Introducing the Concept of Social Noise

Social Noise is a term I have coined to describe the influence of personal and relational factors on social media information behavior. Knowing that others in the social network may observe posts, comments, and, likes, a user may interact differently with information than if they encountered it privately. This social pressure of observation by peers, colleagues, family, and other members of the social network may amplify, confuse, or distort information being communicated. Under the influence of Social Noise, a user may moderate their communication based on external cues regarding what behavior is acceptable or desirable, consciously or unconsciously attempting to present themselves in a more desirable way within the network. The objective of this study is to investigate how observation by members of the social network influences social media users’ information behavior. The Social Noise Model serves as the theoretical framework for this exploratory study. Using Shannon’s Mathematical Model of Communication and Alfred Bandura’s Social Cognitive Theory as inspiration, the Social Noise Model introduced here is designed to represent and characterize this new facet of human information behavior. The model illustrates information being received by the individual and filtered through personal and environmental factors prior to the observable information behavior. Data analytics, including LDA, LSA, and clustering, were performed to identify the presence of Social Noise in a large dataset of Facebook posts and comments, but they could not provide information about users’ motivations and thinking behind their observable information behavior. Twenty user observations and semi-structured interviews provided insight into how Social Noise influenced the way information was received, understood, and acted upon on Facebook. Four key constructs of Social Noise were identified, and sub-codes were assigned within each construct as patterns emerged, providing insight into the different facets of Social Noise. Additionally, in most instances more than one of the four constructs were present, layering their influence on the information behavior. Based on these findings, social media users are not always interacting with information based on true personal beliefs or desires; instead, concerns surrounding their personal image, relationships with others, core beliefs, and online conflict are influencing their observable information behavior. The results of this study provide a basis to further develop the Social Noise Model. Qualitative data provides insight into the thinking and motivations behind social media users’ observable information behavior, specifically in the areas of Cultural Agency, Relationship Management, Image Curation, and Conflict Engagement

Factors impacting social media users' information behavior: The concept of social noise

Social media communication involves the discussion and sharing of information in an environment subject to the influence of online relationships and perceived expectations of those in the social network. The ability to filter the resulting noise depends largely on our understanding of Social Noise and its underlying constructs. We introduce the concept of Social Noise and investigate methods of identifying it using a quantitative, data analytics approach. Understanding this phenomenon has taken on increasing importance as it can influence attitudes and behavior surrounding social issues, political campaigns, and other core areas of society. Results from the topic modeling and data clustering techniques represent part of ongoing research into Social Noise and general keywords and combinations of keywords related to its underlying constructs

Paralegal Students’ and Paralegal Instructors’ Perceptions of Synchronous and Asynchronous Online Paralegal Course Effectiveness: A Comparative Study

