Definition:
The HyFlex model gives students the choice to attend class in person or via synchronous remote stream and to make that choice on a daily basis. In other words, unlike online and hybrid models which typically have a fixed course structure for the entire semester, the HyFlex model does not require students to make a choice at the beginning of term and then stick with it whether their choice works for them or not; rather students are able to make different choices each day depending on what works best for them on that day (hence the format is “flexible”) (Miller and Baham, 2018, to be published in the Proceedings of the 10th International Conference on Teaching Statistics).
Definition from Horizon Report, HIgher Ed edition, 2014. p. 10 integration of Online Hybrid and Collaborative Learning
Definition from U of Arizona (https://journals.uair.arizona.edu/index.php/itet/article/view/16464/16485)
Beatty (2010) defines HyFlex courses to be those that “enable a flexible participation policy for students whereby students may choose to attend face-to-face synchronous class sessions or complete course learning activities online without physically attending class”
Online courses
Definition
Goette, W. F., Delello, J. A., Schmitt, A. L., Sullivan, J. R., & Rangel, A. (2017). Comparing Delivery Approaches to Teaching Abnormal Psychology: Investigating Student Perceptions and Learning Outcomes. Psychology Learning and Teaching, 16(3), 336–352. https://doi.org/10.1177/1475725717716624
p.2.Online classes are a form of distance learning available completely over the Internet with no F2F interaction between an instructor and students (Helms, 2014).
https://www.oswego.edu/human-resources/section-6-instructional-policies-and-procedures
An online class is a class that is offered 100% through the Internet. Asynchronous courses require no time in a classroom. All assignments, exams, and communication are delivered using a learning management system (LMS). At Oswego, the campus is transitioning from ANGEL to Blackboard, which will be completed by the Fall 2015 semester. Fully online courses may also be synchronous. Synchronous online courses require student participation at a specified time using audio/visual software such as Blackboard Collaborate along with the LMS.
Web-enhanced courses
Web enhanced learning occurs in a traditional face-to-face (f2f) course when the instructor incorporates web resources into the design and delivery of the course to support student learning. The key difference between Web Enhanced Learning versus other forms of e-learning (online or hybrid courses) is that the internet is used to supplement and support the instruction occurring in the classroom rather than replace it. Web Enhanced Learning may include activities such as: accessing course materials, submitting assignments, participating in discussions, taking quizzes and exams, and/or accessing grades and feedback.”
Blended/Hybrid Learning
Definition
Goette, W. F., Delello, J. A., Schmitt, A. L., Sullivan, J. R., & Rangel, A. (2017). Comparing Delivery Approaches to Teaching Abnormal Psychology: Investigating Student Perceptions and Learning Outcomes. Psychology Learning and Teaching, 16(3), 336–352. https://doi.org/10.1177/1475725717716624
p.3.
Helms (2014) described blended education as incorporating both online and F2F character- istics into a single course. This definition captures an important confound to comparing course administration formats because otherwise traditional F2F courses may also incorp- orate aspects of online curriculum. Blended learning may thus encompass F2F classes in which any course content is available online (e.g., recorded lectures or PowerPoints) as well as more traditionally blended courses. Helms recommended the use of ‘‘blended’’ over ‘‘hybrid’’ because these courses combine different but complementary approaches rather than layer opposing methods and formats.
Blended learning can merge the relative strengths of F2F and online education within a flexible course delivery format. As such, this delivery form has a similar potential of online courses to reduce the cost of administration (Bowen et al., 2014) while addressing concerns of quality and achievement gaps that may come from online education. Advantages of blended courses include: convenience and efficiency for the student; promotion of active learning; more effective use of classroom space; and increased class time to spend on higher- level learning activities such as cooperative learning, working with case studies, and discuss- ing big picture concepts and ideas (Ahmed, 2010; Al-Qahtani & Higgins, 2013; Lewis & Harrison, 2012).
Although many definitions of hybrid and blended learning exist, there is a convergence upon three key points: (1) Web-based learning activities are introduced to complement face-to-face work; (2) “seat time” is reduced, though not eliminated altogether; (3) the Web-based and face-to-face components of the course are designed to interact pedagogically to take advantage of the best features of each.
