One major goal of psychological science is the construction, evaluation, and revision of explanations of behavior and/or mental processes.  Psychological explanations are generalizations that describe the mechanisms by which those phenomena are produced by causal variables in people's external and/or internal environment.  A mechanism is the pattern of changes precipitated by the operation and interaction of causal variables that produce explanandum phenomena within a specific range of circumstances.  Mechanisms explain how causal influence is precipitated by the operation and interaction of causal variables and how this causal influence progresses over time to produce and maintain the phenomena to be explained within the context (set of circumstances) within which a given mechanism operates.  Explanatory mechanisms provide a deeper level of understanding of explanandum phenomena because they place constraints on relevant causal dependencies, the range of circumstances within which those dependencies hold, and the circumstances within which they may change or fail to hold altogether.  Because of the depth of understanding they provide, psychologists can use potential explanatory mechanisms to pose a wider range of empirically testable questions about behaviors and mental processes that they could with descriptive accounts alone.

Explanatory modeling activities require students to identify constraints on the occurrence and manifestations of phenomena, use those constraints to derive hypotheses about those phenomena, and evaluate these hypotheses by comparing them to relevant empirical evidence. These kinds of activities can help students overcome many of their misconceptions about psychological phenomena by restructuring and systematizing their relevant background knowledge in ways that promote conceptual change.

Current research in education suggests that new knowledge is often constructed through a continuous interaction of thought and action.  Students' existing conceptual structure guides their choice of goal directed actions and these actions guide their selection of relevant knowledge for the current task.  The goal of explanatory modeling is to construct a model of the functional architecture of the system of causal variables that operate and interact to produce and maintain the phenomenon to be explained. The recommended strategies for  achieving this goal include the following activities.  Initial candidate models are retrieved or constructed by using relevant domain knowledge to identify (1) a class of real world systems that are capable of producing the phenomenon to be explained and (2) the class of mechanisms (mechanism schema) by which those systems generates these phenomena.  Then, the hypothesized mechanism schema is used to construct an algorithm for mapping known inputs (antecedent conditions) to outputs (the explanandum phenomenon and byproducts of the system's operation).  Inferences are made by mentally simulating the operation of the candidate model in accordance with the algorithm to generate new states. As the simulation proceeds, the new states that are generated are compared to relevant empirical evidence and feedback from these comparisons is used to revise or elaborate existing models or reject them and start again. The modeling procedure involves a process of generating new and modifying existing models until a model is achieved that the student considers to be satisfactory. During these modeling activities, students' existing concepts will guide their (1) use of relevant knowledge to retrieve or construct initial models, (2) search for and interpret relevant empirical evidence, and (3) use of relevant evidence to modify their existing models at each stage of the modeling process.

The degree to which students' proposed model components capture relevant constraints on the phenomenon under study will determine how well their models can account for its occurrence.  When students' models fail to provide a good account of particular occurrences of the target phenomenon, subsequent reflection and discussion can lead students to revise their models, and this can lead to further model evaluation.  This process of model evaluation and revision often leads students to modify their relevant concepts.

The benefits of student engagement in explanatory modeling activities can be enhanced by activities that involve metacognitive reflection on and discussion of their modeling activities. For example, students might reflect on how the structure of their explanatory concepts motivates the construction, evaluation, and revision of their models.  Students might also reflect on their use of psychological concepts to separate things into distinct kinds, and how they relate these kinds to each other.

When properly integrated with other kinds of instructional methods, explanatory modeling activities can help students to better understand and evaluate important theoretical and methodological issues in psychology.  These issues are often difficult for students to understand because they involve the applications of scientific methods of investigation to behaviors that are extremely complex and endlessly variable. Evaluation presupposes understanding because students cannot properly evaluate what they do not properly understand. Evaluation is particularly important in the social and behavioral sciences where alternative theories often compete for acceptance. 

The complexity and variability of psychological phenomena reflects the common causal structure of the kinds of situations in which those phenomena typically occur.  In real world situations, the behavior of humans and other animal species are embedded in an ongoing process of continuous, dynamic activity that is maintained by constantly changing stimuli in their internal and external environment.  GLN Consulting can recommend ways of helping students to better understand these behaviors and the situations in which they occur by bringing together information resources, explanatory modeling activities, and instructional strategies.