Vast advances in computing, the cloud and virtualization technology, along with widely available high-speed internet, has made it possible to access almost all types of tools and platforms for teaching and learning. Technology systems that were previously available only at corporations or large computer centers at colleges and universities can now be offered as hands-on learning labs for individual learners to use with just an internet connection and a web browser.
Companies are using online learning labs for hands-on exercises in a vast variety of topics, including coding, machine learning and data science. There are no downloads required and no concerns about crashing systems. If learners make a mistake, they simply restart the lab and have another go, or two or three, at the problem.
Online learning lab platforms are now connected to the top learning management systems (LMSs) using LTI-based bidirectional integration and are integrated with MOOC platforms that deliver learning to tens of thousands of students around the world. They support self-paced, instructor-led, blended, synchronous and asynchronous delivery formats. In addition to accessing deep learner data and analytics, instructors can monitor each active lab session and even guide their students virtually. Organizations can also use learning labs to score exercises and assessments and provide feedback to learners. Sophisticated scripts in the labs determine if learners successfully completed exercises, and the results are automatically recorded.
Learning labs vary in complexity, from simple sandboxes to complex tools that support guided instructor-led training with high-stakes assessment capabilities. The Learning Labs Maturity Model (L2MM) provides a way for instructors and organizations to assess their requirements for learning labs and determine which of the rich set of learning lab capabilities to use at each stage.
Stage 1: Ad Hoc Learning
In the first stage, learners are expected to figure things out for themselves. A simple sandbox that is accessible through the web is sufficient at this stage. At the minimum, the sandbox should have all the components required to learn about the topic or direct users to repositories (such as GitHub) to download onto the sandbox.
Stage 2: Defined Learning
In the defined stage, the training organization has developed specific hands-on exercises with clear objectives, and learners are expected to complete all the exercises as part of a curriculum. In this stage, the organization should use an online learning labs with the ability to provide browser overlays. These overlays are configured with lab steps and guide the learners through each activity. The sandbox should be fully configured with all the resources required to complete the exercises.
Stage 3: Managed Learning
While in the preceding stage, every course is an island, in the managed stage, courses are banded together to form learning paths. To support learning paths, tight integration with the organization’s learning management system becomes imperative. The organization should select a learning lab that can integrate with an LMS, preferably through standards-based integration, such as LTI. When the lab is integrated with the LMS, learners can move back and forth between the lab and the LMS using single sign-on capabilities. The learning lab should also be able to sequence labs, adapt to learner choices and set controlled time frames.
Stage 4: Leveraged Learning
In the leveraged stage, learning labs are considered a core element of the learning journey across the institution, and a quantitative and qualitative focus on pedagogical aspects of learning labs becomes important. In addition to the capabilities outlined earlier, the organization should select a learning lab that provides tools such as session-sharing, deep learner and lab analytics, and performance-based testing.
Stage 5: Optimized Learning
In the optimized stage, there is focus on continuous improvement of the all capabilities of learning labs and on enhancing pedagogical outcomes. The organization can use data from learner analytics and assessments to determine the level of reinforcement needed and the difficulty and clarity of the subject matter and exercises. Learning leaders should evaluate and deploy new capabilities and innovations, such as gamification, credentialing and augmented reality, that they can integrate into the learning labs.
“All models are wrong,” British statistician George Fox reportedly said, “but some are useful.” The Learning Lab Maturity Model is a useful guide for stakeholders as they navigate the dynamic and exciting hands-on learning lab technology landscape.