The amount of in class time varies in hybrids from school to school. Some require more than 50% must be in class, others say more than 50% must be online. Others indicate that 20% – 80% must be in class (or online). There is consensus that generally the time is split 50-50, but it depends on the best pedagogy for what the instructor wants to achieve.
definition
“Backchannel” is a mode of communication created by audience members to connect with other observers both inside and outside of the presentation space, with or without the speaker’s awareness (Atkinson, 2009).
ARS (or CRS) are[audience] response systems are instructional technologies that allow instructors to rapidly collect and analyze student responses to questions posed during class” (Bruff, 2009, p. 1)
Distance learning courses are indicated in the schedule of classes on BU Brain with an Instructional Method of Online Asynchronous (OA), Online Synchronous (OS), Online Combined (OC), or Online Hybrid (OH). Online Asynchronous courses are those in which the instruction is recorded/stored and then accessed by the students at another time. Online Synchronous courses are those in which students are at locations remote from the instructor and viewing the instruction as it occurs. Online Combined courses are those in which there is a combination of asynchronous and synchronous instruction that occurs over the length of the course. Online Hybrid courses are those in which there is both in-person and online (asynchronous and/or synchronous) instruction that occurs over the length of the course.
social media – call centers in Iowa, where agriculture is expected. not an awesome job. http://sched.co/D7pQ
Caleris as featured in New York Times.
Sarah Roberts talk about psychological effects of working at Caleris; it resembles the effect of air strikes on the drone pilots
Drupal based. Google Analytics like. Bookmarks. objects list can be shared through social media, email, etc. Pachyderm used to have timeline like Islandora. still images, audio, video
Over the past 10 years, new learning management systems (LMSs) have sprung on the scene to rival the Blackboards and Moodles of old. On the EdSurge Product Index alone, 56 products self-identify and fall into the LMS category. And with certain established companies like Pearson pulling out of the LMS ranks, where do you start?
As University of Central Florida’s Associate Vice President of Distributed Learning, Tom Cavanagh, wrote in an article for EDUCAUSE, “every institute has a unique set of instructional and infrastructure circumstances to consider when deciding on an LMS,” but at the same time, “all institutions face certain common requirements”—whether a small charter school, a private university or a large public school district.
The LMS Checklist
#1: Is the platform straightforward and user-friendly?
#2: Who do we want to have access to this platform, and can we adjust what they can see?
#3: Can the instructor and student(s) talk to and communicate with each other easily?
“Students and faculty live a significant portion of their daily lives online in social media spaces,” writes University of Central Florida’s Tom Cavanagh in his article on the LMS selection process. “Are your students and faculty interested in these sorts of interplatform connections?”
#5: Does this platform plug in with all of the other platforms we have?
“Given the pace of change and the plethora of options with educational technology, it’s very difficult for any LMS vendor to keep up with stand-alone tools that will always outperform built-in tools,” explains Michael Truong, executive director of innovative teaching and technology at Azusa Pacific University. According to Truong, “no LMS will be able to compete directly with tools like Piazza (discussion forum), Socrative (quizzing), EdPuzzle (video annotation), etc.”
As a result, Truong says, “The best way to ‘prepare’ for future technological changes is to go with an LMS that plays well with external tools.”
Effective communication is one critical characteristics of effective and successful school principal. Research on effective schools and instructional leadership emphasizes the impact of principal leadership on creating safe and secure learning environment and positive nurturing school climate (Halawah, 2005, p. 334)
Halawah, I. (2005). The Relationship between Effective Communication of High School Principal and School Climate. Education, 126(2), 334-345.
Selection of school principals in Hong Kong. The findings confirm a four-factor set of expectations sought from applicants; these are Generic Managerial Skills; Communication and Presentation Skills; Knowledge and Experience; and Religious Value Orientation.
