The Active Implementation Hub is a free, online learning environment for use by any interested party — practitioners, educators, coaches, trainers, purveyors — involved in the active implementation and scaling up of programs and innovations. The site goal is to increase the knowledge and improve the performance of persons actively implementing any program or practice.
But there is so much more to the AI Hub behind the scenes! The publicly available, online lessons on the Hub are leveraged as part of an innovative and detailed blended learning strategy that NIRN provides for its partners.
What kind of blended learning do we provide with AI Hub lessons?
When we come on-site, our Implementation Support Providers (or ISPs) develop relationships with the teams we support. We get to know them, and we tailor our content and approach accordingly. Unfortunately, this kind of personalized and tailored approach does not come to mind when we hear or use the term “blended learning.” Typically, blended learning is a reduced real-time experience with digital add-ons, lowering rather than elevating the learning experience. In contrast, high-quality blended learning involving synchronous and asynchronous elements improves the effectiveness and efficiency of the overall instructional experience by leveraging the logistic conveniences of asynchronous instruction and the technical capabilities of learning technology to save time and money while enhancing the ability to personalize and tailor professional learning delivered synchronously.
This kind of high-quality blended synchronous and asynchronous learning requires a strategy that includes identifying learning objectives, designing learner experiences, and planning the collection and use of learner activity data from asynchronous instruction. Learner activity data reflects any learner behavior—not just multiple choice answers but also decisions made during a narrative scenario and answers to open-ended questions. Onsite support utilizes robust, personalized data captured through our AI Hub lessons (which are completed by learners as pre-work before synchronous sessions), allowing support providers to review, extend, enhance, or skip areas during synchronous instruction depending on existing learner knowledge. This data-forward blended learning strategy maximizes the effectiveness of onsite work.
How are we making that happen?
To collect learner activity data through interactive lessons, NIRN requires a learning technology platform. You may be familiar with one such platform: a Learning Management System (or LMS). NIRN is located within a university setting (UNC-Chapel Hill) that organizes classwork with an LMS, but the LMS utilized for UNC students would not be a good fit. An LMS platform requires pre-registration of all learners and routes all traffic through a specific interface. However, NIRN already has its website presence, and not all learners are known to NIRN before engaging in the AI Hub lessons.
Furthermore, the kind of data LMS platforms typically collect is not useful for NIRN. Most platforms capture data called SCORM (which stands for Sharable Content Object Reference Model). SCORM is very good at collecting single scores and completion data but, in order to be informative for NIRN’s implementation support practitioners, learner activity data must be conceptually meaningful, reflecting specific learner choices within custom-developed interactions.
Instead of an LMS, NIRN has a unique infrastructure that allows us to collect a specific kind of learner activity data called xAPI (or Experience API). API stands for Application Programming Interface; APIs are mechanisms that enable two software components to communicate with each other using a set of definitions and protocols. xAPI is a particular protocol—a particular kind of data organized into statements with parts like actor, verb, and object. But there are lots of other parts that can be added to that statement, such as result and context, so it can become as complex or specific as is required by our data strategy. By using xAPI statements, we tailor the learner activity data collected to the needs of each project, creating meaningful variables. Our data is then reported to a specific kind of database called a Learning Record Store (or LRS), designed for this type of data. Furthermore, online learning experiences are portable within our learning ecosystem: they can live on any website and still report the data we need to our LRS.
Such customization offers a great deal of freedom and the ability to implement instructional strategy, leading to well-designed training products and processes. Custom learner activity variables can be correlated with training and research outcomes to assess the effectiveness of not only each lesson and module but also each interaction within each module. The customized data possible with xAPI allows us to implement a strategic blended learning design.
What does this look like to NIRN staff?
Pivotal to the blended learning strategy at NIRN is a very effective data visualization tool we call the "traffic light" dashboard.
With one row per learner, this visualization of data (Figure 1) is organized by learning objectives and details learners' strengths and weaknesses across the target skills, as evidenced by their behavior throughout the asynchronous lesson. In the last row, a group evaluation is provided.
Figure 1: Traffic light dashboard (Note: names have been removed to allow anonymity)
Selecting any individual “traffic light” will produce a “drill-down” report (Figure 2) that provides greater detail of the behavior of that learner relevant to the learning objective:
Figure 2: Drilldown report (Note: name has been removed to allow anonymity; report scrolls to show 14 questions)
The design of each drill-down (Figure 3) is determined by the information most useful to tailor follow-up instruction.
Figure 3: A drill-down report with a different design
The traffic light data visualization tool allows NIRN staff to utilize robust, personalized data captured through asynchronous pre-work to personalize and tailor their synchronous support. It provides insight that allows them to make the best use of their time by skipping or truncating their coverage of strength areas so that they can extend to more advanced skills and/or review and further support areas of need. The result is more effective and efficient synchronous group work.
What does it look like to the teams we support?
The blended learning that leads to more effective and efficient support from NIRN undergirds the development of team readiness and capacity. That means that AI Hub assignments are not busywork! While our lessons provide universal TA to anyone who wants it, they are also a crucial part of the targeted and intensive support we offer. Our synchronous support is tailored to the needs and strengths indicated through each team’s unique path through the AI Hub lessons. This means that before we even meet your team we have started developing a relationship.
For example, if NIRN was supporting a team learning to administer a capacity assessment, the data represented in Figure 1 above would indicate that they might skip or extend to more advanced information related to the 2nd learning objective (defining the key purpose of the assessment) while reviewing and providing more support for the 1st learning objective (identifying relevant assessments).
We will be utilizing this learner activity data to support our micro-credentialing program in two important ways: 1) learners will have the opportunity to access their own data to inform their individualized learning plans, and 2) staff will use data from micro-credentialing participants to determine the focus of the participants’ Communities of Practice.
How can your team benefit from this?
Would your team benefit from this kind of innovative, blended support from NIRN? Would your team like to have its own data dashboard like NIRN’s? Contact us; we’ll be happy to help you!