Searching for "algorithms"

education algorithms

https://www.edsurge.com/news/2016-06-10-humanizing-education-s-algorithms

predictive algorithms to better target students’ individual learning needs.

Personalized learning is a lofty aim, however you define it. To truly meet each student where they are, we would have to know their most intimate details, or discover it through their interactions with our digital tools. We would need to track their moods and preferences, their fears and beliefs…perhaps even their memories.

There’s something unsettling about capturing users’ most intimate details. Any prediction model based off historical records risks typecasting the very people it is intended to serve. Even if models can overcome the threat of discrimination, there is still an ethical question to confront – just how much are we entitled to know about students?

We can accept that tutoring algorithms, for all their processing power, are inherently limited in what they can account for. This means steering clear of mythical representations of what such algorithms can achieve. It may even mean giving up on personalization altogether. The alternative is to pack our algorithms to suffocation at the expense of users’ privacy. This approach does not end well.

There is only one way to resolve this trade-off: loop in the educators.

Algorithms and data must exist to serve educators

 

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more on algorithms in this IMS blog
blog.stcloudstate.edu/ims?s=algor

social media algorithms

How algorithms impact our browsing behavior? browsing history?
What is the connection between social media algorithms and fake news?
Are there topic-detection algorithms as they are community-detection ones?
How can I change the content of a [Google] search return? Can I? 

Larson, S. (2016, July 8). What is an Algorithm and How Does it Affect You? The Daily Dot. Retrieved from https://www.dailydot.com/debug/what-is-an-algorithm/
Berg, P. (2016, June 30). How Do Social Media Algorithms Affect You | Forge and Smith. Retrieved September 19, 2017, from https://forgeandsmith.com/how-do-social-media-algorithms-affect-you/
Oremus, W., & Chotiner, I. (2016, January 3). Who Controls Your Facebook Feed. Slate. Retrieved from http://www.slate.com/articles/technology/cover_story/2016/01/how_facebook_s_news_feed_algorithm_works.html
Lehrman, R. A. (2013, August 11). The new age of algorithms: How it affects the way we live. Christian Science Monitor. Retrieved from https://www.csmonitor.com/USA/Society/2013/0811/The-new-age-of-algorithms-How-it-affects-the-way-we-live
Johnson, C. (2017, March 10). How algorithms affect our way of life. Desert News. Retrieved from https://www.deseretnews.com/article/865675141/How-algorithms-affect-our-way-of-life.html
Understanding algorithms and their impact on human life goes far beyond basic digital literacy, some experts said.
An example could be the recent outcry over Facebook’s news algorithm, which enhances the so-called “filter bubble”of information.
personalized search (https://en.wikipedia.org/wiki/Personalized_search)
Kounine, A. (2016, August 24). How your personal data is used in personalization and advertising. Retrieved September 19, 2017, from https://www.tastehit.com/blog/personal-data-in-personalization-and-advertising/
Hotchkiss, G. (2007, March 9). The Pros & Cons Of Personalized Search. Retrieved September 19, 2017, from http://searchengineland.com/the-pros-cons-of-personalized-search-10697
Magid, L. (2012). How (and why) To Turn Off Google’s Personalized Search Results. Forbes. Retrieved from https://www.forbes.com/sites/larrymagid/2012/01/13/how-and-why-to-turn-off-googles-personalized-search-results/#53a30be838f2
Nelson, P. (n.d.). Big Data, Personalization and the No-Search of Tomorrow. Retrieved September 19, 2017, from https://www.searchtechnologies.com/blog/big-data-search-personalization

gender

Massanari, A. (2017). #Gamergate and The Fappening: How Reddit’s algorithm, governance, and culture support toxic technocultures. New Media & Society19(3), 329-346. doi:10.1177/1461444815608807

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community detection algorithms:

Bedi, P., & Sharma, C. (2016). Community detection in social networks. Wires: Data Mining & Knowledge Discovery6(3), 115-135.

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CRUZ, J. D., BOTHOREL, C., & POULET, F. (2014). Community Detection and Visualization in Social Networks: Integrating Structural and Semantic Information. ACM Transactions On Intelligent Systems & Technology5(1), 1-26. doi:10.1145/2542182.2542193

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Bai, X., Yang, P., & Shi, X. (2017). An overlapping community detection algorithm based on density peaks. Neurocomputing2267-15. doi:10.1016/j.neucom.2016.11.019

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topic-detection algorithms:

Zeng, J., & Zhang, S. (2009). Incorporating topic transition in topic detection and tracking algorithms. Expert Systems With Applications36(1), 227-232. doi:10.1016/j.eswa.2007.09.013

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topic detection and tracking (TDT) algorithms based on topic models, such as LDA, pLSI (https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis), etc.

