PowerPoint 프레젠테이션

PICKON: Context-Based Emoticon
Recommendation Service on Mobile
Instant messaging
Yujung Yun
Interaction Science
Sungkyunkwan University, Korea
[email protected]
Thesis Supervisor:
Jun-Dong Cho
Committee:
Dong-hee Shin, Sangwon-Lee
1
Contents
1. Introduction
4. Results
1.
Emoticon in CMC
1.
Reliability & Validity
2.
Related studies
2.
Result1, 2
3.
Research Question
5. Conclusion
2. Literature Review
1.
Emoticon Classification
2.
Technology Acceptance Model(TAM)
1.
Discussion
2.
Limitations of the Research
3.
Future Research
3. Methodology
1.
Participants
2.
Questionnaire design
3.
Procedure
2
Introduction_
Emoticon in Computer-mediated Communication (CMC)
• Reduced nonverbal cues
• Impersonal
• Anonymity
3
Introduction_
Emoticon usage and sales statistics
Transfering emoticons through Line are
18 billions in one day,
10-15% of all messages in Kakaotalk are
Emoticon exchange (about 5 billions)
4
Attributes
Chat
Instant
Messaging(IM)
Mobile Instant
Messaging(MIM)
Interactive
mode
Nearly
synchronous
Nearly synchronous
Synchronous
Message type
Medium
Shorter
Shorter
Communicatio
n frequency
Varied
Frequently within a
time period
Frequent
Medium
Desktop, laptop
Desktop, laptop
Smartphone.
Typical
software
example
sayclub
MSN, Skype, Nateon
Kakaotalk, Line
5
Related Studies
Study
Tested
medium
Research summary
Dependent variable
Ptaszynski, M.,
et al (2010).
Web-based
program
Presented CAO, a system for affect
analysis of emoticons in japanese
online communication. (based on
database of text emoticon)
Capability to sufficiently detect
and extract any emoticon.
Reddy, V. R.
(2004)
Web-based
program
Developed 3D character
conversational agent that allow
natural language conversation
Preference,
intention to use, trust,
presence
Nevlarouskaya,
A. et al
(2010)
Second
life(3d virtual
world)
Recognition of affective content
conveyed through text, and
automatic visualization of
emotional expression of avatars
Usefulness, helpful in
conveying mood/
emotions,ease of use, improve
communication asked in
interview
Sánchez,J.A.,et
al (2006)
IM
Developed instant messageing and
smile emoticons based on Russell’s
circumplex model of affect.
Appropriateness of
conveyed emotion,
qualitative result from
Interview
6
How do you select
emoticon?
7
purpose of study
Emoticon
selection
Emotional
Status
Context-based emoticon recommendation system
Examine effect of context-based emoticon recommendation
system in mobile instant messaging (MIM). Specially, on the
relationships between emoticon selection and usability factors.
8
Research Question
For mobile instant messaging user what is the relationship between
types of emoticon selection and perceived usefulness, perceived ease
of use, attitude, appropriateness, enjoyment, and intention to use.
• Independent variable: Type of emoticon selection :
- Flicking UI, Presenting emoticons irregularly.
- Touch & Drag to scrolling handle, Presenting
emoticons based on context(emotional state, text)
• Dependent variable:
usefulness, ease of use, attitude, appropriateness, enjoyment, intention to use
9
Literature Review_
Emoticon classification in selecting emoticon
•
James Russell’s circumplex model
10
Theoretical framework 2
• Technology Acceptance Model(TAM)
[10] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information
technology. MIS quarterly, 319-340.
[11] Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison
of two theoretical models. Management science, 35(8), 982-1003.
11
Research model
[28]
[49]
Perceived
Ease of use
Emoticon
selection
Perceived
Usefulness
Appropriateness
[29]
[4]
Perceived
Enjoyment
Attitude
[28],[24]
[49], [51]
Intention
to Use
[2]
[18]
[10]
[12],
[45]
[12] Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in
the workplace1. Journal of applied social psychology, 22(14), 1111-1132.