To improve online learning pedagogy within the field of paralegal education, this study investigated how paralegal students and paralegal instructors perceived the effectiveness of synchronous and asynchronous online paralegal courses.  This study intended to inform paralegal instructors and course developers how to better design, deliver, and evaluate effective online course instruction in the field of paralegal studies.Survey results were analyzed using independent samples t-test and correlational analysis, and indicated that overall, paralegal students and paralegal instructors positively perceived synchronous and asynchronous online paralegal courses.  Paralegal instructors reported statistically significant higher perceptions than paralegal students: (1) of instructional design and course content in synchronous online paralegal courses; and (2) of technical assistance, communication, and course content in asynchronous online paralegal courses.  Instructors also reported higher perceptions of the effectiveness of universal design, online instructional design, and course content in synchronous online paralegal courses than in asynchronous online paralegal courses.  Paralegal students reported higher perceptions of asynchronous online paralegal course effectiveness regarding universal design than paralegal instructors.  No statistically significant differences existed between paralegal students’ perceptions of the effectiveness of synchronous and asynchronous online paralegal courses. A strong, negative relationship existed between paralegal students’ age and their perceptions of effective synchronous paralegal courses, which were statistically and practically significant.  Lastly, this study provided practical applicability and opportunities for future research. Akyol, Z., & Garrison, D. R. (2008). The development of a community of inquiry over time in an online course: Understanding the progression and integration of social, cognitive and teaching presence. Journal of Asynchronous Learning Networks, 12, 3-22.  Retrieved from https://files.eric.ed.gov/fulltext/EJ837483.pdf Akyol, Z., Garrison, D. R., & Ozden, M. Y. (2009). Online and blended communities of inquiry: Exploring the developmental and perceptional differences. The International Review of Research in Open and Distributed Learning, 10(6), 65-83.  Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/765/1436 Allen, I. E., & Seaman, J. (2014). Grade change: Tracking online education in the United States. Babson Park, MA:  Babson Survey Research Group and Quahog Research Group, LLC.  Retrieved from https://www.utc.edu/learn/pdfs/online/sloanc-report-2014.pdf Alreck, P. L., & Settle, R. B. (2004). The Survey Research Handbook (3rd ed.) New York, NY: McGraw-Hill Irwin. American Association for Paralegal Education (2013, Oct.).  AAfPE core competencies for paralegal programs.  Retrieved from https://cdn.ymaws.com/www.aafpe.org/resource/resmgr/Docs/AAfPECoreCompetencies.pdf American Bar Association, Standing Committee on Paralegals.  (2017). https://www.americanbar.org/groups/paralegals.html American Bar Association, Standing Committee on Paralegals (2013, September).  Guidelines for the approval of paralegal education programs.  Retrieved from https://www.americanbar.org/content/dam/aba/administrative/paralegals/ls_prlgs_2013_paralegal_guidelines.authcheckdam.pdf Astani, M., Ready, K. J., & Duplaga, E. A. (2010). Online course experience matters: Investigating students’ perceptions of online learning. Issues in Information Systems, 11(2), 14-21.  Retrieved from http://iacis.org/iis/2010/14-21_LV2010_1526.pdf Bailey, C. J., & Card, K. A. (2009). Effective pedagogical practices for online teaching: Perception of experienced instructors. The Internet and Higher Education, 12, 152-155. doi: 10.1016/j.iheduc.2009.08.002 Bernard, R., Abrami, P., Borokhovski, E., Wade, C., Tamim , R., Surkes, M., & Bethel, E. (2009).  A meta-analysis of three types of interaction treatments in distance education.  Review of Educational Research, 79, 1243-1289.  doi: 10.3102/0034654309333844 Cherry, S. J., & Flora, B. H. (2017). Radiography faculty engaged in online education: Perceptions of effectiveness, satisfaction, and technological self-efficacy. Radiologic Technology, 88(3), 249-262.  http://www.radiologictechnology.org/ Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New York: Taylor & Francis Group. Colorado, J. T., & Eberle, J. (2010). Student demographics and success in online learning environments.  