We know that many of you have been interested in exploring Turnitin in the past, so we are excited to bring you an exclusive standardized price and more information on the roll out of Feedback Studio, replacing the Turnitin you have previously seen. We would like to share some exciting accessibility updates, how Feedback Studio can help faculty deliver formative feedback to students and help students become writers. Starting today thru December 31st non-integrated Feedback Studio will be $2.50 and integrated Feedback Studio will be $3 for new customers! Confused by the name? Don’t be! Turnitin is new and improved! Check out this video to learn about Feedback Studio!
Meet your exclusive Turnitin Team!
Ariel Ream – Account Executive, Indianapolis aream@turnitin.com – 317.650.2795
Juliessa Rivera – Relationship Manager, Oakland jrivera@iparadigms.com – 510.764.7698
Juan Valladares – Account Representative, Oakland
jvalladares@turnitin.com – 510.764.7552
To learn more, please join us for a WebEx on September 21st. We will be offering free 30 day pilots to anyone who attends!
Turnitin Webinar
Wednesday, September 21, 2016
11:00 am | Central Daylight Time (Chicago) | 1 hr
Meeting number (access code): 632 474 162
https://mnscu.webex.com/mnscu/j.php?MTID=mebaec2ae9d1d25e6774d16717719008d
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my notes from the webinar
I am prejudiced against TI and I am not hiding it; that does not mean that I am wrong.
For me, TurnitIn (TI) is an anti-pedagogical “surfer,” using the hype of “technology” to ride the wave of overworked faculty, who hope to streamline increasing workload with technology instead of working on pedagogical resolutions of not that new issues.
Low and behold, Juan, the TI presenter is trying to dazzle me with stuff, which does not dazzle me for a long time.
WCAG 2.0 AA standards of the W3C and section 508 of the rehabilitation act.
the sales pitch: 79% of students believe in feedback, but only %50+ receive it. HIs source is TurnitIn surveys from 2012 to 2016 (very very small font size (ashamed of it?))
It seems to me very much like “massaged” data.
Testimonials: one professor and one students. Ha. the apex of qualitative research…
next sales pitch: TurnitIn feedback studio. Not any more the old Classic. It assesses the originality. Drag and drop macro-style notes. Pushing rubrics. but we still fight for rubrics in D2L. If we have a large amount of adjuncts. Ha. another gem. “I know that you are, guys, IT folks.” So the IT folks are the Trojan horse to get the faculty on board. put comments on
This presentation is structured dangerously askew: IT people but no faculty. If faculty is present, they will object that they ARE capable of doing the same which is proposed to be automated.
More , why do i have to pay for another expensive software, if we have paid already Microsoft? MS Word can do everything that has been presented so far. Between MS Word and D2L, it becomes redundant.
why the heck i am interested about middle school and high school.
TI was sued for illegal collection of paper; paper are stored in their database without the consent of the students’ who wrote it. TI goes “great length to protect the identity of the students,” but still collects their work [illegally?}
November 10 – 30 day free trial
otherwise, $3 per student, prompts back: between Google, MS Word and D2L (which we already heftily pay for), why pay another exuberant price.
D2L integration: version, which does not work. LTI.
“small price to pay of such a beauty” – it does not matter how quick and easy the integration is, it is a redundancy, which already can be resolved with existing tools, part of which we are paying hefty price for
https://d2l.custhelp.com/app/answers/detail/a_id/1668/
#1: Adjust Your Content Mix
On Facebook, go to Insights > Posts > Post Types to review the engagement by the type of content you posted (post, link, image, video). On Twitter, you can see a snapshot of each post you’ve made by going to Settings > Analytics > Tweets.
#2: Fine-tune Your Posting Schedule
On Facebook, go to Insights > Posts > When Your Fans Are Online. For Twitter, you can use a tool such a Tweriod to find out when the bulk of your followers are online.
#3: Inform Your Messaging
On Facebook, open the Ads Manager and go to Audience Insights. On Twitter, you can check your audience data by going to Settings > Twitter Ads > Analytics > Audience Insights.
