1.
ATPI Dissertation
Proposal of Laura A.
Pasquini
Department of Learning Technologies - College of
Information, University of North Texas
Major Professor: Dr. Jeff M. Allen
Committee: Dr. Kim Nimon & Dr. Mark Davis
1

2.
Social Media Guidance in Higher
Education: Using Latent Semantic
Analysis to Review Social Media
Guideline and Policy Documents
Laura A. Pasquini, B.A., M.S. Ed.
Department of Learning Technologies,
College of Information - University of
North Texas
2

3.
Research Study
Examination of social media guideline & policy
documents, which are accessible online from postsecondary education (PSE) institutions using the
text mining method, Latent Semantic Analysis
(LSA).
3

4.
Background
• Social
media use has increased in higher education
(Brenner & Smith, 2013); however guideline and policy
documents have rarely been examined (Joosten, 2012;
Joosten et al., 2013; Reed, 2013)
• Institutions
direct & moderate how students, staff, faculty &
administrators use social media on campus (Blankenship,
2011; Moran, Seaman, & Tinti-Kane, 2011)
4
pp. 3-5

5.
Need for Study
•
Social learning and learning cultures experience
(Vygotsky, 1962; Bandura, 1977; Brown, 2001)
•
Communities of practice (Wenger, 1999)
•
Personal learning networks (Warlick, 2009)
•
Social media creates an information network
where information, ideas, learning & passion
grows (Thomas & Brown, 2011)
5
pp. 5-8

6.
How is Social Media Being
“Guided” in Higher Ed?
Mergel et al. (2012)
• Create a social media policy before using social media
or experimentation with social media within the
organization to generate and apply guidance.
Wandel (2009) and Joosten et al. (2013)
• Security and privacy are two of the primary concerns
Rodriguez (2011)
• Deal with challenges related to privacy, ownership of
intellectual property, legal use, identity management,
and literacy development
6
pp. 8-11

7.
Purpose
The purpose of this study is to analyze social
media guideline and policy documents that are
accessible online from post-secondary education
(PSE) institutions.
7
p. 11

8.
Theoretical Framework
Latent Semantic Analysis (LSA) is a
Theory of Meaning:
•“the
meaning of a yet is largely
conveyed by the words from which it is
composed”
(Landauer, McNamara, Dennis, & Kintsch, 2013)
8

9.
Research Questions
•
R1. What latent semantic factors are relevant
to structuring the body of textual data in
current higher education social media
guideline and policy documents?
•
R2. What naturally emerging inherent
categories and themes, can be identified in
higher education social media guidelines and
policies?
9
p. 14

10.
Limitations
Latent Semantic Analysis (LSA is):
•Text content only
•Dimension reduction of the dataset
•Orthogonal (Lee, Song & Kim, 2010)
•Polysemy issues (Li & Joshi, 2012)
10
pp. 14-15

11.
Delimitations
No indicates bound of the study
controlled by the researcher that might
influence the validity of the study. The
researcher will follow LSA methodological
recommendations for this type of text
mining procedure.
(Evangelopoulos, Zhang, & Prybutok, 2012)
11
p. 15

12.
Terms & Definitions
Social Media (pp. 15-16)
Post-Secondary Education Institutions (p. 17)
Guideline and Policy Documents (pp. 17-18)
12

13.
Literature Review
Greenhow and Robelia (2009)
•create rich experiences for learners to improve educational
achievement and student engagement
Joosten (2012), Chapman andRussell (2009), & Dohn (2009)
•Instructors use for pedagogical practice and to supplement the
face-to-face classroom learning
Danciu and Grosseck (2011)
•Digital literacy development for continuous discovery, digital
curation, network development, and connected engagement
Silius, Kailanto, and Tervakari (2011)
•Allow for a hands-on, interactive approach for engagement…
enhance teamwork and collaboration
13
pp.18-24

14.
Why Social Media Guideline and
Policy Document Analysis in PSE?
•
•
•
•
•
•
•
•
Limited research
Recruitment and
admissions
Student-led initiatives
Scholars & researchers
Peer-review publications
Enhances student learning
Privacy & control
Institutional leadership &
implementation
14
pp.18-24

15.
Other concerns…
15
pp. 24-26

16.
Methodology
Data mining (Romero, Ventura, &
Garcia, 2008).
Text mining
•(Hearst, 1997; Feldman & Dagan,
1995; Fayyad, Piatesky-Shapiro, &
Smyth, 1996; Simoudis, 1996).
16
pp.26-28

17.
Research Methods
Latent Semantic Analysis (LSA)
• a computational research method that
simulates human like analysis with
language (Landauer, 2011)
• used for information retrieval query
optimization (Deerwester, Dumais,
Furnas, Landauer, & Harshman, 1990,
Dumais 2004).
17
p. 30

18.
Vector Space Model (VSM)
(Salton, 1975; Evangelopoulos, Zhang, & Prybutok, 2010).
18
pp. 28-29

19.
Research Design
Step 1: Establish the Corpus
Step 2: Pre-Process the Data
Step 3: Extract Knowledge
19
p. 36

20.
Research Design
20
p. 36

21.
Sampling
21
pp. 43-44

22.
Gathering Guideline & Policy
Documents
www.socialmediaguidance.wordpress.com
22

23.
Data Collection
www.socialmediaguidance.wordpress.com
23

24.
Institutional Research Board
24

25.
Instrumentation
•
Appendix B
25
pp. 82-89

26.
Data Cleaning & Organization
•
Appendix A
26
pp. 68-81

27.
Data Analysis
•
Appendix A
27
pp. 68-81

28.
Next Steps
• Comments
• Run
& feedback from committee
data analysis
• Analyze
findings
• Discuss
findings & implications
• Revisions
(committee suggestions)
• Professional
editor
• Revisions/edits
• Defense
28

29.
Questions
29