Outlining My Research

I had one main accomplishment last week. This past week I produced a rough draft of an outline, which will serve to guide me as I write my literature review.

One big challenge I faced in compiling this outline was in finding enough information on anxiety or vulnerability, my latest direction of research. This topic is mainly covered by articles dealing with clinically severe (and innate trait) anxiety, not the state-based anxiety that occurs naturally during human-human interaction. Thus, I did a lot of digging around. The interaction of anxiety and vulnerability with rapport, namely that more anxiety contributes to forming lower rapport, led me to believe that self disclosure was important, because anxiety reduces the amount of self disclosure being reciprocated by a tutor-tutee dyad.

Self disclosure is an important part of the rapport building model. Specifically, the model for rapport building that I find most useful is the model presented by Zhao et al (2014), reproduced below (Figure 2).



Most models of rapport building strategies include self disclosure, coordination, and mutual attentiveness, whether the model was created for tutor-tutee relationships, business-customer relationships, or boss-employee relationships. I have looked previously at economics research in rapport building and found many components of the Zhao et al model. The reason certain elements of the model are ubiquitous is because, as it seems, all human rapport building boils down to being able to sharing personal information about oneself in order to create a salient boundary between the outside world and your group of friends, which leads to better support and understanding for each other (note Fig. 2 mentions supporting the “true-self”). Going forward, any time I refer to a rapport model, or strategies for building rapport, I am always referring to the Zhao et al model above.

Last week I spent 5 hours watching an re-watching all the video data available to me. This data was collected for a prototype virtual algebra tutor (which mine and my colleagues’ research will improve), which spoke to a middle school student to offer suggestions on how to solve an algebra problem. I had to watch these videos because I had to make sure that the topics I’m looking for in my review of the literature (namely, anxiety and rapport and self disclosure), are going to be found in the data. If this is not the case, then I’m just wasting time learning about topics that I will never be able to find evidence for in our data corpus. Interestingly, some students indeed seemed more anxious than others, and levels of self disclosure varied greatly, as did apparent extroversion and face threatening behavior (perhaps due to personality differences). Of course, a formal statistical test is required to see if these are significant interactions, but at least now I know my research is not completely detached from the reality of my data.

The other 7 hours were spent meeting with my CREU team-mate and research mentor (2 hours) and doing research (5 hours). Note, research was not a major component of the time I worked last week because that step is almost complete.

I have attached my outline to this post, but note it is rough, and certain sections need to be taken out to make the scope of the outline less broad and more focused.



  1. Persona Effect
    1. People universally agree computers do not have human understanding of emotion, yet some people treat them nicely…”This rejection of anthropomorphism stands in stark contrast to people’s actual behavior in our labs and in more naturalistic settings. In this article, we will argue that there is clear evidence that individuals mindlessly (Langer, 1989) apply social rules and expectations to computers.”
    2. Mindlessness is defined by Nass and Moon (2000) to be the conscious attention giving to a subset of contexts, so that you are responding to overly simplistic scripts that ignore details, like when a human ignores that a virtual agent that is smiling is not really there, but is just pixels., and human smiles back. Many computer-human social interactions are due to a degree of mindlessness, and many source have suggested there is som subconscious understanding of virtual agent as social actor, such as when humans reported feeling more connected with agents that smiled more but they were not aware than agents were programmed to smile more or less. Here is the theory of mindless behavior:: “. We can conclude that individuals are responding mindlessly to computers to the extent that they apply social scripts—scripts for human-human interaction—that are inappropriate for human-computer interaction, essentially ignoring the cues that reveal the essential asocial nature of a computer” (Nass and Moon, 2000).
  2. Self efficacy
    1. Self efficacy effects: Self-efficacy beliefs influence academic choices as students are prone to engage in tasks in which they feel confident and avoid those in which they do not.
    2. Self efficacy is the belief in one’s ability to do something successfully, and it is a better predictor for academic success than prior academic success and academic interest.
    3. But there are some limits to the use of self efficacy as a predictor for academic success: “The effects of efficacy beliefs on achievement are usually stronger for high school and college students than for elementary students”
  3. Self disclosure
    1. Al this ties into self disclosure. Self disclosure causes a risk beause one could reveal a secret that causes rejection by peers (Kelly 1996). Kelly also says that the worth of revealing secrets is a complex calculation that is successful based on whether the secret keeper will act friendly after having benn told the secret (Kelly 1996). So, when a child reveals something to a virtual agent, he is calculating how worthy a vvirtual agent is of receiving the secret.
    2. Kang and Gratch (2012) said that computers with human back stories lead people to reveal more about themselves than using computer back stories, which makes sense. Anxious people seem to self disclose more.
    3. Zhao et al (2014) claim self dislosure is used to build coordination and other researchers claim slef disclosure helps the friends differentiate themselves from the outside universe (eg. This is you and me, we are different from them), which is a way to establish rapport.
    4. Is self disclosure part of the larger idea of reciprocation?
    5. How to define “comfortablness” with a virtual agent

Works Cited

Nass and Moon (2000). Machines and Mindlessness. Journal of Social Issues, Vol. 56, No. 1, 2000, pp. 81–103

Kelly, A. E., & McKillop, K. J. (1996). Consequences of revealing personal secrets. Psychological Bulletin, 120(3), 450-465.

