A Vision and Roadmap for Education Statistics

Curated on Posted on by Stefaan Verhulst

Report by he National Academies of Sciences, Engineering, and Medicine: “The education landscape in the United States has been changing rapidly in recent decades: student populations have become more diverse; there has been an explosion of data sources; there is an intensified focus on diversity, equity, inclusion, and accessibility; educators and policy makers at all levels want more and better data for evidence-based decision making; and the role of technology in education has increased dramatically. With awareness of this changed landscape the Institute of Education Sciences at the U.S. Department of Education asked the National Academies of Sciences, Engineering, and Medicine to provide a vision for the National Center for Education Statistics (NCES)—the nation’s premier statistical agency for collecting, analyzing, and disseminating statistics at all levels of education.

A Vision and Roadmap for Education Statistics (2022) reviews developments in using alternative data sources, considers recent trends and future priorities, and suggests changes to NCES’s programs and operations, with a focus on NCES’s statistical programs. The report reimagines NCES as a leader in the 21st century education data ecosystem, where it can meet the growing demands for policy-relevant statistical analyses and data to more effectively and efficiently achieve its mission, especially in light of the Foundations for Evidence-Based Policymaking Act of 2018 and the 2021 Presidential Executive Order on advancing racial equity. The report provides strategic advice for NCES in all aspects of the agency’s work including modernization, stakeholder engagement, and the resources necessary to complete its mission and meet the current and future challenges in education…(More)”.

An intro to AI, made for students

Curated on Posted on by Stefaan Verhulst

Reena Jana at Google: “Adorable, operatic blobs. A global, online guessing game. Scribbles that transform into works of art. These may not sound like they’re part of a curriculum, but learning the basics of how artificial intelligence (AI) works doesn’t have to be complicated, super-technical or boring.

To celebrate Digital Learning Day, we’re releasing a new lesson from Applied Digital Skills, Google’s free, online, video-based curriculum (and part of the larger Grow with Google initiative). “Discover AI in Daily Life” was designed with middle and high school students in mind, and dives into how AI is built, and how it helps people every day.

AI for anyone — and everyone

“Twenty or 30 years ago, students might have learned basic typing skills in school,” says Dr. Patrick Gage Kelley, a Google Trust and Safety user experience researcher who co-created (and narrates) the “Discover AI in Daily Life” lesson. “Today, ‘AI literacy’ is a key skill. It’s important that students everywhere, from all backgrounds, are given the opportunity to learn about AI.”

“Discover AI in Daily Life” begins with the basics. You’ll find simple, non-technical explanations of how a machine can “learn” from patterns in data, and why it’s important to train AI responsibly and avoid unfair bias.

First-hand experiences with AI

“By encouraging students to engage directly with everyday tools and experiment with them, they get a first-hand experience of the potential uses and limitations of AI,” says Dr. Annica Voneche, the lesson’s learning designer. “Those experiences can then be tied to a more theoretical explanation of the technology behind it, in a way that makes the often abstract concepts behind AI tangible.”…(More)”.

Turning the Principle of Participation into Practice: Empowering Parents to Engage on Data and Tech

Curated on Posted on by Stefaan Verhulst

Guest Blog by Elizabeth Laird at Responsible Data for Children: “Two years into the pandemic, questions about parental rights in school have taken center stage in public debates, particularly in school board meetings and state houses across the United States. Not surprisingly, this extends to the use of data and technology in schools.

CDT recently released research that found that parental concerns around student privacy and security protection have risen since the spring, growing from 60% in February 2021 to 69% in July 2021. Far from being ambivalent, we also found that parents and students expressed eagerness to play a role in decisions about technology and data but indicate these desires are going unmet. Most parents and students want to be consulted but few have been asked for input: 93% of surveyed parents feel that schools should engage them regarding how student data is collected and used, but only 44% say their school has asked for their input on these issues.

While much of this debate has focused on the United States and similar countries, these issues have global resonance as all families have a stake in how their children are educated. Engaging students and families has always been an important component of primary and secondary education, from involving parents in their children’s individual experiences to systemic decision-making; however, there is significant room for improvement, especially as it relates to the use of education data and technology. Done well, community engagement (aligned with the Participatory principle in the Responsible Data for Children (RD4C) initiative) is a two-way, mutually beneficial partnership between public agencies and community members in which questions and concerns are identified, discussed, and decided jointly. It benefits public agencies by building trust, helping them achieve their mission, and minimizing risks, including community pushback. It helps communities by assisting agencies to better meet community needs and increasing transparency and accountability.

To assist education practitioners in improving their community engagement efforts, CDT recently released guidance that focuses on four important steps…(More)”.

