Paper reading #1: Oh, dear Stacy! Social interaction, elaboration, and Learning with Teachable agents

Introduction
Title: Oh, dear Stacy! Social interaction, elaboration, and Learning with Teachable agents.
Author bios:
Amy Ogan- is a postdoctoral student at Carnegie Mellon University in the Human-Computer Interaction Institute. She works on virtual agents for learning with other students
Samantha Finklestein- is a doctoral student at Carnegie Mellon University in the Human-Computer Interaction Institute.
Elijah Mayfield- is a doctoral student at Carnegie Mellon University in the Human-Computer Interaction Institute.
Claudia D'Adamo-
Noboru Matsuada
Justine Cassell

Summary

What they did was basically observe how students from different grades interacted with a teachable agent that could supply a few different social responses to the child. They were trying to see what would have the most learning gain and how the way the children interacted with Stacy affected their learning gains. The hypothesis was: how do increased cognitive reflection moves, inside-system vs. outside-system language and increased social moves correlate with learning. The agent Stacy was to be taught linear equations from the child and the authors would observe how the child conversed with Stacy, if they called the agent "she" or "her" it was inside, if it was called "it" it was outside. They then evaluated the results by comparing how the student talked to Stacy and how much she learned or didn't learn and how much they learned.

Related Work

The work was novel and the way the authors talked about related work was appropriate and helped with the overview of what has already been looked at.

Designing learning by teaching agents: The Betty's Brain system

Virtual peers as partners in storytelling and literacy learning

A social-cognitive framework for pedagogical agents as learning companions

Measuring self-regulating learning skills through social interactions in a teachable agent environment

Modeling student behaviors in an open-ended learning environment.

A Science Learning Environment using a Computational Thinking Approach

Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution.

Supporting Student Learning using Conversational Agents in a Teachable Agent Environment.

Relating Student Performance to Action Outcomes and Context in a Complex, Choice-Rich Learning Environment.

Identifying Students Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning.

All of these papers do with learning from a teachable agent or interactive program, but none of them talk about what the authors of this paper talk about. Which is how social interaction can affect learning from a teachable agent

Evaluation
Their results were evaluated systemically, they took all of the data they collected from each child put it together and created a coding scheme that was applied to the things the children said aka "utterances" as they were teaching Stacy. The categories the utterances were put in were: a social utterance, a tutoring move, an alignment bases pronoun use, a cognitive assessment,a correctness evaluation and they also had a none category. Once they put all the utterances into the right category they looked for correlations between the categories and the learning gains. They also looked at shifts in behavior and how specific behaviors in the chilled affected upcoming alignment on a turn by turn basis. Since they took down the words the child said I think it was also evaluated quantitatively with a little bit of qualitative since they measured the learning gains with a pre and post test and found a normalized gain.

Discussion
I thought that the work and contribution were very interesting, I didn't think that teaching someone helped you learn but when I thought about it it made perfect sense. Also I didn't know that there were teachable agents that help with this. The way they evaluated the results seemed to be pretty appropriate to me since I couldn't think of another way to look at the data they acquired. The contribution was novel in my opinion the field has been researched but not in the way these authors did, the overall contribution should help with the future development of teachable agents to help kids learn in a different way.

Introduction

Email: gdbrown09@gmail.com
Class: 5th year Senior
I'm taking this class because I thought that it would be pretty interesting. I don't really bring much experience about this particular field to this class the only experience I have is the classes I've taken up to this course. Professional life goals are to have a career that I love and won't get tired of going to everyday, personal life goals are pretty much the same as most people good job, family, good house. After I graduate I want to get a job in the CS field so that I can start getting experience that will go toward getting the eventual career that I want. In 10 years I hope to have a good job and still be on my own in any city really. I have no idea what the next technology will be but I'm sure it will be great. I would go back and meet Abraham Lincoln, because he was a good president in my opinion. My favorite shoes are any shoes that are new shoes, because new shoes are always clean and they look great for a few days. I wouldn't want to speak a different but I would want to have a cool accent like Irish that would be pretty cool, I just really like the way irish people sound with their accents.
Interesting Fact: I tore my acl in high school