. Link VR content to course outcomes. If you want to VR to succeed in your college classroom, you have to look at how 360-degree audio and video adds value. The forensic-science department, for example, is trying to get a close approximation of a crime scene so that students can acclimate to the job environment and take a real-world approach to investigations. Adding VR without adding value will not be effective. 2. Do a proof-of-concept app first. The history reenactment app was a great starting point, as it was a simple-to-film, single-location shoot that didn’t require much editing. You want to start simple to get an early win. They learned valuable lessons during that shoot, such as best camera placement to minimize distractions.
3. Get buy-in at the highest levels. Marketing students in the capstone project are presenting the final apps to the President, Provost, and other administration officials. Once you get buy-in at an administrative level, it’s easier to secure funding for more equipment and more promotion of your work to other departments.
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
Minecraft for Higher Ed? Try it. Pros, Cons, Recommendations?
Description: Why Minecraft, the online video game? How can Minecraft improve learning for higher education? We’ll begin with a live demo in which all can participate (see “Minecraft for Free”). We’ll review “Examples, Not Rumors” of successful adaptations and USES of Minecraft for teaching/learning in higher education. Especially those submitted in advance And we’ll try to extract from these activities a few recommendations/questions/requests re Minecraft in higher education.
Callaghan, N. (2016). Investigating the role of Minecraft in educational learning environments. Educational Media International, 53(4), 244-260. doi:10.1080/09523987.2016.1254877
Noelene Callaghan dissects the evolution in Australian education from a global perspective. She rightfully draws attention (p. 245) to inevitable changes in the educational world, which still remain ignored: e.g., the demise of “traditional” LMS (Educase is calling for their replacement with digital learning environments https://blog.stcloudstate.edu/ims/2017/07/06/next-gen-digital-learning-environment/ and so does the corporate world of learning: https://blog.stcloudstate.edu/ims/2017/03/28/digital-learning/ ), the inevitability of BYOD (mainly by the “budget restrictions and sustainability challenges” (p. 245); by the assertion of cloud computing, and, last but not least, by the gamification of education.
p. 245 literature review. In my paper, I am offering more comprehensive literature review. While Callaghan focuses on the positive, my attempt is to list both pros and cons: http://scsu.mn/1F008Re
246 General use of massive multiplayer online role playing games (MMORPGs)
levels of interaction have grown dramatically and have led to the creation of general use of massive multiplayer online role playing games (MMORPGs)
247 In teaching and learning environments, affordances associated with edugames within a project-based learning (PBL) environment permit:
These affordances develop both social and cognitive abilities of students
Nebel, S., Schneider, S., Beege, M., Kolda, F., Mackiewicz, V., & Rey, G. (2017). You cannot do this alone! Increasing task interdependence in cooperative educational videogames to encourage collaboration. Educational Technology Research & Development, 65(4), 993-1014. doi:10.1007/s11423-017-9511-8
Abrams, S. S., & Rowsell, J. (2017). Emotionally Crafted Experiences: Layering Literacies in Minecraft. Reading Teacher, 70(4), 501-506.
Nebel, S., Schneider, S., & Daniel Rey, G. (2016). Mining Learning and Crafting Scientific Experiments: A Literature Review on the Use of Minecraft in Education and Research. Source: Journal of Educational Technology & Society, 19(192), 355–366. Retrieved from http://www.jstor.org/stable/jeductechsoci.19.2.355
Cipollone, M., Schifter, C. C., & Moffat, R. A. (2014). Minecraft as a Creative Tool: A Case Study. International Journal Of Game-Based Learning, 4(2), 1-14.