[50] Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: a meta-analysis of the TAM:
Part 1. Journal of Modelling in Management, 2(3), 251-280.
12
Hypothesis


H1a. Emoticon selection in prototype1 has a positive effect on perceived ease of use(ES>PEOU)
H1b. Emoticon selection in prototype2 has a positive effect on perceived ease of use(ES>PEOU)


H2a. Emoticon selection in prototype1 has a positive effect on perceived appropriateness(ES>APP)
H2b. Emoticon selection in prototype2 has a positive effect on perceived appropriateness(ES>APP)


H3a. Emoticon selection in prototype1 has a positive effect on perceived enjoyment. (PI>PE)
H3b. Emoticon selection in prototype2 has a positive effect on perceived enjoyment. (PI>PE)


H4a. Perceived ease of use while using prototype1 has a positive effect on perceived usefulness(PEOU>PU)
H4b. Perceived ease of use while using prototype2 has a positive effect on perceived usefulness(PEOU>PU)


H5a. Perceived Enjoyment while using prototype1 has a positive effect on Intention to use. (ES>IU)
H5b. Perceived Enjoyment while using prototype2 has a positive effect on Intention to use. (ES>IU)


H6a. Appropriateness while using prototype1 has a positive effect on attitude. (APP>ATT)
H6b. Appropriateness while using prototype2 has a positive effect on attitude. (APP>ATT)


H7a. Attitude while using prototype1 has a positive effect on Intention to use. (ATT>IU)
H7b. Attitude while using prototype2 has a positive effect on Intention to use. (ATT>IU)
13
Methodology
Stimulus material
(Prototype application)
design
Measurement
(Questionnaire)
design
Online Survey
(for 2days)
14
Experiment Design & execution
What is PICKON?_App design process.
15
Reliability & Validity 1_Reliability
Construct
Measured items
No. of items
Cronbach's α
Perceived Ease of use
PEOU1, PEOU2, PEOU3, PEOU4
4
.904
Perceived Usefulness
PU1, PU2, PU3, PU4
4
.885
APP1, APP2, APP3, APP4
4
.884
ATT2, ATT3
2
.846
PE1, PE2, PE3
3
.909
IU1, IU2
2
.929
Appropriateness
Attitude
Perceived Enjoyment
Intention to Use
ATT1, ATT4, IU were eliminated during reliability check
.60 considered acceptable for exploratory purposes,
.70 considered adequate for confirmatory purposes,
.80 considered good for confirmatory purposes
All the estimated Cronbach's α value exceeded the cut-off value (0.70)
16
Experiment Design & execution
Emoticons are
displayed based on
emotional state by
adjusting handle
1
A Indicator is divided into 3
part(negative, neutral, positive)
1
Sign for each
emotion
Color association on
the side of slides
(red, grey, and blue)
17
Which one is better for user?
Prototype1
Prototype2
18
Summary of difference in prototypes
Prototype1
Way to
Manipulate
Emoticon
Arrangement
Order
Way to Access
Prototype2(PICKON)
Flicking (place a finger on the
Touch & Drag to scrolling handle
screen and quickly swipe the
among three point(negative,
panel)
neutral, positive)
Context-based(affective status
Random, partially arranged
selected by user), arranged by
according to character
emoticon type(smile, emoticon
w/ face, sticker)
Touch smile tap icon on
Touch a PICKON tap on
prototype1’s bottom
prototype1’s bottom
19
Participants
19-22
23-26
>27
male
female
N= 200
Measure
Gender
Age
items
frequency
percent
Female
100
50%
Male
100
50%
19-22
58
29%
23-26
92
46%
>27
50
25%
20
Measurement
Criteria
Questionnaire items
Q1. Using prototype1/ prototype2 (PICKON) would enable me to select emoticon more quickly.
Perceived
Q2. Using prototype1/ prototype2 (PICKON) would improve selecting emoticon.
usefulness
Q3. Using prototype1/ prototype2 (PICKON) would enhance effectiveness on selecting emoticon.
Q4. Using prototype1/ prototype2 (PICKON) would make it easier to select emoticon.
