75 Matching Annotations
  1. May 2026
    1. the unemployment rate for recent college graduates rose to 5.6%, while the underemployment rate (the share of graduates working in jobs that typically do not require a college degree) reached 42.5%, its highest level since the covid pandemic

      5.6%的毕业生失业率与42.5%的未充分就业率形成鲜明对比,后者是前者的7.5倍多。这一巨大差异表明,虽然失业率相对可控,但大量毕业生被迫从事低于其教育水平的工作,这可能对长期职业发展产生负面影响。

    2. the unemployment rate for recent college graduates rose to 5.6%, while the underemployment rate (the share of graduates working in jobs that typically do not require a college degree) reached 42.5%

      5.6%的失业率和42.5%的低就业率是衡量应届毕业生就业状况的重要指标。这一数据来自纽约联邦储备银行,具有较高的可信度。42.5%的低就业率是自疫情以来的最高水平,表明高等教育文凭的价值正在受到挑战。这些数据与AI对初级工作的影响可能相关,但文章也指出不能确定AI是唯一原因。

    1. In sub-Saharan Africa and India, we are creating AI-powered apps that support foundational literacy and numeracy programs

      这一数据点指出了AI在教育领域的具体应用区域:撒哈拉以南非洲和印度。这些地区通常面临教育资源不足的问题,AI可能有较大帮助。然而,文章没有提供这些地区的人口数量、教育水平基线数据,也没有说明预计的覆盖范围和效果评估指标。

  2. May 2025
  3. Sep 2024
    1. Die Fossilindustrie finanziert seit Jahrzehten Universitäten und fördert damit Publikationen in ihrem Interesse, z.B. zu false solutions wie #CCS. Hintergrundbericht anlässlich einer neuen Studie: https://www.theguardian.com/business/article/2024/sep/05/universities-fossil-fuel-funding-green-energy

      Studie: https://doi.org/10.1002/wcc.904

  4. Sep 2023
    1. Recent work has revealed several new and significant aspects of the dynamics of theory change. First, statistical information, information about the probabilistic contingencies between events, plays a particularly important role in theory-formation both in science and in childhood. In the last fifteen years we’ve discovered the power of early statistical learning.

      The data of the past is congruent with the current psychological trends that face the education system of today. Developmentalists have charted how children construct and revise intuitive theories. In turn, a variety of theories have developed because of the greater use of statistical information that supports probabilistic contingencies that help to better inform us of causal models and their distinctive cognitive functions. These studies investigate the physical, psychological, and social domains. In the case of intuitive psychology, or "theory of mind," developmentalism has traced a progression from an early understanding of emotion and action to an understanding of intentions and simple aspects of perception, to an understanding of knowledge vs. ignorance, and finally to a representational and then an interpretive theory of mind.

      The mechanisms by which life evolved—from chemical beginnings to cognizing human beings—are central to understanding the psychological basis of learning. We are the product of an evolutionary process and it is the mechanisms inherent in this process that offer the most probable explanations to how we think and learn.

      Bada, & Olusegun, S. (2015). Constructivism Learning Theory : A Paradigm for Teaching and Learning.

  5. Aug 2022
  6. May 2022
    1. I like how Dr. Pacheco-Vega outlines some of his research process here.

      Sharing it on Twitter is great, and so is storing a copy on his website. I do worry that it looks like the tweets are embedded via a simple URL method and not done individually, which means that if Twitter goes down or disappears, so does all of his work. Better would be to do a full blockquote embed method, so that if Twitter disappears he's got the text at least. Images would also need to be saved separately.

  7. Apr 2022
    1. A New York Times article uses the same temperature dataset you have been using to investigate the distribution of temperatures and temperature variability over time. Read through the article, paying close attention to the descriptions of the temperature distributions.

      Unfortunately, like most NYT content, this article is behind a paywall. I'm partly reading this as I plan to develop a set of open education resources myself and the problem of how to manage dead/unavailable links looks like a key stumbling block.