Kang, Gratch (2012). Socially anxious people reveal more personal information with virtual counselors that talk about themselves using intimate human back stories.

Kang et al. (2008). “Agreeable People Like Agreeable Virtual Tutors”.

This source determines specific components of rapport (either coordination, attentiveness, or positivity) that are benefit disproportionately from specific personality types (Big Five personality types).


Erik E. Noftle and Phillip R. Shaver (2006). “Attachment Dimensions & Personality Traits: Associations and Comparative Ability to Predict Relationship Quality.” Journal of Research in Personality, (40)  179-208.

This source is useful because it shows that there IS a correlation between certain personality types and certain relationship types. For example, attachment avoidance is highly positively correlated with neuroticism (the tendency to be cold or depressed or anxious). This shows that in human-human relationships, personality matters.


Ogan et al (2011). “Rudeness and Rapport: Insults and Learning Gains in Peer Tutoring.” page 1.

This study describes how positivity decreases as strangers become friends. Ogan shows that insults are correlated with lower learning gains in stranger dyads but higher learning gains in friendly dyads. Also, some examples of insults are provided. It is shown that in general, students are more playful with language, whereas tutors are more focused on the task at hand

Cutrone (2011) “Face theory, japanese culture”.

This source claims the concept of face DOES exist in japanese culture, and probably across all cultures, even though previous sociologists said the concept of face is alien to japanese culture.

Johnson, L., Paola, R. “Politeness in Dialog: ‘Run the factory, that’s what I’d do.'” Intelligent Tutoring Systems, 206 – 243. 2004.

This source describes very concisely and neatly the “persona effect”. This effect somewhat answers the question “how believable should we make the tutoring system?” because the answer is that the system need not be believable, but it MUST be able to account for the persona effect, and to do that it must respond to social cues in a human-like way. This means humans are comforted by the system, even if they know it’s not a real sentient being.

Furthermore, this  source has a good definition of the politeness theory of Brown and Levinson, which includes the definition of “Face”. Their theory said that every culture has a conecpt of face, and positive face is the desire to be approved by others, whereas negative face is the desire to do what one wants regardless of others’ opinions. When tutors threaten negative face (by being forcefull or giving orders) or positive face (by saying the tutee is wrong too harshly), the learning gains suffer.

Park, A., Ickes, W., & Robinson, R. L. (2014). More f#!%ing rudeness: Reliable personality predictors of verbal rudeness and other ugly confrontational behaviors. Journal of Aggression, Conflict and Peace Research, 6(1), 26-43. Retrieved from

The source above is surprisingly NOT about the role of laughter specifically, but rather how in a story telling dyadic paradigm, the listener’s prosody, nonverbal behavior, and affective stance affect the telling of the story by the teller.

Park, A., Ickes, W., & Robinson, R. L. (2014). More f#!%ing rudeness: Reliable personality predictors of verbal rudeness and other ugly confrontational behaviors. Journal of Aggression, Conflict and Peace Research, 6(1), 26-43. Retrieved from

The paper above tries to demonstrate certain personalities are predisposed to rudeness and ugly confrontational behavior in general. This supports my hypothesis that certain personalities can be predisposed to confront a virtual peer tutor.

Mendoza-Denton, Norma (1999). Turn initial “no”: collaborative opposition among Latina adolescents. Source is a chapter in a larger book, “Reinventing Identities: The Gendered Self In discourse” (1999). Eds, Mary Bucholtz, A. C. Liang, Laurel A. Sutton. Oxford University Press. ISBN 3 1735 040 492 310. (From Hillman Library, location P-120, S48, R47, 1999).

This source states that stance is composed of both verbal and nonverbal cues. For example, gesture, intonation, and physical action are all deployed in concert to embody stance in girls’ conflictive evaluation of one another’s turns in hopscotch. Subjects were all Latinas from Mexico in an American high school. This paper is a response to other theories that polarize (and generalize) all women’s behavior as cooperative and all men’s behavior as conflictive. This paper aims to demonstrate conflict AND cooperation working in women’s interactions. This debunks myths like Latina submissiveness.  To the end of shedding light on women’s interactions, this paper describes turn initial “no” as a “no” that is a discourse marker. In linguistics, discourse markers signal cataphoric or anaphoric relations between units of talk, “bracketing” them. The point is turn initial “no” is not just the negation word “no”, but rather has polysemy (many meanings).

Che et al (2012). Problem Solving Strategies of Girls and Boys in Problem Solving Strategies.

More than double as many girls as boys wrote down an answer using a procedure that was memorized or poorly explained. However, both boys and girls were mature in their problem solving thinking.

Zhao et al (2014). Towards a Dyadic Copmutational Model of Rapport Management.

This source outlines a model of rapport building. It is useful because this source is from the Articulab, where all the rest of my research is taking place, so I will be using this model going forward. The original purpose of the paper was to propose a computer architecture that can support a computer program that is able to check the rapport state, and adjust its outputs to this rapport state (using the FIgure 2 model aforementioned). I believe this model was implemented in the prototype tutor experiment that I watched videos of.

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