122 Matching Annotations
  1. May 2022
    1. faciliter l’accès aux données des systèmes du MES, notamment par rapport à laréussite de groupes ciblés d’étudiants (par exemple, les étudiants en situationde handicap, les étudiants autochtones, les étudiants issus de l’immigration, lesétudiants de première génération3, les étudiants internationaux)
  2. Feb 2022
    1. Contemporary digital learning technologies generate, store, and share terabytes of learner data—which must flow seamlessly and securely across systems. To enable interoperability and ensure systems can perform at-scale, the ADL Initiative is developing the Data and Training Analytics Simulated Input Modeler (DATASIM), a tool for producing simulated learner data that can mimic millions of diverse user interactions. view image full screen DATASIM application screen capture. DATASIM is an open-source platform for generating realistic Experience Application Programming Interface (xAPI) data at very large scale. The xAPI statements model realistic behaviors for a cohort of simulated learner/users, producing tailorable streams of data that can be used to benchmark and stress-test systems. DATASIM requires no specialized hardware, and it includes a user-friendly graphical interface that allows precise control over the simulation parameters and learner attributes.
    1. The video profile of the xAPI was created to identify and standardize the common types of interactions that can be tracked in any video player.
  3. Jan 2022
    1. xAPI Wrapper Tutorial Introduction This tutorial will demonstrate how to integrate xAPI Wrapper with existing content to capture and dispatch learning records to an LRS.

      roll your own JSON rather than using a service like xapi.ly

    1. Storyline 360 xAPI Updates (Winter 2021)Exciting xAPI update for Storyline users! Articulate has updated Storyline 360 to support custom xAPI statements alongside a few other xAPI-related updates. (These changes will likely come to Storyline 3 soon, though not as of November 30, 2021.)
    1. Making xAPI Easier Use the xapi.ly® Statement Builder to get more and better xAPI data from elearning created in common authoring platforms. xapi.ly helps you create the JavaScript triggers to send a wide variety of rich xAPI statements to the Learning Record Store (LRS) of your choice.

      criteria for use and pricing listed on site

    1. Here you will find a well curated list of activities, activity types, attachments types, extensions, and verbs. You can also add to the registry and we will give you a permanently resolvable URL - one less thing you have to worry about. The registry is a community resource, so that we can build together towards a working Tin Can data ecosystem.

      **participant in the Spring 2022 XAPI cohort suggested that 'Registry is not maintained, and they generally suggest using the Vocab Server (which is also the data source for components in the Profile Server).'

  4. www.json.org www.json.org
    1. JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language Standard ECMA-262 3rd Edition - December 1999. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. These properties make JSON an ideal data-interchange language.
    1. The xAPI Vocabulary and Profile Server is a curated list of xAPI vocabulary concepts and profiles maintained by the xAPI community.
    1. xAPI Foundations Leverage xAPI to develop more comprehensive learning experiences. This on-demand e-learning course is available online immediately after purchase. Within the course, you will have the opportunity to personalize your learning by viewing videos, interacting with content, hearing from experts, and planning for your future. You will have access to the course(s) for 12 months from your registration date.
    1. Learning program analytics seek to understand how an overall learning program is performing. A learning program typically encompasses many learners and many learning experiences (although it could easily contain just a few).
    2. Learning experience analytics seek to understand more about a specific learning activity. 
    3. Learner analytics seek to understand more about a specific person or group of people engaged in activities where learning is one of the outputs.
    4. There are many types of learning analytics and things you can measure and analyze. We segment these analytics into three categories: learning experience analytics, learner analytics, and learning program analytics.
    5. Learning analytics is the measurement, collection, analysis, and reporting of data about learners, learning experiences, and learning programs for purposes of understanding and optimizing learning and its impact on an organization’s performance.
    1. Social learning This is a feature the LXP has really expanded. Although some of the more advanced LMSs boast social features, the Learning Experience Platform is better formatted for them and far more likely to provide.  Firstly, the LXP caters for a broader range of learning options than the LMS. It’s usually not difficult to use your LXP to set up an online class or webinar.  LXPs also provide a chance for learners to share their opinions on content: liking, sharing, or commenting on an article or online class. Users can follow and interact with others, above or below them in the organisation. Sometimes LXPs even provide people curation, matching learners and mentors.  Users also have a chance to make the LXP their own by setting up a personalised profile page. It might seem low-priority, but a sense of ownership usually corresponds with a boost in engagement.  As well prepared as Learning & Development leaders are, there’ll be things that people doing a job every day will know that you won’t. They can use their personal experience to recommend or create learning content in an LXP. This helps on-the-job learning and gives employees a greater chance of picking up the skills they need to progress in their role. 
    1. How, exactly, can we design for engagement and conversation? In comparison to content-focused educational technology such as the Learning Management System (LMS), our (not so secret) recipe is this:1. Eliminate the noise2. Bring people into the same room3. Make conversation easy and meaningful4. Create modularity and flexibility

