AutoTutor

• Outer loop: AutoTutor (http://demo.autotutor.org/) teaches by engaging students in a natural language (English) dialogue
• For AutoTutor, a task corresponds to a single question, such as the one shown in the upper right of Figure 4, that has a complex answer. Its outer loop consists of selecting such a question and working with the student to get it completely answered.
• Inner loop: The inner loop starts with the student typing in an initial answer to the top level question (see Figure 4; the student types into the lower right window; the whole dialogue is displayed in the lower left window).
• AutoTutor has been used to compare output modalities.
• An AutoTutor dialogue is composed of tutor turns alternating with student turns. On most of the student turns, the student makes a small contribution toward completing the whole task. Those student turns count as steps, because they are a user interface event that contributes to a solution of the whole task
• Step analysis:
• These are conclusions that are produced by applying knowledge components. For instance, the first two items above correspond to distinct learning events, wherein the student has applied the same Knowledge Component,
• In addition to having a list of all anticipated correct learning events, such as the ones mentioned above, AutoTutor has a list of several of the most important incorrect learning events
• To find out which learning events underlie the student’s step, AutoTutor measures the semantic similarity between the text of the Learning Event and the text of the step. It uses a measure called Latent Semantic Analysis