  8. Feb 2022
  9. Jan 2022
    1. Ryan Imgrund. (2022, January 2). If schools are not a source of transmission for COVID-19, why were school board per capita rates of infection 1.77x HIGHER than their surrounding community? SOURCE: Ministry of Education data; Compiled on December 17th, 2021; Calculations are mine. Https://t.co/94trbDvw2C [Tweet]. @imgrund. https://twitter.com/imgrund/status/1477683538971529217

  10. Dec 2021
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  14. May 2021
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  17. Nov 2020
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  22. May 2020
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  24. Jul 2019
  25. Aug 2018
  26. Nov 2017
    1. An institution has implemented a learning management system (LMS). The LMS contains a learning object repository (LOR) that in some aspects is populated by all users across the world  who use the same LMS.  Each user is able to align his/her learning objects to the academic standards appropriate to that jurisdiction. Using CASE 1.0, the LMS is able to present the same learning objects to users in other jurisdictions while displaying the academic standards alignment for the other jurisdictions (associations).

      Sounds like part of the problem Vitrine technologie-éducation has been tackling with Ceres, a Learning Object Repository with a Semantic core.

  27. Sep 2016
    1. Research: Student data are used to conduct empirical studies designed primarily to advance knowledge in the field, though with the potential to influence institutional practices and interventions. Application: Student data are used to inform changes in institutional practices, programs, or policies, in order to improve student learning and support. Representation: Student data are used to report on the educational experiences and achievements of students to internal and external audiences, in ways that are more extensive and nuanced than the traditional transcript.

      Ha! The Chronicle’s summary framed these categories somewhat differently. Interesting. To me, the “application” part is really about student retention. But maybe that’s a bit of a cynical reading, based on an over-emphasis in the Learning Analytics sphere towards teleological, linear, and insular models of learning. Then, the “representation” part sounds closer to UDL than to learner-driven microcredentials. Both approaches are really interesting and chances are that the report brings them together. Finally, the Chronicle made it sound as though the research implied here were less directed. The mention that it has “the potential to influence institutional practices and interventions” may be strategic, as applied research meant to influence “decision-makers” is more likely to sway them than the type of exploratory research we so badly need.

    1. the use of data in scholarly research about student learning; the use of data in systems like the admissions process or predictive-analytics programs that colleges use to spot students who should be referred to an academic counselor; and the ways colleges should treat nontraditional transcript data, alternative credentials, and other forms of documentation about students’ activities, such as badges, that recognize them for nonacademic skills.

      Useful breakdown. Research, predictive models, and recognition are quite distinct from one another and the approaches to data that they imply are quite different. In a way, the “personalized learning” model at the core of the second topic is close to the Big Data attitude (collect all the things and sense will come through eventually) with corresponding ethical problems. Through projects vary greatly, research has a much more solid base in both ethics and epistemology than the kind of Big Data approach used by technocentric outlets. The part about recognition, though, opens the most interesting door. Microcredentials and badges are a part of a broader picture. The data shared in those cases need not be so comprehensive and learners have a lot of agency in the matter. In fact, when then-Ashoka Charles Tsai interviewed Mozilla executive director Mark Surman about badges, the message was quite clear: badges are a way to rethink education as a learner-driven “create your own path” adventure. The contrast between the three models reveals a lot. From the abstract world of research, to the top-down models of Minority Report-style predictive educating, all the way to a form of heutagogy. Lots to chew on.

  28. Jul 2016
  29. Mar 2016
  30. Dec 2015
    1. Among the most useful summaries I have found for Linked Data, generally, and in relationship to libraries, specifically. After first reading it, got to hear of the acronym LODLAM: “Linked Open Data for Libraries, Archives, and Museums”. Been finding uses for this tag, in no small part because it gets people to think about the connections between diverse knowledge-focused institutions, places where knowledge is constructed. Somewhat surprised academia, universities, colleges, institutes, or educational organisations like schools aren’t explicitly tied to those others. In fact, it’s quite remarkable that education tends to drive much development in #OpenData, as opposed to municipal or federal governments, for instance. But it’s still very interesting to think about Libraries and Museums as moving from a focus on (a Web of) documents to a focus on (a Web of) data.

  31. Nov 2015
  32. Aug 2015