what i find most important: Future IT Workforce: Deploying a broad array of modern recruitment, retention, and employment practices to develop a resilient IT talent pipeline for the institution
Digital Integrations: Ensuring system interoperability, scalability, and extensibility, as well as data integrity, security, standards, and governance, across multiple applications and platforms
Engaged Learning: Incorporating technologies that enable students to create content and engage in active learning in course curricula
Student Retention and Completion: Developing the capabilities and systems to incorporate artificial intelligence into student services to provide personalized, timely support
Administrative Simplification: Applying user-centered design, process improvement, and system reengineering to reduce redundant or unnecessary efforts and improve end-user experiences
Improved Enrollment: Using technology, data, and analytics to develop an inclusive and financially sustainable enrollment strategy to serve more and new learners by personalizing recruitment, enrollment, and learning experiences
Workforce of the Future: Using technology to develop curriculum, content, and learning experiences that prepare students for the evolving workforce
Holistic Student Success: Applying technology and data, including artificial intelligence, to understand and address the numerous contributors to student success, from finances to health and wellness to academic performance and degree planning (my note: this is what Christine Waisner, Mark Gill and Plamen Miltenoff are trying to do with their VR research)
Improved Teaching: Strengthening engagement among faculty, technologists, and researchers to achieve the true and expanding potential of technology to improve teaching
Student-Centric Higher Education: Creating a student-services ecosystem to support the entire student life cycle, from prospecting to enrollment, learning, job placement, alumni engagement, and continuing education
The Oculus Quest is mainly being marketed as an all-in-one VR gaming system, but I see much potential for classroom lessons.
The Oculus Go delivered a VR view, but the Oculus Quest provides us with interactions.
One major difference between the Quest and the Go is the lack of motion sickness with the new device.
The 6 degrees of freedom (6DoF) provides mobility for the student to walk forward, backward, left, right, jump up and squat down. In other words, they can move around just like they would in real life.
The affordable starting price of $399 for 64 GB is comparable to other classroom devices, such as Chromebooks, laptops and iPads.
between the Quest and the Go is the high cost of the apps. By contrast, the majority of my Oculus Go apps were free.
Mental health of college students and Lee’s new book: “Delivering College Mental Health”
Join Bryan Alexander and Lee Keyes, executive director, Counseling Center at the University of Alabama, and author of Delivering Effective College Mental Health Services for an engaging live discussion on the future of mental health in higher education.
Bryan plans to ask Lee about unfolding trends in college student mental health and his thoughts around the rise in anxiety and stress. We will explore how universities are changing their approaches to student mental health and what roles technology may play in harming or helping psychological well-being.
What questions or thoughts do you have? Join and take part in the discussion!
Lee about “Mobile First” – like First Aid. Often by text and email. after Bryan asked how Adjuncts can deal with such situations, if
Counseling Centers need those additions.
Mobile First apps.
most crisis situations are a form of panic. if addressed quickly, one can prevent growing and turning into a major episode.
mindfulness can be different for the different type of issues of students.
libraries as the campus community center.
can be done on
conflation of immaturity and irresponsibility with stress and panic. Latter might be expressed in a way it is immature, but one has to meet them where they are, not judgement and denial, which will make it worse. Tough love will not help. Upholding classroom expectations and rules, but can be supportive at the same time. When pressed by time
Daniel Stanford De Paul. Cohort fundamentals of good teaching. instead of “fail safely”
In May 2018, Google announced a partnership with Labster, a virtual lab simulator, to develop immersive high school and college biology and anatomy courses.
Course title: IM 554 Developing Skills for Online Teaching and Learning
Topic for this week: Game-based learning, Virtual Reliability, and Augmented Reality
Audience: IM Graduate students working for K12 schools or in business
2. How did GBL change in the past year? Who is the leader in this research (country)? Is K12 the “playground” for GBL and DGBL?
China: Liao, C., Chen, C., & Shih, S. (2019). The interactivity of video and collaboration for learning achievement, intrinsic motivation, cognitive load, and behavior patterns in a digital game-based learning environment. Computers & Education, 133, 43–55. https://doi.org/10.1016/j.compedu.2019.01.013
Finalnd: Brezovszky, B., Mcmullen, J., Veermans, K., Hannula-Sormunen, M., Rodríguez-Aflecht, G., Pongsakdi, N., … Lehtinen, E. (2019). Effects of a mathematics game-based learning environment on primary school students’ adaptive number knowledge. Computers & Education, 128, 63–74. https://doi.org/10.1016/j.compedu.2018.09.011
Tunesia: Denden, M., Tlili, A., Essalmi, F., & Jemni, M. (2018). Implicit modeling of learners’ personalities in a game-based learning environment using their gaming behaviors. Smart Learning Environments, 5(1), 1–19. https://doi.org/10.1186/s40561-018-0078-6
Pitarch, R. (2018). An Approach to Digital Game-based Learning: Video-games Principles and Applications in Foreign Language Learning. Journal of Language Teaching and Research, 9(6), 1147–1159. https://doi.org/10.17507/jltr.0906.04
min 29 from start: University of Connecticut (chapter 1)
min 58 from start: Dan Getz with Penn State (chapter 2)
hour 27 min from start: Randy Rode, Yale (chapter 3)
https://sched.co/JAqk
the type of data: wikipedia. the dangers of learning from wikipedia. how individuals can organize mitigate some of these dangers. wikidata, algorithms.