Q5. Learning to operate prototype1/ prototype2 (PICKON) would be easy for me
Perceived
Q6. My interaction with prototype1/ prototype2 (PICKON) would be clear and understandable
Ease of Use
Q7. It would be easy for me to become skillful at using prototype1/ prototype2 (PICKON)
Q8. It would find prototype1/ prototype2(PICKON) easy to use.
Q9. (interface)The interface is appropriate to find certain emoticon right now.
Perceived
Q10. (emotion) The emoticons are appropriate for emotion I intended right now.
Appropriateness Q11. (system) The way of displaying recommend emoticons list is appropriate(make sense).
Q12.(emoticon) The recommended emoticons are appropriate to me.
Perceived
Q13. Using prototype1/prototype2 (PICKON) wise idea.
Attitude
Q14. I like the idea of using the prototype1/ prototype2 (PICKON).
Intention to Use
Perceived
Enjoyment
Q15. Assuming I had access to the prototype1/ prototype2 (PICKON), I intend to use it.
Q16. Given that I had access to the system, I predict that I would use it.
Q17. Using prototype1/ prototype2 (PICKON) is fun
Q18. prototype1/ prototype2 (PICKON) is pleasant
Q19. I find prototype1/ prototype2 (PICKON) to be enjoyable
21
Reliability & Validity_results of Confirmatory Factor Analysis(CFA)
Fit index
Recommended Value
Structural Model
𝑥 2 /df
<3.00
2.384
Tucker-lewis index (TLI)
>.90
.929
Comparative fit index (CFI)
>.90
.934
RMSEA
<.08
.083
The Root Mean Square Error of Approximation (RMSEA):
When numerical value of RMSEA is between 0.05-0.08; better model
When numerical value of RMSEA is between 0.08-0.10; acceptable model
If greater than 0.10; it indicates that the model don’t fit well
The structural model had acceptable fit indices.
Doll, W. J., Hendrickson, A., & Deng, X. (1998). Using Davis's Perceived Usefulness and Ease‐of‐use
Instruments for Decision Making: A Confirmatory and Multigroup Invariance Analysis. Decision Sciences,
29(4), 839-869.
22
- 타당도가 있다없다가 아니라 적절하다 높다 낮다로 표현
-이 검사는 무엇을 측정하는데 타당하다고 표현.
Reliability & Validity
_Descriptive analysis and Discriminant validity of the measurement
PEOU
PU
APP
ATT
PE
PEOU
1
PU
.60
1
APP
.75
.84
1
ATT
.58
.81
.83
1
PE
.60
.75
.72
.74
1
IU
.60
.81
.73
.82
.77
IU
1
AVE
Composite
reliability
0.587
0.850
0.520
0.810
0.531
0.819
0.649
0.786
0.655
0.851
0.774
0.873
Average Shared squared variance(AVE): >.50
Composite reliability: >.70
AVE values are greater than .50 and less than composite reliability:
adequate level of convergent validity
23
Result 1
•
•
•
•
NS : non-significant
0.01< p < 0.05: code as one*
0.001< p <0.01: code as two*(**); usual cutoff p<0.05
P <0.001, code as ***
24
Result 1_Summary of hypothesis tests
Hypotheses
𝛽
SE
t
p-value
Supported
H1
Emoticon selection
--->
PEOU
-0.083
0.123
-1.36
0.174
No
H2
Emoticon selection
--->
APP
-0.052
0.143
-0.699
0.485
No
H3
Emoticon selection
--->
PE
-0.06
0.181
-0.809
0.419
No
H4
APP
--->
ATT
0.828
0.079
12.305
***
Yes
H5
PEOU
--->
PU
1.112
0.125
7.52
***
Yes
H6
ATT
--->
IU
0.452
0.112
4.079
***
Yes
H7
PU
--->
IU
0.331
0.146
3.033
0.002**
Yes
H8
PE
--->
IU
0.318
0.047
6.319
***
Yes
𝛽; Beta(standardized regression coefficient):
The greater value affect more to the dependent variable
P-value:
p-values quantify the probability of observing
Concomitant events under the null hypothesis.