#4: Boost Your Engagement
On Twitter, go to Settings > Analytics > Tweets and take a look at which post topics get the most engagement. On Facebook, go to Insights > Posts > Post Types and then switch the engagement metrics in Facebook to show reactions, comments, and shares for each post rather than post clicks or general engagement.
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more on social media analytics in this blog
https://blog.stcloudstate.edu/ims?s=social+media+analytics
http://www.rubedo.com.br/2016/08/38-great-resources-for-learning-data.html
Learn data mining languages: R, Python and SQL
W3Schools – Fantastic set of interactive tutorials for learning different languages. Their SQL tutorial is second to none. You’ll learn how to manipulate data in MySQL, SQL Server, Access, Oracle, Sybase, DB2 and other database systems.
Treasure Data – The best way to learn is to work towards a goal. That’s what this helpful blog series is all about. You’ll learn SQL from scratch by following along with a simple, but common, data analysis scenario.
10 Queries – This course is recommended for the intermediate SQL-er who wants to brush up on his/her skills. It’s a series of 10 challenges coupled with forums and external videos to help you improve your SQL knowledge and understanding of the underlying principles.
TryR – Created by Code School, this interactive online tutorial system is designed to step you through R for statistics and data modeling. As you work through their seven modules, you’ll earn badges to track your progress helping you to stay on track.
Leada – If you’re a complete R novice, try Lead’s introduction to R. In their 1 hour 30 min course, they’ll cover installation, basic usage, common functions, data structures, and data types. They’ll even set you up with your own development environment in RStudio.
Advanced R – Once you’ve mastered the basics of R, bookmark this page. It’s a fantastically comprehensive style guide to using R. We should all strive to write beautiful code, and this resource (based on Google’s R style guide) is your key to that ideal.
Swirl – Learn R in R – a radical idea certainly. But that’s exactly what Swirl does. They’ll interactively teach you how to program in R and do some basic data science at your own pace. Right in the R console.
Python for beginners – The Python website actually has a pretty comprehensive and easy-to-follow set of tutorials. You can learn everything from installation to complex analyzes. It also gives you access to the Python community, who will be happy to answer your questions.
PythonSpot – A complete list of Python tutorials to take you from zero to Python hero. There are tutorials for beginners, intermediate and advanced learners.
Read all about it: data mining books
Data Jujitsu: The Art of Turning Data into Product – This free book by DJ Patil gives you a brief introduction to the complexity of data problems and how to approach them. He gives nice, understandable examples that cover the most important thought processes of data mining. It’s a great book for beginners but still interesting to the data mining expert. Plus, it’s free!
Data Mining: Concepts and Techniques – The third (and most recent) edition will give you an understanding of the theory and practice of discovering patterns in large data sets. Each chapter is a stand-alone guide to a particular topic, making it a good resource if you’re not into reading in sequence or you want to know about a particular topic.
Mining of Massive Datasets – Based on the Stanford Computer Science course, this book is often sighted by data scientists as one of the most helpful resources around. It’s designed at the undergraduate level with no formal prerequisites. It’s the next best thing to actually going to Stanford!
Hadoop: The Definitive Guide – As a data scientist, you will undoubtedly be asked about Hadoop. So you’d better know how it works. This comprehensive guide will teach you how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. Make sure you get the most recent addition to keep up with this fast-changing service.
Online learning: data mining webinars and courses
DataCamp – Learn data mining from the comfort of your home with DataCamp’s online courses. They have free courses on R, Statistics, Data Manipulation, Dynamic Reporting, Large Data Sets and much more.
Coursera – Coursera brings you all the best University courses straight to your computer. Their online classes will teach you the fundamentals of interpreting data, performing analyzes and communicating insights. They have topics for beginners and advanced learners in Data Analysis, Machine Learning, Probability and Statistics and more.
Udemy – With a range of free and pay for data mining courses, you’re sure to find something you like on Udemy no matter your level. There are 395 in the area of data mining! All their courses are uploaded by other Udemy users meaning quality can fluctuate so make sure you read the reviews.
CodeSchool – These courses are handily organized into “Paths” based on the technology you want to learn. You can do everything from build a foundation in Git to take control of a data layer in SQL. Their engaging online videos will take you step-by-step through each lesson and their challenges will let you practice what you’ve learned in a controlled environment.