IBM Watson is using wikipedia by algorythms making sense, AI system
youtube videos debunked of conspiracy theories by using wikipedia.
semantic relatedness, Word2Vec
how does algorithms work: large body of unstructured text. picks specific words
lots of AI learns about the world from wikipedia. the neutral point of view policy. WIkipedia asks editors present as proportionally as possible. Wikipedia biases: 1. gender bias (only 20-30 % are women).
conceptnet. debias along different demographic dimensions.
citations analysis gives also an idea about biases. localness of sources cited in spatial articles. structural biases.
geolocation on Twitter by County. predicting the people living in urban areas. FB wants to push more local news.
danger (biases) #3. wikipedia search results vs wkipedia knowledge panel.
collective action against tech: Reddit, boycott for FB and Instagram.
data labor: what the primary resources this companies have. posts, images, reviews etc.
boycott, data strike (data not being available for algorithms in the future). GDPR in EU – all historical data is like the CA Consumer Privacy Act. One can do data strike without data boycott. general vs homogeneous (group with shared identity) boycott.
the wikipedia SPAM policy is obstructing new editors and that hit communities such as women.
how to access at different levels. methods and methodological concerns. ethical concerns, legal concerns,
tweetdeck for advanced Twitter searches. quoting, likes is relevant, but not enough, sometimes screenshot
engagement option
social listening platforms: crimson hexagon, parsely, sysomos – not yet academic platforms, tools to setup queries and visualization, but difficult to algorythm, the data samples etc. open sources tools (Urbana, Social Media microscope: SMILE (social media intelligence and learning environment) to collect data from twitter, reddit and within the platform they can query Twitter. create trend analysis, sentiment analysis, Voxgov (subscription service: analyzing political social media)
graduate level and faculty research: accessing SM large scale data web scraping & APIs Twitter APIs. Jason script, Python etc. Gnip Firehose API ($) ; Web SCraper Chrome plugin (easy tool, Pyhon and R created); Twint (Twitter scraper)
Facepager (open source) if not Python or R coder. structure and download the data sets.
TAGS archiving google sheets, uses twitter API. anything older 7 days not avaialble, so harvest every week.
social feed manager (GWUniversity) – Justin Litman with Stanford. Install on server but allows much more.
legal concerns: copyright (public info, but not beyond copyrighted). fair use argument is strong, but cannot publish the data. can analyize under fair use. contracts supercede copyright (terms of service/use) licensed data through library.
methods: sampling concerns tufekci, 2014 questions for sm. SM data is a good set for SM, but other fields? not according to her. hashtag studies: self selection bias. twitter as a model organism: over-represnted data in academic studies.
methodological concerns: scope of access – lack of historical data. mechanics of platform and contenxt: retweets are not necessarily endorsements.
ethical concerns. public info – IRB no informed consent. the right to be forgotten. anonymized data is often still traceable.
table discussion: digital humanities, journalism interested, but too narrow. tools are still difficult to find an operate. context of the visuals. how to spread around variety of majors and classes. controversial events more likely to be deleted.
takedowns, lies and corrosion: what is a librarian to do: trolls, takedown,
development kit circulation. familiarity with the Oculus Rift resulted in lesser reservation. Downturn also.
An experience station. clean up free apps.
question: spherical video, video 360.
safety issues: policies? instructional perspective: curating,WI people: user testing. touch controllers more intuitive then xbox controller. Retail Oculus Rift
app Scatchfab. 3modelviewer. obj or sdl file. Medium, Tiltbrush.
College of Liberal Arts at the U has their VR, 3D print set up.
Penn State (Paul, librarian, kiniseology, anatomy programs), Information Science and Technology. immersive experiences lab for video 360.
CALIPHA part of it is xrlibraries. libraries equal education. content provider LifeLiqe STEM library of AR and VR objects. https://www.lifeliqe.com/
libraians, IT staff, IDs. help faculty with course design, primarily online, master courses. Concordia is GROWING, mostly because of online students.
solve issues (putting down fires, such as “gradebook” on BB). Librarians : research and resources experts. Librarians helping with LMS. Broadening definition of Library as support hub.
Do we need to pay for services such as Turnitin? Are there comparable services for free? Do we need services such as those ones or we rather have faculty and students educated on plagiarism and faculty trained to detect plagiarism? Is it supposed to be a “mechanical” process or educational activity?
These questions following a posting of today from the Educause Blended and Online Learning Group
Are any of you using a non-Turnitin plagiarism checker that you’re happy with (besides Google or Grammarly’s free service)?
Thanks,
Jenn Stevens (she, her, hers)
Director, Instructional Technology Group
403C Walker Building
Emerson College | 120 Boylston St | Boston, MA 02116
(617) 824-3093
At Ursinus, we use PlagScan, which is affordable and meets our needs.