25
Result 2_Summary of hypothesis tests
Hypotheses
Prototype1
Prototype2
𝛽
SE
t
p-value
Supported
H4_a
APP → ATT
0.646
0.101
6.745
***
Yes
H5_a
PEOU → PU
0.797
0.079
9.084
***
Yes
H6_a
ATT → IU
0.415
0.103
0.103
***
Yes
H7_a
PU → IU
0.555
0.094
0.094
***
Yes
H8_a
PE → IU
-0.005
0.062
0.062
0.943
No
H4_b
APP → ATT
0.874
0.104
0.104
***
Yes
H5_b
PEOU → PU
0.684
0.13
0.13
***
Yes
H6_b
ATT → IU
0.334
0.111
0.111
***
Yes
H7_b
PU → IU
0.256
0.157
0.157
***
Yes
H8_b
PE → IU
0.689
0.062
0.062
***
Yes
Prototype1, H8_a(PE-IU) was not supported among hypotheses.
However, H4_a, H5_a, H6, and H7_a, were supported.
Prototype2, All the hypotheses were supported.
26
Result 2_Critical ratios for differences between parameters
Prototype1
Prototype2
APP → ATT
ATT → IU
PU → IU
PE → IU
PEOU → PU
APP → ATT
2.064
3.996
3.639
8.153
1.971
ATT → IU
-2.273
-0.369
-0.886
2.718
-2.799
PU → IU
-1.6
-0.072
-0.474
2.294
-1.931
PE → IU
-0.594
1.783
1.247
7.008
-1.105
PEOU → PU
-0.367
1.352
0.939
4.334
-0.665
• c.r(Cirtical Ratio) of ATT-->IU, PU-->IU, PEOU-->PU , had no statistically
significant differences between parameters in path coefficient
(smaller than t(±1.96))
• c.r of APP-->ATT, PE-->IU were considered that there were statistically
significant difference in path coefficient in parameters (bigger than t(±1.96))
•
Prototype2 affected more than Prototype1 in H4 (APP-->ATT) and H8 (PE-->IU)11
27
Conclusion_Discussion
• Confirmed TAM path correlation toward emoticon selection
• Explored determinants of intention to use,
- PEOU> PU, APP>ATT,PE
• PU was affected most toward IU in Prototype1, PE was the most
important determinant of IU in Prototype2.
• Contributed to mobile instant messaging design by providing an
understanding of which emoticon selection style may be good in
what aspects.
28
Conclusion_ Limitation of Research
• Conclusion drawn are based on specific emoticon selection
technology.(hard to generalize)
• Lack of existing studies on the use of emoticon in ‘mobile
instant messaging’(difficult to interpret results)
• Participants who well known using emoticon
(need controlling ages)
29
Conclusion_
Future Research
Emoticon
selection
Spatiotemporal
property
Emotional
Status
Text
recognition
Context-based emoticon recommendation system
30
Conclusion_
Future Research
• Quantitative study + Qualitative data
(To obtain detailed opinion from the users)
• Design new set of emoticons for emotion based emoticon
recommendation
(referred to Ekman’s study)
• Improve prototype fuction (automation of inputting context)
31
References
•
•
•
•
•
•
•
•
•
•
•
•
•
[1] Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information
technology: a replication. MIS quarterly, 227-247.
[2] Ajzen, I. (2001). Nature and operation of attitudes. Annual review of psychology, 52(1), 27-58.
[3] Azuma, J., & Ebner, M. (2008, June). A Stylistic Analysis of Graphic Emoticons: Can they be Candidates for a
Universal Visual Language of the Future?. In World Conference on Educational Multimedia, Hypermedia and
Telecommunications (Vol. 2008, No. 1, pp. 972-979).
[4] Babin, B. J., Chebat, J. C., & Michon, R. (2004). Perceived appropriateness and its effect on quality, affect and
behavior. Journal of Retailing and Consumer Services, 11(5), 287-298.