      Spring 2022 #xAPICohort resource

    1. To learn more, there are two books I highly recommend. "Digital Body Language," by Steve Woods, and "Big Data: Does Size Matter?" by Timandra Harkness. If you would like a deeper dive into data-driven learning design, there's a free e-book and toolkit you can download from my blog. You can also reach me there at loriniles.com. Remember, start with the data you have readily available. Data does not have to be intimidating Excel spreadsheets. Be prepared with data every single time you meet with your stakeholders. And before you design any strategy, ask what data you have to support every decision. You're on an exciting journey to becoming a more well-rounded HR leader. Get started, and good luck.

      Spring 2022 #xAPICohort resource

    1. LXPs and LMSs accomplish two different objectives. An LMS enables administrators to manage learning, while an LXP enables learners to explore learning. Organizations may have an LXP, an LMS or both. If they have both, they may use the LXP as the delivery platform and the LMS to handle the administrative work.

      Spring 2022 #xAPICohort resource

    2. 4. Highly intuitive interfaces

      Spring 2022 #xAPICohort resource

    3. 3. Supports various types of learning

      Spring 2022 #xAPICohort resource

    4. 2. Rich learning experience through deeper personalization

      Spring 2022 #xAPICohort resource

    5. Here are some other characteristics that set LXPs apart from LMS’s: 1. Extensive integration capabilities

      Spring 2022 #xAPICohort resource

    6. The gradual shift, from one-time pay to cloud-based subscription-based business has lead learning platforms to also offer Software-as-a-Service (SaaS) models to their clients. As such content becomes part of digital learning networks, they are integrated into commercial learning solutions and then become part of broader LXPs. Looking back at all these developments, from how new data consumption platforms evolved, to the emergence of newer content development approaches and publishing channels, it’s easy to understand why LXPs naturally evolved as a result of DXPs.

      Spring 2022 #xAPICohort resource

    7. The growth of social learning has also created multiple learning opportunities for people to share their knowledge and expertise. As they socialize on these platforms (Facebook, LinkedIn, YouTube, Instagram and many others), individuals and groups learn from each other through various types of social interactions – sharing content, exchanging mutually-liked links to external content. LXP leverage similar approaches in corporate learning environments, and scale learning experience and opportunities with such user-generated content as found in social and community-based learning.

      Spring 2022 #xAPICohort resource

    8. Integrations are also possible with AI. If you integrate LXP and your Human Resource Management (HRM) system, the corporate intranet, your Learning Record Store (LRS) or the enterprise Customer Relationship Management (CRM) system, and collect the data from all of them, you can identify many different trends and patterns. And based on those patterns, all stakeholders can make informed training and learning decisions. Standard LMS’s cannot do any of that. And though LMS developers are trying to get there, they’ve still got a long way to go to bridge the functionality gap with LXPs. As a result, there was an even greater impetus to the emergence of LXPs.

      Spring 2022 #xAPICohort resource

    9. Another driver for the emergence of LXP’s is the standards adopted by modern-day LMS’s – which are SCORM-based. While SCORM does “get results”, it is limited in what it can do. One of the main goals of any corporate learning platform is to connect learning with on-the-job performance. And SCORM makes it very difficult to decide how effective the courses really are, or how learners benefit from these courses. Experience API (xAPI) on the other hand – the standard embraced by LXPs – offers significantly enhanced capabilities to the platform. When you use xAPI, you can follow different parameters both while you learn and perform on the job tasks. And, what’s even better is that you can do that on a variety of digital devices.

      Spring 2022 #xAPICohort resource

    10. LMS’s primarily served as a centralized catalog of corporate digital learning assets. Users of those platforms often found it hard to navigate through vast amounts of content to find an appropriate piece of learning. LMS providers sought to bridge that gap by introducing smart searches and innovative querying features – but that didn’t entirely address the core challenge: LMS’s were still like huge libraries where you should only go to when you have an idea of what you need, and then spend inordinate amounts of time searching for what you specifically want!

      Spring 2022 #xAPICohort resource

    1. Experience API (xAPI) is a tool for gaining insight into how learners are using, navigating, consuming, and completing learning activities. In this course, Anthony Altieri provides an in-depth look at using xAPI for learning projects, including practical examples that show xAPI in action.

      Spring 2022 #xAPICohort resource

    1. The xAPI Learning Cohort is a free, vendor-neutral, 12-week learning-by-doing project-based team learning experience about the Experience API. (Yep, you read that right – free!) It’s an opportunity for those who are brand new to xAPI and those who are looking to experiment with it to learn from each other and from the work itself.