Kwan, P. (2012). Assessing school principal candidates: perspectives of the hiring superintendents. International Journal Of Leadership In Education, 15(3), 331-349. doi:10.1080/13603124.2011.617838
Yee, D. L. (2000). Images of school principals’ information and communications technology leadership. Journal of Information Technology for Teacher Education, 9(3), 287–302. https://doi.org/10.1080/14759390000200097
Catano, N., & Stronge, J. H. (2007). What do we expect of school principals? Congruence between principal evaluation and performance standards. International Journal of Leadership in Education, 10(4), 379–399. https://doi.org/10.1080/13603120701381782
Communication can consist of two large areas:
broadcasting information: PR, promotions, notifications etc.
two-way communication: collecting feedback, “office hours” type of communication, backchanneling, etc.
Further communication initiated by/from principals can have different audiences
Reyes, P., & Hoyle, D. (1992). Teachers’ Satisfaction With Principals’ Communication. The Journal of Educational Research, 85(3), 163–168. https://doi.org/10.1080/00220671.1992.9944433
parents: involvement, feeling of empowerment, support, volunteering
students
board members
community
Epstein, J. L. (1995). School/family/community partnerships – ProQuest. Phi Delta Kappan, 76(9), 701.
Others
Communication and Visualization
The ever-growing necessity to be able to communicate data to different audiences in digestible format.
considering the information discussed in class, split in groups of 4 and develop your institution strategy for effective and modern communication across and out of your school.
Applications for the 2018 Institute will be accepted between December 1, 2017 and January 27, 2018. Scholars accepted to the program will be notified in early March 2018.
Title:
Learning to Harness Big Data in an Academic Library
Abstract (200)
Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions. Introduction to the problem. Limitations
The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.
Big Data is becoming an omnipresent term. It is widespread among different disciplines in academia (De Mauro, Greco, & Grimaldi, 2016). This leads to “inconsistency in meanings and necessity for formal definitions” (De Mauro et al, 2016, p. 122). Similarly, to De Mauro et al (2016), Hashem, Yaqoob, Anuar, Mokhtar, Gani and Ullah Khan (2015) seek standardization of definitions. The main connected “themes” of this phenomenon must be identified and the connections to Library Science must be sought. A prerequisite for a comprehensive definition is the identification of Big Data methods. Bughin, Chui, Manyika (2011), Chen et al. (2012) and De Mauro et al (2015) single out the methods to complete the process of building a comprehensive definition.
In conjunction with identifying the methods, volume, velocity, and variety, as defined by Laney (2001), are the three properties of Big Data accepted across the literature. Daniel (2015) defines three stages in big data: collection, analysis, and visualization. According to Daniel, (2015), Big Data in higher education “connotes the interpretation of a wide range of administrative and operational data” (p. 910) and according to Hilbert (2013), as cited in Daniel (2015), Big Data “delivers a cost-effective prospect to improve decision making” (p. 911).
The importance of understanding the process of Big Data analytics is well understood in academic libraries. An example of such “administrative and operational” use for cost-effective improvement of decision making are the Finch & Flenner (2016) and Eaton (2017) case studies of the use of data visualization to assess an academic library collection and restructure the acquisition process. Sugimoto, Ding & Thelwall (2012) call for the discussion of Big Data for libraries. According to the 2017 NMC Horizon Report “Big Data has become a major focus of academic and research libraries due to the rapid evolution of data mining technologies and the proliferation of data sources like mobile devices and social media” (Adams, Becker, et al., 2017, p. 38).
Power (2014) elaborates on the complexity of Big Data in regard to decision-making and offers ideas for organizations on building a system to deal with Big Data. As explained by Boyd and Crawford (2012) and cited in De Mauro et al (2016), there is a danger of a new digital divide among organizations with different access and ability to process data. Moreover, Big Data impacts current organizational entities in their ability to reconsider their structure and organization. The complexity of institutions’ performance under the impact of Big Data is further complicated by the change of human behavior, because, arguably, Big Data affects human behavior itself (Schroeder, 2014).
De Mauro et al (2015) touch on the impact of Dig Data on libraries. The reorganization of academic libraries considering Big Data and the handling of Big Data by libraries is in a close conjunction with the reorganization of the entire campus and the handling of Big Data by the educational institution. In additional to the disruption posed by the Big Data phenomenon, higher education is facing global changes of economic, technological, social, and educational character. Daniel (2015) uses a chart to illustrate the complexity of these global trends. Parallel to the Big Data developments in America and Asia, the European Union is offering access to an EU open data portal (https://data.europa.eu/euodp/home ). Moreover, the Association of European Research Libraries expects under the H2020 program to increase “the digitization of cultural heritage, digital preservation, research data sharing, open access policies and the interoperability of research infrastructures” (Reilly, 2013).
