Because the questionnaire data comprised both Likert scales and open questions, they were analyzed quantitatively and qualitatively. Textual data (open responses) were qualitatively analyzed by coding: each segment (e.g. a group of words) was assigned to a semantic reference category, as systematically and rigorously as possible. For example, “Using an iPad in class really motivates me to learn” was assigned to the category “positive impact on motivation.” The qualitative analysis was performed using an adapted version of the approaches developed by L’Écuyer (1990) and Huberman and Miles (1991, 1994). Thus, we adopted a content analysis approach using QDAMiner software, which is widely used in qualitative research (see Fielding, 2012; Karsenti, Komis, Depover, & Collin, 2011). For the quantitative analysis, we used SPSS 22.0 software to conduct descriptive and inferential statistics. We also conducted inferential statistics to further explore the iPad’s role in teaching and learning, along with its motivational effect. The results will be presented in a subsequent report (Fievez, & Karsenti, 2013)
The 20th century notion of conducting a qualitative research by an oral interview and then processing manually your results had triggered in the second half of the 20th century [sometimes] condescending attitudes by researchers from the exact sciences.
The reason was the advent of computing power in the second half of the 20th century, which allowed exact sciences to claim “scientific” and “data-based” results.
One of the statistical package, SPSS, is today widely known and considered a magnificent tools to bring solid statistically-based argumentation, which further perpetuates the superiority of quantitative over qualitative method.
At the same time, qualitative researchers continue to lag behind, mostly due to the inertia of their approach to qualitative analysis. Qualitative analysis continues to be processed in the olden ways. While there is nothing wrong with the “olden” ways, harnessing computational power can streamline the “olden ways” process and even present options, which the “human eye” sometimes misses.
Below are some suggestions, you may consider, when you embark on the path of qualitative research.
excellent guide to the structure of a qualitative research
Palys, T., & Atchison, C. (2012). Qualitative Research in the Digital Era: Obstacles and Opportunities. International Journal Of Qualitative Methods, 11(4), 352-367.
Palys and Atchison (2012) present a compelling case to bring your qualitative research to the level of the quantitative research by using modern tools for qualitative analysis.
1. The authors correctly promote NVivo as the “jaguar’ of the qualitative research method tools. Be aware, however, about the existence of other “Geo Metro” tools, which, for your research, might achieve the same result (see bottom of this blog entry).
2. The authors promote a new type of approach to Chapter 2 doctoral dissertation and namely OCR-ing PDF articles (most of your literature as of 2017 is mostly either in PDF or electronic textual format) through applications such as
Abbyy Fine Reader, https://www.abbyy.com/en-us/finereader/
OmniPage, http://www.nuance.com/for-individuals/by-product/omnipage/index.htm
Readirus http://www.irislink.com/EN-US/c1462/Readiris-16-for-Windows—OCR-Software.aspx
The text from the articles is processed either through NVIVO or related programs (see bottom of this blog entry). As the authors propose: ” This is immediately useful for literature review and proposal writing, and continues through the research design, data gathering, and analysis stages— where NVivo’s flexibility for many different sources of data (including audio, video, graphic, and text) are well known—of writing for publication” (p. 353).
In other words, you can try to wrap your head around huge amount of textual information, but you can also approach the task by a parallel process of processing the same text with a tool.
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Here are some suggestions for Computer Assisted / Aided Qualitative Data Analysis Software (CAQDAS)for a small and a large community applications):
text mining: https://en.wikipedia.org/wiki/Text_mining Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. https://ischool.syr.edu/infospace/2013/04/23/what-is-text-mining/
Qualitative data is descriptive data that cannot be measured in numbers and often includes qualities of appearance like color, texture, and textual description. Quantitative data is numerical, structured data that can be measured. However, there is often slippage between qualitative and quantitative categories. For example, a photograph might traditionally be considered “qualitative data” but when you break it down to the level of pixels, which can be measured.
word of caution, text mining doesn’t generate new facts and is not an end, in and of itself. The process is most useful when the data it generates can be further analyzed by a domain expert, who can bring additional knowledge for a more complete picture. Still, text mining creates new relationships and hypotheses for experts to explore further.
Pros and Cons of Computer Assisted Qualitative Data Analysis Software
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more on quantitative research:
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
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literature on quantitative research:
St. Cloud State University MC Main Collection – 2nd floor
AZ195 .B66 2015
p. 161 Data scholarship in the Humanities
p. 166 When Are Data?
