Whether you’re flipping your courses, creating videos to help your students understand specific concepts or recording lectures for exam review, these tips can help you optimize your production setup on a tight budget.
1) Speak Into the Microphone
2) Reconsider Whether You Want to be a Talking Head
Record your video and upload it to YouTube. YouTube will apply its machine transcription to the audio as a starting point. Then you can download the captions into your caption editor and improve on the captions from there. Afterward, you can delete the video from YouTube and add it to your institution’s platform.
how the digital medium will foster engagement and enhance learning outcomes.
aware of the implications of having students post content on third-party services (those not provided or hosted by your institution).
Social media usage in the classroom intersects with both FERPA and Copyright Compliance, so keep this checklist handy as you develop your class activity.
Include details about the activity in your syllabus & course description.
Use contracts.
Link to institutional policies.
Use aliases for social media accounts.
Teach your students to use digital media responsibly.
Know where to provide assignment feedback.
Don’t use personal accounts for university business.
Understand the Terms of Service.
Children who use smartphones, tablets, and video games for more than seven hours a day are more likely to experience premature thinning of the cortex, the outermost layer of the brain that processes thought and action, a 2018 study found. https://t.co/OJe6ZTBVkx
But others say banning laptops can be counterproductive, arguing these devices can create opportunity for students to discover more information during class or collaborate. And that certain tools and technologies are necessary for learners who struggle in a traditional lecture format.
Flanigan, who studies self-regulation, or the processes students use to achieve their learning goals, began researching digital distraction after confronting it in the classroom as a graduate instructor.
Digital distraction tempts all of us, almost everywhere. That’s the premise of Digital Minimalism: Choosing a Focused Life in a Noisy World by Cal Newport, an associate professor of computer science at Georgetown University.
The professor is upset. The professor has taken action, by banning laptops.
Bruff, whose next book, Intentional Tech: Principles to Guide the Use of Educational Technology in College Teaching, is set to be published this fall, is among the experts who think that’s a mistake. Why? Well, for one thing, he said, students are “going to have to graduate and get jobs and use laptops without being on Facebook all day.” The classroom should help prepare them for that.
When Volk teaches a course with 50 or 60 students, he said, “the idea is to keep them moving.”Shifting the focal point away from the professor can help, too. “If they are in a small group with their colleagues,” Volk said, “very rarely will I see them on their laptops doing things they shouldn’t be.”
Professors may not see themselves as performers, but if they can’t get students’ attention, nothing else they do matters. “Learning doesn’t happen without attention,” said Lang, who is writing a book about digital distraction, Teaching Distracted Minds.
One aspect of distraction Lang plans to cover in his book is its history. It’s possible, he said, to regard our smartphones as either too similar or dissimilar from the distractions of the past. And it’s important, he said, to remember how new this technology really is, and how much we still don’t know about it.
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Study: Use of digital devices in class affects students’ long-term retention of information
A new study conducted by researchers at Rutgers University reveals that students who are distracted by texts, games, or videos while taking lecture notes on digital devices are far more likely to have their long-term memory affected and to perform more poorly on exams, even if short-term memory is not impacted, EdSurge reports.
Exam performance was not only poorer for students using the devices, but also for other students in classes that permitted the devices because of the distraction factor, the study found.
After conducting the study, Arnold Glass, the lead researcher, changed his own policy and no longer allows his students to take notes on digital devices.
A nationally representative Gallup poll conducted in March showed that 42% of K-12 teachers feel that the use of digital devices in the classroom are “mostly helpful” for students, while only 28% feel they are “mostly harmful.” Yet 69% of those same teachers feel the devices have a harmful impact on student mental health and 55% feel they negatively affect student physical health.
According to a 2016 study of college students, student waste about 20% of their class time for “non-class” purposes — texting, emailing, or using social media more than 11 times in a typical day. In K-12, increased dependence on digital devices often interferes with homework completion as well.
Though the new study focused on long-term retention, past studies have also shown that indicate a negative correlation between use of digital devices during class and exam scores. A 2015 study by the London School of Economics revealed that pupils in schools that banned cell phones performed better on exams and that the differences were most notable for low-performing students.
