Face work with emojis in a - didactic peer-feedback setting: A pragmatic analysis

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Face work with emojis in a - didactic peer-feedback setting: A pragmatic analysis
Face work with emojis in a
   didactic peer-feedback setting:
        A pragmatic analysis

     Michael Beißwenger · Steffen Pappert
         Universität Duisburg-Essen

Final Workshop. 18th–20th October 2018, Zurich
Face work with emojis in a - didactic peer-feedback setting: A pragmatic analysis
Focus of our talk

Results of an exploratory, pragmatic analysis of the use of emojis
in a digital learning environment (inverted classroom /
game-based learning scenario).
 Data set: The total of posts written by learners in a learning
  environment using a wiki platform (N=680)
 Task: provide peer feedback for other learners‘ suggestions on
  how to solve a certain spelling phenomenon. The use of emojis
  was voluntary.
 Hypothesis: Providing feedback on the results of other learners
  is a challenging task as it requires to criticise people who are on
  the same level. Learners adopt emojis as a resource for
  managing this challenge: They use emojis as a means of
  politeness in interaction (as a resource for face work).
Face work with emojis in a - didactic peer-feedback setting: A pragmatic analysis
Three perspectives
Media linguistics:
Linguistic analysis of CMC; special focus on the functions of emojis
and on politeness strategies in written interactions.
Pragmatic analysis of emerging practices in mediated interaction:
How do interlocutors adopt resources provided by the
technological environment (in our case: emojis) for the
organisation of social interaction?
Research on ‚digital didactics‘:
What devices can/should a learning environment – as an eco-logy
for learning and teaching activities – provide to support learners
with performing given or anticipated tasks?
 Linguistics can contribute to the evaluation and further
  development of scenarios for digitally enhanced learning.
Face work with emojis in a - didactic peer-feedback setting: A pragmatic analysis
Structure of our talk

 Linguistic perspectives on emojis:
  Previous and related work
 Face work in interaction:
  Basic concepts for our study on emojis in a learning
  environment
 Face work with emojis:
  results and examples from our study
  (Beißwenger/Pappert to appear 2019)
 Discussion & outlook
Face work with emojis in a - didactic peer-feedback setting: A pragmatic analysis
Emoijs as a means of managing interaction in CMC

Research on emojis with a linguistic focus:
  e.g., Danesi 2017, Dürscheid/Frick 2014, Dürscheid/Siever
  2018, Herring/Dainas 2017, Pappert 2017, Siebenhaar 2017
Suggestions from previous research on the functions of emojis:
 graphical function: emojis function “as allographs, as
  ideograms, and as border and sentence intention signals”
  (Dürscheid/Siever 2017: 266; 278) + indexical function
  (Dürscheid/Siever 2017: 274-275)
 commentary function; representation function; illustration
  function (Dürscheid/Frick 2016: 105; cf. Dürscheid/Frick 2014:
  173)
 Siebenhaar (2018): Emojis function “as a substitute for
  complex propositions” (758) + as a “substitute for entire
  communicative actions” (760)
Face work with emojis in a - didactic peer-feedback setting: A pragmatic analysis
Emoijs as a means of managing interaction in CMC

Function types (Pappert 2017):
(1)    Framing function (Rahmen)
(2)    Economizing function (Ökonomisieren)
(3)    Relationship building/management function
       (Beziehungsgestaltung)
(4)    Modalizing function (Modalisieren)
(5)    Commentary/evaluative function (Kommentieren/Evaluieren)
(6)    Structuring function (Strukturieren)
(7)    Representation function (Darstellen)
(8)    Ludic function (Ludische Funktion)
(9)    Ornament function (Ausschmückung)
Face work with emojis in a - didactic peer-feedback setting: A pragmatic analysis
Emoijs as a means of managing interaction in CMC

                          I forgot the sandwich! Crap!
                          Can you please take it? !
                          I will.

                          I‘m arrived!

                          I’m free on July 04!