The challenges posed by Big Data to human and social behavior (Schroeder, 2014) are no less significant to the impact of Big Data on learning. Cohen, Dolan, Dunlap, Hellerstein, & Welton (2009) propose a road map for “more conservative organizations” (p. 1492) to overcome their reservations and/or inability to handle Big Data and adopt a practical approach to the complexity of Big Data. Two Chinese researchers assert deep learning as the “set of machine learning techniques that learn multiple levels of representation in deep architectures (Chen & Lin, 2014, p. 515). Deep learning requires “new ways of thinking and transformative solutions (Chen & Lin, 2014, p. 523). Another pair of researchers from China present a broad overview of the various societal, business and administrative applications of Big Data, including a detailed account and definitions of the processes and tools accompanying Big Data analytics. The American counterparts of these Chinese researchers are of the same opinion when it comes to “think about the core principles and concepts that underline the techniques, and also the systematic thinking” (Provost and Fawcett, 2013, p. 58). De Mauro, Greco, and Grimaldi (2016), similarly to Provost and Fawcett (2013) draw attention to the urgent necessity to train new types of specialists to work with such data. As early as 2012, Davenport and Patil (2012), as cited in Mauro et al (2016), envisioned hybrid specialists able to manage both technological knowledge and academic research. Similarly, Provost and Fawcett (2013) mention the efforts of “academic institutions scrambling to put together programs to train data scientists” (p. 51). Further, Asomoah, Sharda, Zadeh & Kalgotra (2017) share a specific plan on the design and delivery of a big data analytics course. At the same time, librarians working with data acknowledge the shortcomings in the profession, since librarians “are practitioners first and generally do not view usability as a primary job responsibility, usually lack the depth of research skills needed to carry out a fully valid” data-based research (Emanuel, 2013, p. 207).
Borgman (2015) devotes an entire book to data and scholarly research and goes beyond the already well-established facts regarding the importance of Big Data, the implications of Big Data and the technical, societal, and educational impact and complications posed by Big Data. Borgman elucidates the importance of knowledge infrastructure and the necessity to understand the importance and complexity of building such infrastructure, in order to be able to take advantage of Big Data. In a similar fashion, a team of Chinese scholars draws attention to the complexity of data mining and Big Data and the necessity to approach the issue in an organized fashion (Wu, Xhu, Wu, Ding, 2014).
Bruns (2013) shifts the conversation from the “macro” architecture of Big Data, as focused by Borgman (2015) and Wu et al (2014) and ponders over the influx and unprecedented opportunities for humanities in academia with the advent of Big Data. Does the seemingly ubiquitous omnipresence of Big Data mean for humanities a “railroading” into “scientificity”? How will research and publishing change with the advent of Big Data across academic disciplines?
Reyes (2015) shares her “skinny” approach to Big Data in education. She presents a comprehensive structure for educational institutions to shift “traditional” analytics to “learner-centered” analytics (p. 75) and identifies the participants in the Big Data process in the organization. The model is applicable for library use.
Being a new and unchartered territory, Big Data and Big Data analytics can pose ethical issues. Willis (2013) focusses on Big Data application in education, namely the ethical questions for higher education administrators and the expectations of Big Data analytics to predict students’ success. Daries, Reich, Waldo, Young, and Whittinghill (2014) discuss rather similar issues regarding the balance between data and student privacy regulations. The privacy issues accompanying data are also discussed by Tene and Polonetsky, (2013).
Privacy issues are habitually connected to security and surveillance issues. Andrejevic and Gates (2014) point out in a decision making “generated by data mining, the focus is not on particular individuals but on aggregate outcomes” (p. 195). Van Dijck (2014) goes into further details regarding the perils posed by metadata and data to the society, in particular to the privacy of citizens. Bail (2014) addresses the same issue regarding the impact of Big Data on societal issues, but underlines the leading roles of cultural sociologists and their theories for the correct application of Big Data.
