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!
Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners – This book is a must read for anyone who needs to do applied data mining in a business setting (ie practically everyone). It’s a complete resource for anyone looking to cut through the Big Data hype and understand the real value of data mining. Pay particular attention to the section on how modeling can be applied to business decision making.
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.
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.
Yochai Benklerexplains: “The various formats of the networked public sphere provide anyone with an outlet to speak, to inquire, to investigate, without need to access the resources of a major media organization.”
Democratic bodies are typically elected in periods of three to five years, yet citizen opinions seem to fluctuate daily and sometimes these mood swings grow to enormous proportions. When thousands of people all start tweeting about the same subject on the same day, you know that something is up. With so much dynamic and salient political diversity in the electorate, how can policy-makers ever reach a consensus that could satisfy everyone?
At the same time, it would be a grave mistake to discount the voices of the internet as something that has no connection to real political situations.
What happened in the UK was not only a political disaster, but also a vivid example of what happens when you combine the uncontrollable power of the internet with a lingering visceral feeling that ordinary people have lost control of the politics that shape their lives.
Polarization as a driver of populism
People who have long entertained right-wing populist ideas, but were never confident enough to voice them openly, are now in a position to connect to like-minded others online and use the internet as a megaphone for their opinions.
The resulting echo chambers tend to amplify and reinforce our existing opinions, which is dysfunctional for a healthy democratic discourse. And while social media platforms like Facebook and Twitter generally have the power to expose us to politically diverse opinions, research suggests that the filter bubbles they sometimes create are, in fact, exacerbated by the platforms’ personalization algorithms, which are based on our social networks and our previously expressed ideas. This means that instead of creating an ideal type of a digitally mediated “public agora”, which would allow citizens to voice their concerns and share their hopes, the internet has actually increased conflict and ideological segregation between opposing views, granting a disproportionate amount of clout to the most extreme opinions.
The disintegration of the general will
In political philosophy, the very idea of democracy is based on the principal of the general will, which was proposed by Jean-Jacques Rousseau in the 18th century. Rousseau envisioned that a society needs to be governed by a democratic body that acts according to the imperative will of the people as a whole.
There can be no doubt that a new form of digitally mediated politics is a crucial component of the Fourth Industrial Revolution: the internet is already used for bottom-up agenda-setting, empowering citizens to speak up in a networked public sphere, and pushing the boundaries of the size, sophistication and scope of collective action. In particular, social media has changed the nature of political campaigning and will continue to play an important role in future elections and political campaigns around the world.
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more on the impact of technology on democracy in this IMS blog:
At a Ford Foundation conference dubbed Fairness by Design, officials, academics and advocates discussed how to address the problem of encoding human bias in algorithmic analysis. The White House recently issued a report on the topic to accelerate research into the issue.
U.S. CTO Megan Smith said the government has been “creating a seat for these techies,” but that training future generations of data scientists to tackle these issues depends on what we do today. “It’s how did we teach our children?” she said. “Why don’t we teach math and science the way we teach P.E. and art and music and make it fun?”
“Ethics is not just an elective, but some portion of the main core curriculum.”
DARPA’s holographic imaging system hopes to show objects behind a wall or around a corner – Eraser anyone?
04/28/2016 – 18:21 Kim Cobb
SMU’s Lyle School of Engineering will lead a multi-university team funded by the Defense Advanced Research Projects Agency (DARPA) to build a theoretical framework for creating a computer-generated image of an object hidden from sight around a corner or behind a wall.
The core of the proposal is to develop a computer algorithm to unscramble the light that bounces off irregular surfaces to create a holographic image of hidden objects.
Similar technologies purused by MS Hololense as reported in this IMS blog entry:
Are you ready to deal with “denial of sleep” attacks? Those are attacks using malicious code, propagated through the Internet of Things, aimed at draining the batteries of your devices by keeping them awake.
Security. threats extend well beyond denial of sleep: “The IoT introduces a wide range of new security risks and challenges to the IoT devices themselves, their platforms and operating systems, their communications, and even the systems to which they’re connected.
