74 Matching Annotations
  1. Mar 2019
    1. Adult Learning Theory

      This article by the University of Utah discusses Lindeman's and Knowles's theories on adult learning. Andragogy uses the teacher differently from pedagogy: the teacher in an adult learning environment becomes a facilitator instead of the knower. I think this is an important distinction to make for people who go from teaching children to teaching adults. There are two of these people on my team at work. One taught third grade and one taught sixth grade, and both of them tend to try to put the instructor in the knower's position instead of the facilitator's position. They have to catch themselves often and rework some instruction to be more student-focused instead of content-focused. 8/10

    1. when we put our tools down and stand back from the furnace, the letter press, or the paper mill, what will we turn to build instead?

      A retired scientific glassblower, after 40 years "in front of the furnace" my job disappeared. Running away to join the circus and spending my golden years making glass unicorns "on the Midway" was one option, Returning to college to "update my skills" was the worst decision of my life. Barely surviving "higher education" as an adult student, I now have two associate's degrees and am the first person in Metropolitan State University's College of Individualized Studies authorized to complete my bachelor's degree by documenting my prior learning on my own domain. I'm not convinced the institution is capable of assessing digital objects for credit, but there will always be a market for glass unicorns.

    2. “Bartleby, the Scrivener,” Herman Melville

      Ms. Massicot assigned this novella in her English class my junior year in high school. Fifty years later, Bartleby still informs my decision making everyday. Especially when responding to academics and educational institutions.

    1. stages of personalized learning: infographic This is here because it shows the progression of personalized learning from teacher centered to learner center to learner driven. It has other links to learn more about personalized learning. Usability for the article is adequate but less than ideal for the infographic (which nonetheless has useful information).

  2. Nov 2018
    1. 1Engaging Adults Learners with Technology

      The pdf provides information from The Twin Cities Campus Library with instruction incorporating technology into teaching adult students.

      It includes a review of instructional technology, assessment for learning, framework for teaching adult learners and a workshop. This 14 page pdf provides the essentials necessary in understanding basic learning needs of adult learners.

      RATING: 3/5 (rating based upon a score system 1 to 5, 1= lowest 5=highest in terms of content, veracity, easiness of use etc.)

    1. This article focuses on creating online tutorials for adult learners. Andragogy theory is used to build online programs for adult learners that are learner centered and engaging.

      8/10

  3. Nov 2017
    1. Although we're currently nowhere near this idea, how can businesses, educational institutions, and governments alike not consider the importance of giving individuals control over their digital archives? Or their learning analytics data?17
    2. mandate the use of "learning management systems."

      Therein lies the rub. Mandated systems are a radically different thing from “systems which are available for use”. This quote from the aforelinked IHE piece is quite telling:

      “I want somebody to fight!” Crouch said. “These things are not cheap -- 300 grand or something like that? ... I want people to want it! When you’re trying to buy something, you want them to work at it!”

      In the end, it’s about “procurement”, which is quite different from “adoption” which is itself quite different from “appropriation”.

    1. On this model, students are responsible for their own education, often forming communities or societies to collaborate. Professors typically worked one-on-one with students, but from time to time would be enlisted to offer a series - or 'course' - of lectures on a given topic. The lectures could be (and often were) public, and were frequently attended by other professors in the same field.

      Reminds me of @KevinCarey1 describe the original university of Bologna, in his End of College. Don’t have the quote handy (one of many cases where #OpenAccess would allow for more thoughtful discussion), but the gist of that paragraph sounds similar to what @Downes is describing here

    1. If we want Domain of One’s Own to flourish as a space for student agency than we need to balance structured guidance with playfulness and empowerment.
    2. how to balance supporting a system as complex as Domain of One’s Own without dictating how people use it
    3. realize that the web was not something that happened to them but they were happening to
    4. An overarching value we try to embrace when we talk about the domain choice is one of agency: participants should be able determine for themselves how they wish to be known and found on the Web.
    5. give a student an individually-controlled space for reflection and growth
    6. Oh, and some people also thought that perhaps it would benefit a student to have an online presence that they could create, develop, and take with them when they graduated from UMW.
    7. The other reason I worry about our dependence on WordPress is that we run the risk of recreating the very dynamic that Domain of One’s Own seeks to challenge
    1. Mount St. Mary’s use of predictive analytics to encourage at-risk students to drop out to elevate the retention rate reveals how analytics can be abused without student knowledge and consent

      Wow. Not that we need such an extreme case to shed light on the perverse incentives at stake in Learning Analytics, but this surely made readers react. On the other hand, there’s a lot more to be said about retention policies. People often act as though they were essential to learning. Retention is important to the institution but are we treating drop-outs as escapees? One learner in my class (whose major is criminology) was describing the similarities between schools and prisons. It can be hard to dissipate this notion when leaving an institution is perceived as a big failure of that institution. (Plus, Learning Analytics can really feel like the Panopticon.) Some comments about drop-outs make it sound like they got no learning done. Meanwhile, some entrepreneurs are encouraging students to leave institutions or to not enroll in the first place. Going back to that important question by @sarahfr: why do people go to university?