We haven’t been able to get it to fully integrate within our LMS yet but hopefully we will be able to soon.
Christine Iannicelli
Instructional Technology Librarian
Library and IT
Library 124
Phone: 610-409-3466 ciannicelli@ursinus.edu
Colleges around the country have also started hiring staff members with titles like OER Coordinator and Affordable Content Librarian. Our series looked into how the movement is changing, and the research into the costsand benefits. You can even hear a podcast version here.
Flipped classrooms seem to be growing exponentially
Robert Talbert, a professor of mathematics at Grand Valley State University and author of the book Flipped Learning. Talbert recently tabulated how many scholarly articles are published each year about “flipping” instruction, meaning that traditional lecture-style material is delivered before class (often using videos) so that classroom time can be used for discussion and other more active learning.
More professors are looking to experts to help them teach. (Though some resist.)
By 2016, there were an estimated 13,000 instructional designers on U.S. campuses, according to a report by Intentional Futures. And that number seems to be growing.
There’s also a growing acceptance of the scholarly discipline known as “learning sciences,” a body of research across disciplines of cognitive science, computer science, psychology, anthropology and other fields trying to unlock secrets of how people learn and how to best teach.
Students are also finding their own new ways to learn online, by engaging in online activism. The era of a campus bubble seems over in the age of Twitter
Colleges are still struggling to find the best fit for online education
And what does it mean to teach an age of information overload and polarization?
Perhaps the toughest questions of all about teaching in the 21st century is what exactly is the professor’s role in the Internet age. Once upon a time the goal was to be the ‘sage on the stage,’ when lecturing was king. Today many people argue that the college instructor should be more of a ‘guide on the side.’ But as one popular teaching expert notes, even that may not quite fit.
And in an era of intense political polarization, colleges and professors are looking for best to train students to become digitally literate so they can play their roles as informed citizens. But just how to do that is up for debate, though some are looking for a nonpartisan solution.
DataSense, a data management platform developed by Brightbytes.
DataSense is a set of professional services that work with K-12 districts to collect data from different data systems, translate them into unified formats and aggregate that information into a unified dashboard for reporting purposes.
DataSense traces its origins to Authentica Solutions, an education data management company founded in 2013.
A month later, BrightBytes acquired Authentica. The deal was hailed as a “major milestone in the industry” and appeared to be a complement to BrightBytes’ flagship offering, Clarity, a suite of data analytics tools that help educators understand the impact of technology spending and usage on student outcomes.
Of the “Big Five” technology giants, Microsoft has become the most acqui-hungry as of late in the learning and training space. In recent years it purchased several consumer brand names whose services reach into education, including LinkedIn (which owns Lynda.com, now a part of the LinkedIn Learning suite), Minecraft (which has been adapted for use in the classroom) and Github (which released an education bundle).
Last year, Microsoft also acquired a couple of smaller education tools, including Flipgrid, a video-discussion platform popular among teachers, and Chalkup, whose services have been rolled into Microsoft Teams, its competitor to Slack.
Eureka: machine learning tool, brainstorming engine. give it an initial idea and it returns similar ideas. Like Google: refine the idea, so the machine can understand it better. create a collection of ideas to translate into course design or others.
Netlix:
influencers and microinfluencers, pre- and doing the execution
a machine can construct a book with the help of a person. bionic book. machine and person working hand in hand. provide keywords and phrases from lecture notes, presentation materials. from there recommendations and suggestions based on own experience; then identify included and excluded content. then instructor can construct.
Design may be the least interesting part of the book for the faculty.
multiple choice quiz may be the least interesting part, and faculty might want to do much deeper assessment.
use these machine learning techniques to build assessment. how to more effectively. inquizitive is the machine learning
students engagements and similar prompts
presence in the classroom: pre-service teachers class. how to immerse them and practice classroom management skills
First class: marriage btw VR and use of AI – an environment headset: an algorithm reacts how teachers are interacting with the virtual kids. series of variables, oppty to interact with present behavior. classroom management skills. simulations and environments otherwise impossible to create. apps for these type of interactions
facilitation, reflection and research
AI for more human experience, allow more time for the faculty to be more human, more free time to contemplate.
Jason: Won’t the use of AI still reduce the amount of faculty needed?
Christina Dumeng: @Jason–I think it will most likely increase the amount of students per instructor.
Andrew Cole (UW-Whitewater): I wonder if instead of reducing faculty, these types of platforms (e.g., analytic capabilities) might require instructors to also become experts in the various technology platforms.
Dirk Morrison: Also wonder what the implications of AI for informal, self-directed learning?
Kate Borowske: The context that you’re presenting this in, as “your own jazz band,” is brilliant. These tools presented as a “partner” in the “band” seems as though it might be less threatening to faculty. Sort of gamifies parts of course design…?
Dirk Morrison: Move from teacher-centric to student-centric? Recommender systems, AI-based tutoring?
Andrew Cole (UW-Whitewater): The course with the bot TA must have been 100-level right? It would be interesting to see if those results replicate in 300, 400 level courses