Niemeyer, D. J., & Gerber, H. R. (2015). Maker culture and Minecraft : implications for the future of learning. Educational Media International, 52(3), 216-226. doi:10.1080/09523987.2015.1075103
Nebel, S., Schneider, S., & Daniel Rey, G. (2016). Mining Learning and Crafting Scientific Experiments: A Literature Review on the Use of Minecraft in Education and Research. Journal of Educational Technology & Society, 19(192), 355–366. Retrieved from http://www.jstor.org/stable/jeductechsoci.19.2.355
Wilkinson, B., Williams, N., & Armstrong, P. (2013). Improving Student Understanding, Application and Synthesis of Computer Programming Concepts with Minecraft. In The European Conference on Technology in the Classroom 2013. Retrieved from http://iafor.info/archives/offprints/ectc2013-offprints/ECTC2013_0477.pdf
Uusi-Mäkelä, M., & Uusi-Mäkelä, M. (2014). Immersive Language Learning with Games: Finding Flow in MinecraftEdu. EdMedia: World Conference on Educational Media and Technology (Vol. 2014). Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/noaccess/148409/
Birt, J., & Hovorka, D. (2014). Effect of mixed media visualization on learner perceptions and outcomes. In 25th Australasian Conference on Information Systems (pp. 1–10). Retrieved from http://epublications.bond.edu.au/fsd_papers/74
Al Washmi, R., Bana, J., Knight, I., Benson, E., Afolabi, O., Kerr, A., Hopkins, G. (2014). Design of a Math Learning Game Using a Minecraft Mod. https://doi.org/10.13140/2.1.4660.4809
My Note: when stripped from the commercialized plug in for Apple, this article makes a good memorization exercise for pedagogues.
According to American psychologist Abraham Maslow, all humans have the same fundamental needs (food, clothing and shelter), and these needs must be met before an individual is motivated to look beyond these basic needs. This motivational theory is commonly referred to as Maslow’s hierarchy of needs.
Self-actualization: achieving one’s full potential
Maslow’s hierarchy of needs can serve as an analogy for what is possible with instructionally-designed technology
1. Device Deployment = Basic Needs
Device deployment is the first basic need of any school looking to leverage education technology. If schools are unable to procure devices and if IT is unable to get these devices into the hands of students and educators, there is no moving forward.
2. Communication = Safety Needs
Beyond basic communications functions, apps must be made available and installed for an additional layer of connectivity. For example, learning management systems (LMS) enable communication beyond classroom walls and empower students with the learning resources they need while at home or in the community. However, how do we ensure access off-campus for those without ubiquitous internet connections
3. Productivity = Love Needs
Communication that encourages higher-level thinking and problem solving is where dramatic learning happens.
4. Transformation = Esteem and Self-Actualization Needs
IT and educators are pairing innovative teaching methods such as blended learning (a mix of technology and traditional learning) or flipped classrooms (teaching is done at home and exercises during class time) with education apps (productivity layer).
5. Let Mobile Device Management (MDM) Be Your Stepladder
Tobin, T. J., Mandernach, B. J., & Taylor, A. H. (2015). Evaluating Online Teaching: Implementing Best Practices (1 edition). San Francisco, CA: Jossey-Bass.
5 measurable faculty competencies for on line teaching:
attend to unique challenges of distance learning
Be familiar with unique learning needs
Achieve mastery of course content, structure , and organization
Respond to student inquiries
Provide detailed feedback
Communicate effectively
Promote a safe learning environment
Monitor student progress
Communicate course goals
Provide evidence of teaching presence.
Best practices include:
Making interactions challenging yet supportive for students
Asking learners to be active participants in the learning process
Acknowledging variety on the ways that students learn best
Providing timely and constructive feedback
Evaluation principles
Instructor knowledge
Method of instruction
Instructor-student rapport
Teaching behaviors
Enthusiastic teaching
Concern for teaching
Overall
8. The American Association for higher Education 9 principle4s of Good practice for assessing student learning from 1996 hold equally in the F2F and online environments:
the assessment of student learning beings with educational values
assessment is most effective when it reflects an understanding of learning as multidimensional, integrated and revealed in performance over time
assessment works best when the programs it seeks to improve have clear, explicitly stated purposes.
Assessment requires attention to outcomes but also and equally to the experiences that lead to those outcomes.
Assessment works best when it is ongoing, not episodic
Assessment fosters wider improvement when representatives from across the educational community are involved
Assessment makes a difference when it begins with issues of use and illumines questions that people really care bout
Assessment is most likely to lead to improvements when it is part of the large set of conditions that promote change.
Through assessment, educators meet responsibilities to students and to the public.
9 most of the online teaching evaluation instruments in use today are created to evaluate content design rather than teaching practices.