    1. At the very least, the tool should allow for robust formative assessment, and should be capable of giving timely, helpful feedback to learners.

      The “at the very least” part makes it sound as though this were the easy part.

    1. created by people working together on their own.
    2. And I see no good reason why we should require the production of educators and students to be fair game for resellers who want to pluck it for free out of the commons and charge money for it to those not lucky enough to be a part of our community.

      To many a student, the notion that somebody else could profit from their “free labour” is particularly offputting. Including (or especially) those who prepare to become the heads of commercial entities.

  4. Oct 2017
    1. The learning analytics and education data mining discussed in this handbook hold great promise. At the same time, they raise important concerns about security, privacy, and the broader consequences of big data-driven education. This chapter describes the regulatory framework governing student data, its neglect of learning analytics and educational data mining, and proactive approaches to privacy. It is less about conveying specific rules and more about relevant concerns and solutions. Traditional student privacy law focuses on ensuring that parents or schools approve disclosure of student information. They are designed, however, to apply to paper “education records,” not “student data.” As a result, they no longer provide meaningful oversight. The primary federal student privacy statute does not even impose direct consequences for noncompliance or cover “learner” data collected directly from students. Newer privacy protections are uncoordinated, often prohibiting specific practices to disastrous effect or trying to limit “commercial” use. These also neglect the nuanced ethical issues that exist even when big data serves educational purposes. I propose a proactive approach that goes beyond mere compliance and includes explicitly considering broader consequences and ethics, putting explicit review protocols in place, providing meaningful transparency, and ensuring algorithmic accountability. Export Citation: Plain Text (APA
  5. Sep 2017
    1. Over the course of many years, every school has refined and perfected the connections LMSs have into a wide variety of other campus systems including authentication systems, identity management systems, student information systems, assessment-related learning tools, library systems, digital textbook systems, and other content repositories. APIs and standards have decreased the complexity of supporting these connections, and over time it has become easier and more common to connect LMSs to – in some cases – several dozen or more other systems. This level of integration gives LMSs much more utility than they have out of the box – and also more “stickiness” that causes them to become harder to move away from. For LMS alternatives, achieving this same level of connectedness, particularly considering how brittle these connections can sometimes become over time, is a very difficult thing to achieve.
  6. Aug 2017
    1. This has much in common with a customer relationship management system and facilitates the workflow around interventions as well as various visualisations.  It’s unclear how the at risk metric is calculated but a more sophisticated predictive analytics engine might help in this regard.

      Have yet to notice much discussion of the relationships between SIS (Student Information Systems), CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), and LMS (Learning Management Systems).

  7. Oct 2016
    1. Outside of the classroom, universities can use connected devices to monitor their students, staff, and resources and equipment at a reduced operating cost, which saves everyone money.
    2. Devices connected to the cloud allow professors to gather data on their students and then determine which ones need the most individual attention and care.
    1. For G Suite users in primary/secondary (K-12) schools, Google does not use any user personal information (or any information associated with a Google Account) to target ads.

      In other words, Google does use everyone’s information (Data as New Oil) and can use such things to target ads in Higher Education.

  8. Sep 2016
    1. Application Modern higher education institutions have unprecedentedly large and detailed collections of data about their students, and are growing increasingly sophisticated in their ability to merge datasets from diverse sources. As a result, institutions have great opportunities to analyze and intervene on student performance and student learning. While there are many potential applications of student data analysis in the institutional context, we focus here on four approaches that cover a broad range of the most common activities: data-based enrollment management, admissions, and financial aid decisions; analytics to inform broad-based program or policy changes related to retention; early-alert systems focused on successful degree completion; and adaptive courseware.

      Perhaps even more than other sections, this one recalls the trope:

      The difference probably comes from the impact of (institutional) “application”.

    2. the risk of re-identification increases by virtue of having more data points on students from multiple contexts

      Very important to keep in mind. Not only do we realise that re-identification is a risk, but this risk is exacerbated by the increase in “triangulation”. Hence some discussions about Differential Privacy.

