My Dissertation Proposal Defense


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Defending chapter 1, 2 & 3 of my final dissertation; my contract to analyze data & finish!

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  • Over 75% of the incoming 2013 class use social media for enrollment decisions (Uversity, 2013)
    41% of faculty use social media for teaching (Seaman & Tinti-Kane, 2013; Pearson, 2012)
    Social media guideline and policy document analysis has the potential to inform use (e.g. teaching, engagement, etc.), implementation, and policy design in higher education
  • Using text mining, specifically Latent Semantic Analysis (LSA) methods, to identify topics, themes, and categories from current social media guideline and policy documents in higher education.
  • Analysis of textual content only i.e. no images, screenshots, videos, photos, or URLs
    Latent semantic analysis (LSA) is dimension reduction of the original dataset; determination of dimension factors is based on a subjective researcher judgment.
    LSA has orthogonal characteristics, which means multiple occurrences of words from different factors (topics) are usually prevented and words in a certain topics will have a high relation with words in that topic, whereas will be limited in connection to other topics.
    (Lee, Song & Kim, 2010)
    LSA will not be able to resolve polysemy issues (coexistence of many possible word or phrase meanings).
    (Li & Joshi, 2012)
    It makes no use of word order, thus of syntactic relations or logic, or of morphology. Remarkably, it manages to extract correct reflections of passage and word meanings quite well without these aids, but it must still be suspected of incompleteness or likely error on some occasions.
  • Social Media (pp. 15-16) - social, user-generated web applications and platforms; “virtual places where people share; everybody and anybody can share anything anywhere anytime” (Joosten, 2012, p. 14).  
    Post-Secondary Education Institutions (p. 17) - includes all higher education entities to create a robust corpus; community colleges, universities, etc.
    Guideline and Policy Documents (pp. 17-18) - published online; publicly accessible (e.g. HTML, PDF or Word); may include: tips, regulations, rules, strategies, beliefs, etc.
  • Research & developing peer-review publications (Daniels, 2013)
    Faculty, instructors, instructional designers & staff explore how it can successfully enhance student learning (Bennett et. al., 2011)
    Concerns about lack of privacy and perceived loss of control (Fuchs-Kittowski et al., 2009).
    Institutional leadership needs to guide and prepare managers who are not ready to embrace the implementation of social media (Li, 2010).
  • Institutional brand and broadcasting messages (Joosten et al., 2013)
    Legal liabilities and implications imposed by social media use (Lindsay, 2011)
    Regulate student athlete behaviors (Woodhouse, 2012)
    Privacy concerns (Barnes 2006)
    Course communication with SNS (Roblyer, McDaniel, Webb, Herman, & Witty, 2010)
    Learning policies about technology (Hemmi, Bayne, & Land, 2009)
    Judicial implications for academic dishonesty (Brown, 2008)
    Guidelines are primitive and often grassroots (Rodriquez, 2011; Joosten, 2012)
    Students, staff & faculty are unfamiliar social media (Sullivan, 2012)
    How social media applications engage & impact learning outcomes (Bennett, et. al., 2011)
    Influence communication & marketing practices (Constantinides & Zinck Stagno, 2011)
    Affect adjustment and interventions on campus (DeAndrea et al., 2011)
    Impact college search and decision of students (Nyangau & Bado, 2012)
    Answer concerns about digital privacy and fair use (Rodriguez, 2011)
    Online instructional scaffolding with self-regulated learning (Rourke & Coleman, 2011)
    Online learning to supplement face-to-face courses (Hung & Yuen, 2010)
    Student motivation to learn with social media (Tay & Allen, 2011)
  • Data mining to both predict clusters and results for a significant amount of data (Romero, Ventura, & Garcia, 2008).
    Text mining
    extracting interesting and non-trivial patterns or knowledge from unstructured text documents (Hearst, 1997; Feldman & Dagan, 1995; Fayyad, Piatesky-Shapiro, & Smyth, 1996; Simoudis, 1996).
    uses fast processing by consolidating a vase amount of data, reduce coding bias, and limit researcher influence (Cronin, Stiffler, & Day, 1993; Litecky, Aken, Ahmad, & Nelson, 2010).
    “text classification, text clustering, ontology and taxonomy creation, document summarization and latent corpus analysis” (Feinerer, Hornik, & Meyer, 2008)
  • LSA is a text mining approach to index words and concepts. Essentially, LSA is a computational model that learned word meanings from vast amounts of text and identified the degree to which two words or passages have the same meaning (Landauer, 2011).
  • The vector of terms will be represented by VSM, where the value and importance of a term is determined by its frequency of appearance in the document, known as the “bag of words”
  • This study will follow established text mining procedures as discussed in prior studies (Evangelopoulos et al., 2010; Hossain et al, 2011; Li & Joshi, 2012) and utilize the following three-step process of text mining using LSA as described in Elder, Hill,
    Delen, and Fast’s (2012) methodology as outlined in Figure
    Step 1: Establish the Corpus - search online, website gathering, social media
    Step 2: Pre-Process the Data - Word (carriage returns), to Excel docs (macros), combine all - clean URLs, videos, images, etc text only
