Simulated Ecological Environments for Education (SEEE) Model, (2006-Present). An investigation into the empirical models with the data generated by the simulations to understand the causal models and the relationships between the design factors of complex user interfaces in these virtual environments on human thought and action. Future research will investigate the complexity and causality of such interactions between the child’s mental model, the virtual environment, and the user interface in the form of regression equations and Markov models.
The SEEE Tripartite Model
I am deeply interested in how humans perceive and learn about their environment. Especially in how information systems may be used to help mediate communication, interpretation, understanding, learning, decision support, action selection and creativity. This interest led me to investigate fields of human-computer interaction and then scientific visualization, as humans are excellent at pattern recognition, thus we can be most efficient with interactions in our environment when our information systems are designed with the human factors at the forefront.
The main direction of my research is theoretical and firmly framed in the domain of information science. Investigation is into 3D user-interfaces and the interplay of virtual environments, simulations, content, the mental model of the user and the search, navigation, augmentation, and annotation possibilities of the user-interface. Quantitative and causal models are used to explain the role that ecological context has in the dynamic interaction with cognitive models, and is at the core of my research program. This is the type of research program that requires collaboration between computer scientists, psychologists, educators and scientists. The research aim is to use qualitative data fitting techniques to investigate the design parameter interaction with mental models and thus evaluate the SEEE Tripartite Model. An investigation into the empirical models with the data generated by the simulations to understand the causal models and the relationships between the design factors of complex user interfaces in these virtual environments on human thought and action. It is defined as a tripartite model, as it is believed to be a Markov Model.
The overreaching goal is to develop practical, empirically grounded theories to inform HCI design, which will make it possible for educators and user-interface designers to produce high-impact learning simulators for children. The pragmatic goal is to create solutions to complex, real-world problems, and to make them beautiful, innovative, efficient, and transformational, with impact on education, research, outreach, and even on the commercialization processes. Interdisciplinary, collaborative, research in art, technology, science, and social sciences, especially in understanding the phenomena of human, technology, and environment interaction, and the quantitative modeling of dynamic interactions in understanding situations, meaning, and aesthetics. The scientific goal, is to build an empirical understanding of the quantitative and causal models, explaining the role of ecological context in the dynamic interaction with humans, and the interplay of the search, navigation, augmentation, and annotation possibilities of the user-interface design to support intrinsic learning and acts of creativity. The research aim is to use qualitative data fitting techniques to investigate the design parameter interaction with mental models, and thus evaluate and apply my major contribution to the field, the work started in my dissertation, Simulated Ecological Environments for Education (SEEE) Tripartite Model. It is defined as a tripartite model, as it is believed to be a Markov Model.
The user interface (UI) is designed to enhance the perception of signals (various redundancy-gain techniques such as highlighting and scientific visualization) and to intentionally capture the user’s attention. Very much like an art student being trained to actively and consciously observe, see, and analyze the visual object, then to re-create it as artifacts, check it for error, and iteratively reduce the error, the user interface should support the user in learning to see more actively and consciously the details in the virtual environment (VE). The user interface can use various abstraction techniques to highlight important information, direct attention, and reduce cognitive overload. Changes in the one's knowledge (Δ Knowledge) are represented in the framework as a novice’s ontology of the domain, a subset of expert’s, which, after interacting with the simulation (VE) and the user interface (UI), may iteratively expand outwards and towards the experts’ ontology of the domain. The experts’ knowledge is representative of both the declarative knowledge of the domain ontology, and procedural knowledge as the algorithms (rules) and the heuristics of the domain. Changes in the virtual environment result in new visual signals for the child to perceive. New signals present opportunities for the child to either recognize (accessible in memory) or not recognize (not in memory); that is, it is the opportunity to inquire. The active, chosen act of inquiry, based on the child’s understanding that they do not know, is a user-initiated action or a set of actions via the user-interface, requesting semantic information about that visual signal. This is active user-initiated inquiry, which is different from a passive reception of new information about an unrecognized visual signal, and is a very important distinction for this research. Since we only perceive what we can see and know to look for, we need to recognize unknown signals and seek to know them.
Figure 1: Published in my dissertation as (Figure 6), is a graphical representation of the information, signals, and their relationships to each other, quantified in the SEEE Tripartite Model: a theory of Human-Computer Environment Interaction. Harrington, Maria, C. R. (2008). “Simulated Ecological Environments for Education (SEEE): A Tripartite Model Framework of HCI Design Parameters for Situational Learning in Virtual Environments,” Dissertation Abstracts International. July 17, 2008. University of Pittsburgh, Pittsburgh, PA.
Maria C. R. Harrington, Ph.D. University of Pittsburgh Lecture at UPMC. Colloquium Presentations and Invited Talks:
Harrington, Maria, C. R. (2010). COMET/Colloquium: Reality Matters in Virtual Reality for Learning Sciences Research: The Virtual Trillium Trail Empirical Data and Future fMRI Studies. University of Pittsburgh, UPMC, School of Medicine, Pittsburgh, PA. http://halley.exp.sis.pitt.edu/comet/presentColloquium.do?col_id=700