                          Forgot my tobacco. That never
                          happened to me before.
                          Wonderful!!!!!!
Face work with emojis in a - didactic peer-feedback setting: A pragmatic analysis
Model of linguistic politeness: face work approach
                  (GOFFMAN 1986, BROWN/LEVINSON 1987)

Basic concept:
o face: the public self-image that every member of a society wants to
  claim for him- oder herself and that we typically protect and
  negotiate in interaction.
Two components:
o positive face: the want to be respected and appreciated by others;
o negative face: the want for privacy and autonomy („I do not want to
  be disturbed when not necessary and do not want to be expected by
  others to do something that I do not want by myself “).
Face-threats in interaction:
o face threatening acts (FTAs): acts which have the potential to –
  completely or partially – disrespect the face-wants of an interaction
  partner and, thus, affect their positive and/or negative face.
Face work with emojis in a - didactic peer-feedback setting: A pragmatic analysis
Model of linguistic politeness: face work approach
                  (GOFFMAN 1986, BROWN/LEVINSON 1987)

Politeness strategies:
 help to deal with potential face-threats in a socially acceptable way
 are applied to protect the positive and/or negative face of the
  interaction partner (or of oneself):
    o by performing ‛redressive actions‘ in order to soften (modalize) a
      potential face-threat;
    o by boosting the partnerʼs face
      (‛face-flattering acts‘, KERBAT-ORECCHIONI 2005; politeness as a
      ‛social accelerator‘, BROWN/LEVINSON 1987: 103)
In our study we are interested in:
 the detection of (i) face-threatening acts (FTAs) and
                   (ii) face-flattering acts (FFAs) in the data,
 the identification of emoji instances which in the given context serve
  as visual boosters or softeners for FTAs/FFAs.
Face work with emojis in a - didactic peer-feedback setting: A pragmatic analysis
The learning scenario – in a nutshell:
           The cooperative Learning game Ortho & Graf

                                                        https://udue.de/orthoundgraf

Beißwenger, Michael; Meyer, Lena (2018): Ortho & Graf: ein Wiki-basiertes Planspiel zur Förderung von Rechtschreibkompe-
  tenzen in der Sekundarstufe II. In: Steffen Gailberger & Frauke Wietzke (Hrsg.): Deutschunterricht in einer digitalen
  Gesellschaft. Unterrichtsanregungen für die Sekundarstufen. Weinheim: Beltz Juventa, 296-330.
Beißwenger, Michael; Meyer, Lena (2019, in press): Gamification als Schlüssel zu „trockenen“ Themen? Beobachtungen und
  Analysen zu einem webbasierten Planspiel zur Förderung orthographischer Kompetenz. In: Karin Beckers & Marvin Wasser-
  mann (Hrsg.): Wissenskommunikation im Web. Sprachwissenschaftliche und didaktische Perspektiven. Frankfurt: Peter Lang.
game phase 1: investigation request

                                                       game phase 3:
                                                       internal audit

game phase 2: case file
with investigation result
& rationale
Task for game phase 3

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Please
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Performing the task implies face-threats in several respect:

 (1) w.r.t. the negative face of the addressees:
     It can be seen as a limitation of their autonomy
     (“Your case file needs to be revised”).

 (2) w.r.t. the positive face of the addressees
     as their work is criticised.

 (3) w.r.t. the positive face of the author
     as the addressees may consider her or him as a smart aleck.

 Hypothesis:

     Since the students had to perform the task in order to
     successfully complete the seminar, they try to solve this risky
     task in a socially acceptable way. Emojis are adopted for
     softening face threats and for being polite.
How students solve the task: an example
The data

Data set: 680 posts from three instantiations of the game in seminars
with teacher students at the UDE during the summer semester 2017
(95 students in total, 65 actively participating in game phase 3).