Library organizations have been traditional proponents of core democratic values such as protection of privacy and elucidation of related ethical questions (Miltenoff & Hauptman, 2005). In recent books about Big Data and libraries, ethical issues are important part of the discussion (Weiss, 2018). Library blogs also discuss these issues (Harper & Oltmann, 2017). An academic library’s role is to educate its patrons about those values. Sugimoto et al (2012) reflect on the need for discussion about Big Data in Library and Information Science. They clearly draw attention to the library “tradition of organizing, managing, retrieving, collecting, describing, and preserving information” (p.1) as well as library and information science being “a historically interdisciplinary and collaborative field, absorbing the knowledge of multiple domains and bringing the tools, techniques, and theories” (p. 1). Sugimoto et al (2012) sought a wide discussion among the library profession regarding the implications of Big Data on the profession, no differently from the activities in other fields (e.g., Wixom, Ariyachandra, Douglas, Goul, Gupta, Iyer, Kulkami, Mooney, Phillips-Wren, Turetken, 2014). A current Andrew Mellon Foundation grant for Visualizing Digital Scholarship in Libraries seeks an opportunity to view “both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets (Hwangbo, 2014).
The importance of the library with its traditional roles, as described by Sugimoto et al (2012) may continue, considering the Big Data platform proposed by Wu, Wu, Khabsa, Williams, Chen, Huang, Tuarob, Choudhury, Ororbia, Mitra, & Giles (2014). Such platforms will continue to emerge and be improved, with librarians as the ultimate drivers of such platforms and as the mediators between the patrons and the data generated by such platforms.
Every library needs to find its place in the large organization and in society in regard to this very new and very powerful phenomenon called Big Data. Libraries might not have the trained staff to become a leader in the process of organizing and building the complex mechanism of this new knowledge architecture, but librarians must educate and train themselves to be worthy participants in this new establishment.
Method
The study will be cleared by the SCSU IRB.
The survey will collect responses from library population and it readiness to use and use of Big Data. Send survey URL to (academic?) libraries around the world.
Data will be processed through SPSS. Open ended results will be processed manually. The preliminary research design presupposes a mixed method approach.
The study will include the use of closed-ended survey response questions and open-ended questions. The first part of the study (close ended, quantitative questions) will be completed online through online survey. Participants will be asked to complete the survey using a link they receive through e-mail.
Mixed methods research was defined by Johnson and Onwuegbuzie (2004) as “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts, or language into a single study” (Johnson & Onwuegbuzie, 2004 , p. 17). Quantitative and qualitative methods can be combined, if used to complement each other because the methods can measure different aspects of the research questions (Sale, Lohfeld, & Brazil, 2002).
Sampling design
Online survey of 10-15 question, with 3-5 demographic and the rest regarding the use of tools.
1-2 open-ended questions at the end of the survey to probe for follow-up mixed method approach (an opportunity for qualitative study)
data analysis techniques: survey results will be exported to SPSS and analyzed accordingly. The final survey design will determine the appropriate statistical approach.
Project Schedule
Complete literature review and identify areas of interest – two months
Prepare and test instrument (survey) – month
IRB and other details – month
Generate a list of potential libraries to distribute survey – month
Contact libraries. Follow up and contact again, if necessary (low turnaround) – month
Collect, analyze data – two months
Write out data findings – month
Complete manuscript – month
Proofreading and other details – month
Significance of the work
While it has been widely acknowledged that Big Data (and its handling) is changing higher education (https://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (https://blog.stcloudstate.edu/ims/2016/03/29/analytics-in-education/), it remains nebulous how Big Data is handled in the academic library and, respectively, how it is related to the handling of Big Data on campus. Moreover, the visualization of Big Data between units on campus remains in progress, along with any policymaking based on the analysis of such data (hence the need for comprehensive visualization).
This research will aim to gain an understanding on: a. how librarians are handling Big Data; b. how are they relating their Big Data output to the campus output of Big Data and c. how librarians in particular and campus administration in general are tuning their practices based on the analysis.
Based on the survey returns (if there is a statistically significant return), this research might consider juxtaposing the practices from academic libraries, to practices from special libraries (especially corporate libraries), public and school libraries.
