student outcomes, including learning, persistence, or attitudes.

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)

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.

learning analytics

xAPI profiles make learning design, development, and analytics better.

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.

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.

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.)

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.

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.

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.

The xAPI Vocabulary and Profile Server is a curated list of xAPI vocabulary concepts and profiles maintained by the xAPI community.

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.

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).

Learning experience analytics seek to understand more about a specific learning activity. 

Learner analytics seek to understand more about a specific person or group of people engaged in activities where learning is one of the outputs.

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.

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.

xAPI

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. 

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

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.

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.

4. Highly intuitive interfaces

3. Supports various types of learning

2. Rich learning experience through deeper personalization

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

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.

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.

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.

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.

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!

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.

Intro to xAPI: What Do Instructional Designers Need to Know

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.

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.

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.

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.

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.

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.

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.

Distance learning, learning analytics, COVID-19, technology-enhanced learning

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.

ow the human made use of that time.

Which segments of text are being highlighted?

What types of annotations are being created?

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

Session Profile

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?

niversity’s General Counsel to Resign

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

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.

full Caliper Analytics compliance

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.

If students do not complete the courses they need to graduate, they can’t progress.

Ethics and privacy in learning analytics

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.

this kind of assessmen

Data should extend our senses, not be a substitute for them. Likewise, analytics should augment rather than replace our native sense-making capabilities.

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.

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.

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.

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

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.

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

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.

what do we do with that information?

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.”

not as a way to monitor and regulate

demanded by education policies — for more data

more efficient (whatever that means)

data being collected about individuals for purposes unknown to these individuals

which applicants are most likely to matriculate

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.

"We know the day before the course starts which students are highly unlikely to succeed,"

there is some disparity and implicit bias

nothing we did is visible to our analytics systems

It shifted its work to faculty-driven initiatives.

learning agenda on learning analytics

Learning analytics cannot be left to the researchers, IT leadership, the faculty, the provost or any other single sector alone.

An executive at a large provider of digital learning tools pushed back against what he saw as Thille’s “complaint about capitalism.”

a debate over who should control the field of learning analytics

What teachers want in a data dashboard

timely

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

While generally misused today, analytics can (theoretically) be used to predict and personalize many facets of teaching & learning, inc. pace, complexity, content, and more.

Researchers have tracked student emotions while using Crystal Island–a game-based learning environment– and used that research to predict how students will react in other learning situations

Ranty Blog Post about Big Data, Learning Analytics, & Higher Ed

The future of learning analytics, a future in which it is not passé, is one in which learning comes before management

focus groups where students self-report the effectiveness of the materials are common, particularly among textbook publishers

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

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

I will investigate the details on this, including the relevant contractual clauses, when I get the chance.

taking a swipe at Knewton

they are making a bet against the software as a replacement for the teacher and against big data

a set of algorithms designed to optimize the commitment of knowledge to long-term memory

numbers have to be interpreted by those who do the daily work of classroom teaching

The challenge, of course, is how to balance concerns of the Hawthorne effect with privacy.

Regular readers know that I am a big fan of the standard-in-development.

If Knewton were doing more of this, I wouldn’t be as critical.

Personalized course materials made possible by real data

Soon, data will make learning outcomes for these courses highly transparent

when outcomes are non-transparent

wring out every ounce of performance

increased investment in professional development and teaching-friendly tenure and promotion practices

The most popular project for the MUA to tackle was Learning Analytics

Lambda Solutions [Corrected.]

grows exponentially.

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

read by any Caliper-compliant system

Caliper WordPress plugin

most blogs have a feature called “pingbacks,”

adaptive learning with multiple levels of support.

technology-assisted differentiated instruction

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.

e-learning technologies and standards: SCORM, LOM, IMS-LD, IMS-CC, IMS-QTI, IMS-CP, LDL, SLD

Commercial publishers and content producers say there's reason to doubt the quality of open resources