Digital Literacy for St. Cloud State University
Facebook had evidence its algorithms were dividing people, but top executives killed or weakened proposed solutions from r/technology
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more on facebook in this IMS blog
https://blog.stcloudstate.edu/ims?s=facebook
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.
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more on algorithms in this IMS blog
blog.stcloudstate.edu/ims?s=algor
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?
gender
Massanari, A. (2017). #Gamergate and The Fappening: How Reddit’s algorithm, governance, and culture support toxic technocultures. New Media & Society, 19(3), 329-346. doi:10.1177/1461444815608807
community detection algorithms:
Bedi, P., & Sharma, C. (2016). Community detection in social networks. Wires: Data Mining & Knowledge Discovery, 6(3), 115-135.
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 & Technology, 5(1), 1-26. doi:10.1145/2542182.2542193
Bai, X., Yang, P., & Shi, X. (2017). An overlapping community detection algorithm based on density peaks. Neurocomputing, 2267-15. doi:10.1016/j.neucom.2016.11.019
topic-detection algorithms:
Zeng, J., & Zhang, S. (2009). Incorporating topic transition in topic detection and tracking algorithms. Expert Systems With Applications, 36(1), 227-232. doi:10.1016/j.eswa.2007.09.013
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 Web, 17(3), 377-404. doi:10.1007/s11280-013-0207-7
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
journalism
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https://blog.stcloudstate.edu/ims?s=algorithm
more on algorithms in this IMS blog
see also
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.
The Digital Services Act will reshape the online world
The EU has agreed on another ambitious piece of legislation to police the online world.
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.
a 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
A 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
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.
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.
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.
three elements supporting business model transformation: digital enhancements, information-based service extensions, and multisided platforms.
Our political discourse, and what we search for online, is contributing to truth decay.
It’s time that it stops. #EWOpinion https://t.co/JZue4HzhZn pic.twitter.com/GJinaCZF2g
— Education Week Teacher (@EdWeekTeacher) January 26, 2021
https://www.edweek.org/policy-politics/opinion-are-you-contributing-to-truth-decay/2021/01
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
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
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