        post 1
        post 2

        post 3

        post 4
Classification of the data
(1) Identify all posts which contain a reactive component
    (i.e. which do not only perform the feedback task but also reply to a
    previous post on the same talk page) and remove them to keep the
    data set homogeneous.
(2) For all remaining posts:
    Identify all posts which contain at least one FTA.
    FTA is the case when an utterance suggests revision of a case file
    and/or criticises the case file or parts of it and/or presents an
    alternative opinion about the solution of the case described in the
    file. Remove all posts which do not contain an FTA.
(3) For all remaining posts: Remove posts which do not include at least
    one emoji or emoticon.
(4) For all remaining posts: Besides the FTAs, also identify all FFAs.
    FFAs = „acts, which are like the positive side of the FTAs, acts that
    reinforce the other’s face“ (KERBRAT-ORECCHIONI 2005: 30-31)
Classification of the data

Data set: 680 posts from three instantiations of the game in seminars
with teacher students at the UDE during the summer semester 2017
(95 students in total, 65 actively participating in game phase 3).
Classification results:

   Posts which include a reactive component:                       120

   Posts which contain FFAs only:                                  216

   Posts which contain at least one FTA and no reactive
                                                                    343
    component
          ... plus at least one emoji / emoticon:             229

          (Subset 1) ... and only FTAs:                 62

          (Subset 2) ... and both FTAs and FFAs:       167
How emojis contribute to face work in our data

(1) as boosters: they support and intensify the effects of
    face-related acts:
   (1.1) FFA boosters: emojis supporting a face-flattering act.
   (1.2) FTA boosters: emojis supporting a face-threatening act.

(2) as softeners: they are used to modalise the effect of an FTA:
   (2.1) modalising by positioning: the emoji is used to mitigate the
         claim of validity for an act of critique by indicating that the
         critique might be unjustified.
   (2.2) modalising by characterising an FTA as facetious
   (2.3) modalising by characterising the FTA as an action within a
         ludic context
How emojis contribute to face work in our data

FFA boosters:
How emojis contribute to face work in our data

(1) as boosters: they support and intensify the effects of
    face-related acts:
   (1.1) FFA boosters: emojis supporting a face-flattering act.
   (1.2) FTA boosters: emojis supporting a face-threatening act.

(2) as softeners: they are used to modalise the effect of an FTA:
   (2.1) modalising by positioning: the emoji is used to mitigate the
         claim of validity for an act of critique by indicating that the
         critique might be unjustified.
   (2.2) modalising by characterising an FTA as facetious
   (2.3) modalising by characterising the FTA as an action within a
         ludic context
How emojis contribute to face work in our data

FTA boosters:
How emojis contribute to face work in our data

(1) as boosters: they support and intensify the effects of
    face-related acts:
   (1.1) FFA boosters: emojis supporting a face-flattering act.
   (1.2) FTA boosters: emojis supporting a face-threatening act.

(2) as softeners: they are used to modalise the effect of an FTA:
   (2.1) modalising by positioning: the emoji is used to mitigate the
         claim of validity for an act of critique by indicating that the
         critique might be unjustified.
   (2.2) modalising by characterising an FTA as facetious
   (2.3) modalising by characterising the FTA as an action within a
         ludic context
How emojis contribute to face work in our data

softeners: modalising by positioning:
How emojis contribute to face work in our data

(1) as boosters: they support and intensify the effects of
    face-related acts:
   (1.1) FFA boosters: emojis supporting a face-flattering act.
   (1.2) FTA boosters: emojis supporting a face-threatening act.

(2) as softeners: they are used to modalise the effect of an FTA:
   (2.1) modalising by positioning: the emoji is used to mitigate the
         claim of validity for an act of critique by indicating that the
         critique might be unjustified.
   (2.2) modalising by characterising an FTA as facetious
   (2.3) modalising by characterising the FTA as an action within a
         ludic context
How emojis contribute to face work in our data

softeners: modalising by characterising the FTA as facetious:
How emojis contribute to face work in our data

(1) as boosters: they support and intensify the effects of
    face-related acts:
   (1.1) FFA boosters: emojis supporting a face-flattering act.
   (1.2) FTA boosters: emojis supporting a face-threatening act.