[5] Baron, N. S. (2004). See you online. Gender issues in college student use of Instant Messaging. Journal of Language
and Social Psychology, 23(4), 397-423.
[6] Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model.
MIS quarterly, 351-370.
[7] Cha, Y. J. (2007). Identifying cross-cultural differences of emoticons in computer-mediated communication: A
comparison of North American (US) and South Korean emotional responses to emoticons (Doctoral dissertation, faculty
of the School of Informatics in partial fulfillment of the requirements for the degree Master of Science in HumanComputer Interaction, Indiana University).
[8] Cyr, D., Head, M., & Ivanov, A. (2006). Design aesthetics leading to m-loyalty in mobile commerce. Information &
Management, 43(8), 950-963.
[9] Danesi, M. (2009). Dictionary of media and communications. New York & London: M. E. Sharpe, Inc
[10] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology.
MIS quarterly, 319-340.
[11] Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of
two theoretical models. Management science, 35(8), 982-1003.
[12] Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the
workplace1. Journal of applied social psychology, 22(14), 1111-1132.
[13] Derks, D., Bos, A. E., & Grumbkow, J. V. (2007). Emoticons and social interaction on the Internet: the importance
of social context. Computers in Human Behavior, 23(1), 842-849.
32
[14] Dey, A. K. (2001). Understanding and using context. Personal and ubiquitous computing, 5(1), 4-7.
[15] Dresner, E., & Herring, S. C. (2010). Functions of the nonverbal in CMC: Emoticons and illocutionary force.
Communication Theory, 20(3), 249-268.
[16] Double, K. (2007). Computer-mediated communication and Internet language: An exploration of emoticons as
medium-specific elements of a developing Internet language. New Media, 2, 17-21.
[17] Ekman, P., & Friesen, W. V. (2003). Unmasking the face: A guide to recognizing emotions from facial clues. Ishk.
[18] Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research.
[19] Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: a study of ecommerce adoption. Journal of the Association for Information Systems, 1(1), 8.
[20] Holbrook, M. B., Chestnut, R. W., Oliva, T. A., & Greenleaf, E. A. (1984). Play as a consumption experience: The roles of
emotions, performance, and personality in the enjoyment of games. Journal of Consumer Research, 728-739.
[21] Huang, A. H., Yen, D. C., & Zhang, X. (2008). Exploring the potential effects of emoticons. Information & Management,
45(7), 466-473.
[22] Huffaker, D., & Calvert, S. (2005). Gender, identity, and language use in teenage blogs.Journal of Computer-Mediated
Communication, 10, article 1. Retrieved May 5, 2008,from http://jcmc.indiana.edu/vol10/issue2/huffaker.html.
[23] Kiesler, S., Siegel, J., & McGuire, T. W. (1984). Social psychological aspects of computer-mediated communication.
American psychologist, 39(10), 1123.
[24] Kim, K. J., & Sundar, S. S. (2014). Does Screen Size Matter for Smartphones? Utilitarian and Hedonic Effects of Screen
Size on Smartphone Adoption. Cyberpsychology, Behavior, and Social Networking.
[25] Lancaster, S., Yen, D. C., Huang, A. H., & Hung, S. Y. (2007). The selection of instant messaging or e-mail: College
students' perspective for computer communication. Information management & computer security, 15(1), 5-22.
[26] Lee, M., & Cho, J. D. (2014, September). Logmusic: context-based social music recommendation service on mobile
device. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct
Publication (pp. 95-98). ACM.
[27] Lim, S. H., Kim, D., & Watts, S. (2011). An Empirical Study of the Usage Performance of Mobile Emoticons. Journal of
Information Technology Applications & Management, 18(4), 21-40.
[28] Lu, J., Liu, C., Yu, C. S., & Wang, K. (2008). Determinants of accepting wireless mobile data services in China.
Information & Management, 45(1), 52-64.
[29] MacInnis, D. J., & Park, C. W. (1991). The differential role of characteristics of music on high-and low-involvement
consumers' processing of ads. Journal of consumer Research, 161-173.
[30] Malone, T. (1982). What makes computer games fun? (Vol. 13, No. 2-3, p. 143). ACM.
