Proposal defense

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Other concerns. Methodology data mining (Romero, ventura, garcia, 2008). Text mining (Hearst, 1997; Feldman dagan, 1995; fayyad, piatesky-shapiro, smyth, 1996; Simoudis, 1996). 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). Vector Space model (VSM) (Salton, 1975; evangelopoulos, Zhang, prybutok, 2010). Research Design Step 1: Establish the corpus Step 2: Pre-Process the data Step 3: Extract Knowledge.

What naturally emerging inherent categories and themes, can online be identified in higher education social media guidelines and policies? Limitations Latent Semantic Analysis (lsa is text content only dimension reduction of the dataset Orthogonal (lee, song kim, 2010) Polysemy issues (li joshi, 2012). 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). Terms definitions Social Media (pp. 15-16) Post-Secondary Education Institutions (p. 17) guideline and Policy documents (pp. Literature review Greenhow and Robelia (2009) create rich experiences summary 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. 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.18-24.

proposal defense

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How is Social Media being guided in Higher Ed? (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. (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. Purpose The purpose of this study is to analyze social media guideline and policy documents that are accessible online from post-secondary education (PSE) make institutions. 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). What latent semantic factors are relevant to structuring the body of textual data in current higher education social media guideline and policy documents?

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During the extraction process in lsa, the key values should emerge from the matrix. Atpi dissertation Proposal of laura. Pasquini department of learning Technologies - college of Information, University of North Texas Major Professor:. Mark davis. Social Media guidance in Higher Education: Using Latent Semantic Analysis to review Social Media guideline and Policy documents laura. Department of learning Technologies, college of Information - university of North Texas. 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). Background social media use has will increased in higher education (Brenner smith, 2013 however guideline and policy documents have rarely been examined (Joosten, 2012; joosten., 2013; reed, 2013) Institutions direct moderate how students, staff, faculty administrators use social media on campus (Blankenship, 2011; Moran. 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).

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.

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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., 2006 and improve interpretability of lsa results (Sidorova., 2008). Many different methods of factor rotation exist (Kim amp; mueller, 1978).

Although these methods for have your not been utilized in text mining, the varimax rotation has been used successfully to identify factors (Sidorova., 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. Beginning with the lt matrix is the recommended strategy because it facilitates factor interpretation (Sidorova., 2008). Once a solution matrix m is recovered, it is also applied to the ld matrix (Sidorova., 2008). The factors represent topics strength in the documents.

Text classification, text clustering, ontology and taxonomy creation, document summarization and latent corpus analysis (Feinerer, hornik, amp; 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., 2010; Hossain et al, 2011; li amp; Joshi, 2012) and utilize the following three-step process of text mining using lsa as described in Elder, hill, delen, and Fasts (2012).


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, bachelors and associate degrees, etc.).

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Impact college search and decision of students (Nyangau amp; Bado, 2012). Answer concerns about digital privacy and fair use (Rodriguez, 2011). Online instructional scaffolding with self-regulated learning (Rourke amp; Coleman, 2011). Online learning to supplement face-to-face courses (Hung amp; yuen, 2010). Student motivation to learn with social media (tay amp; Allen, 2011). Data mining to both predict clusters and results for a significant amount of data (Romero, ventura, amp; Garcia, 2008). Text mining extracting interesting and non-trivial patterns or knowledge from unstructured text documents (Hearst, 1997; Feldman amp; Dagan, 1995; fayyad, piatesky-shapiro, amp; Smyth, 1996; Simoudis, 1996). Uses fast processing by consolidating a vase paper amount of data, reduce coding bias, and limit researcher influence (Cronin, Stiffler, amp; day, 1993; Litecky, aken, Ahmad, amp; Nelson, 2010).

proposal defense

Privacy concerns (Barnes 2006 course communication with sns (Roblyer, McDaniel, webb, herman, amp; homework Witty, 2010). Learning policies about technology (Hemmi, bayne, amp; Land, 2009). Judicial implications for academic dishonesty (Brown, 2008). Guidelines are primitive and often grassroots (Rodriquez, 2011; joosten, 2012). Students, staff amp; faculty are unfamiliar social media (Sullivan, 2012). How social media applications engage amp; impact learning outcomes (Bennett,. Influence communication amp; marketing practices (Constantinides amp; Zinck Stagno, 2011). Affect adjustment and interventions on campus (DeAndrea., 2011).

can successfully enhance student learning (Bennett. Concerns about lack of privacy and perceived loss of control (Fuchs-Kittowski., 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., 2013). Legal liabilities and implications imposed by social media use (Lindsay, 2011). Regulate student athlete behaviors (Woodhouse, 2012).

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, writing whereas will be limited in connection to other topics. (lee, song amp; Kim, 2010 lSA will not be able to resolve polysemy issues (coexistence of many possible word or phrase meanings). (li amp; 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. 15-16) - social, user-generated web applications and platforms; virtual places where people share; everybody and anybody can share anything anywhere anytime (Joosten, 2012,. Post-Secondary Education Institutions (p. 17) - includes all higher education entities to create a robust corpus; community colleges, universities, etc.

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Successfully reported this slideshow. My interests dissertation Proposal Defense, upcoming SlideShare, loading. Show More, no downloads, no notes for slide, over 75 of the incoming 2013 class use social media for enrollment decisions (Uversity, 2013) 41 of faculty use social media for teaching (Seaman amp; Tinti-kane, 2013; pearson, 2012). Social media guideline and policy document analysis has the potential to inform use (e.g. 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. 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.


proposal defense
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