Voke VR, a virtual reality (VR) company founded by two former Washington State University (WSU) professors, is working to build Intel-backed immersive tech for live events.
At the core of the platform is Voke’s TrueVR product, which delivers full stereoscopic 3D video that is integrated with augmented content in a 360-degree VR environment. It uses multiple camera angles with zoom capabilities and synchronized DVR, so that viewers can control what they want to watch. Additionally, with TrueVR, content is captured, encoded, synced with scores, metadata and audio and delivered in real time to multiple platforms.
The default Mastodon web interface is a little more complex than Twitter, but no more than, for instance, TweetDeck. There are, of course, other interfaces you could use with Mastodon, including mobile apps or custom Atom feeds or custom web apps using Mastodon’s rich API. Since everyone starts out with the web UI, however, it’s a good thing to look at to get used to Mastodon’s capabilities.
Document analysis is a systematic procedure for reviewing or evaluating documents—both printed and electronic (computer-based and Internet-transmitted) material. Like other analytical methods in qualitative research, document analysis requires that data be examined and interpreted in order to elicit meaning, gain understanding, and develop empirical knowledge(Corbin&Strauss,2008;seealsoRapley,2007).
Document analysis is often used in combination with other qualitative research methods as a means of triangulation—‘the combination of methodologies in the study of the same phenomenon’ (Denzin, 1970, p. 291)
The qualitative researcher is expected to draw upon multiple (at least two) sources of evidence; that is, to seek convergence and corroboration through the use of different data sources and methods. Apart from documents, such sources include interviews, participant or non-participant observation, and physical artifacts (Yin,1994).By triangulating data, the researcher attempts to provide ‘a confluence of evidence that breeds credibility’ (Eisner, 1991, p. 110). By examining information collected through different methods, the researcher can corroborate findings across data sets and thus reduce the impact of potential biases that can exist in a single study. According to Patton (1990), triangulation helps the researcher guard against the accusation that a study’s findings are simply an artifact of a single method, a single source, or a single investigator’s bias. Mixed-method studies (which combine quantitative and qualitative research techniques)sometimes include document analysis. Here is an example: In their large-scale, three-year evaluation of regional educational service agencies (RESAs), Rossman and Wilson (1985) combined quantitative and qualitative methods—surveys (to collect quantitative data) and open ended, semi structured interviews with reviews of documents (as the primary sources of qualitative data). The document reviews were designed to identify the agencies that played a role in supporting school improvement programs.
Document Analysis
Document analysis is a form of qualitative research in which documents are interpreted by the researcher to give voice and meaning around an assessment topic. Analyzing documents incorporates coding content into themes similar to how focus group or interview transcripts are analyzed. A rubric can also be used to grade or score a document. There are three primary types of documents:
• Public Records: The official, ongoing records of an organization’s activities. Examples include student transcripts, mission statements, annual reports, policy manuals, student handbooks, strategic plans, and syllabi.
• Personal Documents: First-person accounts of an individual’s actions, experiences, and beliefs. Examples include calendars, e-mails, scrapbooks, blogs, Facebook posts, duty logs, incident reports, reflections/journals, and newspapers.
• Physical Evidence: Physical objects found within the study setting (often called artifacts). Examples include flyers, posters, agendas, handbooks, and training materials.
As with all research, how you collect and analyse the data should depend on what you want to find out. Since you haven’t told us that, it is difficult to give you any precise advice. However, one really important matter in using documents as sources, whatever the overall aim of your research, is that data from documents are very different from data from speech events such as interviews, or overheard conversations.So the first analytic question you need to ask with regard to documents is ‘how are these data shaped by documentary production ?’ Something which differentiates nearly all data from documents from speech data is that those who compose documents know what comes at the end while still able to alter the beginning; which gives far more opportunity for consideration of how the recepient of the utterances will view the provider; ie for more artful self-presentation. Apart from this however, analysing the way documentary practice shapes your data will depend on what these documents are: for example your question might turn out to be ‘How are news stories produced ?’ – if you are using news reports, or ‘What does this bureaucracy consider relevant information (and what not relevant and what unmentionable) ? if you are using completed proformas or internal reports from some organisation.
An analysis technique is just like a hardware tool. It depends where and with what you are working to choose the right one. For a nail you should use a hammer, and there are lots of types of hammers to choose, depending on the type of nail.
So, in order to tell you the bettet technique, it is important to know the objectives you intend to reach and the theoretical framework you are using. Perhaps, after that, We could tell you if you should use content analysis, discourse or grounded theory (which type of it as, like the hammer, there are several types of GTs).
Estimated Hours of Learning: 28 Certificate of Completion available upon request
Learning outcomes
After participating in this eCourse, you will:
Gain experience using some of the best apps available and understand how they enable learning
Learn how to evaluate and review mobile apps
Learn how tablets complement laptops, and how their capabilities are creating new learning opportunities
Learn how apps are being used by people with special needs, and where to find additional resources for learning more
Receive guidance for creating your own app guides, offering workshops, and advising colleagues
In this 5-week eCourse, you’ll learn about the most useful apps available on tablet and mobile devices and how they can be applied in your library to create the best learning experiences for your patrons and students.
Mobile apps are empowering for people of all ages and abilities. Contrary to the popular idea that apps are only useful for “consumption,” the best apps are being used effectively as tools to enable learning and knowledge creation. In this eCourse, Nicole Hennig will show you how to incorporate apps as learning tools at your library.
