The retriever-connector model: Matching Classroom Data and Agent-Based Computer Models to Simulate Students' Use of Multiple Epistemological Resources.

The retriever-connector model: Matching Classroom Data and Agent-Based Computer Models to Simulate Students’ Use of Multiple Epistemological Resources Paulo Blikstein (paulob@stanford.edu) TLT Lab, School of Education and (by courtesy) Computer Science Department, Stanford University 520 Galvez Mall, CERAS 232, Stanford, CA 94305 USA Abstract Relevance to cognitive research Utilizing data from a classroom intervention with 8 th graders, I employ agent-based computer modeling to simulate the cognitive processes at play during the intervention, in which students transition between using multiple epistemological resources. The model substantiates the hypothesis of manifold epistemological resources, which can be activated with simple prompts and have a non-linear impact on learning. Agent-based modeling in cognitive research could address the limitations of current methodologies. First, because experiments with human subjects cannot be indefinitely conducted, replicating findings or exploring a wide parameter space is costly and oftentimes impossible. In the case of research in schools, once the classroom data are collected, the researchers can revisit the videotapes and transcriptions; however, they can never relive the situations. Second, as the field moves toward theories that conceptualize learning as a dynamic and adaptive phenomenon, the traditional medium of scientific discourse—static linear text—becomes limited in its capacity to express these theories. Both of these flaws could be addressed with a set of dynamic, adaptive computer models of learning. Third, tools such as brain imaging cannot yet offer the speed and resolution required to evaluate complex learning processes at a neuronal level, so such models are still far from being applicable to real classrooms. Lastly, ethnographic or micro-genetic methods still cannot offer a ‘runnable,’ systemic, task-independent account of human learning. The ultimate goal of using agent-based simulation to explore human learning is to enable researchers to generalize and play “what-if” scenarios using in-depth interviews and ethnographic data and to help them investigate internal cognitive structures by observing external behaviors. This work builds on previous seminal contributions to the field, in which theoretical models of cognition were implemented by using computer programs to attempt to predict human reasoning (Newell & Simon, 1972) in tasks such as shape classification (Hummel & Biederman, 1992), language acquisition (Goldman & Varma, 1995), memory (Anderson, Bothell, Lebiere, & Matessa, 1998); and other more general-purpose models (Anderson, 1983; Anderson & Bellezza, 1993; Anderson & Lebiere, 1998). My design, however, differs from extant approaches in two ways: (1) Grain Size: Selecting a unit of analysis toward bridging the micro and macro perspective on learning. Theories which slice human learning into diminutive pieces, when reintegrated into the larger context of classroom learning, could not account for any meaningful macro-cognitive phenomena, and (2) Accessibility: Democratizing modeling- based research. Most computational theories of the mind are so mathematically complex that only specialized researchers can examine and critique them; the intricacies Keywords: Cognitive modeling; agent-based modeling; classroom research; epistemological resources. Introduction Agent-based modeling (ABM) has been increasingly used by scientists to study a wide range of phenomena such as the interactions of species in an ecosystem, the collisions of molecules in a chemical reaction, and the food-gathering behavior of ants (Bonabeau, Dorigo, & Theraulaz, 1999; Wilensky & Reisman, 2006). Such phenomena, in which the agents in a system (molecules, or ants) follow simple rules and interaction patterns, but exhibit complex emergent macroscopic behaviors, are studied in a young interdisciplinary field called complex systems or complexity studies (Holland, 1995). Although complex-systems perspectives initially arose from the natural sciences, complexity, emergence, and multi-level descriptions of phenomena are all highly relevant to social science research. In fact, recent decades have observed a surge in social- science studies employing ABM (Epstein & Axtell, 1996; Axelrod, 1997). Recently, ABM has also been used to illustrate aspects of cognitive development (Abrahamson & Wilensky, 2005; Blikstein, Abrahamson & Wilensky, 2006; Smith & Conrey, 2006), and collaboration and group work in classrooms (Abrahamson, Blikstein, & Wilensky, 2007). ABM has the potential to advance theory in multiple ways, which I illustrate in this paper: (a) explicitizing— ABM demands a high level of specificity in expressing a theoretical model, and it provides the tools and standard practices to express those models; (b) dynamics—ABM enables researchers to mobilize an otherwise static list of conjectured behaviors and observe the macroscopic patterns that may enfold; (c) emergence—ABM can examine cognition and social behaviors as a collection of decentralized, simple rules; and (d) interdisciplinary collaboration—the lingua franca of ABM enables researchers from different fields to understand, critique and challenge each other’s theories by modifying and extending the computational algorithms that underlie their theoretical models.