Emporia State Research Studies, 46(1), 4-10.  Retrieved from https://esirc.emporia.edu/bitstream/handle/123456789/380/205.2.pdf?sequence=1 Dutcher, C. W., Epps, K. K., & Cleaveland, M. C. (2015). Comparing business law in online and face to face formats: A difference in student learning perception. Academy of Educational Leadership Journal, 19, 123-134.  http://www.abacademies.org/journals/academy-of-educational-leadership-journal-home.html Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175-191.  Retrieved from http://www.gpower.hhu.de/fileadmin/redaktion/Fakultaeten/Mathematisch-Naturwissenschaftliche_Fakultaet/Psychologie/AAP/gpower/GPower3-BRM-Paper.pdf Field, A. (2009).  Discovery statistics using SPSS. (3rd ed.).  Thousand Oaks, CA:  Sage Publications, Inc. Gall M., Borg, W., & Gall, J. (1996). Educational research: An introduction (6th ed.). White Plains, NY: Longman Press. 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.  Retrieved from http://cde.athabascau.ca/coi_site/documents/Garrison_Anderson_Archer_CogPres_Final.pdf Green, S. B., & Salkind, N. J. (2005). Using SPSS for Windows and Macintosh: Internal consistency estimates of reliability. Upper Saddle River, NJ: Pearson Prentice Hall. Harrell, I. L. (2008). Increasing the Success of Online Students. Inquiry, 13(1), 36-44.  Retrieved from http://files.eric.ed.gov/fulltext/EJ833911.pdf Horspool, A., & Lange, C. (2012). Applying the scholarship of teaching and learning: student perceptions, behaviours and success online and face-to-face. Assessment & Evaluation in Higher Education, 37, 73-88.  doi: 10.1080/02602938.2010.496532 Inman, E., Kerwin, M., & Mayes, L. (1999). Instructor and student attitudes toward distance learning. Community College Journal of Research & Practice, 23, 581-591.  doi:10.1080/106689299264594 Institute of Legal Executives (ILEX).  https://www.cilexcareers.org.uk/ Johnson, J. & Taggart, G. (1996).  Computer assisted instruction in paralegal education: Does it help? Journal of Paralegal Education and Practice, 12, 1-21. Johnstone, Q. & Flood, J. (1982).  Paralegals in English and American law offices.  Windsor YB Access to Justice 2, 152. Jones, S. J. (2012). Reading between the lines of online course evaluations: Identifiable actions that improve student perceptions of teaching effectiveness and course value. Journal of Asynchronous Learning Networks, 16(1), 49-58.  doi:http://dx.doi.org/10.24059/olj.v16i1.227 Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and psychological measurement, 30, 607-610.  http://journals.sagepub.com/home/epm Liu, S., Gomez, J., Khan, B., & Yen, C. J. (2007). Toward a learner-oriented community college online course dropout framework. International Journal on ELearning, 6(4), 519-542.  https://www.learntechlib.org/j/IJEL/ Lloyd, S. A., Byrne, M. M., & McCoy, T. S. (2012). Faculty-perceived barriers of online education. Journal of online learning and teaching, 8(1), 1-12.  Retrieved from http://jolt.merlot.org/vol8no1/lloyd_0312.pdf Lockee, B., Burton, J., & Potter, K. (2010, March). Organizational perspectives on quality in distance learning. In D. Gibson & B. Dodge (Eds.), Proceedings of SITE 2010—Society for Information Technology & Teacher Education International Conference (pp. 659-664). San Diego, CA:  Association for the Advancement of Computing in Education (AACE).  https://www.learntechlib.org/p/33419/ Lowerison, G., Sclater, J., Schmid, R. F., & Abrami, P. C. (2006). Student perceived effectiveness of computer technology use in post-secondary classrooms. Computers & Education, 47(4), 465-489.  doi:10.1016/j.compedu.2004.10.014  Retrieved from https://pdfs.semanticscholar.org/fc9c/13f0187d3967217aa82cc96c188427e29ec9.pdf Martins, L. L., & Kellermanns, F. W. (2004). A model of business school students' acceptance of a web-based course management system. Academy of Management Learning & Education, 3(1), 7-26.  doi: 10.5465/AMLE.2004.12436815 Mayes, J. T. (2001). Quality in an e-University. Assessment & Evaluation in Higher Education, 26, 465-473.  doi:10.1080/02602930120082032 McCabe, S. (2007).  A brief history of the paralegal profession.  Michigan Bar Journal, 86(7), 18-21.  Retrieved from https://www.michbar.org/file/barjournal/article/documents/pdf4article1177.pdf McMillan, J. H. (2008). Educational Research: Fundamentals for the customer.  Boston, MA:  Pearson Education, Inc. Myers, C. B., Bennett, D., Brown, G., & Henderson, T. (2004). Emerging online learning environments and student learning: An analysis of faculty perceptions. Educational Technology & Society, 7(1), 78-86.  