    —pre-processing and term reduction; SVD; term frequency matrix
    Step 3: Extract Knowledge
  • This study will follow established text mining procedures as discussed in prior studies (Evangelopoulos et al., 2010; Hossain et al, 2011; Li & Joshi, 2012) and utilize the following three-step process of text mining using LSA as described in Elder, Hill,
    Delen, and Fast’s (2012) methodology as outlined in Figure
    Step 1: Establish the Corpus - search online, website gathering, social media
    Step 2: Pre-Process the Data - Word (carriage returns), to Excel docs (macros), combine all - clean URLs, videos, images, etc text only
    —pre-processing and term reduction; SVD; term frequency matrix
    Step 3: Extract Knowledge
  • To be eligible for this study the social media guideline and/or policy document must be available electronically and accessed through the post-secondary institutional website or general web search. The text documents may “guide” social media from a department- or institutional-level within the post-secondary education organization. These guiding documents may be directed to students, staff, researchers, faculty, and other members of the campus community. The sample will include all guideline and policy documents from institutions; however they must be published electronically in a single language, English, for effective text analysis.
  • To ensure the corpus for this study would be robust for latent semantic analysis procedures, the researcher conducted a preliminary online search of social media guideline and policy documents to form the database from October 2013 until January 2014. The database currently contains at least 20, 000 documents from approximately 240 post-secondary education institution representing various geographic locations (countries), size of campus (by student population), and institutional types (e.g. public, private, bachelor’s and associate degrees, etc.). The researcher will continue to solicit for submissions for social media guidelines and policy documents that are directed at students, staff, faculty, researchers, and campus stakeholders from the post-secondary education sector via an online form ( embedded into a research website
  • Factor Interpretation.
    The high-loading terms and documents help researchers interpret the factors. For each solution, there will be a table of high-loading terms and documents will be sorted by term frequency. These terms will help to categorize (label) the factor. The process of labeling the factors will include examination of the terms and the documents (social media atomic concepts) related to a particular factor, interpreting the underlying area, and determining an appropriate label.
    Factor rotation aids in the simplification of a factor structure to achieve a more meaningful solution (Hair et al., 2006), and improve interpretability of LSA results (Sidorova et al., 2008). Many different methods of factor rotation exist (Kim & Mueller, 1978). Although these methods for have not been utilized in text mining, the varimax rotation has been used successfully to identify factors (Sidorova et al., 2008). Varimax rotation maximizes the sum of variance for the squared loadings. Rotation can begin with either the term loadings LT matrix or the documents loadings LD. Beginning with the LT matrix is the recommended strategy because it facilitates factor interpretation (Sidorova et al., 2008). Once a solution matrix M is recovered, it is also applied to the LD matrix (Sidorova et al., 2008). The factors represent topics in the documents. These topics are defined by the associated words found in the frequency matrix and loading values.
    Measuring the Strength of Document Terms and Concepts.
    To assess the different social media guideline and policy document themes, the strength of the document theme will be related to the corresponding factor. Each atomic document (concept) will be classified into the particular social media guidance area by its factor loadings. Specifically, the document will be classified to the social media guideline and policy document topics that possess strength of the category.
    Documents will be associated with only the key factors by topic, and noise across documents will be suppressed. When the factors are rotated and loadings are suppressed, the researcher will interpret and analyze the results. During the extraction process in LSA, the key values should emerge from the matrix.
  • My Dissertation Proposal Defense

    1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 12. Terms & Definitions Social Media (pp. 15-16) Post-Secondary Education Institutions (p. 17) Guideline and Policy Documents (pp. 17-18) 12
    13. 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. 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. 15. Other concerns… 15 pp. 24-26
    16. 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. 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. 18. Vector Space Model (VSM) (Salton, 1975; Evangelopoulos, Zhang, & Prybutok, 2010). 18 pp. 28-29
    19. 19. Research Design Step 1: Establish the Corpus Step 2: Pre-Process the Data Step 3: Extract Knowledge 19 p. 36
    20. 20. Research Design 20 p. 36
    21. 21. Sampling 21 pp. 43-44
    22. 22. Gathering Guideline & Policy Documents 22
    23. 23. Data Collection 23
    24. 24. Institutional Research Board 24
    25. 25. Instrumentation • Appendix B 25 pp. 82-89
    26. 26. Data Cleaning & Organization • Appendix A 26 pp. 68-81
    27. 27. Data Analysis • Appendix A 27 pp. 68-81
    28. 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. 29. Questions 29