Analytics. IoT will require a new approach to analytics. “New analytic tools and algorithms are needed now, but as data volumes increase through 2021, the needs of the IoT may diverge further from traditional analytics,” according to Gartner.
Device (Thing) Management. IoT things that are not ephemeral — that will be around for a while — will require management like every other device (firmware updates, software updates, etc.), and that introduces problems of scale.
Low-Power, Short-Range IoT Networks. Short-range networks connecting IT devices will be convoluted. There will not be a single common infrastructure connecting devices.
Low-Power, Wide-Area Networks. Current solutions are proprietary, but standards will come to dominate.
Processors and Architecture. Designing devices with an understanding of those devices’ needs will require “deep technical skills.”
Operating Systems. There’s a wide range of systems out there that have been designed for specific purposes.
Event Stream Processing. “Some IoT applications will generate extremely high data rates that must be analyzed in real time.
Platforms. “IoT platforms bundle many of the infrastructure components of an IoT system into a single product.
Standards and Ecosystems. as IoT devices proliferate, new ecosystems will emerge, and there will be “commercial and technical battles between these ecosystems” that “will dominate areas such as the smart home, the smart city and healthcare.
In November 2015, the Open University released the latest edition of its ‘Innovating Pedagogy’ report, the fourth rendition of an annual educational technology and teaching techniques forecast. While the timelines and publishing interval may remind you of the Horizon Report, the methodology for gathering the trends is different.
The NMC Horizon Team uses a modified Delphi survey approach with a panel of experts.
10 Innovative Pedagogy Trends from the 2015 Edition:
Crossover Learning: recognition of diverse, informal achievements with badges.
Learning through Argumentation: To fully understand scientific ideas and effectively participate in public debates students should practice the kinds of inquiry and communication processes that scientists use, and pursue questions without known answers, rather than reproducing facts.
Incidental Learning: A subset of informal learning, incidental learning occurs through unstructured exploration, play and discovery. Mobile technologies can support incidental learning. An example is the app and website Ispot Nature.
Context-based Learning:Mobile applications and augmented reality can enrich the learners’ context. An example is the open source mobile game platform ARIS.
Computational Thinking: The skills that programmers apply to analyze and solve problems are seen as an emerging trend . An example is the programming environment SCRATCH.
Learning by Doing Science with Remote Labs: A collection of accessible labs is ilab
Embodied learning:involving the body is essential for some forms of learning, how physical activities can influence cognitive processes.
Adaptive Teaching:intelligent tutoring systems – computer applications that analyse data from learning activities to provide learners with relevant content and sequence learning activities based on prior knowledge.
Analytics of Emotions: As techniques for tracking eye movements, emotions and engagement have matured over the past decade, the trend prognoses opportunities for emotionally adaptive learning environments.
Stealth Assessment: In computer games the player’s progress gradually changes the game world, setting increasingly difficult problems through unobtrusive, continuous assessment.
6 Themes of Pedagogical Innovation
Based upon a review of previous editions, the report tries to categorize pedagogical innovation into six overarching themes:
“What started as a small set of basic teaching methods (instruction, discovery, inquiry) has been extended to become a profusion of pedagogies and their interactions. So, to try to restore some order, we have examined the previous reports and identified six overarching themes: scale, connectivity, reflection, extension, embodiment, and personalisation.”
Delivering education at massive scale.
Connecting learners from different nations, cultures and perspectives.
Fostering reflection and contemplation.
Extending traditional teaching methods and settings.
Recognizing embodied learning (explore, create, craft, and construct).
Creating a personalized path through educational content.
Further Reading
Follow these links to blog posts and EdITLib resources to further explore selected trends:
Interested in the Innovating Pedagogy report? Read our review of the 2014 edition, and reflect which trends are closer to becoming common practice.
LITA listsrev has an excellent discussion on password management.