(2) as softeners: they are used to modalise the effect of an FTA:
   (2.1) modalising by positioning: the emoji is used to mitigate the
         claim of validity for an act of critique by indicating that the
         critique might be unjustified.
   (2.2) modalising by characterising an FTA as facetious
   (2.3) modalising by characterising the FTA as an action within a
         ludic context
How emojis contribute to face work in our data

softeners: characterising the FTA as an action within a ludic context:
Politeness strategies with emojis:
results from a classification of emojis occurrences in the data
  Data set: 680 posts from three instantiations of the game in seminars
  with teacher students at the UDE during the summer semester 2017
  (95 students in total, 65 actively participating in game phase 3).
  Three categories of posts:

  (1) Posts which include a reactive component:                       120

  (2) Posts which contain FFAs only:                                  216

  (3) Posts which contain at least one FTA and no reactive
                                                                      343
      component
     (3.1) ... plus at least one emoji / emoticon:              229

            (3.1.1) ... and only FTAs:                    62

            (3.1.2) ... and both FTAs and FFAs:          167
Politeness strategies with emojis:
results from a classification of emojis occurrences in the data

  emojis are rather used to soften than to boost posts containing FTAs
Politeness strategies with emojis:
results from a classification of emojis occurrences in the data

  When occuring together with FTAs (69 cases), emojis are rather used
   as softeners (53) than as boosters (16).
  Emojis are used twice as often as FFA boosters than as FTA softeners.
  57% of all posts in the data set (87) contain FFA boosters only.
A frequent pattern: softening FTAs by boosting an FFA
                (87 out of 153 cases)
Face work with emojis: interpretation of our findings

 Even though the given task („provide peer feedback“) encou-
  rages learners to critically discuss each other‘s results, the
  authors of feedback posts rather use emojis to soften than to
  highlight their critique.
 Besides that, the „1st choice strategy“ for mitigating the effects
  of FTAs using emojis is obviously not the use of emojis as moda-
  lisers; instead, authors of feedback posts use emojis as boosters
  for FFAs while FTAs performed in the same post are not accom-
  panied by an emoji at all.
 “bird‘s eye view politeness”: In posts of that type the FFA
  component is visually highlighted as salient whereas the FTAs
  are not discovered until the addressee actually reads the post.
 Strategy: collect social credits in advance and make the
  addressee feel great before s/he discovers the critique.
Conclusion and outlook

Media Linguistics / Pragmatic analyses of practices:
o This study: modeling the coontribution of emojis to face work practices
  on the basis of data from a ‘controlled’ environment (the analysed
  posts are all approaches to the solution of one and the same task).
  Findings can form the basis for investigations on face work practices in
  CMC in other contexts (e.g. everyday whatsapp interactions).
o Corpora needed for analyses on a broader basis – e.g. the Whats Up,
  Switzerland (Ueberwasser & Stark 2017) or the MoCoDa2 corpus
  (Beißwenger, Fladrich, Imo & Ziegler 2018).
Media Didactics:
o Emojis are used by the learners not only as nice little „gimmicks“ but
  as resources to deal with a given task. Findings can contribute to the
  design and further development of scenarios for digitally enhanced
  learning where learners are expected to perform tasks which may
  result in potential face threats.
Book chapter (in press)

Michael Beißwenger/Steffen Pappert (2019, in press):
Face work mit Emojis.
Was linguistische Analysen zum Verständnis sprachlichen Handelns
in digitalen Lernumgebungen beitragen können.
In: Michael Beißwenger & Matthias Knopp (eds.):
Soziale Medien in Schule und Hochschule: Linguistische, sprach- und
mediendidaktische Perspektiven.
Frankfurt: Peter Lang (Reihe „Forum Angewandte Linguistik“).
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Face work with emojis in a
   didactic peer-feedback setting:
        A pragmatic analysis

     Michael Beißwenger · Steffen Pappert
         Universität Duisburg-Essen

Final Workshop. 18th–20th October 2018, Zurich
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