References:
Adams Becker, S., Cummins M, Davis, A., Freeman, A., Giesinger Hall, C., Ananthanarayanan, V., … Wolfson, N. (2017). NMC Horizon Report: 2017 Library Edition.
Andrejevic, M., & Gates, K. (2014). Big Data Surveillance: Introduction. Surveillance & Society, 12(2), 185–196.
Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190. https://doi.org/10.1111/dsji.12125
Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75–86.
Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD Skills: New Analysis Practices for Big Data. Proc. VLDB Endow., 2(2), 1481–1492. https://doi.org/10.14778/1687553.1687576
Daniel, B. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., … Chuang, I. (2014). Privacy, Anonymity, and Big Data in the Social Sciences. Commun. ACM, 57(9), 56–63. https://doi.org/10.1145/2643132
De Mauro, A. D., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122–135. https://doi.org/10.1108/LR-06-2015-0061
De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings, 1644(1), 97–104. https://doi.org/10.1063/1.4907823
Eaton, M. (2017). Seeing Library Data: A Prototype Data Visualization Application for Librarians. Publications and Research. Retrieved from http://academicworks.cuny.edu/kb_pubs/115
Emanuel, J. (2013). Usability testing in libraries: methods, limitations, and implications. OCLC Systems & Services: International Digital Library Perspectives, 29(4), 204–217. https://doi.org/10.1108/OCLC-02-2013-0009
Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255–261. https://doi.org/10.1177/2043820613513121
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47(Supplement C), 98–115. https://doi.org/10.1016/j.is.2014.07.006
Laney, D. (2001, February 6). 3D Data Management: Controlling Data Volume, Velocity, and Variety.
Miltenoff, P., & Hauptman, R. (2005). Ethical dilemmas in libraries: an international perspective. The Electronic Library, 23(6), 664–670. https://doi.org/10.1108/02640470510635746
Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275(Supplement C), 314–347. https://doi.org/10.1016/j.ins.2014.01.015
Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
Reyes, J. (2015). The skinny on big data in education: Learning analytics simplified. TechTrends: Linking Research & Practice to Improve Learning, 59(2), 75–80. https://doi.org/10.1007/s11528-015-0842-1
Schroeder, R. (2014). Big Data and the brave new world of social media research. Big Data & Society, 1(2), 2053951714563194. https://doi.org/10.1177/2053951714563194
Sugimoto, C. R., Ding, Y., & Thelwall, M. (2012). Library and information science in the big data era: Funding, projects, and future [a panel proposal]. Proceedings of the American Society for Information Science and Technology, 49(1), 1–3. https://doi.org/10.1002/meet.14504901187
Tene, O., & Polonetsky, J. (2012). Big Data for All: Privacy and User Control in the Age of Analytics. Northwestern Journal of Technology and Intellectual Property, 11, [xxvii]-274.
van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society; Newcastle upon Tyne, 12(2), 197–208.
Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77–84. https://doi.org/10.1111/jbl.12010
West, D. M. (2012). Big data for education: Data mining, data analytics, and web dashboards. Governance Studies at Brookings, 4, 1–0.
Willis, J. (2013). Ethics, Big Data, and Analytics: A Model for Application. Educause Review Online. Retrieved from https://docs.lib.purdue.edu/idcpubs/1
Wixom, B., Ariyachandra, T., Douglas, D. E., Goul, M., Gupta, B., Iyer, L. S., … Turetken, O. (2014). The current state of business intelligence in academia: The arrival of big data. CAIS, 34, 1.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107. https://doi.org/10.1109/TKDE.2013.109
Wu, Z., Wu, J., Khabsa, M., Williams, K., Chen, H. H., Huang, W., … Giles, C. L. (2014). Towards building a scholarly big data platform: Challenges, lessons and opportunities. In IEEE/ACM Joint Conference on Digital Libraries (pp. 117–126). https://doi.org/10.1109/JCDL.2014.6970157
Apеster (https://app.apester.com/): can be played asynchronously (yet, restricted in time). Kahoot is a simultaneous game. EdPuzzle also lke Apester can be asynchronous, but like Kahoot requires an account, whereas Apester can be played by anyone.