Using laptops in class harms academic performance, study warns. Researchers say students who use computers score half a grade lower than those who write notes
findings, published in the journal Economics of Education Review in a paper, based on an analysis of the grades of about 5,600 students at a private US liberal arts college, found that using a laptop appeared to harm the grades of male and low-performing students most significantly.
While the authors were unable to definitively say why laptop use caused a “significant negative effect in grades”, the authors believe that classroom “cyber-slacking” plays a major role in lower achievement, with wi-fi-enabled computers providing numerous distractions for students.
High schoolers assigned a laptop or a Chromebook were more likely to take notes in class, do internet research, create documents to share, collaborate with their peers on projects, check their grades and get reminders about tests or homework due dates.
Blended Learning – the idea of incorporating technology into the every day experience of education – can save time, raise engagement, and increase student retention.
Lets face it, our students are addicted to their phones. Like…drugs addicted. It is not just a bad habit, it is hard wired in their brains(literally) to have the constant stimulation of their phones.
If you are interested in the research, there is a lot out there to read about how it happens and how bad it is.
a Scientific American article published about a recent study of nomophobia – on adults (yes, many of us are addicted too).
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
the Sloan Consortium defined hybrid courses as those that “integrate online with traditional face-to-face class activities in a planned, pedagogically valuable manner.” Educators probably disagree on what qualifies as “pedagogically valuable,” but the essence is clear: Hybrid education uses online technology to not just supplement, but transform and improve the learning process.
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.
Do you know any fact checking sites? Can you identify spot sponsored content? Do you understand syndication? What do you understand under “media literacy,” “news literacy,” “information literacy.” https://blog.stcloudstate.edu/ims/2017/03/28/fake-news-resources/
what is social media (examples). why is called SM? why is so popular? what makes it so popular?
use SM tools for your research and education:
– Determining your topic. How to?
Digg http://digg.com/, Reddit https://www.reddit.com/ , Quora https://www.quora.com
Facebook, Twitter – hashtags (class assignment 2-3 min to search)
LinkedIn Groups
YouTube and Slideshare (class assignment 2-3 min to search)
Flickr, Instagram, Pinterest for visual aids (like YouTube they are media repositories)
The biggest Ohio e-school story over the past year: a push by the state education department to audit attendance at the full-time online schools using student-login records. A first round of reviews found that nine e-schools had overstated their enrollment by a combined 12,000 students. A Dispatch analysis of ECOT’s audit showed that nearly 70 percent of the school’s students missed enough school to be considered truant under state law.
Across the country, the question of how to best track and report student attendance in a full-time online school remains unresolved, contributing to the significant uncertainty around e-schools’ funding and performance.
how data is produced, collected and analyzed. make accessible all kind of data and info
ask good q/s and find good answers, share finding in meaningful ways. this is where digital literacy overshadows information literacy and this the fact that SCSU library does not understand; besides teaching students how to find and evaluate data, I also teach them how to communicate effectively using electronic tools.
connecting people tools and resources and making it easier for everybody. building collaborative, open and interdisciplinary
robust data computational literates. developing workshops, project and events to practice new skills. to position the library as the interdisciplinary nexus
what are data: definition. items of information, facts, traces of content and form. higher level, conception discussion about data in terms of social effects: matadata capturing information about the world, social political and economic changes. move away the mystic conceptions about data. nothing objective about data.
the emergence of IoT – digital meets physical. cyber physical systems. smart objects driven by industry. . proliferation of sensor and device – smart devices.
what does privacy looks like ? what is netneutrality when IoT? library must restructure : collaborate across institutions about collections of data in opien and participatory ways. put IoT in the hands of make and break things (she is maker space aficionado)
make and break things hackathons – use cheap devices such as Arduino and Pi.
data literacy programs with higher level conception exploration; libraries empower the campus in data collection. data science norms, store and share data to existing repositories and even catalogs. commercial services to store and connect data, but very restrictive and this is why libraries must be involved.
linked data and dark data
linked data – draw connections around online data most of the data are locked. linked data uses metadata to link related information in ways computers can understand.