29 stakeholders for the evaluation of online teaching
faculty members with online teaching experience
campus faculty members as a means of establishing equitable evaluation across modes of teaching
contingent faculty members teaching online
department or college administrators
members of faculty unions or representative governing organizations
administrative support specialists
distance learning administrators
technology specialists
LMS administrators
Faculty development and training specialists
Institutional assessment and effectiveness specialists
Students
Sample student rating q/s
University resources
Rate the effectiveness of the online library for locationg course materials
Based on your experience,
148. Checklist for Online Interactive Learning COIL
150. Quality Online Course Initiative QOCI
151 QM Rubric
154 The Online Insturctor Evaluation System OIES
163 Data Analytics: moving beyond student learning
# of announcments posted per module
# of contributions to the asynchronous discussion boards
Quality of the contributions
Timeliness of posting student grades
Timelines of student feedback
Quality of instructional supplements
Quality of feedback on student work
Frequency of logins
180 understanding big data
reliability
validity
factor structure
187 a holistics valuation plan should include both formative evaluation, in which observations and rating are undertaken with the purposes of improving teaching and learning, and summative evaluation, in which observation and ratings are used in order to make personnel decisions, such as granting promotion and tenure, remediation, and asking contingent faculty to teach again.
195 separating teaching behaviors from content design
some of the findings in Kahoot!’s first-ever EdTrends Report : Google is gaining a stronghold in United States classrooms, with Chrome OS expanding its presence on school computers, while Apple’s iOS has been on the decline since the first quarter of 2015 among students and teachers.
Chromebook had the highest number of users among teachers (44 percent) and students (46 percent), when they were asked about their top devices used. Google’s Productivity Suite (G Suite or Classroom) was the most widely used productivity suite in U.S. classrooms, with 57 percent saying they used it, compared to 23 percent saying they used Microsoft Office 365.
a majority of educators (more than 60 percent) said the purpose of adopting education technology was to increase student productivity and efficiency. Their key educational priorities for 2017-18 are “to improve student learning and outcomes” (88 percent), and to “better leverage available time and motivate students” (71 percent).
Educators saw the top ed tech trends in the next school year as:
Digital platforms for teaching, learning and assessment;
Personalized learning;
Computational thinking, coding and robotics;
Increased understanding of data; and
Gamificiation.
Some other key findings in the report include:
A majority of U.S. public school educators surveyed said they are challenged with budget restraints and lack of resources when it comes to implementing education technology;
A majority of U.S. private school educators said they lack training to understand or adopt new technology;
Many public and private school educators said they saw the adoption of “technology for the sake of technology” as a challenge;
Educators in California struggle with lack of training and “technology for the sake of technology,” while teachers in Texas struggle with bureaucracy, budget constraints and a lack of resources.
MPS students will be receiving devices that come with 3GB of high-speed LTE data (with unlimited data available at 2G speeds if usage exceeds that amount). Students can keep their device up to four years while they are in high school no cost, according to initiative site. Additionally, devices are equipped with filters to block adult content that cannot be disabled and are Free Children’s Internet Protection Act (CIPA) compliant.
The roles of librarians change with changes in user needs and demands and the technology employed. A survey conducted for Research Libraries UK found skill gaps in nine key areas in which subject librarians could be supporting researchers’ needs. Even though many librarians may want to hire new staff with these skills, a survey found that the reality for most will be training existing staff.
Definitions of library services will change. We need to grow the ways users can engage with whatever they value from libraries, whether papyrus rolls, maker spaces or data management instruction.
p. 19
What is the Unique Selling Point (USP) of libraries vis-à-vis other information service providers?
p. 21
Librarians should measure the effectiveness of services based on the users’ perceptions of success. Librarians also should move beyond surveys of how library space is being used and should conduct structured observations and interviews with the people using the space. It is not enough to know that the various spaces, whether physical or virtual, are busy. Librarians need to understand when and how the spaces are being used.
p. 33 What is Enough? Satisficing Information Needs
Role theory explains that: “When people occupy social positions their behavior is determined mainly by what is expected of that position rather than by their own individual characteristics” (Abercrombie et al., 1994, p. 360).
Rational choice theory is based on the premise that complex social behavior can be understood in terms of elementary individual actions because individual action is the elementary unit of social life. Rational choice theory posits that individuals choose or prefer what is best to achieve their objectives or pursue their interests, acting in their self-interest (Green, 2002). Stated another way, “When faced with several courses of action, people usually do what they believe is likely to have the best overall outcome” (Scott, 2000).