The proliferation of mobile devices and the adoption of learning applications in higher education simplifies formative assessment. Professors can, for example, quickly create a multi-modal performance that requires students to write, draw, read, and watch video within the same assessment. Other tools allow for automatic grade responses, question-embedded documents, and video-based discussion.
Multi-Modal Assessments – create multiple-choice and open-ended items that are distributed digitally and assessed automatically. Student responses can be viewed instantaneously and downloaded to a spreadsheet for later use.
Formative (http://www.goformative.com) allows professors to upload charts or graphic organizers that students can draw on with a stylus. Formative also allows professors to upload document “worksheets” which can then be augmented with multiple-choice and open-ended questions.
Nearpod (http://www.nearpod.com) allows professors to upload their digital presentations and create digital quizzes to accompany them. Nearpod also allows professors to share three-dimensional field trips and models to help communicate ideas.
Video-Based Assessments – Question-embedded videos are an outstanding way to improve student engagement in blended or flipped instructional contexts. Using these tools allows professors to identify if the videos they use or create are being viewed by students.
Playposit (http://www.playposit.com) are two leaders in this application category. A second type of video-based assessment allows professors to sustain discussion-board like conversation with brief videos.
Flipgrid (http://www.flipgrid.com), for example, allows professors to posit a video question to which students may respond with their own video responses.
Quizzing Assessments – ools that utilize close-ended questions that provide a quick check of student understanding are also available.
Kahoot (http://www.kahoot.com) are relatively quick and convenient to use as a wrap up to instruction or a review of concepts taught.
Integration of technology is aligned to sound formative assessment design. Formative assessment is most valuable when it addresses student understanding, progress toward competencies or standards, and indicates concepts that need further attention for mastery. Additionally, formative assessment provides the instructor with valuable information on gaps in their students’ learning which can imply instructional changes or additional coverage of key concepts. The use of tech tools can make the creation, administration, and grading of formative assessment more efficient and can enhance reliability of assessments when used consistently in the classroom. Selecting one that effectively addresses your assessment needs and enhances your teaching style is critical.
“tendency to possess an expectation of academic success without taking personal responsibility for achieving that success.”
How widespread is it?
The research (and there’s not a lot) reports finding less student entitlement than faculty do.
Can a student be entitled without being rude and disruptive?
Yes. Students can have beliefs like those mentioned above and only discuss them with other students or not discuss them at all. Part of what makes entitlement challenging for teachers are those students who do verbally express the attitudes, often aggressively.
Are millennial students more entitled than previous generations? That’s another widely held assumption in the academic community, but support from research is indirect and inconsistent.
Is entitlement something that only happens in the academic environment? No, it has been studied, written about, and observed in other contexts (like work environments
What’s causing it?
A number think it’s the result of previous educational experiences and/or grade inflation. Some blame technology that gives students greater access to teachers and the expectation of immediate responses. Fairly regularly, student evaluations are blamed for the anonymous power and control they give students. And finally, there’s the rise in consumerism that’s now associated with education. Students (and their parents) pay (usually a lot) for college and the sense that those tuition dollars entitle them to certain things, is generally not what teachers think education entitles learners to receive.
How should teachers respond?
It helps if teachers clarify their expectations with constructive positive language and even more importantly with discussions of the rationales on which those expectations rest. Teacher authority gets most students to follow the rules, but force doesn’t generally change attitudes and those are what need to be fixed in this case.
References: Elias, R. Z. (2017). Academic entitlement and its relationship to cheating ethics. Journal of Education for Business, 92 (4), 194-199.
Greenberger, E., et. al. (2008). Self-entitled college students: Contributions of personality, parenting and motivational factors. Journal of Youth Adolescence, 37, 1193-1204.
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Responding to Disruptive Students
Negative attention doesn’t help difficult students change their ways, but teachers can alter classroom dynamics through this exercise.
Draw a map of your classroom, including doors, windows, desks, blackboards—all significant items and areas. I’m sure you’ve already got a clear idea of where the most challenging students usually sit. Now imagine teaching class on a regular day. Trace the paths you usually take across the room. Do you sometimes speed up for a particular reason?
Now put your breathing on the map. Are you conscious of the way you breathe during class? Use a new color and draw a wavy line on top of the lines and arrows you’ve already sketched. Does the wavy line look even, or have you drawn some chaotic or nervous zigzags? Could it be that you’ve sometimes forgotten to breathe?