Retrieved from http://www.ifets.info/journals/7_1/9.pdf Myers, K. (2002). Distance education: A primer.  Journal of Paralegal Education & Practice, 18, 57-64. Nunnaly, J. (1978). Psychometric theory. New York: McGraw-Hill. Otter, R. R., Seipel, S., Graeff, T., Alexander, B., Boraiko, C., Gray, J., Petersen, K., & Sadler, K. (2013). Comparing student and faculty perceptions of online and traditional courses. The Internet and Higher Education, 19, 27-35.  doi:10.1016/j.iheduc.2013.08.001 Popham, W. J. (2000). Modern educational measurement: Practical guidelines for educational leaders. Boston, MA:  Allyn & Bacon. Rich, A. J., & Dereshiwsky, M. I. (2011). Assessing the comparative effectiveness of teaching undergraduate intermediate accounting in the online classroom format. Journal of College Teaching and Learning, 8(9), 19.  https://www.cluteinstitute.com/ojs/index.php/TLC/ Robinson, C., & Hullinger, H. (2008).  New benchmarks in higher education:  Student engagement in online learning.  The Journal of Education for Business, 84(2), 101-109.  Retrieved from http://anitacrawley.net/Resources/Articles/New%20Benchmarks%20in%20Higher%20Education.pdf Salkind, N. J. (2008). Statistics for people who think they hate statistics. Los Angeles, CA: Sage Publications. Santos, J. (1999, April). Cronbach's Alpha: A tool for assessing the reliability of scales.  Journal of Extension, 37, 2. Retrieved from https://www.joe.org/joe/1999april/tt3.php Seok, S., DaCosta, B., Kinsell, C., & Tung, C. K. (2010). Comparison of instructors' and students' perceptions of the effectiveness of online courses. Quarterly Review of Distance Education, 11(1), 25.  Retrieved from http://online.nuc.edu/ctl_en/wp-content/uploads/2015/08/Online-education-effectiviness.pdf Sheridan, K., & Kelly, M. A. (2010). The indicators of instructor presence that are important to students in online courses. Journal of Online Learning and Teaching, 6(4), 767-779.  Retrieved from http://jolt.merlot.org/vol6no4/sheridan_1210.pdf Shook, B. L., Greer, M. J., & Campbell, S. (2013). Student perceptions of online instruction. International Journal of Arts & Sciences, 6(4), 337.  Retrieved from https://s3.amazonaws.com/academia.edu.documents/34496977/Ophoff.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1508119686&Signature=J1lJ8VO0xardd%2FwH35pGj14UeBg%3D&response-content-disposition=inline%3B%20filename%3DStudent_Perceptions_of_Online_Learning.pdf Song, L., Singleton, E. S., Hill, J. R., & Koh, M. H. (2004). Improving online learning: Student perceptions of useful and challenging characteristics. The Internet and Higher Education, 7, 59-70.  doi:10.1016/j.iheduc.2003.11.003 Steiner, S. D., & Hyman, M. R. (2010). Improving the student experience: Allowing students enrolled in a required course to select online or face-to-face instruction. Marketing Education Review, 20, 29-34.  doi:10.2753/MER1052-8008200105 Stoel, L., & Hye Lee, K. (2003). Modeling the effect of experience on student acceptance of web-based courseware. Internet Research, 13(5), 364-374.  http://www.emeraldinsight.com/loi/intr Taggart, G., & Bodle, J. H. (2003). Example of assessment of student outcomes data from on-line paralegal courses: Lessons learned. Journal of Paralegal Education & Practice, 19, 29-36. Tanner, J. R., Noser, T. C., & Totaro, M. W. (2009). Business faculty and undergraduate students' perceptions of online learning: A comparative study. Journal of Information Systems Education, 20, 29-40.  http://jise.org/ Tung, C.K. (2007).  Perceptions of students and instructors of online and web-enhanced course effectiveness in community colleges (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database (Publication No. AAT 3284232). Vodanovich, S. J.  & Piotrowski, C., & (2000). Are the reported barriers to Internet-based instruction warranted? A synthesis of recent research. Education, 121(1), 48-53.  http://www.projectinnovation.com/education.html Ward, M. E., Peters, G., & Shelley, K. (2010). Student and faculty perceptions of the quality of online learning experiences. The International Review of Research in Open and Distributed Learning, 11, 57-77.  Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/867/1610? Wilkes, R. B., Simon, J. C., & Brooks, L. D. (2006). A comparison of faculty and undergraduate students' perceptions of online courses and degree programs. Journal of Information Systems Education, 17, 131-140. http://jise.org/