Zhou, E., Zhong, N., & Li, Y. (2014). Extracting news blog hot topics based on the W2T Methodology. World Wide Web17(3), 377-404. doi:10.1007/s11280-013-0207-7

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The W2T (Wisdom Web of Things) methodology considers the information organization and management from the perspective of Web services, which contributes to a deep understanding of online phenomena such as users’ behaviors and comments in e-commerce platforms and online social networks.  (https://link.springer.com/chapter/10.1007/978-3-319-44198-6_10)

ethics of algorithm

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679

journalism

Malyarov, N. (2016, October 18). Journalism in the age of algorithms, platforms and newsfeeds | News | FIPP.com. Retrieved September 19, 2017, from http://www.fipp.com/news/features/journalism-in-the-age-of-algorithms-platforms-newsfeeds

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https://blog.stcloudstate.edu/ims?s=algorithm
more on algorithms in this IMS blog

see also

the Platform Transparency and Accountability Act

Meta, TikTok and YouTube may finally have to start sharing data with researchers

A Senate hearing this week and a new law in Europe show how “transparency” advocates are winning

the Platform Transparency and Accountability Act, was introduced in December by (an ever-so-slightly) bipartisan group of senators.

“YouTube, TikTok, Telegram, and Snapchat represent some of the largest and most influential platforms in the United States, and they provide almost no functional transparency into their systems. And as a result, they avoid nearly all of the scrutiny and criticism that comes with it.”

When we do hear about what happens inside a tech company, it’s often because a Frances Haugen-type employee decides to leak it.

Cruz expressed great confusion about why he got relatively few new Twitter followers in the days before Elon Musk said he was going to buy it, but then got many more after the acquisition was announced.

The actual explanation is that Musk has lots of conservative fans, they flocked back to the platform when they heard he was buying it, and from there Twitter’s recommendation algorithms kicked into gear.

As usual, though, Europe is much further ahead of us. The Digital Services Act, which regulators reached an agreement on in April, includes provisions that would require big platforms to share data with qualified researchers. The law is expected to go into effect by next year. And so even if Congress dithers after today, transparency is coming to platforms one way or another. Here’s hoping it can begin to answer some very important questions.

new EU legislation for Google, Meta

Google, Meta, and others will have to explain their algorithms under new EU legislation

The Digital Services Act will reshape the online world

https://www.theverge.com/2022/4/23/23036976/eu-digital-services-act-finalized-algorithms-targeted-advertising

The EU has agreed on another ambitious piece of legislation to police the online world.

  • argeted advertising based on an individual’s religion, sexual orientation, or ethnicity is banned. Minors cannot be subject to targeted advertising either.
  • “Dark patterns” — confusing or deceptive user interfaces designed to steer users into making certain choices — will be prohibited. The EU says that, as a rule, canceling subscriptions should be as easy as signing up for them.
  • Large online platforms like Facebook will have to make the working of their recommender algorithms (used for sorting content on the News Feed or suggesting TV shows on Netflix) transparent to users. Users should also be offered a recommender system “not based on profiling.” In the case of Instagram, for example, this would mean a chronological feed (as it introduced recently).
  • Hosting services and online platforms will have to explain clearly why they have removed illegal content as well as give users the ability to appeal such takedowns. The DSA itself does not define what content is illegal, though, and leaves this up to individual countries.
  • The largest online platforms will have to provide key data to researchers to “provide more insight into how online risks evolve.”
  • Online marketplaces must keep basic information about traders on their platform to track down individuals selling illegal goods or services.
  • Large platforms will also have to introduce new strategies for dealing with misinformation during crises (a provision inspired by the recent invasion of Ukraine).

hese tech companies have lobbied hard to water down the requirements in the DSA, particularly those concerning targeted advertising and handing over data to outside researchers.

automated proctoring

https://www.edsurge.com/news/2021-11-19-automated-proctoring-swept-in-during-pandemic-it-s-likely-to-stick-around-despite-concerns

law student sued an automated proctoring company, students have complained about their use in student newspaper editorials and professors have compared them to Big Brother.

ProctorU, which has decided not to sell software that uses algorithms to detect cheating

recent Educause study found that 63 percent of colleges and universities in the U.S. and Canada mention the use of remote proctoring on their websites.

One reason colleges are holding onto proctoring tools, Urdan adds, is that many colleges plan to expand their online course offerings even after campus activities return to normal. And the pandemic also saw rapid growth of another tech trend: students using websites to cheat on exams.

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More on proctoring in this blog
https://blog.stcloudstate.edu/ims?s=proctoring

New Elements of Digital Transformation

The New Elements of Digital Transformation

https://sloanreview-mit-edu.cdn.ampproject.org/c/s/sloanreview.mit.edu/article/the-new-elements-of-digital-transformation/amp

2014, “The Nine Elements of Digital Transformation

It requires that companies become what we call digital masters. Digital masters cultivate two capabilities: digital capability, which enables them to use innovative technologies to improve elements of the business, and leadership capability, which enables them to envision and drive organizational change in systematic and profitable ways. Together, these two capabilities allow a company to transform digital technology into business advantage.

We found that the elements of leadership capability have endured, but new elements of digital capability have come to the fore.