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
In its latest edition, the Associated Press Stylebook — a widely used reference for journalists — is embracing the use of “they” as a singular pronoun.
meetings with Chief Learning Officers, talent management leaders, and vendors of next generation learning tools.
The corporate L&D industry is over $140 billion in size, and it crosses over into the $300 billion marketplace for college degrees, professional development, and secondary education around the world.
Digital Learning does not mean learning on your phone, it means “bringing learning to where employees are.” In other words, this new era is not only a shift in tools, it’s a shift toward employee-centric design. Shifting from “instructional design” to “experience design” and using design thinking are key here.
1) The traditional LMS is no longer the center of corporate learning, and it’s starting to go away.
LMS platforms were designed around the traditional content model, using a 17 year old standard called SCORM. SCORM is a technology developed in the 1980s, originally intended to help companies like track training records from their CD-ROM based training programs.
the paradigm that we built was focused on the idea of a “course catalog,” an artifact that makes sense for formal education, but no longer feels relevant for much of our learning today.
not saying the $4 billion LMS market is dead, but the center or action has moved (ie. their cheese has been moved). Today’s LMS is much more of a compliance management system, serving as a platform for record-keeping, and this function can now be replaced by new technologies.
We have come from a world of CD ROMs to online courseware (early 2000s) to an explosion of video and instructional content (YouTube and MOOCs in the last five years), to a new world of always-on, machine-curated content of all shapes and sizes. The LMS, which was largely architected in the early 2000s, simply has not kept up effectively.
2) The emergence of the X-API makes everything we do part of learning.
In the days of SCORM (the technology developed by Boeing in the 1980s to track CD Roms) we could only really track what you did in a traditional or e-learning course. Today all these other activities are trackable using the X-API (also called Tin Can or the Experience API). So just like Google and Facebook can track your activities on websites and your browser can track your clicks on your PC or phone, the X-API lets products like the learning record store keep track of all your digital activities at work.
3) As content grows in volume, it is falling into two categories: micro-learning and macro-learning.
4) Work Has Changed, Driving The Need for Continuous Learning
Why is all the micro learning content so important? Quite simply because the way we work has radically changed. We spend an inordinate amount of time looking for information at work, and we are constantly bombarded by distractions, messages, and emails.
5) Spaced Learning Has Arrived
If we consider the new world of content (micro and macro), how do we build an architecture that teaches people what to use when? Can we make it easier and avoid all this searching?
“spaced learning.”
Neurological research has proved that we don’t learn well through “binge education” like a course. We learn by being exposed to new skills and ideas over time, with spacing and questioning in between. Studies have shown that students who cram for final exams lose much of their memory within a few weeks, yet students who learn slowly with continuous reinforcement can capture skills and knowledge for decades.
6) A New Learning Architecture Has Emerged: With New Vendors To Consider
One of the keys to digital learning is building a new learning architecture. This means using the LMS as a “player” but not the “center,” and looking at a range of new tools and systems to bring content together.
On the upper left is a relatively new breed of vendors, including companies like Degreed, EdCast, Pathgather, Jam, Fuse, and others, that serve as “learning experience” platforms. They aggregate, curate, and add intelligence to content, without specifically storing content or authoring in any way. In a sense they develop a “learning experience,” and they are all modeled after magazine-like interfaces that enables users to browse, read, consume, and rate content.
The second category the “program experience platforms” or “learning delivery systems.” These companies, which include vendors like NovoEd, EdX, Intrepid, Everwise, and many others (including many LMS vendors), help you build a traditional learning “program” in an open and easy way. They offer pathways, chapters, social features, and features for assessment, scoring, and instructor interaction. While many of these features belong in an LMS, these systems are built in a modern cloud architecture, and they are effective for programs like sales training, executive development, onboarding, and more. In many ways you can consider them “open MOOC platforms” that let you build your own MOOCs.
The third category at the top I call “micro-learning platforms” or “adaptive learning platforms.” These are systems that operate more like intelligent, learning-centric content management systems that help you take lots of content, arrange it into micro-learning pathways and programs, and serve it up to learners at just the right time. Qstream, for example, has focused initially on sales training – and clients tell me it is useful at using spaced learning to help sales people stay up to speed (they are also entering the market for management development). Axonify is a fast-growing vendor that serves many markets, including safety training and compliance training, where people are reminded of important practices on a regular basis, and learning is assessed and tracked. Vendors in this category, again, offer LMS-like functionality, but in a way that tends to be far more useful and modern than traditional LMS systems. And I expect many others to enter this space.
