Our full paper can be here at: https://link.springer.com/chapter/10.1007/978-3-030-52237-7_27#aboutcontent Lin Y., Yu R., Dowell N. (2020) LIWCs the Same, Not the Same: Gendered Linguistic Signals of Performance and Experience in Online STEM Courses. In: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163. Springer, Cham

1. “LIWCs” the Same, Not the Same
Gendered Linguistic Signals of Performance
and Experience in Online STEM Courses
Yiwen Lin, Renzhe Yu, Nia Dowell
1

2. 01
Background:
Gender in STEM
2

3. Representation
X3
Intention to
Major
3
National Science
Foundation Report (2017)
Persistent rates
Griffith, A. L. (2010); Chen, X. (2013).
Bottia, Stearns, Mickelson,
Moller, & Valentino(2015)
Modality
preference
Wladis, Hachey, Conway
(2015)

4. - Different computer-mediated communication style between male and
female students (Sullivan et al, 2015; Tsai et al, 2015).
- Male and female students exhibit different linguistics characteristics
that are reflective of sociocognitive engagement. (Prinsen et al, 2007;
Lin et al, 2019)
Previous work
4
Goal: Explore whether language signals students learning outcomes in
different ways across gender.

5. 02
Theoretical
Framework:
Community of Inquiry
Social
presence
Cognitive
presence
Teaching
presence
5
Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of
distance education, 15(1), 7-23.

6. performance
Social
presence
Cognitive
presence
Sense of Belonging
Language
6

7. Quantifying language
7
Linguistic Inquiry and Word
Count (Pennebaker, Boyd, Jordan & Blackburn, 2015)

8. 8
Analytic: Formal, logical, and hierarchical thinking.
Clout: Relative social status, confidence, or leadership displayed
through writing.
Tone: positive and negative emotion words used in the writing.
Authentic: signals “humble, vulnerable” positions in social relations.
LIWC dimensions
Summary
Variables
Variables are derived from algorithms based on combinations of
individual LIWC variables.(Pennebaker, Boyd, Jordan & Blackburn, 2015)

9. LIWC-based analysis offer distinct proxies of cognitive
presence. (Kovanovic, Joksimovic, Gasevic & Hatala,
2014; Joksimovic, Gasevic, Kovanovic, Adesope &
Hatala, 2014)
Alignment with CoI
9
Social
presence
Cognitive
presence
LIWC linguistic elements illustrate students analytical
thinking, social processes, social orientation and may be
used as a form of assessment in STEM maker projects
(Oliver, Houchins, Moore, Wang, 2020).

10. Analytic
Clout
Tone
Authentic
Cognitive process (Cause, think,should)
Social process (friends, talk)
Personal pronouns ( I, we, they, you)
Variables of interest
Composite
Variables
Individual
Variables
Social
presence
Cognitive
presence
10

11. 03
Research
Questions
11

12. 01
What linguistic features of students’ forum post are associated
with performance and learning experience(i.e. Sense of belonging)
in an online introductory STEM course?
03
02
Do male and female students exhibit different
linguistic features in their posts?
Do these linguistic features correlate with outcomes
differently for male and female students?
12
OutcomesLanguage
?
Language
?
OutcomesLanguage
?

13. 04
Research
Context
13

14. four-year public
university in the US
Research Context
300 full-time
residential college
students
Fully online gateway
course in chemistry
in 2019
14

15. week1 week2 week3 ...week10
Course setup
Random groups (~10),
assigned in Week 3
week2week1
Weekly reflection
post on readings
Pre-course
survey
Post-course
survey
15

16. 16

17. Data
17
Content and metadata
of reflection posts from
Canvas LMS
Course gradebook
Student background
characteristics from
administrative records
Pre- and post-course survey
responses

18. Analysis
18
01
What linguistic features of students’ forum post are
associated with performance and learning experience(i.e.
Sense of belonging) in an online introductory STEM course?
03
02
Do male and female students exhibit different
linguistic features in their posts?
Do these linguistic features correlate with
outcomes differently for male and female
students?
What linguistic features of students’ forum post are
associated with performance and learning experience(i.e.
Sense of belonging) in an online introductory STEM course?
Language
?
Language
?
Language
?
Logistic regression
Multiple regression
Independent t-tests
Logistic regression
Multiple regression

19. 05
Results
19

20. RQ1
20

21. 21
RQ2

22. RQ3
22
Analytic
Clout
Authentic
Tone
Cognitive
Social
I
We
You
They
-.0922
-.643**
-.163
-.0034
-.469
-1.23**
.468
.983**
.66*
.757**
-.197
-.0364
.302
-.863***
.937***
-.961*
-.251
.547
.15
.19
Analytic
Clout
Authentic
Tone
Cognitive
Social
I
We
You
They
.0523
.00176
-.102
-.0993
-.0681
-.193
-.0668
.131
-.189
.0791
.0861
-.0367
.0457
.105
.391***
.11
-.0788
-.326**
-.111
-.133

23. 01
03
02
23

24. 06
Discussion
24

25. Discussion
25
Cognitive language might be a
stronger indicator for females’
performance and sense of
belonging
Group composition might also
influence the results, which
require further investigation in
the future
Female students might benefit
more from making intellectual
contributions

26. Implications
26
Diversifying course
assignments may offer
valuable insights about
students’ learning
Integration of linguistic
analytic tools for real-time
observation
Model building needs to
consider the potential
divergence across student
subgroup
Gender differences may
not manifest in the
most obvious way

27. Questions?
Thanks!
27
Yiwen Lin
yiwenl21@uci.edu
Twitter: @iwwwlyn
Renzhe Yu
renzhey@uci.edu
Twitter: @renzheyu
This theme is adapted from template in Slidesgo.
Special thanks to Peter McPartlan, Qiujie Li, Teya
Rutherford.