[31] Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management,
38(4), 217-230.
[33] Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2007, January). An expressive avatar for instant messaging endowed
with emotional intelligence. In Intelligent Virtual Agents (pp. 395-396). Springer Berlin Heidelberg.
[34] Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2010). User study on AffectIM, an avatar-based Instant Messaging
system employing rule-based affect sensing from text. International journal of human-computer studies, 68(7), 432-450.
33
[35] Petrantonakis, P. C., & Hadjileontiadis, L. J. (2010). Emotion recognition from EEG using higher order crossings.
Information Technology in Biomedicine, IEEE Transactions on, 14(2), 186-197.
[36] Phillips, L. W., & Sternthal, B. (1977). Age differences in information processing: a perspective on the aged consumer.
Journal of Marketing Research, 444-457.
[37] Plutchik, R. (2001). The nature of emotions. American Scientist, 89(4), 344-350.
[39] Reddy, V. R. (2004). Communicating emotions to virtual agents: An emoticon-based approach (Doctoral dissertation,
Master’s thesis, International School of New Media, Universitat zu Lubeck, Master of Science in Digital Media (June 2004)).
[40] Riva, G. (2002). The sociocognitive psychology of computer-mediated communication: The present and future of
technology-based interactions. Cyberpsychology & behavior, 5(6), 581-598.
[41] Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological review, 110(1), 145.
[42] Sánchez, J. A., Kirschning, I., Palacio, J. C., & Ostróvskaya, Y. (2005, October). Towards mood-oriented interfaces for
synchronous interaction. In Proceedings of the 2005 Latin American conference on Human-computer interaction (pp. 1-7).
ACM.
[43] Sánchez, J. A., Hernández, N. P., Penagos, J. C., & Ostróvskaya, Y. (2006, November). Conveying mood and emotion in
instant messaging by using a two-dimensional model for affective states. In Proceedings of VII Brazilian symposium on
Human factors in computing systems (pp. 66-72). ACM.
[44] Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. Digital
media, youth, and credibility, 73-100.
[45] Thelwall, M., Wilkinson, D., & Uppal, S. (2010). Data mining emotion in social network communication: Gender
differences in MySpace. Journal of the American Society for Information Science and Technology, 61(1), 190-199.
[46] Utz, S. (2000). Social information processing in MUDs: The development of friendships in virtual worlds. Journal of
Online Behavior, 1 (1). Retrieved September 15, 2009, from http://www.behavior.net/JOB/v1n1/utz.html
[48] Walther, J. B. (1996). Computer-mediated communication impersonal, interpersonal, and hyperpersonal interaction.
Communication research, 23(1), 3-43.
[49_1]Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion
into the technology acceptance model. Information systems research, 11(4), 342-365.
[48] Walther, J. B., & D’Addario, K. P. (2001). The impacts of emoticons on message interpretation in computer-mediated
communication. Social science computer review, 19(3), 324-347.
[49] Wu, L. L., Lu, H. P., & Tao, Y. H. (2010). The effect of emoticons in simplex and complex task-oriented communication:
An empirical study of instant messaging. Computers in Human Behavior, 26(5), 889-895.
[50] Yigit, O. T. (2005). Emoticon usage in task-oriented and socio-emotional contexts in online discussion boards.
[51] Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: a meta-analysis of the TAM: Part 1.
Journal of Modelling in Management, 2(3), 251-280.
[52] Zarmpou, T., Saprikis, V., Markos, A., & Vlachopoulou, M. (2012). Modeling users’ acceptance of mobile services.
Electronic Commerce Research, 12(2), 225-248.
[53] Zimmermann, A., Lorenz, A., & Oppermann, R. (2007). An operational definition of context. In Modeling and using
context (pp. 558-571). Springer Berlin Heidelberg.
[54] Zimmerman, D. P. (1987). Effects of computer conferencing on the language use of emotionally disturbed adolescents.
34
Behavior Research Methods, Instruments, & Computers, 19(2), 224-230.
Thank you!
35
Q&A
36