Posts Tagged ‘big data in education’

personalized learning and achievement gap

https://www.edsurge.com/news/2022-03-28-can-personalized-learning-be-scaled-to-ease-teacher-burdens-and-close-achievement-gaps

McGraw Hill Plus, a new tool, Focusing first on math and then expanding to ELA and science, its objective is to make personalized learning scalable.

Smith: The modern classroom sits at the intersection of blended learning, competency-based learning and personalized learning.

reimagine instructional time and use technology to scale personalized learning.

First, pulling data into one place is the key fundamental driver that will change the teacher workflow. Second, we need to manipulate that data into some advanced data visualization tools, so it’s easy for teachers to understand and use. Third, we need to be able to visualize student performance and take action on it.
Using these data analytics, we can drive personalized learning based on student performance. And the last thing is the automation of teacher workflow.

eachers get data visualization from different sources, such as an adaptive software solution like our ALEKS program, our Redbird Mathematics, or our recently acquired Achieve3000 Literacy.

The dark side of education research

The dark side of education research: widespread bias

Johns Hopkins study finds that insider research shows 70 percent more benefits to students than independent research

https://hechingerreport.org/the-dark-side-of-education-research-widespread-bias/

The study, “Do Developer-Commissioned Evaluations Inflate Effect Sizes?

There are a number of reasons for why developer studies tend to show stronger results, according to Wolf, whose full time work is to evaluate educational programs. The first is that a company is unlikely to publish unfavorable results. Wolf speculates that developers are more likely to “brand a failed trial a ‘pilot’ and file it away.”

This isn’t the first study to detect bias in education research. The problem of hiding unfavorable results from publication was documented as far back as 1995. In 2016, one of Wolf’s co-authors, Robert Slavin, wrote about the positive results that researchers get when they devise their own measures to prove that their inventions work.

AI and ed research

https://www.scienceopen.com/document/read?vid=992eaf61-35dd-454e-aa17-f9f8216b381b

This article presents an examination of how education research is being remade as an experimental data-intensive science. AI is combining with learning science in new ‘digital laboratories’ where ownership over data, and power and authority over educational knowledge production, are being redistributed to research assemblages of computational machines and scientific expertise.

Research across the sciences, humanities and social sciences is increasingly conducted through digital knowledge machines that are reconfiguring the ways knowledge is generated, circulated and used (Meyer and Schroeder, 2015).

Knowledge infrastructures, such as those of statistical institutes or research-intensive universities, have undergone significant digital transformation with the arrival of data-intensive technologies, with knowledge production now enacted in myriad settings, from academic laboratories and research institutes to commercial research and development studios, think tanks and consultancies. Datafied knowledge infrastructures have become hubs of command and control over the creation, analysis and exchange of data (Bigo et al., 2019).

The combination of AI and learning science into an AILSci research assemblage consists of particular forms of scientific expertise embodied by knowledge actors – individuals and organizations – identified by categories including science of learning, AIED, precision education and learning engineering.

Precision education overtly uses psychological, neurological and genomic data to tailor or personalize learning around the unique needs of the individual (Williamson, 2019). Precision education approaches include cognitive tracking, behavioural monitoring, brain imaging and DNA analysis.

Expert power is therefore claimed by those who can perform big data analyses, especially those able to translate and narrate the data for various audiences. Likewise, expert power in education is now claimed by those who can enact data-intensive science of learning, precision education and learning engineering research and development, and translate AILSci findings into knowledge for application in policy and practitioner settings.

the thinking of a thinking infrastructure is not merely a conscious human cognitive process, but relationally performed across humans and socio-material strata, wherein interconnected technical devices and other forms ‘organize thinking and thought and direct action’.
As an infrastructure for AILSci analyses, these technologies at least partly structure how experts think: they generate new understandings and knowledge about processes of education and learning that are only thinkable and knowable due to the computational machinery of the research enterprise.

Big data-based molecular genetics studies are part of a bioinformatics-led transformation of biomedical sciences based on analysing exceptional volumes of data (Parry and Greenhough, 2018), which has transformed the biological sciences to focus on structured and computable data rather than embodied evidence itself.

Isin and Ruppert (2019) have recently conceptualized an emergent form of power that they characterize as sensory power. Building on Foucault, they note how sovereign power gradually metamorphosed into disciplinary power and biopolitical forms of statistical regulation over bodies and populations.
Sensory power marks a shift to practices of data-intensive sensing, and to the quantified tracking, recording and representing of living pulses, movements and sentiments through devices such as wearable fitness monitors, online natural-language processing and behaviour-tracking apps. Davies (2019: 515–20) designates these as ‘techno-somatic real-time sensing’ technologies that capture the ‘rhythms’ and ‘metronomic vitality’ of human bodies, and bring about ‘new cyborg-type assemblages of bodies, codes, screens and machines’ in a ‘constant cybernetic loop of action, feedback and adaptation’.

