1. Analyzing Chat Dialogue with Taghelper Tools Catherine Chase Stanford University PSLC Summer Institute June 22, 2007

2. Study Design (Kumar et al., 2007) 16 dyads of 6 th grade students Math Talk tutor built with CTAT and Tutalk Two conditions: Mathtalk with social dialogue enhancers Mathtalk alone Dependent measure analyzed: Chat dialogue from collaborative Math Talk sessions Kumar, R., Gweon, G., Joshi, M., Cui, Y., & Rose, C. (2007). Supporting Students Working Together on Math with Social Dialogue.

3. Math Talk

4. Social Dialogue Agent Tutor asks Student1: “Do you like tacos or hamburgers?” Tutor asks Student2: “Do you like to hang out on the weekends or after school?” Tutor uses students responses to generate a story problem: Alice thought she bought enough tacos for 11 of her friends one day on the weekend. But 8 of her friends finished 2/3 of the food. How many times more food should she have bought?

5. Research Questions Do social dialogue agents affect students’ sense of belonging to a group? Do social dialogue agents affect the attributions students make towards solutions to a math problem? Do social dialogue agents impact student affect?

6. The Process Data clean-up in Excel Developed and applied coding schemes Created coding models using TagHelper Tools Evaluated model performance Debugged model Altered features in model creation Coded more data or different types of data

7. Coding Scheme: Sense of belonging Trained a model to code for group, self, and partner-referenced language using only simple user-defined rules Group = we, us, our, ours, ourselves Self = I, I am, I’m, Im, my, mine, me Partner = you, u, your, yours, you’re Using a decision tree algorithm, the model achieved 94% reliability

8. Coding Scheme: Attributions Trained a model to code for attributions to any problem-solving step using a set of pre- coded data Group: “we did it!” Self: “I figured it out” Partner: “u multiplied by the wrong number” Only achieved reliability of 34% Fewer instances of attributions in the data More ambiguous language made it more difficult for the program to find meaningful patterns Is “nice work!” an attribution to the partner?

9. Coding Scheme: Affect Part 1 Coded for specific types of affective states or behaviors Insult: “you are stoopid”, “FOOL” Annoyance: “This is annoying” Boredom: “I am bored” Achieved low reliability level of 39% The model used meaningless keywords like “it” and “I” to classify codes into certain categories Sample of coded data for model training was not representative of the full distribution of examples within each category

10. Coding Scheme: Affect Part 2 Coded broader categories of positive and negative emotion Positive: “sweet”, “awesome!”, “yeah!” Negative: “this is annoying”, “you are stoopid”, “stop it” Achieved high reliability level of 69%, however, data coded by the trained model contained many incorrectly classified cases Coded as Negative by the model: “I know that” Coded as Positive by the model: “it should be 12!” Coded data for training was not representative of all instances of positive/negative affect

11. Next Steps Conduct statistical analyses on sense of belonging data Continue to “debug” training models for attribution and affect coding schemes Code more data Code more varied examples within data Test out different algorithm types and user- defined features Examine erroneous coding to determine problems with the model

12. Lessons Learned  Both typical and atypical coded examples are necessary to train an accurate model  Experimentation with various features and algorithms often leads to successful models  TagHelper is particularly useful for:  large data sets  training a model to analyze multiple data sets

13. Special thanks to… Carolyn Rosé Rohit Kumar Pittsburgh Science of Learning Center