I personally am using LastPass for two years: great free option, paid one can be used on mobiles.
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From:lita-l-request@lists.ala.org [mailto:lita-l-request@lists.ala.org] On Behalf Of Michael J. Paulmeno Sent: Wednesday, January 06, 2016 1:36 PM To:lita-l@lists.ala.org Subject: RE: [lita-l] Question on password management
I second Keepass. Not only is it free, open source, and multi-OS, but it lives on your computer, not in the cloud (although the database can be put on a shared drive or in DropBox for access across devices). Personally that makes me feel much safer. There are clients available for Windows, Mac, Linux, IPhone, Android and even Blackberry.
I use lastpass as well. However, LastPass was just bought by LogMeIn, so lots of people are holding their breath hoping that things stay good. Another open source, multi-os, alternative is keepass (keepass.info)
I would like offer or recommend a password management tool to my library staff that’s reliable and easy to use. Do any of you have one you can recommend?
From:lita-l-request@lists.ala.org [mailto:lita-l-request@lists.ala.org] On Behalf Of O’English, Lorena Sent: Wednesday, January 06, 2016 12:51 PM To:lita-l@lists.ala.org Subject: RE: [lita-l] Question on password management
I really like Dashlane (dashlane.com) – it has a lot of options, including the ability to give someone else access to your passwords in certain situations (plus, they support Firefox financially via low-impact ads). I think of this sometimes when I think about what would happen if a piano fell on me tomorrow – what a mess it would be for my spouse to cope with my digital life! That said, although I use Dashlane, I still have not quite managed to get myself to use all its functionality.
—–Original Message—–
From: lita-l-request@lists.ala.org [mailto:lita-l-request@lists.ala.org] On Behalf Of Cary Gordon
Sent: Wednesday, January 06, 2016 12:37 PM
To: lita-l@lists.ala.org
Subject: Re: [lita-l] Question on password management
1Password ++
————–
—–Original Message—–
From: lita-l-request@lists.ala.org [mailto:lita-l-request@lists.ala.org] On Behalf Of COLLINS, MATTHEW
Sent: Wednesday, January 06, 2016 12:35 PM
To: lita-l@lists.ala.org
Subject: RE: [lita-l] Question on password management
I have used Roboform for at least 10 years and never had a problem. It manages passwords for logins and bookmarks on my PCs, my iPhone and iPad. It synchs online so work, home, tablet and phone all have the same info. It also stores personal info (name & multiple addresses) and confidential notes and other info.
Note: CAPS is used here and there to call attention without extra Gmail formatting, not to shout at anyone. Still…I know I look like I yell here. I have flogged myself, I will now bathe in the River Salt.
MWoT
Ok, check it out.
Plugins, macros, group/profile/source/target/timing locks, separate DBs and separate metadata for these if you like, INTERNALLY-ROTATING SUPERKEYS via REGULARKEYTRANSORMATIONS and TWO-CHANNEL AUTO-TYPE OBFUSCATION (for obfuscating your auto-typed passwords or keys, if you select Auto)….!!!…
…and well-reasoned, well-EXPLAINED approaches to certain critical areas of password management in general and to KeePass in particular.
For instance: In the FAQ, read the logic breakdown (thought-by-thought explanation) of why Keepass does NOT lock itself when a SUB-dialogue box is open in Keepass whle the user then LOCKS the workstation. =)
Why doesn’t KeePass lock when Windows locks and a KeePass sub-dialog is open?
My support of Keepass as a primary, then a close alternative, comes from four of my six years in IT being in direct computer and network security roles. Sure, not the most trench years out there, but they are all engineering and tiered-analyst roles for several major US corporations.
I’m proud of that…and in terms of relevance, I worked – and still work – with and around many engineers, analysts, and scientists (data, algorithmic). I look up to these people a great deal, and many of these coworkers come fully assembled having forgotten more than I’ll ever know and still learning faster than I could ever talk about… and even THEY use Keepass and they use it powerfully.