Navigating the role of mobile technologies in shaping information behavior: A meta-synthesis

Mobile technology, primarily via smartphones, has become increasingly ubiquitous in the modern world, and this change is impacting information behavior in important ways. As LIS educators, we must study this new phenomenon and incorporate it in our teaching in order to stay current in the information science field. With this goal in mind, we used the relatively new meta-synthesis methodology to collect qualitative studies that examined the intersection of mobile technology and information behavior, systematically evaluating them for patterns and trends that provide insight into technology-driven change in behavior we are witnessing. Through this process we identified four primary ways mobile technology is affecting information behavior, and these will be incorporated into a graduate level Information Behavior cours

The role of cognitive authority in social media

This poster discusses the role that cognitive authority may play in the context of social media. A term coined by TD Wilson, “cognitive authority” refers to the authority that someone or some sources may have over the thoughts of an individual. When a person gives another person cognitive authority, they give them the opportunity to influence their thoughts without hesitation. This poster aims to see how cognitive authority can be used in the realm of social media. In an environment where people “follow” or “friend” other people or news sources that they trust enough to have a connection with, is cognitive authority an influential factor? If cognitive authority is an influential factor, how does it affect the information seeking process and what potential influence can it have on information literacy? Information literacy requires critically thinking about information and determining its validity; when cognitive authority is at play, how much thought and effort is put into validating the information encountered

Staying InformED: Top emergency Medicine pharmacotherapy articles of 2020

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.The year 2020 was not easy for Emergency Medicine (EM) clinicians with the burden of tackling a pandemic. A large focus, rightfully so, was placed on the evolving diagnosis and management of patients with COVID-19 and, as such, the ability of clinicians to remain up to date on key EM pharmacotherapy literature may have been compromised. This article reviews the most important EM pharmacotherapy publications indexed in 2020. A modified Delphi approach was utilized for selected journals to identify the most impactful EM pharmacotherapy studies. A total of fifteen articles, eleven trials and four meta-analyses, were identified. This review provides a summary of each study, along with a commentary on the impact to the EM literature and EM clinician

‘You want to deal with power while riding on power’: global perspectives on power in participatory health research and co-production approaches

Introduction Power relations permeate research partnerships and compromise the ability of participatory research approaches to bring about transformational and sustainable change. This study aimed to explore how participatory health researchers engaged in co-production research perceive and experience ‘power’, and how it is discussed and addressed within the context of research partnerships. Methods Five online workshops were carried out with participatory health researchers working in different global contexts. Transcripts of the workshops were analysed thematically against the ‘Social Ecology of Power’ framework and mapped at the micro (individual), meso (interpersonal) or macro (structural) level. Results A total of 59 participants, with participatory experience in 24 different countries, attended the workshops. At the micro level, key findings included the rarity of explicit discussions on the meaning and impact of power, the use of reflexivity for examining assumptions and power differentials, and the perceived importance of strengthening co-researcher capacity to shift power. At the meso level, participants emphasised the need to manage co-researcher expectations, create spaces for trusted dialogue, and consider the potential risks faced by empowered community partners. Participants were divided over whether gatekeeper engagement aided the research process or acted to exclude marginalised groups from participating. At the macro level, colonial and ‘traditional’ research legacies were acknowledged to have generated and maintained power inequities within research partnerships. Conclusions The ‘Social Ecology of Power’ framework is a useful tool for engaging with power inequities that cut across the social ecology, highlighting how they can operate at the micro, meso and macro level. This study reiterates that power is pervasive, and that while many researchers are intentional about engaging with power, actions and available tools must be used more systematically to identify and address power imbalances in participatory research partnerships, in order to contribute to improved equity and social justice outcomes

Masking Adherence in K–12 Schools and SARS-CoV-2 Secondary Transmission

OBJECTIVES: Masking is an essential coronavirus 2019 mitigation tool assisting in the safe return of kindergarten through 12th grade children and staff to in-person instruction; however, masking adherence, compliance evaluation methods, and potential consequences of surveillance are currently unknown. We describe two school districts' approaches to promote in-school masking and the consequent impact on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) secondary transmission.METHODS: Two North Carolina school districts developed surveillance programs with daily vs. weekly interventions to monitor in-school masking adherence. Safety teams recorded the proportion of students and staff appropriately wearing masks and provided real-time education after observation of improper masking. Primary infections, within-school transmission, and county-level SARS-CoV-2 infection rates were assessed.RESULTS: Proper mask use was high in both intervention groups and districts. There were variations by grade level, with lower rates in elementary schools, and proper adherence being higher in the weekly surveillance group. Rates of secondary transmission were low in both districts with surveillance programs, regardless of intervention frequency.CONCLUSIONS: Masking surveillance interventions are effective at ensuring appropriate masking at all school levels. Creating a culture of safety within schools led by local leadership is important and a feasible opportunity for school districts with return to in-person school. In our study of schools with high masking adherence, secondary transmission was low