Cultivating intelligent tutoring cognizing agents in ill-defined domains using hybrid approaches

Cognizing agents are those systems that can perceive information from the external environment and can adapt to the changing conditions of that environment. Along the adaptation process a cognizing agent perceives information about the environment and generates reactions. An intelligent tutoring cognizing agent should deal not only with the tutoring system’s world but also with the learner-it should infer and predict new information about the learner and tailor the learning process to fit this specific learner. This paper shows how intelligent tutoring cognizing agents can be cultivated in ill-defined domains using hybrid techniques instantiated in the two example agents AEINS-CA and ALES-CA. These agents offer adaptive learning process and personalized feedback aiming to transfer certain cognitive skills, such as problem solving skills to the learners and develop their reasoning in the two ill-defined domains of ethics and argumentation. The paper focuses on the internal structure of each agent and the reasoning methodology, in which, the cognizing agent administration and construction along with the pedagogical scenarios are described

Tis better to Construct than to Receive? The Effects of Diagram Tools on Causal Reasoning

Previous research on the use of diagrams for argumentation instruction has highlighted, but not conclusively demonstrated, their potential benefits. We examine the relative benefits of using diagrams and diagramming tools to teach causal reasoning about public policy. Sixty-three Carnegie Mellon University students were asked to analyze short policy texts using either: 1) text only, 2) text and a pre-made, correct diagram representing the causal claims in the text, or 3) text and a diagramming tool with which to construct their own causal diagram. After a pretest and training, we tested student performance on a new policy text and found that students given a correct diagram (condition 2 above) significantly outperformed the other groups. Finally, we compared learning by testing students on a third policy problem in which we removed all diagram or tool aids and found that students who constructed their own diagrams (condition 3 above) learned the most. We describe these results and interpret them in a way that foreshadows work we now plan for a cognitive-tutor on causal diagram construction

Identifying critical thinking skills used by experts versus novices to construct argument maps in a computer-aided mapping tool

Research shows that using computer-aided mapping tools improves critical thinking skills, but prior research provides limited evidence to show how the use of specific critical thinking skills increases map quality. This qualitative study observed 4 experts and 5 novices use a computer-aided mapping tool to construct argument maps. The analysis of video recordings with think-aloud protocols and retrospective interviews revealed the use of a five-step argument mapping process (read claims, position conclusion, position claims, link claims, revise links) with the experts using a more sequential application of the five-step process and producing more accurate maps than novices. The novices showed the tendency to position and link claims as a joint action, making map revision more cumbersome. The experts exhibited the tendency to work backward from conclusion to claim while the novices exhibited the reverse tendency. This study’s findings identify processes that differentiate experts from novices and validate specific thinking skills that can be used to improve map quality, and how these processes can be operationalized in terms of discrete mapping behaviors performed on screen that can be mined and analyzed in mapping tools to assess and diagnose students’ mapping skills

The Diagnosticity of Argument Diagrams

Can argument diagrams be used to diagnose and predict argument performance? Argumentation is a complex domain with robust and often contradictory theories about the structure and scope of valid arguments. Argumentation is central to advanced problem solving in many domains and is a core feature of day-to-day discourse. Argumentation is quite literally, all around us, and yet is rarely taught explicitly. Novices often have difficulty parsing and constructing arguments particularly in written and verbal form. Such formats obscure key argumentative moves and often mask the strengths and weaknesses of the argument structure with complicated phrasing or simple sophistry. Argument diagrams have a long history in the philosophy of argument and have been seen increased application as instructional tools. Argument diagrams reify important argument structures, avoid the serial limitations of text, and are amenable to automatic processing. This thesis addresses the question posed above. In it I show that diagrammatic models of argument can be used to predict students' essay grades and that automatically-induced models can be competitive with human grades. In the course of this analysis I survey analytical tools such as Augmented Graph Grammars that can be applied to formalize argument analysis, and detail a novel Augmented Graph Grammar formalism and implementation used in the study. I also introduce novel machine learning algorithms for regression and tolerance reduction. This work makes contributions to research on Education, Intelligent Tutoring Systems, Machine Learning, Educational Datamining, Graph Analysis, and online grading