Detection of each site’s contact (HTTP GET, form forcus, etc) or “touch” can be different with each browser it integrates into, and that’s just for starters. One can also script up a different timing to use before the credentials are passed….;)….one can also relegate references to a central database, or one can refer only to the local system or even just a specific profile that can access said .kdbx file (KeePass database), or one can limit the data source to just one .kdbx single-instance database file, or one can use the .kdbx as a secondary for some other central repository failure, if that happens.
One can make several .kdbx files for different uses…no requirement to have just one! Each a diffferent base of unique data keys, each wtih a different direction administered on when it is referenced, how it is run, and where it lives on a system.
Aaaaaand it can integrate with other DBMs, it’s not an island!
Keepass is not the end-all be-all, but it IS FOSS (Free and Open-Source Software, great for investigating its machinery). Also it is:
– Programmable (via the Plugins model, you can write some yourself if you like!)
– Modularizable (again, via the Plugins model)
– Profile lockable, (<— really neat!)
– SMM (Secure Memory Manageable, for Windows Clipboard and the like)
– and more!
Anyway, Keepass is rad for its cost, but, like the others on this thread, I will second LastPass as well. LastPass is a an alternative to Keepass. =)
is it possible that the Iranian government realized the evolution of social media and his respective obsolescence and this is why they freed him prematurely?
Blogs were gold and bloggers were rock stars back in 2008 when I was arrested.
The hyperlink was a way to abandon centralisation – all the links, lines and hierarchies – and replace them with something more distributed, a system of nodes and networks. Since I got out of jail, though, I’ve realised how much the hyperlink has been devalued, almost made obsolete.
Nearly every social network now treats a link as just the same as it treats any other object – the same as a photo, or a piece of text. You’re encouraged to post one single hyperlink and expose it to a quasi-democratic process of liking and plussing and hearting. But links are not objects, they are relations between objects. This objectivisation has stripped hyperlinks of their immense powers.
At the same time, these social networks tend to treat native text and pictures – things that are directly posted to them – with a lot more respect. One photographer friend explained to me how the images he uploads directly to Facebook receive many more likes than when he uploads them elsewhere and shares the link on Facebook.
Some networks, like Twitter, treat hyperlinks a little better. Others are far more paranoid. Instagram – owned by Facebook – doesn’t allow its audiences to leave whatsoever. You can put up a web address alongside your photos, but it won’t go anywhere. Lots of people start their daily online routine in these cul-de-sacs of social media, and their journeys end there. Many don’t even realise they are using the internet’s infrastructure when they like an Instagram photograph or leave a comment on a friend’s Facebook video. It’s just an app.
A most brilliant paragraph by some ordinary-looking person can be left outside the stream, while the silly ramblings of a celebrity gain instant internet presence. And not only do the algorithms behind the stream equate newness and popularity with importance, they also tend to show us more of what we have already liked. These services carefully scan our behaviour and delicately tailor our news feeds with posts, pictures and videos that they think we would most likely want to see.
Today the stream is digital media’s dominant form of organising information. It’s in every social network and mobile application.
The centralisation of information also worries me because it makes it easier for things to disappear.
But the scariest outcome of the centralisation of information in the age of social networks is something else: it is making us all much less powerful in relation to governments and corporations. Surveillance is increasingly imposed on civilised lives, and it gets worse as time goes by. The only way to stay outside of this vast apparatus of surveillance might be to go into a cave and sleep, even if you can’t make it 300 years.
real-time impact on curriculum structure, instruction delivery and student learning, permitting change and improvement. It can also provide insight into important trends that affect present and future resource needs.
Big Data: Traditionally described as high-volume, high-velocity and high-variety information.
Learning or Data Analytics: The measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
Educational Data Mining: The techniques, tools and research designed for automatically extracting meaning from large repositories of data generated by or related to people’s learning activities in educational settings.
Predictive Analytics: Algorithms that help analysts predict behavior or events based on data.
Predictive Modeling: The process of creating, testing and validating a model to best predict the probability of an outcome.
