CIRCSIM-Tutor (Evans & Michael, 2006) assists students to learn about cardiovascular physiology relating to the regulation of blood pressure, using natural language dialogues. The tutoring strategies used to remediate student errors are a simulation of the tutoring carried out by two experienced human tutors. Students have the freedom to select a procedure that describes a perturbation of the cardiovascular system (shown on the upper right window of Figure 2.13). Then the system requests the students to predict the qualitative changes (increase, decrease, or no change) that will occur in seven cardiovascular parameters during the three time periods of the response: the Direct Response (DR) to the disturbance, the Reflex Response (RR), and the new, final Steady State (SS) (bottom right portion of Figure 2.13). Student input is limited +/-/0 to indicate the increase, decrease and unchanged statuses respectively in the variable that they currently focus on.
The ITS waits until the student has finished making predictions for the current stage (one whole column in the prediction table in Figure 2.13), then it compares the student’s answers with the correct answers and marks the errors with a diagonal line in red. Then the ITS begins a natural language tutoring session to assist the student correct those errors. Within each stage, the variables are discussed in the sequence they are encountered in the solution of the problem. However there is one exception to the timing of the interventions. The system would intervene if the student starts the predictions with the wrong variable. The hint given by the system indicates the physical location in the human physiology system where the first change occurs as a result of the perturbation. The goal of this hint is to help the student to focus on the correct parameter to start predictions. This early intervention aims to avoid a large number of wrong predictions that occur as a result of starting predictions with the incorrect parameter.
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The dialogues facilitated by CIRCSIM-Tutor are known as Directed Line of Reasoning (DLR) (Evans & Michael, 2006). A DLR is a multi-turn dialogue sequence in which the tutor helps the student reason about the problem with a series of questions, prompts and hints. This approach is often used to deliver explanations, summaries and remedies for misconceptions.
Figure 2.13: Interface of CIRSCIM-Tutor, from (Michael, Rovick, Glass, Yujian, & Evens, 2003)
The tutorial interactions consists of a sequence of tutoring dialogues focusing on incorrectly predicted variables, with an occasional additional topic, such as a summary, that is not immediately triggered by a student error. If all predictions are correct, the tutors generally ask a question or two to assess the student’s understanding.
The system is capable of adapting the remedial dialogue to the student’s learning needs as evidenced by his/her predictions and responses to the dialogue. The system can recognise a number of different types of student answers: partially correct but missing some essential
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information, partially correct and partially wrong, near-miss (close that are not fully right), grain of truth answers (contains a correct concept that the tutor can use as the basis for a productive tutoring interaction), “I don’t know” answers and totally wrong answers (Evans & Michael, 2006). This categorization enables a much wider range of responses. For instance, for a partially correct answer, the system acknowledges the correct part and provides a hint for the other part. For a student answer identified as a near miss, the system attempts to bridge the gap between the student answer and the correct answer. The response plans are dynamically generated from rules, but the system has some pre-specified plans to deal with serious misconceptions.
The effectiveness of CIRSCIM-Tutor was evaluated in several studies. The last study that was conducted in November 2002 involved version 2.9 of CIRSCIM-Tutor. The experimental group learnt with CIRSCIM-Tutor while the control group read a specially edited chapter on baroreceptor reflex (Evans & Michael, 2006). Both groups took a pre-test the weekend prior to the scheduled CIRSCIM-Tutor laboratory. The control group sat the post-test after reading the chapter whereas the experimental group did the same after interacting with the system for 1 hour in a scheduled CIRSCIM-Tutor laboratory. The control group consisted of 33 students whereas the experimental group had 40. At the end of study, the experimental group filled out a survey.
The pre and post-tests had 3 parts: (a) focused on the individual relationships between n
cardiovascular variables making up the baroreceptor reflex, (b) a baroreceptor reflex problem to be solved using a prediction table and (c) a set of multiple choice questions posed in a clinical setting that required the application of the understanding of the cardiovascular system.
The results revealed that the CIRSCIM-Tutor was more effective than reading a text in teaching to predict the behaviour of the baroreceptor reflex. Even though the system was capable of assisting students to acquire knowledge of the relevant cardiovascular relationships, it was not as effective as reading the text. However the system was not specifically designed to teach such knowledge. The survey responses indicated that students liked the system and that they felt it was useful in acquiring the targeted knowledge.