When individuals satisfice, they compare the benefits of obtaining “more information” against the additional cost and effort of continuing to search (Schmid, 2004)
p. 38
This paper examines the theoretical concepts—role theory, rational choice, and satisficing—by attempting to explain the parameters within which users navigate the complex information-rich environment and determine what and how much information will meet their needs.
p. 39
The information-seeking and -searching research that explicitly addresses the topic of “what is good enough” is scant, though several studies make oblique references to the stopping stage, or to the shifting of directions for want of adequate information. Kraft and Lee (1979, p. 50) propose three stopping rules:
1. The satiation rule, “where the scan is terminated only when the user becomes satiated by finding all the desired number of relevant documents”;
2. The disgust rule, which “allows the scan to be terminated only when the user becomes disgusted by having to examine too many irrelevant documents”; and
3. The combination rule, “which allows the user to be seen as stopping the scan if he/she is satiated by finding the desired number of relevant documents or disgusted by having to examine too many irrelevant documents, whichever comes first.”
p. 42
Ellis characterizes six different types of information activities: starting, chaining, browsing, differentiating, monitoring and extracting. He emphasizes the information- seeking activities, rather than the nature of the problems or criteria used for determining when to stop the information search process. In a subsequent article, Ellis (1997) observes that even in the final stages of writing, individuals may continue the search for information in an attempt to answer unresolved questions or to look for new literature.
p. 43
Undergraduate and graduate students
Situations creating the need to look for information (meeting assignment requirements):
• Writing research reports; and
• Preparing presentations.
Criteria used for stopping the information search (fulfilling assignment requirements):
1. Quantitative criteria:
— Required number of citations was gathered;
— Required number of pages was reached;
— All the research questions were answered; and
— Time available for preparing.
2. Qualitative criteria:
— Accuracy of information;
— Same information repeated in several sources;
— Sufficient information was gathered; and
— Concept understood.
Criteria used for stopping the information search (fulfilling assignment requirements):
1. Quantitative criteria:
— Required number of citations was gathered;
— Required number of pages was reached;
— All the research questions were answered; and
— Time available for preparing.
2. Qualitative criteria:
— Accuracy of information;
— Same information repeated in several sources;
— Sufficient information was gathered; and
— Concept understood.
p. 44
Faculty
Situations creating the need to look for information (meeting teaching needs):
• Preparing lectures and presentations;
• Delivering lectures and presentations;
• Designing and conducting workshops;
• Meeting scholarly and research needs; and
• Writing journal articles, books and grant proposals.
Criteria used for stopping the information search (fulfilling teaching needs):
1. Quantitative criteria:
— Time available for: preparing lectures and presentations; delivering lectures
— And presentations; and designing and conducting workshops; and
— Fulfilling scholarly and research needs.
2. Qualitative criteria:
— Every possible synonym and every combination were searched;
— Representative sample of research was identified;
— Current or cutting-edge research was found;
— Same information was repeated;
— Exhaustive collection of information sources was discovered;
— Colleagues’ feedback was addressed;
— Journal reviewers’ comments were addressed; and
— Publisher’s requirements were met.
1. Quantitative criteria for stopping:
— Requirements are met;
— Time constraints are limited; and
— Coverage of material for publication is verified by colleagues or reviewers.
2. Qualitative criteria for stopping:
— Trustworthy information was located;
— A representative sample of sources was gathered;
— Current information was located;
— Cutting-edge material was located;
— Exhaustive search was performed; and
— Exhaustive collection of information sources was discovered.
p. 53
“Screenagers” and Live Chat Reference: Living Up to the Promise
p. 81
Sense-Making and Synchronicity: Information-Seeking Behaviors of Millennials and Baby Boomers
p. 84 Millennials specific generational features pertinent to libraries and information-seeking include the following:
Rushkoff (1996) described the non-linearity of the thinking patterns of those he terms “children of chaos,” coining the term “screenagers” to describe those who grew up surrounded by television and computers (p. 3).
p. 85
Rational choice theory describes a purposive action whereby individuals judge the costs and benefits of achieving a desired goal (Allingham 1999; Cook and Levi 1990; Coleman and Fararo 1992). Humans, as rational actors, are capable of recognizing and desiring a certain outcome, and of taking action to achieve it. This suggests that information seekers rationally evaluate the benefits of information’s usefulness and credibility, versus the costs in time and effort to find and access it.
