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Learning Science

MAGE: Driving Learning Effectiveness at Multiverse

By Team Multiverse

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Contents

  1. Measured learning
  2. Applied Learning
  3. Guided learning
  4. Equitable learning

Learning is more important than ever; particularly on-the-job upskilling and reskilling. According to the World Economic Forum, 1.1 billion jobs are likely to be disrupted or transformed by technology by 2030. Leaders must stay ahead of the change curve and ensure their human capital is armed with the skills of tomorrow. As a learning provider we see it as our responsibility to support leaders with this challenge, but we’re only as good as the learning experiences we provide.

We drive learning through MAGE – Measured, Applied, Guided, and Equitable learning. These principles guide our curriculum development and learning experience design. In other words, these pillars are the standards we hold ourselves to when deciding what learning we offer and how we deliver it.

“We unlock economic opportunity and potential for individuals and organizations by closing the skills gaps of today and tomorrow through measured, applied, guided and equitable learning”

Measured learning

Measured Learning means we collect the right data at the right time in order to demonstrate that learning is occurring as anticipated, as well as capturing whether it is having the desired impact. This calls for continuous effort, but at Multiverse, we’re committed to achieving it.

We build upon established learning measurement frameworks like Learning-Transfer Evaluation Model (Thalheimer, W., 2018) to create a map of data we collect throughout the learning journey. Not only do we measure whether knowledge, skills and behaviors can be evidenced; but we measure a litany of other data points that allow us to monitor learning performance and triage support as necessary. In many cases, we also support our learners in highlighting and celebrating the real-world transfer of this learning into workplace performance gains.

"One of our apprentices was able to demonstrate a saving of 20 hours per week through the automation of a previously manual financial process. At the heart of this was an automated dashboard taught on one of our data programs."

Applied Learning

Applied Learning means that our learning happens in a real-world context, learning what you need to know when you need to learn it. As Josh Bersin (2018) states, the more we learn in the flow of work (opens new window)(opens new window) the more impact we can have. We facilitate an active learning environment by encouraging apprentices to apply what they learn within the context of their own role. This makes it a transformative experience that facilitates lasting changes in mindset, perspective, attitudes as well as knowledge and skills. We support this in a myriad of ways including:

  1. We focus on knowledge, skills and behaviors that are in demand; from first principles, our portfolio is assessed against market requirements
  2. We mandate applied learning projects throughout every learning journey to ensure immediate real-world application
  3. We collaborate with managers to ensure learning is applied in line with job requirements

Guided learning

Guided Learning means that learners are continually supported on their learning journey through a unique blend of AI-powered, on-demand and human-centered coaching. Our aim is to ease learners into that “goldilock’s zone” of challenge (Vygotsky, 1978; Wilson et al., 2019); ensuring they can do more with the guidance of knowledgeable experts and other forms of scaffolded support.

The benefits of a guided learning process have long been documented (e.g. Bloom, 1984). However, many learning providers have felt the tension between providing guided experiences and scaling their delivery through remote, productised experiences. At Multiverse, we believe this is a false choice, and aim to do both through our blend of AI and human approaches to coaching. We also believe there are a range of knowledgeable experts within a learning journey, encouraging apprentices to not only learn from their coaches but also their peers and extended Multiverse community.

Equitable learning

Equitable Learning means learning is accessible to everyone and can be used as a means to open up career possibilities. It means that everyone's unique qualities are valued and represented in our learning experience. As such, we’re continuously aiming to assess what makes each learner unique (e.g. how they think, how they learn, what motivates them) such that support and guidance can be tailored to each individual.

Within our Learning Science team, we have a blend of experts in workplace psychology and learning assessment to continue to put each individual learner at the center of each experience. As emphasized by Multiverse’s mission “Providing equitable access to economic opportunity for everyone”, equity and inclusion are at the heart of what we do.

By incorporating the MAGE framework into how we think about, build and deliver learning we can ensure we deliver learning effectively. In particular, this framework has been developed with on-the-job, professional learning in mind; as we at Multiverse help solve your business-critical problems and prepare you for the future of work.

References

  • Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational researcher, 13(6), 4-16.
  • Josh Bersin (2018, July 8) “A New Paradigm For Corporate Training: Learning In The Flow of Work”. Josh Bersin. https://joshbersin.com/2018/06/a-new-paradigm-for-corporate-training-learning-in-the-flow-of-work/
  • Knowles, M. S., Holton III, E. F., & Swanson, R. A. (2014). The adult learner: The definitive classic in adult education and human resource development. Routledge.
  • Thalheimer, W. (2018). The learning-transfer evaluation model: Sending messages to enable learning effectiveness.
  • Vygotsky, L. S., & Cole, M. (1978). Mind in society: Development of higher psychological processes. Harvard university press
  • Wilson, R. C., Shenhav, A., Straccia, M., & Cohen, J. D. (2019). The eighty five percent rule for optimal learning. Nature communications, 10(1), 4646.
  • World Economic Forum. (2024). Reskilling Revolution. World Economic Forum. Retrieved 20/03/2024, from https://initiatives.weforum.org/reskilling-revolution/home(opens new window)

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