Perhaps the most exciting part of tools today is the growth of AI and machine-learning systems, as well as the huge potential for virtual reality.
7) Traditional Coaching, Training, and Culture of Learning Has Not Gone Away
8) A New Business Model for Learning
he days of spending millions of dollars on learning platforms is starting to come to an end. We do have to make strategic decisions about what vendors to select, but given the rapid and immature state of the market, I would warn against spending too much money on any one vendor at a time. The market has yet to shake out, and many of these vendors could go out of business, be acquired, or simply become irrelevant in 3-5 years.
9) The Impact of Microsoft, Google, Facebook, and Slack Is Coming
The newest versions of Microsoft Teams, Google Hangouts and Google Drive, Workplace by Facebook, Slack, and other enterprise IT products now give employees the opportunity to share content, view videos, and find context-relevant documents in the flow of their daily work.
We can imagine that Microsoft’s acquisition of LinkedIn will result in some integration of Lynda.com content in the flow of work. (Imagine if you are trying to build a spreadsheet and a relevant Lynda course opens up). This is an example of “delivering learning to where people are.”
10) A new set of skills and capabilities in L&D
It’s no longer enough to consider yourself a “trainer” or “instructional designer” by career. While instructional design continues to play a role, we now need L&D to focus on “experience design,” “design thinking,” the development of “employee journey maps,” and much more experimental, data-driven, solutions in the flow of work.
lmost all the companies are now teaching themselves design thinking, they are using MVP (minimal viable product) approaches to new solutions, and they are focusing on understanding and addressing the “employee experience,” rather than just injecting new training programs into the company.
Shortly: Limitations are influences that the researcher cannot control. They are the shortcomings, conditions or influences that cannot be controlled by the researcher that place restrictions on your methodology and conclusions. Any limitations that might influence the results should be mentioned. Delimitationsare choices made by the researcher which should be mentioned. They describe the boundaries that you have set for the study. Assumptions are accepted as true, or at least plausible, by researchers and peers who will read your dissertation or thesis.
As social networking platforms proliferate and more interactions take place digitally, there are more opportunities for propagation of misinformation, copyright infringement, and privacy breaches.
Empathy as a critical quality for leaders was popularized in Daniel Goleman’s work about emotional intelligence. It is also a core component of Karol Wasylyshyn’s formula for achieving remarkable leadership. Elizabeth Borges, a women’s leadership program organizer and leadership consultant, recommends a particular practice, cognitive empathy.
What is library leadership? a library leader is defined as the individual who articulates a vision for the organization/task and is able to inspire support and action to achieve the vision. A manager, on the other hand, is the individual tasked with organizing and carrying out the day-to-day operational activities to achieve the vision.Work places are organized in hierarchical and in team structures. Managers are appointed to administer business units or organizations whereas leaders may emerge from all levels of the hierarchical structures. Within a volatile climate the need for strong leadership is essential.
Leaders are developed and educated within the working environment where they act and co-work with their partners and colleagues. Effective leadership complies with the mission and goals of the organization. Several assets distinguish qualitative leadership:
Mentoring. Motivation. Personal development and skills. Inspiration and collaboration. Engagement. Success and failure. Risk taking. Attributes of leaders.
Leaders require having creative minds in shaping strategies and solving problems. They are mentors for the staff, work hard and inspire them to do more with less and to start small and grow big. Staff need to be motivated to work at their optimum performance level. Leadership entails awareness of the responsibilities inherent to the roles of a leader. However, effective leadership requires the support of the upper management.
p. 36. Developments in Technology for Academic and Research Libraries
Digital strategies are not so much technologies as they are ways of using devices and software to enrich teaching, learning, research and information management, whether inside or outside the library. Effective Digital strategies can be used in both information and formal learning; what makes them interesting is that they transcended conventional ideas to create something that feels new, meaningful, and 21st century.
enabling technologies
this group of technologies is where substantive technological innovation begins to be visible.
Internet technologies.
learning technologies
social media technologies. could have been subsumed under the consumer technology category, but they have become so ever-present and so widely used in every part of society that they have been elevated to their own category. As well-established as social media is, it continues to evolve at a rapid pace, with new ideas, tools, and developments coming online constantly.