Readings about security, writing, mobile apps in academia
Your thoughts on the readings (discussion forum)
Optional app review assignment
Week 3 – Reference
The Apps
Dictionaries, encyclopedias
Unit converters, maps, languages
Specialized reference apps
Subscription databases & citations
Readings & Discussion
Readings about jailbreaking, platforms, & mobile web
Apple’s iOS Human Interface Guidelines
Your thoughts on the readings (discussion forum)
Optional app review assignment
Week 4 – Multimedia
The Apps
Art viewing
Art creation
Photography and photo editing
Music listening
Music creation
Video viewing and editing
Readings & Discussion
Readings about technology & children
Your thoughts on the readings (discussion forum)
Optional app review assignment
Week 5 – Accessibility & More
Accessibility features of mobile devices
Readings & Discussion
Readings about assistive technology
Your thoughts on the readings (discussion forum)
Idea generation assignment
Ideas for using apps in library programs & services
Apps that wow
How this eCourse Works
The eCourse begins on June 5, 2017. Your participation will require approximately five to six hours a week, at times that fit your schedule. All activities take place on the website, and you will be expected to:
Read, listen to or view online content
Post to online discussion boards
Complete weekly assignments or activities
Instructor Nicole Hennig will monitor discussion boards regularly during the five-week period, lead group discussions, and will also answer individual questions. All interaction will take place on the eCourse site, which will be available 24 hours a day, 7 days a week. It’s recommended that students log into the site on the first day of class or within a few days for an overview of the content and to begin the first lesson.
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).
It’s the first time mobile learning ranked as the highest priority in the survey. The No. 2 priority is broadband and network capacity, which ranked first last year, and the No. 3 priority is cybersecurity and privacy, with 62 percent of respondents rating them more important than last year.
Understaffing remains a key issue for technology departments in school systems.
Single sign-on (SSO) is the most implemented interoperability initiative
More than one-third of IT leaders expressed no interest in bring your own device (BYOD) initiatives, up from 20 percent in 2014.
Interest in open educational resources (OER) is high
Education technology experience is common among IT leaders
Strong academic backgrounds are also prevalent among IT leaders.
Lack of diversity continues to be an issue for school district technology leaders.
CoSN is a nonprofit association for school system technology leaders. To read or download the full IT leadership survey, visit this CoSN site.
A Strategist’s Guide to Industry 4.0. Global businesses are about to integrate their operations into a seamless digital whole, and thereby change the world.
Industrial revolutions are momentous events. By most reckonings, there have been only three. The first was triggered in the 1700s by the commercial steam engine and the mechanical loom. The harnessing of electricity and mass production sparked the second, around the start of the 20th century. The computer set the third in motion after World War II.
Henning Kagermann, the head of the German National Academy of Science and Engineering (Acatech), did exactly that in 2011, when he used the term Industrie 4.0 to describe a proposed government-sponsored industrial initiative.
The term Industry 4.0 refers to the combination of several major innovations in digital technology
These technologies include advanced robotics and artificial intelligence; sophisticated sensors; cloud computing; the Internet of Things; data capture and analytics; digital fabrication (including 3D printing); software-as-a-service and other new marketing models; smartphones and other mobile devices; platforms that use algorithms to direct motor vehicles (including navigation tools, ride-sharing apps, delivery and ride services, and autonomous vehicles); and the embedding of all these elements in an interoperable global value chain, shared by many companies from many countries.
Companies that embrace Industry 4.0 are beginning to track everything they produce from cradle to grave, sending out upgrades for complex products after they are sold (in the same way that software has come to be updated). These companies are learning mass customization: the ability to make products in batches of one as inexpensively as they could make a mass-produced product in the 20th century, while fully tailoring the product to the specifications of the purchaser
.
Three aspects of digitization form the heart of an Industry 4.0 approach.
• The full digitization of a company’s operations
• The redesign of products and services
• Closer interaction with customers
Making Industry 4.0 work requires major shifts in organizational practices and structures. These shifts include new forms of IT architecture and data management, new approaches to regulatory and tax compliance, new organizational structures, and — most importantly — a new digitally oriented culture, which must embrace data analytics as a core enterprise capability.
Klaus Schwab put it in his recent book The Fourth Industrial Revolution (World Economic Forum, 2016), “Contrary to the previous industrial revolutions, this one is evolving at an exponential rather than linear pace.… It is not only changing the ‘what’ and the ‘how’ of doing things, but also ‘who’ we are.”
This great integrating force is gaining strength at a time of political fragmentation — when many governments are considering making international trade more difficult. It may indeed become harder to move people and products across some national borders. But Industry 4.0 could overcome those barriers by enabling companies to transfer just their intellectual property, including their software, while letting each nation maintain its own manufacturing networks.
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more on the Internet of Things in this IMS blog https://blog.stcloudstate.edu/ims?s=internet+of+things
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
A Stanford studyfound that the majority of middle school students can’t tell the difference between real news and fake news. In fact, 82 percent couldn’t distinguish between a real news story on a website and a “sponsored content” post.
The WSJ: Of the 8,704 students studied (ranging in age from middle school to college level), four in ten high-school students believed that the region near Japan’s Fukushima nuclear plant was toxic after seeing an unsourced photo of deformed daisies coupled with a headline about the Japanese area. The photo, keep in mind, had no source or location attribution. Meanwhile, two out of every three middle-schoolers were fooled by an article on financial preparedness penned by a bank executive.
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.