    3. Responsible Use

      Again, this is probably a more felicitous wording than “privacy protection”. Sure, it takes as a given that some use of data is desirable. And the preceding section makes it sound like Learning Analytics advocates mostly need ammun… arguments to push their agenda. Still, the notion that we want to advocate for responsible use is more likely to find common ground than this notion that there’s a “data faucet” that should be switched on or off depending on certain stakeholders’ needs. After all, there exists a set of data use practices which are either uncontroversial or, at least, accepted as “par for the course” (no pun intended). For instance, we probably all assume that a registrar should receive the grade data needed to grant degrees and we understand that such data would come from other sources (say, a learning management system or a student information system).

    4. Data sharing over open-source platforms can create ambiguous rules about data ownership and publication authorship, or raise concerns about data misuse by others, thus discouraging liberal sharing of data.

      Surprising mention of “open-source platforms”, here. Doesn’t sound like these issues are absent from proprietary platforms. Maybe they mean non-institutional platforms (say, social media), where these issues are really pressing. But the wording is quite strange if that is the case.

    5. captures values such as transparency and student autonomy

      Indeed. “Privacy” makes it sound like a single factor, hiding the complexity of the matter and the importance of learners’ agency.

    6. Activities such as time spent on task and discussion board interactions are at the forefront of research.

      Really? These aren’t uncontroversial, to say the least. For instance, discussion board interactions often call for careful, mixed-method work with an eye to preventing instructor effect and confirmation bias. “Time on task” is almost a codeword for distinctions between models of learning. Research in cognitive science gives very nuanced value to “time spent on task” while the Malcolm Gladwells of the world usurp some research results. A major insight behind Competency-Based Education is that it can allow for some variance in terms of “time on task”. So it’s kind of surprising that this summary puts those two things to the fore.

    7. 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. it’s productive to not only think of schools and colleges as sites of learning, but also as marketplaces where goods, knowledge, and services are consumed and produced

      Agreed that it’s productive. But isn’t it also about framing (formal/institutional) education in purely economic terms? Useful to think about goods and services which have exchange value. May be a bit too easy to slip into the implicit idea that a learner is among the system’s key products.

    2. frame the purposes and value of education in purely economic terms

      Sign of the times? One part is about economics as the discipline of decision-making. Economists often claim that their work is about any risk/benefit analysis and isn’t purely about money. But the whole thing is still about “resources” or “exchange value”, in one way or another. So, it could be undue influence from this way of thinking. A second part is that, as this piece made clear at the onset, “education is big business”. In some ways, “education” is mostly a term for a sector or market. Schooling, Higher Education, Teaching, and Learning are all related. Corporate training may not belong to the same sector even though many of the aforementioned EdTech players bet big on this. So there’s a logic to focus on the money involved in “education”. Has little to do with learning experiences, but it’s an entrenched system.

      Finally, there’s something about efficiency, regardless of effectiveness. It’s somewhat related to economics, but it’s often at a much shallower level. The kind of “your tax dollars at work” thinking which is so common in the United States. “It’s the economy, silly!”

    1. often private companies whose technologies power the systems universities use for predictive analytics and adaptive courseware
    2. 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.

    1. “We need much more honesty, about what data is being collected and about the inferences that they’re going to make about people. We need to be able to ask the university ‘What do you think you know about me?’”
  9. Jul 2016
    1. what do we do with that information?

      Interestingly enough, a lot of teachers either don’t know that such data might be available or perceive very little value in monitoring learners in such a way. But a lot of this can be negotiated with learners themselves.

    2. turn students and faculty into data points

      Data=New Oil

    3. E-texts could record how much time is spent in textbook study. All such data could be accessed by the LMS or various other applications for use in analytics for faculty and students.”
    4. not as a way to monitor and regulate
    5. reduce students, to mere algorithms
    1. It starts by rejecting the canard that a university education is just another commodity.
    2. There are outputs, such as graduates, increased social mobility and higher standards of living.
    3. Don't turn students into consumers – the US proves it's a recipe for disaster
  10. Apr 2016
  11. Mar 2016
    1. Learners are engaged in solving real-world problems. Existing knowledge is activated as a foundation for new knowledge. New knowledge is demonstrated to the learner. New knowledge is applied by the learner. New knowledge is integrated into the learner’s world.

      Not totally on board with this. Perhaps if "learner" role can be filled with student or instructor.

  12. Jan 2016
  13. Dec 2015
  14. Aug 2015
  15. Jun 2015
    1. We do not want to leave the school system behind. We need to keep driving toward where we want everyone to be versus waiting until everyone is ready. The end goal will involve the Internet, and there needs to be a framework for it.

      But we do want to leave it behind--the words we use--"school system" tell us exactly what is at the center--schools. What we learn are learn systems where learning is at the center which implies tacit-wise that the learner is at the center.

      (http://gph.is/1e82Pef)

      learner centric