Udacity – Master a new skill or programming language with Udacity’s unique series of online courses and projects. Each class is developed by a Silicon Valley tech giant, so you know what your learning will be directly applicable to the real world.
Treehouse – Learn from experts in web design, coding, business and more. The video tutorials from Treehouse will teach you the basics and their quizzes and coding challenges will ensure the information sticks. And their UI is pretty easy on the eyes.
Learn from the best: top data miners to follow
John Foreman – Chief Data Scientist at MailChimp and author of Data Smart, John is worth a follow for his witty yet poignant tweets on data science.
DJ Patil – Author and Chief Data Scientist at The White House OSTP, DJ tweets everything you’ve ever wanted to know about data in politics.
Nate Silver – He’s Editor-in-Chief of FiveThirtyEight, a blog that uses data to analyze news stories in Politics, Sports, and Current Events.
Andrew Ng – As the Chief Data Scientist at Baidu, Andrew is responsible for some of the most groundbreaking developments in Machine Learning and Data Science.
Bernard Marr – He might know pretty much everything there is to know about Big Data.
Gregory Piatetsky – He’s the author of popular data science blog
KDNuggets, the leading newsletter on data mining and knowledge discovery.
Christian Rudder – As the Co-founder of OKCupid, Christian has access to one of the most unique datasets on the planet and he uses it to give fascinating insight into human nature, love, and relationships
Dean Abbott – He’s contributed to a number of data blogs and authored his own book on Applied Predictive Analytics. At the moment, Dean is Chief Data Scientist at
SmarterHQ.
Practice what you’ve learned: data mining competitions
Kaggle – This is the ultimate data mining competition. The world’s biggest corporations offer big prizes for solving their toughest data problems.
Stack Overflow – The best way to learn is to teach. Stackoverflow offers the perfect forum for you to prove your data mining know-how by answering fellow enthusiast’s questions.
TunedIT – With a live leaderboard and interactive participation, TunedIT offers a great platform to flex your data mining muscles.
DrivenData – You can find a number of nonprofit data mining challenges on DataDriven. All of your mining efforts will go towards a good cause.
Quora – Another great site to answer questions on just about everything. There are plenty of curious data lovers on there asking for help with data mining and data science.
Meet your fellow data miner: social networks, groups and meetups
Facebook – As with many social media platforms, Facebook is a great place to meet and interact with people who have similar interests. There are a number of very active data mining groups you can join.
LinkedIn – If you’re looking for data mining experts in a particular field, look no further than LinkedIn. There are hundreds of data mining groups ranging from the generic to the hyper-specific. In short, there’s sure to be something for everyone.
Meetup – Want to meet your fellow data miners in person? Attend a meetup! Just search for data mining in your city and you’re sure to find an awesome group near you.
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8 fantastic examples of data storytelling
8 fantastic examples of data storytelling
Data storytelling is the realization of great data visualization. We’re seeing data that’s been analyzed well and presented in a way that someone who’s never even heard of data science can get it.
Google’s Cole Nussbaumer provides a friendly reminder of what data storytelling actually is, it’s straightforward, strategic, elegant, and simple.
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more on text and data mining in this IMS blog
hthttps://blog.stcloudstate.edu/ims?s=data+mining
Utilizing Augmented Reality For Special Needs Learning
Utilizing Augmented Reality For Special Needs Learning
Augmented reality is a variation of virtual environments, but has a few added advantages for special needs learning. With virtual environments the user is completely immersed in a virtual world and cannot see the real environment around him or her. This may cause some confusion for special needs learners and can hinder learning. In contrast, augmented reality allows the user to see the real world with virtual objects superimposed upon or composited with the real world. This provides the greatest benefit as learners remain part of the world around them and learn easily.
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more on the topic
Muñoz, Silvia Baldiris Navarro and Ramón, “Gremlings in My Mirror: An Inclusive AR-Enriched Videogame for Logical Math Skills Learning”, Advanced Learning Technologies (ICALT) 2014 IEEE 14th International Conference on, pp. 576-578, 2014.