reasonable to conclude that expert modeling instruction is likely to be the more appropriate instructional design for inexperienced learners.
Experienced learners in the self-guided modeling group invested less mental effort during instruction than those in the expert modeling group. In this study, expert modeling required experienced learners to invest more mental effort, because if the conceptual model of the expert was redundant for them, they had to integrate previous schema and overload their working memory. However, the experienced learners in the self-guided modeling group did not show more engagement than those in the expert modeling group. They exhibited less confidence and satisfaction about their self-guided modeling instruction. Since experienced learners might have believed that they could achieve the required learning when supplied with a full instructional guidance format, such as expert modeling, they exhibited more confidence and satisfaction in expert modeling than in self-guided modeling.
Regardless of the types of model-centered instruction employed, the inexperienced participants expressed significantly higher levels of attention and satisfaction than did the experienced participants. It seems that the inexperienced participants were fascinated by the use of model-centered instruction.
This dissertation confirms the expertise reversal effect. The study also suggests that, in the future, instructional designers should carefully consider learner expertise when they design model-centered instruction for ill-structured problem solving, particularly ethical decision making in program evaluation.