| Vita | Publications |

Allan C. Jeong
Associate Professor

Dept. of Educational Psychology & Learning Systems
Instructional Systems Program
Florida State University
Stone Building Room 3205E
Tallahassee FL 32306-4453
Email: ajeong@fsu.edu

Key Interests: Learning & visual analytics, modeling learning processes with sequential analysis, distance learning, computer-supported collaborative learning, online discourse, argumentation and critical thinking, argument analysis & diagramming, concept & causal diagramming, social media, mobile learning


Since joining the Instructional Systems program in 2001, I have been developing software tools to visualize and sequentially map cognitive /learning processes exhibited in computer-mediated and collaborative learning environments. These tools provide a means to quantitatively measure, test, and identify sequential patterns in students' behaviors and actions. The goal is to create and apply these visual analytic tools to engage in reverse engineering – a process of identifying the cognitive processes and task sequences successful learners perform to achieve the target goals - in order to formulate instructional models that can be used to help all learners maximize learning and performance. The second goal is develop instructional interventions that are effective in guiding students through the prescribed learning process and to apply the visual analytic tools to determine to what extent the interventions are able to change students’ learning processes, and the extent to which observed changes in students’ learning processes lead to improvements in learning and performance.

The tools I have developed to conduct my earlier studies have culminated into two software programs, Forum ManagerDiscussion Analysis Tool. My paper, "A Guide to Analyzing Message-Response Sequences and Group Interaction Patterns in Computer-Mediated Communication", presents an in-depth discussion of how I have used these software tools to

a.      Determine how certain characteristics of both the messenger and responder (gender, intellectual openness, learning  style), the message (message function, conversational vs. expository style, intensifiers vs. qualifiers, response time, day of posting), and instructional environment (prescribed conversational scripts and message tags, pre-structured/unstructured discussion threads, imposing constraints on message-reply sequences) help to elicit the types of responses and dialog move sequences that produce and sustain critical inquiry.

b.      Produce visual diagrams and stochastic models to concisely convey how specific factors/characteristics affect the processes of critical discourse. See my summary of the research findings in my interactive Google presentation.  To support the application of learning analytics and data mining in future research on online discourse, I've developed a fully functional and customizable threaded discussion board hosted in Google spreadsheets.

In search of new opportunities to apply my tools and methods, I  have broadened my research interests to computational modeling student interactions in online discussions - models that can be used to automate the coding of online postings (based on characteristics of the message, messenger, reply, responder, and group task), and diagnosing discourse processes to improve group decision-making, problem-solving, and learning. In addition, I have developed a software application called jMAP for creating and assessing concept and/or causal maps (video demo). This application can be used to study the interplay between argumentative discourse and causal modeling/understanding. Using both the jMAP and DAT software together enables us to use sequential analysis to computationally model and study how specific processes of argumentation affect and change learners' causal diagrams and causal understanding.

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