Data analytics, or the measurement, collection, analysis and reporting of data, is driving decisionmaking in many institutions. However, because of the unique nature of each district’s or college’s data needs, many are building their own solutions.
For example, in 2014 the nonprofit company inBloom, Inc., backed by $100 million from the Gates Foundation and the Carnegie Foundation for the Advancement of Teaching, closed its doors amid controversy regarding its plan to store, clean and aggregate a range of student information for states and districts and then make the data available to district-approved third parties to develop tools and dashboards so the data could be used by classroom educators.22
Tips for Student Data Privacy
Know the Laws and Regulations
There are many regulations on the books intended to protect student privacy and safety: the Family Educational Rights and Privacy Act (FERPA), the Protection of Pupil Rights Amendment (PPRA), the Children’s Internet Protection Act (CIPA), the Children’s Online Privacy Protection Act (COPPA) and the Health Insurance Portability and Accountability Act (HIPAA)
— as well as state, district and community laws. Because technology changes so rapidly, it is unlikely laws and regulations will keep pace with new data protection needs. Establish a committee to ascertain your institution’s level of understanding of and compliance with these laws, along with additional safeguard measures.
Make a Checklist Your institution’s privacy policies should cover security, user safety, communications, social media, access, identification rules, and intrusion detection and prevention.
Include Experts
To nail down compliance and stave off liability issues, consider tapping those who protect privacy for a living, such as your school attorney, IT professionals and security assessment vendors. Let them review your campus or district technologies as well as devices brought to campus by students, staff and instructors. Finally, a review of your privacy and security policies, terms of use and contract language is a good idea.
Communicate, Communicate, Communicate
Students, staff, faculty and parents all need to know their rights and responsibilities regarding data privacy. Convey your technology plans, policies and requirements and then assess and re-communicate those throughout each year.
“Anything-as-a-Service” or “X-as-a-Service” solutions can help K-12 and higher education institutions cope with big data by offering storage, analytics capabilities and more. These include:
• Infrastructure-as-a-Service (IaaS): Providers offer cloud-based storage, similar to a campus storage area network (SAN)
• Platform-as-a-Service (PaaS): Opens up application platforms — as opposed to the applications themselves — so others can build their own applications
using underlying operating systems, data models and databases; pre-built application components and interfaces
• Software-as-a-Service (SaaS): The hosting of applications in the cloud
• Big-Data-as-a-Service (BDaaS): Mix all the above together, upscale the amount of data involved by an enormous amount and you’ve got BDaaS
Suggestions:
Use accurate data correctly
Define goals and develop metrics
Eliminate silos, integrate data
Remember, intelligence is the goal
Maintain a robust, supportive enterprise infrastructure.
Prioritize student privacy
Develop bullet-proof data governance guidelines
Create a culture of collaboration and sharing, not compliance.
building relationships with students so I can better connect lessons to their interests, hopes and dreams; providing them with many opportunities for organizational and cognitive choice; and creating situations where they can get positive, as well as critical, feedback in a supportive way from me, their classmates and themselves.
Response: Personalized Learning Is ‘Based On Relationships, Not Algorithms’
Too often, the notion of “personalized learning” means choice-based programmed rather than truly personalized. This comes from the tech world, where “personalization” is synonymous with user choice. It’s the idea of giving a thumbs up or a thumbs down on Pandora. It’s the idea of having adaptive programs that change based upon one’s personal preferences. It’s the Facebook algorithm that tells you what information is the most relevant to you. It’s about content delivery rather than user creation.
While tech companies promise personalization, they often promote independent, isolated learning. True personalization is interdependent rather than isolated. True personalization is based upon a horizontal relationship rather than a top-down customization. True personalization is based upon a deeply human relationship rather than a program or an algorithm or a set of scripts. True personalization is a mix between personal autonomy and group belonging. It’s a mix between what someone wants and what someone needs. It’s a chance to make, rather than simply a chance to consume.