When Do Informational Interventions Work? Experimental Evidence from New York City High School Choice

Curated on Posted on by Stefaan Verhulst

Paper by Sarah Cohodes, Sean Corcoran, Jennifer Jennings & Carolyn Sattin-Bajaj: “This paper reports the results of a large, school-level randomized controlled trial evaluating a set of three informational interventions for young people choosing high schools in 473 middle schools, serving over 115,000 8th graders. The interventions differed in their level of customization to the student and their mode of delivery (paper or online); all treated schools received identical materials to scaffold the decision-making process. Every intervention reduced likelihood of application to and enrollment in schools with graduation rates below the city median (75 percent). An important channel is their effect on reducing nonoptimal first choice application strategies. Providing a simplified, middle-school specific list of relatively high graduation rate schools had the largest impacts, causing students to enroll in high schools with 1.5-percentage point higher graduation rates. Providing the same information online, however, did not alter students’ choices or enrollment. This appears to be due to low utilization. Online interventions with individual customization, including a recommendation tool and search engine, induced students to enroll in high schools with 1-percentage point higher graduation rates, but with more variance in impact. Together, these results show that successful informational interventions must generate engagement with the material, and this is possible through multiple channels…(More)”.

Sharing Student Data Across Public Sectors: Importance of Community Engagement to Support Responsible and Equitable Use

Curated on Posted on by Stefaan Verhulst

Report by CDT: “Data and technology play a critical role in today’s education institutions, with 85 percent of K-12 teachers anticipating that online learning and use of education technology at their school will play a larger role in the future than it did before the pandemic.  The growth in data-driven decision-making has helped fuel the increasing prevalence of data sharing practices between K-12 education agencies and adjacent public sectors like social services. Yet the sharing of personal data can pose risks as well as benefits, and many communities have historically experienced harm as a result of irresponsible data sharing practices. For example, if the underlying data itself is biased, sharing that information exacerbates those inequities and increases the likelihood that potential harms fall disproportionately on certain communities. As a result, it is critical that agencies participating in data sharing initiatives take steps to ensure the benefits are available to all and no groups of students experience disproportionate harm.

A core component of sharing data responsibly is proactive, robust community engagement with the group of people whose data is being shared, as well as their surrounding community. This population has the greatest stake in the success or failure of a given data sharing initiative; as such, public agencies have a practical incentive, and a moral obligation, to engage them regarding decisions being made about their data…

This paper presents guidance on how practitioners can conduct effective community engagement around the sharing of student data between K-12 education agencies and adjacent public sectors. We explore the importance of community engagement around data sharing initiatives, and highlight four dimensions of effective community engagement:

  • Plan: Establish Goals, Processes, and Roles
  • Enable: Build Collective Capacity
  • Resource: Dedicate Appropriate People, Time, and Money
  • Implement: Carry Out Vision Effectively and Monitor Implementation…(More)”.

Conceptualizing AI literacy: An exploratory review

Curated on Posted on by Stefaan Verhulst

Paper by Davy Tsz KitNg, Jac Ka LokLeung, Samuel K.W.Chu, and Maggie QiaoShen: “Artificial Intelligence (AI) has spread across industries (e.g., business, science, art, education) to enhance user experience, improve work efficiency, and create many future job opportunities. However, public understanding of AI technologies and how to define AI literacy is under-explored. This vision poses upcoming challenges for our next generation to learn about AI. On this note, an exploratory review was conducted to conceptualize the newly emerging concept “AI literacy”, in search for a sound theoretical foundation to define, teach and evaluate AI literacy. Grounded in literature on 30 existing peer-reviewed articles, this review proposed four aspects (i.e., know and understand, use, evaluate, and ethical issues) for fostering AI literacy based on the adaptation of classic literacies. This study sheds light on the consolidated definition, teaching, and ethical concerns on AI literacy, establishing the groundwork for future research such as competency development and assessment criteria on AI literacy….(More)”.

How behavioral science could get people back into public libraries

Curated on Posted on by Stefaan Verhulst

Article by Talib Visram: “In October, New York City’s three public library systems announced they would permanently drop fines on late book returns. Comprised of Brooklyn, Queens, and New York public libraries, the City’s system is the largest in the country to remove fines. It’s a reversal of a long-held policy intended to ensure shelves stayed stacked, but an outdated one that many major cities, including Chicago, San Francisco, and Dallas, had already scrapped without any discernible downsides. Though a source of revenue—in 2013, for instance, Brooklyn Public Library (BPL) racked up $1.9 million in late fees—the fee system also created a barrier to library access that disproportionately touched the low-income communities that most need the resources.

That’s just one thing Brooklyn’s library system has done to try to make its services more equitable. In 2017, well before the move to eliminate fines, BPL on its own embarked on a partnership with Nudge, a behavioral science lab at the University of West Virginia, to find ways to reduce barriers to access and increase engagement with the book collections. In the first-of-its-kind collaboration, the two tested behavioral science interventions via three separate pilots, all of which led to the library’s long-term implementation of successful techniques. Those involved in the project say the steps can be translated to other library systems, though it takes serious investment of time and resources….(More)”.