libraries take advantage of link data. link data opportunity for semantics, natural language processing etc. if hidden data is relative to our communities, it is a library responsibility to provide it. community data practitioners
dark data
massive data, which cannot be analyzed by relational processing. data not yield significant findings. might be valuable for researchers: one persons trash is another persons’ treasure. preserving data and providing access to info. collaborate with researchers across disciplines and assist decide what is worth keeping and what discarding and how to study.
rich learning experience working with lined and dark data enable fresh perspective and learning how to work with data architecture. data literacy programming.
in context of data is different from open source and open projects. the social side of data science . advising researchers on navigation data, ethical compilations.
open science movement .https://cos.io/ pushing beyond licences and reframe, position ourselves as collaborators
analysis and publishing ; use tools that can be shared and include data, code and executable files.
reproducibility and contestability https://www.lib.ncsu.edu/events/series/summer-of-open-science
In the age of Big Data, there is an abundance of free or cheap data sources available to libraries about their users’ behavior across the many components that make up their web presence. Data from vendors, data from Google Analytics or other third-party tracking software, and data from user testing are all things libraries have access to at little or no cost. However, just like many students can become overloaded when they do not know how to navigate the many information sources available to them, many libraries can become overloaded by the continuous stream of data pouring in from these sources. This session will aim to help librarians understand 1) what sorts of data their library already has (or easily could have) access to about how their users use their various web tools, 2) what that data can and cannot tell them, and 3) how to use the datasets they are collecting in a holistic manner to help them make design decisions. The presentation will feature examples from the presenters’ own experience of incorporating user data in decisions related to design the Bethel University Libraries’ web presence.
silos, IT barrier, focusing on student success, retention, server space is cheap, if
promotion and tenure for faculty can include incentive to work with the librarian
lack of fear, changing the mindset.
deep collaboration both within and cross-consortia
don’t rely on vendor solutions. changing mindset
development = oppty (versus development as “work”)
private higher education is PALNI
3d virtual picture of disastrous areas. unlock the digital information to be digitally accessible to all people who might be interested.
they opened the maps of Katmandu for the local community and they were coming up with the strategies to recover. democracy in action
i can’t stop thinking that the keynote speaker efforts are mere follow up of what Naomi Klein explains in her Shock Doctrine: http://www.naomiklein.org/shock-doctrine: a government country seeks reasons to destroy another country or area and then NGOs from the same country go to remedy the disasters
A question from a librarian from the U about the use of drones. My note: why did the SCSU library have to give up its drone?
Douglas County Library model. too resource intensive to continue
Marmot Library Network
ILS integrated library system – shared with other counties, same sever for the entire consortium. they have a programmer, viewfind, open source, discovery player, he customized viewfind community to viewfind plus. instead of using the ILS public access catalogue, they are using the Vufind interface
Caiifa Enki. public library – single access collection. they purchase ebooks from the publisher and they are using also the viewfind interface. but not integrated with the library catalogs. Kansas public library went from OverDrive to Viewfind. CA State library is funding for the time being this effort.
types of content – publisher will not understand issue, which clear for librarians
PDF and epub formats
purchase content –
title by title selection – academia is tired of selections. although it is intended to buy also collections
library – owned ( and shared collections)
host content from libraries – papers in academic lib, genealogy in pub lib.
options in license models .
e resource content. not only ebooks, after it is taken care of, add other types of digital objects.
instead of replicate, replacement of the commercial aggregators,
Amigos Shelf interface is the product of the presenter
instead of having a young reader collection as SCSU has on the third floor, an academic library is outsourcing through AMigos shelf ebooks for young readers
Harper Collins is too cumbersome and the reason to avoid working with them.
security issues. some of the material sent over ftp and immediately moved to sftp
decisions – use of internal resources only, if now – amazon
programmer used for the pilot. contracted programmers. lack of the ability to see the large picture. eventually hired a full time person, instead of outsourcing. RDA compliant MARC.
ONIX, spreadsheet MARC.
Decision about who to start with : public or academic.
attempt to keep pricing down –
own agreement with the customers, separate from the agreement with the Publisher
current development: web-based online reading, shared-consortial collections and SIP2 authentication