      Spring 2022 #xAPICohort resource

    1. If your current course development tools don't create the activity statements you need, keep in mind that sending xAPI statements requires only simple JavaScript, so many developers are coding their own form of statements from scratch.

      Spring 2022 #xAPICohort resource

    2. An xAPI activity statement records experiences in an "I did this" format. The format specifies the actor, verb, object: the actor (who did it), a verb (what was done), a direct object (what it was done to) and a variety of contextual data, including score, rating, language, and almost anything else you want to track. Some learning experiences are tracked with a single activity statement. In other instances, dozens, if not hundreds, of activity statements can be generated during the course of a learning experience. Activity statements are up to the instructional designer and are driven by the need for granularity in reporting.

      Spring 2022 #xAPICohort resource

    3. xAPI is a simple, lightweight way to store and retrieve records about learners and share these data across platforms. These records (known as activity statements) can be captured in a consistent format from any number of sources (known as activity providers) and they are aggregated in a learning record store (LRS). The LRS is analogous to the SCORM database in an LMS. The x in xAPI is short for "experience," and implies that these activity providers are not just limited to traditional AICC- and SCORM-based e-learning. With experience API or xAPI you can track classroom activities, usage of performance support tools, participation in online communities, mentoring discussions, performance assessment, and actual business results. The goal is to create a full picture of an individual's learning experience and how that relates to her performance.

      Spring 2022 #xAPICohort resource

    1. For any xAPI implementation, these five things need to happen:A person does something (e.g., watches a video).That interaction is tracked by an application.Data about the interaction is sent to an LRS.The data is stored in the LRS and made available for use.Use the data for reporting and personalizing a learning experience.In most implementations, multiple learner actions are tracked by multiple applications, and data may be used in a number of ways. In all cases, there’s an LRS at the center receiving, storing, and returning the data as required.

      Spring 2022 #xAPICohort resource

    2. Experience API (also xAPI or Tin Can API) is a learning technology interoperability specification that makes it easier for learning technology products to communicate and work with one another.

      Spring 2022 #xAPICohort resource

    1. Instructional DesignerWhen implementing xAPI across an organization, there isn’t usually a need for instructional designers to take on new roles or duties. However, they may experience a learning curve that presents an opportunity to understand how to best package and effectively deploy xAPI in newly created content. Your learning designer(s) is a key partner in getting good data, so keep them in the loop regarding your strategy, goals, and expected outcomes.

      Spring 2022 #xAPICohort resource

  5. Nov 2021
  6. Mar 2021
  7. Apr 2019
    1. Annotation Profile Follow learners as they bookmark content, highlight selected text, and tag digital resources. Analyze annotations to better assess learner engagement, comprehension and satisfaction with the materials assigned.

      There is already a Caliper profile for "annotation." Do we have any suggestions about the model?

  8. Mar 2019
  9. Feb 2019
    1. Which segments of text are being highlighted?

      Do we capture this data? Can we?

    2. What types of annotations are being created?

      How is this defined?

    3. Who is posting most often? Which posts create the most replies?

      These apply to social annotation as well.

    4. Session Profile

      Are we capturing the right data/how can Hypothesis contribute to this profile?

    5. Does overall time spent reading correlate with assessment scores? Are particular viewing patterns/habits predictive of student success? What are the average viewing patterns of students? Do they differ between courses, course sections, instructors, or student demographics?

      Can H itself capture some of this data? Through the LMS?

  10. Jan 2018
  11. Nov 2017
    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. Information from this will be used to develop learning analytics software features, which will have these functions: Description of learning engagement and progress, Diagnosis of learning engagement and progress, Prediction of learning progress, and Prescription (recommendations) for improvement of learning progress.

      As good a summary of Learning Analytics as any.

  12. Oct 2017
    1. By giving student data to the students themselves, and encouraging active reflection on the relationship between behavior and outcomes, colleges and universities can encourage students to take active responsibility for their education in a way that not only affects their chances of academic success, but also cultivates the kind of mindset that will increase their chances of success in life and career after graduation.
    2. If students do not complete the courses they need to graduate, they can’t progress.

      The #retention perspective in Learning Analytics: learners succeed by completing courses. Can we think of learning success in other ways? Maybe through other forms of recognition than passing grades?

  13. 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).

  14. Feb 2017
    1. this kind of assessmen

      Which assessment? Analytics aren't measures. We need to be more forthcoming with faculty about their role in measuring student learning. Such as, http://www.sheeo.org/msc

  15. Nov 2016
  16. Oct 2016
    1. 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.

  17. Sep 2016
    1. 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.

    2. 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.

    3. 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. 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.

  18. 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. 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.”
    3. not as a way to monitor and regulate
    1. which applicants are most likely to matriculate
    2. Data collection on students should be considered a joint venture, with all parties — students, parents, instructors, administrators — on the same page about how the information is being used.
    3. "We know the day before the course starts which students are highly unlikely to succeed,"

      Easier to do with a strict model for success.