Visualization technologies. from simple infographics to complex forms of visual data analysis. What they have in common is that they tap the brain’s inherent ability to rapidly process visual information, identify patterns, and sense order in complex situations. These technologies are a growing cluster of tools and processes for mining large data sets, exploring dynamic processes, and generally making the complex simple.
p. 38 Big Data
Big data has significant implications for academic libraries in their roles as facilitators and supporters of the research process. big data use in the form of digital humanities research. Libraries are increasingly seeking to recruit for positions such as research data librarians, data curation specialists, or data visualization specialists
p. 40 Digital Scholarship Technologies
digital humanities scholars are leveraging new tools to aid in their work. ubiquity of new forms of communication including social media, text analysis software such as Umigon is helping researchers gauge public sentiment. The tool aggregates and classifies tweets as negative, positive, or neutral.
p. 42 Library Services Platforms
Diversity of format and materials, in turn, required new approaches to content collection and curation that were unavailable in the incumbent integrated library systems (ILS), which are primarily designed for print materials. LSP is different from ILS in numerous ways. Conceptually, LSPs are modeled on the idea of software as a service (SaaS),which entails delivering software applications over the internet.
p. 44 Online Identity.
incorporated the management of digital footprints into their programming and resources
simplify the idea of digital footprint as“data about the data” that people are searching or using online. As resident champions for advancing digital literacy,304 academic and research libraries are well-positioned to guide the process of understanding and crafting online identities.
Libraries are becoming integral players in helping students understand how to create and manage their online identities. website includes a social media skills portal that enables students to view their digital presence through the lens in which others see them, and then learn how they compare to their peers.
beacons are another iteration of the IoT that libraries have adopted; these small wireless devices transmit a small package of data continuously so that when devices come into proximity of the beacon’s transmission, functions are triggered based on a related application.340 Aruba Bluetooth low-energy beacons to link digital resources to physical locations, guiding patrons to these resources through their custom navigation app and augmenting the user experience with location-based information, tutorials, and videos.
students and their computer science professor have partnered with Bavaria’s State Library to develop a library app that triggers supplementary information about its art collection or other points of interest as users explore the space
Top 10 IT Issues, 2017: Foundations for Student Success
Susan Grajek and the 2016–2017 EDUCAUSE IT Issues Panel Tuesday, January 17, 2017http://er.educause.edu/articles/2017/1/top-10-it-issues-2017-foundations-for-student-successThe 2017 EDUCAUSE Top 10 IT Issues are all about student success
Developing a holistic, agile approach to reduce institutional exposure to information security threats
That program should encompass people, process, and technologies:
Educate users
Develop processes to identify and protect the most sensitive data
Implement technologies to encrypt data and find and block advanced threats coming from outside the network via from any type of device
Who Outside the IT Department Should Care Most about This Issue?
End-users, to understand how to avoid exposing their credentials
Unit heads, to protect institutional data
Senior leaders, to hold people accountable
Institutional leadership, to endorse, fund, and advocate for good information security
Issue #2: Student Success and Completion
Effectively applying data and predictive analytics to improve student success and completion
Predictive analytics allows us to track trends, discover gaps and inefficiencies, and displace “best guess” scenarios based on implicitly developed stories about students.
Issue #3: Data-Informed Decision Making
Ensuring that business intelligence, reporting, and analytics are relevant, convenient, and used by administrators, faculty, and students
Higher education information systems generate vast amounts of data daily (including the classroom/LMS). This potentially rich source of information is underused. Even though most institutions have created reports, dashboards, and other distillations of data, these are not necessarily useful or used to inform strategic objectives such as student success or institutional efficiency.
Issue #4: Strategic Leadership
Repositioning or reinforcing the role of IT leadership as a strategic partner with institutional leadership
CIOs have two challenges in this regard. The first is getting to the table. Contemporary requirements for IT leaders position them well for strategic leadership.18 Those requirements include expertise in management and business practices, project portfolio management, negotiation, and change leadership. However, business-savvy CIOs can alienate some academics, particularly those opposed to administrators as leaders. Worse, not all CIOs are well-equipped for a position at the executive table.
Issue #5: Sustainable Funding
Developing IT funding models that sustain core services, support innovation, and facilitate growth
Two complications have deepened the IT funding challenge in recent years. The first is that information technology is now incontrovertibly core to the mission and function of colleges and universities. The second complication is that at most institutions, digital investments and technology refreshes have been funded with capital expenditures. Yet IT services and infrastructure are moving outside the institution, generally to the cloud, and cloud funding depends on ongoing expenditures rather than one-time investments.
