4.1 Creating answers for your expectations

The expectation answers work the same way as the question answers. Just like your main question's answer, you may have several answers for each expectation, which are necessary because student input will vary from individual to individual.

Unlike the main question's answers, if student input adequately covers one of the expectation answers, the Tutor agent will move on to a different expectation -> hints -> prompts sequence. Remember, if a student adequately covers one of the main question's answers, the Tutor agent will move on to the next question. 

Name & Keys

The Name & Keys tab is where you should 1) name your answer, 2) provide a semantic answer using regular expressions, 3) select which agent you would like to provide feedback for the student input, 4) label the "answer type" and 5) determine the threshold.
  • 1. Name your answer. This is for your use. Name each one of your answers so you can better organize your tutoring pack. 
  • 2. Provide a semantic answer using regular expressions. This is what the AutoTutor Lite system will compare your student's input to. This is very similar to the semantic answers used in both the Self-Reflection assessment type and the Tutoring Assessment type. In the "Keys" box, you should include the essential words that would be needed to fully answer the question. Ideally, you will input these answer keys as regular expressions. The AutoTutor Lite system still takes advantage of LSA cosine similarity when comparing student input to answers, but the use of regular expressions helps make up for some of cosine similarity's shortcomings (i.e, differentiating between positive and negative answers). Because cosine similarity is still used to evaluate student input, the your SKO's semantic engine configuration is still important to consider. 
    • After a question is asked and the user provides input, that input will be compared to each of the keys for each of your answers. If a user answer matches a "good answer" type highly, but a "bad answer" type lowly, the user answer will be considered "Good". 
    • If user input matches a "bad answer" more than a "good answer" one of two things may happen, depending on the rule set being used for this ASAT in AutoTutor Lite interaction. 
      • 1. The Tutor Agent or Student Agent will provide immediate feedback to the user, letting the user know their answer is insufficient and will then trigger an expectation -> hints -> prompts sequence. OR
      • 2. The Student Agent will read what is in the Text & Speech tab for the associated "bad answer" (more information about the Text & Speech tab can be found below). This is in an effort to take some of the negative "heat" off of the user and instead place it on the Student Agent. For this to take place, be sure to select "Student" in the "Agent" drop down menu.
  • 3. Select which agent you would like to provide feedback for the student input. You can select either "Tutor" or "Student" as your agent. This agent read your input in the "Text & Speech" box. For example, if your student input has a high relatedness to your "Good" or "Ideal" answer, the agent you choose will read say what you have inserted into the "Speech" box in the Text & Speech tab, presumably your ideal answer. 
  • 4. Select the "answer type". The drop down menu allows you to select an "answer type". The answer types that are available are based on the rules that have been pre-loaded into your ASAT SKO. 
  • 5. Select your answer threshold. This threshold determines what percentage of answer key coverage is necessary for the student input to be considered as answering the question. For example, if we set the threshold for the "Ideal Answer" to .9, then the student input would need to match (or have semantic overlap with) 90% of the answer key. The Tutor or Student agent (depending on who you selected) will then speak what is written in the Text & Speech tab. 
Text & Speech

The Text & Speech tab under "Answers" is where the spoken and written answer should be placed. This will be read by whoever you selected in the Agent drop down menu.