  19. Jun 2016
    1. nothing we did is visible to our analytics systems

      If it’s not counted, does it count?

    1. It shifted its work to faculty-driven initiatives.

      DIY, grassroots, bottom-up… but not learner-driven.

    2. learning agenda on learning analytics
    3. Learning analytics cannot be left to the researchers, IT leadership, the faculty, the provost or any other single sector alone.
    4. An executive at a large provider of digital learning tools pushed back against what he saw as Thille’s “complaint about capitalism.”

      Why so coy?

      R.G. Wilmot Lampros, chief product officer for Aleks, says the underlying ideas, referred to as Knowledge Space Theory, were developed by professors at the University of California at Irvine and are in the public domain. It's "there for anybody to vet," he says. But McGraw-Hill has no more plans to make its analytics algorithms public than Google would for its latest search algorithm.

      "I know that there are a few results that our customers have found counterintuitive," Mr. Lampros says, but the company's own analyses of its algebra products have found they are 97 percent accurate in predicting when a student is ready to learn the next topic.

      As for Ms. Thille's broader critique, he is unpersuaded. "It's a complaint about capitalism," he says. The original theoretical work behind Aleks was financed by the National Science Foundation, but after that, he says, "it would have been dead without business revenues."

      MS. THILLE stops short of decrying capitalism. But she does say that letting the market alone shape the future of learning analytics would be a mistake.

    5. a debate over who should control the field of learning analytics

      Who Decides?

    1. What teachers want in a data dashboard

      Though much of it may sound trite and the writeup is somewhat awkward (diverse opinions strung together haphazardly), there’s something which can help us focus on somewhat distinct attitudes towards Learning Analytics. Much of it hinges on what may or may not be measured. One might argue that learning happens outside the measurement parameters.

    2. timely

      Time-sensitive, mission-critical, just-in-time, realtime, 24/7…

    3. Data “was something you would use as an autopsy when everything was over,” she said.

      The autopsy/biopsy distinction can indeed be useful, here. Leading to insight. Especially if it’s not about which one is better. A biopsy can help prevent something in an individual patient, but it’s also a dangerous, potentially life-threatening operation. An autopsy can famously identify a “cause of death” but, more broadly, it’s been the way we’ve learnt a lot about health, not just about individual patients. So, while Teamann frames it as a severe limitation, the “autopsy” part of Learning Analytics could do a lot to bring us beyond the individual focus.

    1. While generally misused today, analytics can (theoretically) be used to predict and personalize many facets of teaching & learning, inc. pace, complexity, content, and more.
  20. Apr 2016
  21. Mar 2016
  22. Dec 2015
    1. focus groups where students self-report the effectiveness of the materials are common, particularly among textbook publishers

      Paving the way for learning analytics.

    2. It’s educators who come up with hypotheses and test them using a large data set.

      And we need an ever-larger data set, right?

    3. a good example of the kind of insight that big data is completely blind to

      Not sure it follows directly, but also important to point out.

    1. I will investigate the details on this, including the relevant contractual clauses, when I get the chance.
    2. taking a swipe at Knewton

      Snap!

    3. they are making a bet against the software as a replacement for the teacher and against big data
    4. a set of algorithms designed to optimize the commitment of knowledge to long-term memory
    1. The most popular project for the MUA to tackle was Learning Analytics

      Although Dougiamas claims Moodle already has what is needed in the form of logs and reports: no need for Caliper or xAPI.

    1. Lambda Solutions [Corrected.]

      Oh? They were quite present at MoodleMoot. Wonder what their ties are. Clearly, their solution isn’t free software. Nor is it pushing Open Standards for Learning Analytics.

  23. Nov 2015
    1. grows exponentially.

      As we get into “Big Data”, individual datapoints become less important.

    2. What is the correlation between levels of student responses to each other and outcomes?

      Levels and types of responses. Just read such an analysis, based on Brookfield and Preskill’s “Conversational Moves”.

    3. read by any Caliper-compliant system

      Or any Learning Record Store.

    4. Caliper WordPress plugin

      How long before we get such a thing?

    5. most blogs have a feature called “pingbacks,”

      Annotations should have “pingbacks”, too. But the most important thing is how to process those later on. We do get into the Activity Streams behind much Learning Analytics.

    1. Personal Learning Record will define how to represent, capture and leverage user activity, including ratings, test results and performance measures in a distributed learning and work environment.
  24. Sep 2015
    1. Commercial publishers and content producers say there's reason to doubt the quality of open resources

      Have they demonstrated so clearly that their textbooks have enhanced learning? Oh, wait. They set the criteria by which we assess learning and push for their own brand of Learning Analytics, so…

  25. Aug 2015