Role theory offers a person-in-context framework within the information-seeking situation which situates behaviors in the context of a social system (Mead 1934; Marks 1996). Abercrombie, et al. (1994, p. 360) state, “When people occupy social positions their behavior is determined mainly by what is expected of that position rather than by their own individual characteristics.” Thus the roles of information-seekers in the academic environment influence the expectations for performance and outcomes. For example, faculty would be expected to look for information differently than undergraduate students. Faculty members are considered researchers and experts in their disciplines, while undergraduate students are novices and protégés, roles that place them differently within the organizational structure of the academy (Blumer, 2004; Biddle, 1979; Mead, 1934; Marks, 1996; Marks, 1977).
‘Alternative’ Education: Using Charter Schools to Hide Dropouts and Game the System
School officials nationwide dodge accountability ratings by steering low achievers to alternative programs. In Orlando, Florida, the nation’s tenth-largest district, thousands of students who leave alternative charters run by a for-profit company aren’t counted as dropouts.
Accelerated Learning Solutions (ALS), a more than $1.5 million-a-year “management fee,” 2015 financial records show — more than what the school spends on instruction.
alternative schools at times become warehouses where regular schools stow poor performers to avoid being held accountable.
Concerns that schools artificially boosted test scores by dumping low achievers into alternative programs have surfaced in connection with ongoing litigation in Louisiana and Pennsylvania, and echo findings from a legislative report a decade ago in California. The phenomenon is borne out by national data: While the number of students in alternative schools grew moderately over the past 15 years, upticks occurred as new national mandates kicked in on standardized testing and graduation rates.
The role of charter alternative schools like Sunshine — publicly funded but managed by for-profit companies — is likely to grow under the new U.S. Secretary of Education, Betsy DeVos, an ardent supporter of school choice. In her home state of Michigan, charter schools have been responsible in part for a steep rise in the alternative school population. She recently portrayed Florida as a national model for charters and choice.
No Child Left Behind was supposed to improve educational outcomes for students long overlooked — including those who were black, Hispanic and low-income.
Nationwide, nearly a third of the alternative-school population attends a school that spends at least $500 less per pupil than regular schools do in the same district. Forty percent of school districts with alternative schools provide counseling services only in regular schools. Charter alternative schools — both virtual and bricks-and-mortar — in Ohio, Georgia and Florida have been accused of collecting public money for students who weren’t in classes.
Orlando schools are not unique in using alternative programs to remove struggling students from traditional classrooms. As far back as 2007, a legislative report in California warned that the state’s accountability system allowed traditional schools to shirk responsibility for low-performing students by referring them to alternative schools. The state is currently reviewing its standards for alternative schools.
Companies running schools in this niche often save costs by relying on computer programs that reduce the need for credentialed teachers. The market can be lucrative: As enrollment grew, ALS’ management fees from the schools it operates in Orange County more than doubled from $2.5 million in the 2012 school year to $5.4 million in 2015. The company says the fees pay for back-office services, such as human resources, as well as school-based support for areas such as curriculum, reading, math, security, and professional development.
The company’s affiliate — the controversial Nashville-based Community Education Partners, or CEP — contracted with school districts to serve students with behavior problems. The company, founded by a lawyer and Republican Party operative named Randle Richardson, ran schools for students who had committed disciplinary violations in cities such as Atlanta, Philadelphia, Houston and Orlando for more than a decade. Critics called CEP’s schools prison-like and dangerous, and charged that their academics were sub-par.
My note: naturally, the LinkedIn CEO will make such claim, since it is good for his business. Please take a look at this Chronicle of Higher Ed posting:
Namely,
When it comes to getting students ready for the job market, presidents are not always in agreement with employers and parents on what role the institution should play in the process.
presidents are more divided about whether colleges should provide a broad education or specific training, and one- third of them don’t want to be held accountable for the career outcomes of their students
The LinkedIn CIO claims that:
The notion that an entrepreneur who built his career upon a professional network is now saying that skill level is what matters most in a job search should be a sign to colleges and universities that the era of college as a search for personal discovery is rapidly coming to an end.
Colleges must develop stronger training programs, similar to approaches used in applied science for lab and research exposure, to bolster career readiness in the liberal arts and social sciences. Without it, businesses will soon realize that they can develop these learning outlets on their own, in benefit of their own bottom lines.
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more on LInkedIn in this IMS blog:
