So last week I was busy finalizing my literature review. After speaking with my mentor for an hour and meeting with my CREU-colleague Michelina Astle, who writes great literature reviews, I realized that I was using the wrong name to refer to certain variables. For example, I was using the term Self Disclosure to refer to a student who admits his own misunderstanding of material, but this is really a metacognitive reflection (a reflection made by himself on his own performance that is not simple logic, but rather metacognitive). After clearing this up, and reworking my literaure review, I finalized my research questions using the new terminology.
I have enjoyed exploring the field of virtual agent interaction with humans and the rapport maintenance mechanisms involved in these interactions. Though this topic is more psychological than computational, I find it fascinating, and I never forget that the ultimate goal of this project, even beyond the CREU program, is to develop a socially-sensitive virtual agent. Specifically, the questions I have generated each week reflect this reality; I cannot explore psychological topics that are too complex because our lab would never be able to program those into the virtual agent. For example, our virtual agent will never be a robot, it will always be an image on a screen, so it makes no sense to research interpersonal distance interacting with rapport. One always has to keep such practical limitations in mind when researching, and I think this is a valuable lesson for me to keep in mind as I progress to graduate school. This lesson is more specific to me than just “you can’t do everything at once”. For example, there may be many, many variables that are very much relevant to building rapport, but if your agent has limitations, like the inability to make hundreds of facial expressions, then researching these variables is simply a distraction. I have learned that even topics that seem relevant may be distractions.
On another note, I also learned about technical difficulties in collecting data from social interactions. As Micky Chi once stated in “Analyzing Verbal Data”, conversation and social interaction data tend to be voluminous, containing many utterances; just one hour of data can take many more hours to parse. Additionally, one can have technical difficulties, as our lab had, where the camera frame rate dropped sporadically and ruined the video data we collected.
Perhaps the most important use of my time was the meeting I had with my CREU colleague. She is very adept at locating the main point of my literature review and seeing the moments when I veer off track. Her feedback helped me see the moments when I needed to ask myself more often “why am I claiming this? With what basis?”
To summarize my most recent (and final) literature review process iteration, I essentially found literature supporting the idea that metacognitive self reflection, or self-awareness, is important to motivating students to work through a task. This somewhat answers the question you may remember I used to have, “when is rapport becoming too abundant to be productive?” Motivated students can stay on task longer. In fact, there is a cycle students go through from exploration, to grappling with confusion, to finally solving a problem and wanting to solve more, that becomes interrupted when a student gets “stuck” in a failure state (a state of confusion and frustration). If the student becomes self aware that he is in this state he may be able to motivate himself to get out of it, and this is the idea behind affect-adaptive virtual agents, which try to detect this failure state via proxy measures like skin conductance (measuring excitement about the topic) and smiling behavior (measuring enjoyment obviously). However, I hypothesize that another way to nurture metacognitive self reflective behavior in students, to make them awawre that they’re in this state, is not just by creating an agent that can respond to frustration and curiosity, but one that builds rapport with the user. Note that many behaviors associated with rapport maintenance, such as referring to a shared experience (“Are you bored? Yes? I’m bored too”), are the same behavior the affect-adaptive agents are using, but the developers of these agents have not delved into the “relational effect” (Burleson and Picard 2004) of having a virtual tutor interacting with a human. In other words, they have not explicitly tried to employ rapport-building strategies to encourage metacognitive self reflection. Thus, I ask in my review, are metacognitive self reflective behaviors increased by rapport? Does this interaction differ between a virtual agent-human and human-human dyad?
My literature review is here:
Burleson, W., Picard, R. (2004). Affective agents: Sustaining Motivation to Learn Through Failure and a State of “Stuck”. In: Workshop of Social and Emotional Intelligence in Learning Environments, in conjunction with the 7th International Conference on Intelligent Tutoring Systems