Techno-somatic modes of neural sensing, using neurotechnologies for brain imaging and neural analysis, are the next frontier in AILSci. Real-time brainwave sensing is being developed and trialled in multiple expert settings.

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more on AI in this IMS blog
https://blog.stcloudstate.edu/ims?s=artificial+intelligence

NVivo workshop

Intro to NVivo – January 31
10:00 a.m. – 12:30 p.m.
440 Blegen Hall

NVivo is a qualitative data management, coding and markup tool, that facilitates powerful querying and exploration of source materials for both mixed methods and qualitative analysis. It integrates well with tools that assist in data collection and can handle a wide variety of source materials. This workshop introduces the basic functions of NVivo, with no prior experience necessary. The session is held in a computer lab with the software already installed. Register.

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

data driven education

https://www.kqed.org/mindshift/45396/whats-at-risk-when-schools-focus-too-much-on-student-data

The U.S. Department of Education emphasizes “ensuring the use of multiple measures of school success based on academic outcomes, student progress, and school quality.”

starting to hear more about what might be lost when schools focus too much on data. Here are five arguments against the excesses of data-driven instruction.

1) Motivation (decrease)

as stereotype threat. threatening students’ sense of belonging, which is key to academic motivation.

2) Helicoptering

A style of overly involved “intrusive parenting” has been associated in studies with increased levels of anxiety and depression when students reach college.

3) Commercial Monitoring and Marketing

The National Education Policy Center releases annual reports on commercialization and marketing in public schools. In its most recent report in May, researchers there raised concerns about targeted marketing to students using computers for schoolwork and homework.

Companies like Google pledge not to track the content of schoolwork for the purposes of advertising. But in reality these boundaries can be a lot more porous.

4) Missing What Data Can’t Capture

5) Exposing Students’ “Permanent Records”

In the past few years several states have passed laws banning employers from looking at the credit reports of job applicants.
Similarly, for young people who get in trouble with the law, there is a procedure for sealing juvenile records
Educational transcripts, unlike credit reports or juvenile court records, are currently considered fair game for gatekeepers like colleges and employers. These records, though, are getting much more detailed.

Facial Recognition issues

Chinese Facial Recognition Will Take over the World in 2019

Michael K. Spencer Jan 14, 2018
https://medium.com/futuresin/chinese-facial-recognition-will-take-over-the-world-in-2019-520754a7f966
The best facial recognition startups are in China, by a long-shot. As their software is less biased, global adoption is occurring via their software. This is evidenced in 2019 by the New York Police department in NYC for example, according to the South China Morning Post.
The mass surveillance state of data harvesting in real-time is coming. Facebook already rates and profiles us.

The Tech Wars come down to an AI-War

Whether the NYC police angle is true or not (it’s being hotly disputed), Facebook and Google are thinking along lines that follow the whims of the Chinese Government.

SenseTime and Megvii won’t just be worth $5 Billion, they will be worth many times that in the future. This is because a facial recognition data-harvesting of everything is the future of consumerism and capitalism, and in some places, the central tenet of social order (think Asia).

China has already ‘won’ the trade-war, because its winning the race to innovation. America doesn’t regulate Amazon, Microsoft, Google or Facebook properly, that stunts innovation and ethics in technology where the West is now forced to copy China just to keep up.

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more about facial recognition in schools
https://blog.stcloudstate.edu/ims/2019/02/02/facial-recognition-technology-in-schools/

Data Use and School Leaders

Five Questions About Data Use for School Leaders

https://blogs.edweek.org/edweek/rick_hess_straight_up/2018/08/five_questions_about_data_use_for_school_leaders.html

Anna Egalite, assistant professor of leadership and policy at NC State. Previously, Anna taught elementary school and did a postdoc at Harvard. She’ll be writing about education-leadership research—what we know, where we have good intuitions, and where we’re still very much in the dark. 

It’s back-to-school time and education reporters are highlighting stories about how school leaders are “leaning on data” to promote student learning, making administrative decisions that are “supported by a data-driven process,” and drawing on their experience in “data-driven instruction.”

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more on data use and ed leaders in this IMS blog
https://blog.stcloudstate.edu/ims?s=data+ed+leaders

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