Issue #6: Data Management and Governance
Improving the management of institutional data through data standards, integration, protection, and governance
Data management and governance is not an IT issue. It requires a broad, top-down approach because all departments need to buy in and agree. All stakeholders (data owners as well as IR, IT, and institutional leaders) must collaboratively develop a common set of data definitions and a common understanding of what data is needed, in what format, and for what purposes. This coordination, or governance, will enable constituents to communicate with confidence about the data (e.g., “the single version of truth”) and the standards (e.g., APLU, IPEDS, CDS) under which it is collected.
Institutions often choose to approach data management from three perspectives: (1) accuracy, (2) usability, and (3) privacy. The IT organization has a role to play in creating and maintaining data warehouses, integrating systems to facilitate data exchange, and maintaining standards for data privacy and security.
Issue #7: Higher Education Affordability
Prioritizing IT investments and resources in the context of increasing demand and limited resources
Uncoordinated, redundant expenditures supplant other needed investments, such as consistent classroom technology or dedicated information security staff. Planning needs to occur at the institutional or departmental level, but it also needs a place to coalesce and be assessed regionally, nationally, and in some cases, globally, because there isn’t enough money to do everything that institutional leaders, faculty, and others want or even need to do. Public systems are making some headway in sharing services, but for the most part, local optimization supersedes collaboration and compromise.
Issue #8: Sustainable Staffing
Ensuring adequate staffing capacity and staff retention as budgets shrink or remain flat and as external competition grows
As institutions become more dependent on their IT organizations, IT organizations are more dependent on the expertise and quality of their workforce. New hires need to be great hires, and great staff need to want to stay. Each new hire can change the culture and effectiveness of the IT organizations
Issue #9: Next-Gen Enterprise IT
Developing and implementing enterprise IT applications, architectures, and sourcing strategies to achieve agility, scalability, cost-effectiveness, and effective analytics
Buildings should outlive alumni; technology shouldn’t. IT leaders are examining core enterprise applications, including ERPs (traditionally, suites of financial, HR, and student information systems) and LMSs, for their ability to meet current and future needs.
Issue #10: Digital Transformation of Learning
Collaborating with faculty and academic leadership to apply technology to teaching and learning in ways that reflect innovations in pedagogy and the institutional mission
According to Michael Feldstein and Phil Hill, personalized learning applies technology to three processes: content (moving content delivery out of the classroom and allowing students to set their pace of learning); tutoring (allowing interactive feedback to both students and faculty); and contact time (enabling faculty to observe students’ work and coach them more).
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more on IT in this IMS blog https://blog.stcloudstate.edu/ims?s=information+technology
1. They have self- awareness. Emotionally intelligent leaders understand their own emotions and know how to manage them. They don’t speak out of frustration or anger; they control their emotions and wait to speak up until their feelings have settled and they have processed their thoughts. They don’t react in the heat of the moment but wait to respond.
2. They respond to criticism and feedback. Every leader faces feedback, some of it negative. Emotionally intelligent leaders don’t become defensive or take it personally. They listen, process, and genuinely consider other points of view, and because they’re always looking to improve, they know how to accept sincere critiques.
3. They know how to generate self-confidence. Emotionally intelligent leaders share a healthy dose of confidence but never cross the line into arrogance. When they don’t understand something, they ask open-ended questions that aim to gather information, not challenge or argue. They know how to give and take in a way that generates confidence.
4. They know the importance of checking their ego.Leaders who have to demonstrate their own importance or value are not yet connected to true leadership or emotional intelligence. Those who are know how to speak and act out of concern of others. They don’t always have to be the center of attention, and they would never take credit for the work of others. Secure in their own abilities, they’re generous and gracious to others.
5. They know how to embody empathy. Leaders with emotional intelligence can put themselves in others’ shoes. They listen with genuine interest and attention and make it a point to understand, then give back in a way that benefits themselves and others. They know how to create win-win situations.
6. They know how to engage with empowerment. The best leaders–the ones with the highest EQs–make it their mission to believe in others and empower them to believe in themselves. Instead of focusing on themselves they know it’s the power of the people that makes leadership successful, so that’s where they focus their efforts.
We’ve seen the pendulum swing from a more muscular federal role like we had during No Child Left Behind in the Bush era, to times when the primacy was in the states, which is the case now [with the Every Student Succeeds Act]. We’re now in a phase where states can be incubators of innovation.
At the invitation of Adobe Education, I attended the Educause Annual Conference this year and did a quick series of interviews about the education work that Adobe is doing. A huge highlight for me was reconnecting with futurist Bryan Alexander, whom I’d interviewed in 2012 as a part of my Future of Education series, and whose work and voice I’ve continued to really appreciate.