Investing time in building physical and emotional familiarity with the learning environment, instead of nervously anticipating disruption, changes the educator’s perspective toward the whole class, their interaction with individual students, and their self-awareness. Negative attention stops being a solution—instead it is seen as a hindrance to the process of understanding students’ needs.
– Current initiatives and progress on sustainable models for making monographs openly accessible. Webinar for Open Access Week, Tuesday, October 24, 4 p.m Eastern (10 a.m. HAST; 1 p.m. Pacific; 2 p.m. Mountain; 3 p.m. Central)
Registration is free. Please sign up with this registration form
with a growing number of initiatives, publishers, and economic models, the question is sustainability. There are a number of different models, including Open Book Publishers, Open Humanities Press, and numerous university and commercial publishers who have open monograph publications, thus more initiatives than we could include for this one-hour webinar. We have invited a selected number of representatives from various open monograph publishing initiatives to participate in a panel discussion about their current economic models and future of open access monographs. Each panelist will give a brief statement about their initiative, their editorial review process, their funding model, and their perspectives on the future of open access monographs. Following their brief statements, we will have a question and answer period moderated by Kevin Smith, the Dean of Libraries at the University of Kansas.
Participants for the panel include:
AAUP Open Access Monograph Publishing Initiative– Wendy Pradt Lougee, University Librarian and McKnight Presidential Professor, University of Minnesota, Twin Cities. The Association of American Universities (AAU), Association of Research Libraries (ARL), and Association of American University Presses (AAUP) are implementing a new initiative with 13 universities and 60 university presses participating. Universities will provide subventions for open digital monographs, to be published by university presses.
Lever Pressand Knowledge Unlatched – Charles Watkinson, Associate University Librarian for Publishing, University of Michigan Library, and Director, University of Michigan Press. University of Michigan Press and Amherst Press are partners in the Lever Press which is supported by pledging institutions. University of Michigan Press has also been an active participant in Knowledge Unlatched, which uses a crowd -source funding model to make previously published works openly available. Charles is also a Board Member of Knowledge Unlatched Research and will compare Lever Press with KU.
Luminos– Erich van Rijn, Assistant Director, Director of Publishing Operations at University of California Press. The financial model is shared costs between author, institution, publisher, and libraries.
University of Ottawa Press– Lara Mainville, Director of University of Ottawa Press. OA publications are funded by the University of Ottawa libraries.
Moderator: Kevin Smith, Dean of Libraries at the University of Kansas. Prior to joining the University of Kansas, Kevin served as Director of Copyright and Scholarly Communications at the Duke University Libraries.
95 percent of larger programs (those with 2,500 or more online program students) are “wholly asynchronous” while 1.5 percent are mainly or completely synchronous. About three-quarters (73 percent) of mid-sized programs (schools with between 500 and 2,499 online program students) and 62 percent of smaller programs are fully asynchronous.
The asynchronous nature of this kind of education may explain why threaded discussions turned up as the most commonly named teaching and learning technique, mentioned by 27.4 percent of respondents, closely followed by practice-based learning, listed by 27.3 percent of survey participants.
While the LMS plays a significant role in online programming, the report pointed to a distinct lack of references to “much-hyped innovations,” such as adaptive learning, competency-based education systems, simulation or game-based learning tools. (my note: my mouth run dry of repeating every time people start becoming orgasmic about LMS, D2L in particular)
four in 10 require the use of instructional design support, three in 10 use a team approach for online course design and one in 10 outsources the work. Overall, some 80 percent of larger programs use instructional design expertise.
In the smallest programs, instructional design support is treated as a “faculty option” for 53 percent of institutions. Another 18 percent expect faculty to develop their online courses independently. For 13 percent of mid-sized programs, the faculty do their development work independently; another 64 percent may choose whether or not to bring in instructional design help. (my note: this is the SCSU ‘case’)
Measuring Quality
Among the many possible quality metrics suggested by the researchers, the five adopted most frequently for internal monitoring were:
Student achievement of program objectives (83 percent);
Student retention and graduation rates (77 percent);