Developing indicators to support the implementation of education policies

Curated on Posted on by Stefaan Verhulst

OECD Report: “Across OECD countries, the increasing demand for evidence-based policy making has further led governments to design policies jointly with clear measurable objectives, and to define relevant indicators to monitor their achievement. This paper discusses the importance of such indicators in supporting the implementation of education policies.

Building on the OECD education policy implementation framework, the paper reviews the role of indicators along each of the dimensions of the framework, namely smart policy design, inclusive stakeholder engagement, and conducive environment. It draws some lessons to improve the contribution of indicators to the implementation of education policies, while taking into account some of their perennial challenges pertaining to the unintended effects of accountability. This paper aims to provide insights to policy makers and various education stakeholders, to initiate a discussion on the use and misuse of indicators in education, and to guide future actions towards a better contribution of indicators to education policy implementation…..(More)”.

What Do Teachers Know About Student Privacy? Not Enough, Researchers Say

Curated on Posted on by Stefaan Verhulst

Nadia Tamez-Robledo at EdTech: “What should teachers be expected to know about student data privacy and ethics?

Considering so much of their jobs now revolve around student data, it’s a simple enough question—and one that researcher Ellen B. Mandinach and a colleague were tasked with answering. More specifically, they wanted to know what state guidelines had to say on the matter. Was that information included in codes of education ethics? Or perhaps in curriculum requirements for teacher training programs?

“The answer is, ‘Not really,’” says Mandinach, a senior research scientist at the nonprofit WestEd. “Very few state standards have anything about protecting privacy, or even much about data,” she says, aside from policies touching on FERPA or disposing of data properly.

While it seems to Mandinach that institutions have historically played hot potato over who is responsible for teaching educators about data privacy, the pandemic and its supercharged push to digital learning have brought new awareness to the issue.

The application of data ethics has real consequences for students, says Mandinach, like an Atlanta sixth grader who was accused of “Zoombombing” based on his computer’s IP address or the Dartmouth students who were exonerated from cheating accusations.

“There are many examples coming up as we’re in this uncharted territory, particularly as we’re virtual,” Mandinach says. “Our goal is to provide resources and awareness building to the education community and professional organization…so [these tools] can be broadly used to help better prepare educators, both current and future.”

This week, Mandinach and her partners at the Future of Privacy Forum released two training resources for K-12 teachers: the Student Privacy Primer and a guide to working through data ethics scenarios. The curriculum is based on their report examining how much data privacy and ethics preparation teachers receive while in college….(More)”.

Enrollment algorithms are contributing to the crises of higher education

Curated on Posted on by Stefaan Verhulst

Paper by Alex Engler: “Hundreds of higher education institutions are procuring algorithms that strategically allocate scholarships to convince more students to enroll. In doing so, these enrollment management algorithms help colleges vary the cost of attendance to students’ willingness to pay, a crucial aspect of competition in the higher education market. This paper elaborates on the specific two-stage process by which these algorithms first predict how likely prospective students are to enroll, and second help decide how to disburse scholarships to convince more of those prospective students to attend the college. These algorithms are valuable to colleges for institutional planning and financial stability, as well as to help reach their preferred financial, demographic, and scholastic outcomes for the incoming student body.

Unfortunately, the widespread use of enrollment management algorithms may also be hurting students, especially due to their narrow focus on enrollment. The prevailing evidence suggests that these algorithms generally reduce the amount of scholarship funding offered to students. Further, algorithms excel at identifying a student’s exact willingness to pay, meaning they may drive enrollment while also reducing students’ chances to persist and graduate. The use of this two-step process also opens many subtle channels for algorithmic discrimination to perpetuate unfair financial aid practices. Higher education is already suffering from low graduation rates, high student debt, and stagnant inequality for racial minorities—crises that enrollment algorithms may be making worse.

This paper offers a range of recommendations to ameliorate the risks of enrollment management algorithms in higher education. Categorically, colleges should not use predicted likelihood to enroll in either the admissions process or in awarding need-based aid—these determinations should only be made based on the applicant’s merit and financial circumstances, respectively. When colleges do use algorithms to distribute scholarships, they should proceed cautiously and document their data, processes, and goals. Colleges should also examine how scholarship changes affect students’ likelihood to graduate, or whether they may deepen inequities between student populations. Colleges should also ensure an active role for humans in these processes, such as exclusively using people to evaluate application quality and hiring internal data scientists who can challenge algorithmic specifications. State policymakers should consider the expanding role of these algorithms too, and should try to create more transparency about their use in public institutions. More broadly, policymakers should consider enrollment management algorithms as a concerning symptom of pre-existing trends towards higher tuition, more debt, and reduced accessibility in higher education….(More)”.