While strong leadership capability is even more essential than ever, its core elements — vision, engagement, and governance — are not fundamentally changed, though they are informed by recent innovations. The elements of digital capability, on the other hand, have been more profoundly altered by the rapid technological advances of recent years.

The New Elements of Digital Capability

Experience design: Customer experience has become the ultimate battleground for many companies and brands.

Customer intelligence: Integrating customer data across silos and understanding customer behavior

Emotional engagement: Emotional connections with customers are as essential as technology in creating compelling customer experiences.

As ever, well-managed operations are essential to converting revenue into profit, but now we’re seeing a shift in the focus of digital transformation in this arena.

Core process automation: Amazon’s distribution centers deliver inventory to workers rather than sending workers to collect inventory. Rio Tinto, an Australian mining company, uses autonomous trucks, trains, and drilling machinery so that it can shift workers to less dangerous tasks, leading to higher productivity and better safety.

Connected and dynamic operations: Thanks to the growing availability of cheap sensors, cloud infrastructure, and machine learning, concepts such as Industry 4.0, digital threads, and digital twins have become a reality. Digital threads connecting machines, models, and processes provide a single source of truth to manage, optimize, and enhance processes from requirements definition through maintenance.

Data-driven decision-making: from backward-looking reports to real-time data. Now, connected devices, new machine learning algorithms, smarter experimentation, and plentiful data enable more-informed decisions.

Transforming Employee Experience

Augmentation: Warnings that robots will replace humans have given way to a more nuanced and productive discussion.
Workers in Huntington Ingalls Industries’ shipyard use augmented reality to help build giant complex vessels such as aircraft carriers and submarines. They can “see” where to route wires or pipes or what is behind a wall before they start drilling into it.

Future-readying: providing employees with the skills they need to keep up with the pace of change. In the past few years, this has given rise to new models of managing learning and development in organizations, led by a new kind of chief learning officer, whom we call the transformer CLO

Flexforcing: To respond to fast-paced digital opportunities and threats, companies also need to build agility into their talent sourcing systems. As automation and AI applications take over tasks once performed by humans, some companies are multiskilling employees to make the organization more agile.

Transforming Business Models

three elements supporting business model transformation: digital enhancements, information-based service extensions, and multisided platforms.

 

truth decay

https://www.edweek.org/policy-politics/opinion-are-you-contributing-to-truth-decay/2021/01

2011 Ted Talk by Eli Pariser called Beware of Online Filter Bubbles

 

Besides the algorithms that contribute to this truth decay, there is something equally as powerful that contributes to it as well. That other contributor is our confirmation bias.

“At its core, Media Literacy (ML) is made up of several specific competencies, such as the abilities to access, analyze, evaluate, and communicate media messages in a variety of forms. Experts and organizations typically define media literacy using this or similar collections of competencies, which in the past two decades have evolved to focus more on the active construction of media and participation in the information ecosystem.”

Huguet, A.; Kavanagh, J.; Baker, G.; Blumenthal, M. (2018). Exploring Media Literacy Education as a Tool for Mitigating Truth Decay. Rand Corporation.

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more on fake news in this IMS blog
https://blog.stcloudstate.edu/ims?s=fake+news

Data, Information, Knowledge, Wisdom

What is the difference between Data, Information, Knowledge and Wisdom?

Data: Anything represented in digital form, including non-executing knowledge stored in digital form.

Information: The momentary extraction of structure from data that modifies the perspective to the interpreter by creating new data or insight. Information only exists at the time of active data interpretation. Information creates the context that reveals discontinuities between what is known and what is new, triggering the need for learning.

Knowledge: Rules, algorithms, interpreters (such as pattern recognizers) or other mechanisms, including those that exist in the human brain (regardless of our ability to describe those mechanisms) that transform data into information. Knowledge may be changed by its interaction with information.

Wisdom: Specialized knowledge that acts to filter/active the knowledge that is best used to extract the appropriate information from data. Like, knowledge, wisdom may also be changed by the experience of its use through positive or negative reinforcement.

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more on knowledge in this IMS blog
https://blog.stcloudstate.edu/ims?s=knowledge

plagiarism detection tools

link to the “Grammarly” thread:
https://www.facebook.com/groups/onlinelearningcollective/permalink/717390118891689/

Hi all, I don’t use Grammarly, but I hear that a lot of people find it useful. I am also hearing that some instructors/universities find its use problematic. Several years ago, a student that I knew was not a good writer was accused of plagiarism by another instructor. She claimed that her nearly flawless papers were written with the help of Grammarly. I am curious to know if you encourage or prohibit Grammarly in your classes and if your department or university has a policy concerning its use.

My summation of this thread:
naturally, opinions are for and against:
pros –
it helps/forces students understand the need to proofread
partially replaces the very initial work of instructor
cons –
algorithms/technology are/is not perfect
it does not replace a living person (sic!!!)
e.g. it detect passive voice, but does not teach the replacement

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more on grammarly in this IMS blog
https://blog.stcloudstate.edu/ims?s=grammarly

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