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://hybridpedagogy.org/our-bodies-encoded-algorithmic-test-proctoring-in-higher-education/
While in-person test proctoring has been used to combat test-based cheating, this can be difficult to translate to online courses. Ed-tech companies have sought to address this concern by offering to watch students take online tests, in real time, through their webcams.
Some of the more prominent companies offering these services include Proctorio, Respondus, ProctorU, HonorLock, Kryterion Global Testing Solutions, and Examity.
Algorithmic test proctoring’s settings have discriminatory consequences across multiple identities and serious privacy implications.
While racist technology calibrated for white skin isn’t new (everything from photography to soap dispensers do this), we see it deployed through face detection and facial recognition used by algorithmic proctoring systems.
While some test proctoring companies develop their own facial recognition software, most purchase software developed by other companies, but these technologies generally function similarly and have shown a consistent inability to identify people with darker skin or even tell the difference between Chinese people. Facial recognition literally encodes the invisibility of Black people and the racist stereotype that all Asian people look the same.
As Os Keyes has demonstrated, facial recognition has a terrible history with gender. This means that a software asking students to verify their identity is compromising for students who identify as trans, non-binary, or express their gender in ways counter to cis/heteronormativity.
These features and settings create a system of asymmetric surveillance and lack of accountability, things which have always created a risk for abuse and sexual harassment. Technologies like these have a long history of being abused, largely by heterosexual men at the expense of women’s bodies, privacy, and dignity.
Their promotional messaging functions similarly to dog whistle politics which is commonly used in anti-immigration rhetoric. It’s also not a coincidence that these technologies are being used to exclude people not wanted by an institution; biometrics and facial recognition have been connected to anti-immigration policies, supported by both Republican and Democratic administrations, going back to the 1990’s.
Borrowing from Henry A. Giroux, Kevin Seeber describes the pedagogy of punishment and some of its consequences in regards to higher education’s approach to plagiarism in his book chapter “The Failed Pedagogy of Punishment: Moving Discussions of Plagiarism beyond Detection and Discipline.”
my note: I am repeating this for years
Sean Michael Morris and Jesse Stommel’s ongoing critique of Turnitin, a plagiarism detection software, outlines exactly how this logic operates in ed-tech and higher education: 1) don’t trust students, 2) surveil them, 3) ignore the complexity of writing and citation, and 4) monetize the data.
Cheating is not a technological problem, but a social and pedagogical problem.
Our habit of believing that technology will solve pedagogical problems is endemic to narratives produced by the ed-tech community and, as Audrey Watters writes, is tied to the Silicon Valley culture that often funds it. Scholars have been dismantling the narrative of technological solutionism and neutrality for some time now. In her book “Algorithms of Oppression,” Safiya Umoja Noble demonstrates how the algorithms that are responsible for Google Search amplify and “reinforce oppressive social relationships and enact new modes of racial profiling.”
Anna Lauren Hoffmann, who coined the term “data violence” to describe the impact harmful technological systems have on people and how these systems retain the appearance of objectivity despite the disproportionate harm they inflict on marginalized communities.
This system of measuring bodies and behaviors, associating certain bodies and behaviors with desirability and others with inferiority, engages in what Lennard J. Davis calls the Eugenic Gaze.
Higher education is deeply complicit in the eugenics movement. Nazism borrowed many of its ideas about racial purity from the American school of eugenics, and universities were instrumental in supporting eugenics research by publishing copious literature on it, establishing endowed professorships, institutes, and scholarly societies that spearheaded eugenic research and propaganda.
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more on privacy in this IMS blog
https://blog.stcloudstate.edu/ims?s=privacy
Project Information Literacy, a nonprofit research institution that explores how college students find, evaluate and use information. It was commissioned by the John S. and James L. Knight Foundation and The Harvard Graduate School of Education.
focus groups and interviews with 103 undergraduates and 37 faculty members from eight U.S. colleges.
To better equip students for the modern information environment, the report recommends that faculty teach algorithm literacy in their classrooms. And given students’ reliance on learning from their peers when it comes to technology, the authors also suggest that students help co-design these learning experiences.
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more on media literacy in this IMS blog
https://blog.stcloudstate.edu/ims?s=media+literacy
more on news literacy in this IMS blog
https://blog.stcloudstate.edu/ims?s=news+literate
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.
“Facebook’s business model has evolved into social engineering via psychological warfare,” she declared. “The platform weaponizes user data to fuel algorithmic manipulation in order to maximize ad sales—not just for products, but for ideas like the disinformation that led to the conspiracy theories associated with the January 6 Capitol attack.”
“One thing is clear: Facebook and the other digital platforms that rely on an extractive business model will not change on their own,” the letter states. “Congress needs to step in.”
“The secretive collection, sale, and algorithmic manipulation of our personal data by platforms like Facebook must end,” he said. “It is a primary driver of the virality of the misinformation, hate speech, and online radicalization that people across the political spectrum are worried about.”
https://www.searchenginejournal.com/instagram-carousel-tips/429880
Studies have shown that carousels are the most engaging type of post on the platform.
One of the most effective techniques: know your audience and talk directly to them as individuals.
you’re leading with the most compelling information or image for the reader, and think about “what’s in it for them” throughout the carousel.
add a visual signal in the images, like an arrow pointing to the right in all but the final image.
You want every slide to stand on its own.
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more on instagram in this IMS blog
https://blog.stcloudstate.edu/ims?s=instagram
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
