Applying Blooms Taxonomy in Corporate eLearning with mLevel

Workplace training should not only engrain knowledge, it should promote the use of that knowledge.  In order for training programs to be effective, learners need to bridge the gap between their knowledge and the application of that knowledge.  But not all learning objectives and content should be forced into the same delivery mechanism.  Using Bloom’s Taxonomy, you can group your corporate eLearning objectives into the various levels of learning and use the appropriate activity to distribute the content.  It’s not always necessary to include all levels of Bloom’s Taxonomy into all training programs, just include the levels that are appropriate to the learning goals and objectives.

mLevel’s corporate eLearning platform provides a way to distribute learning content in a number of different methods associated with the six levels of learning in Bloom’s Taxonomy.

Blooms Taxonomy Pyramid

Lets take a closer look at the Bloom’s Taxonomy and how mLevel can help you apply Bloom’s Taxonomy in corporate eLearning.  The following is a short description of each level of Bloom’s taxonomy, a list of mLevel activities which can be used to help target them, and how these activities apply to their associated level of Bloom’s Taxonomy.

blooms taxonomy remember   Remember

In this domain, you’re evaluating the learner’s ability to recall previously learned information.  Examples of this can include reciting policies, and quoting prices and product information from memory.

  • Academy – This activity provides an open-book style assessment, along with adaptive questioning and real-time player analytics.  Learners’ memories are tested by examining information and answering questions sequentially.
  • Block Party – Blow up blocks by tapping two or more of the same color.  The more blocks in the group, the more points you earn.  Receive bonus multipliers by answering questions correctly.
  • Check Point – Visit three levels, typically cities, advancing to each location by answering questions correctly.  Stay focused; your time is limited!
  • ShortFuze – Choose the correct answer before the fuse runs out.  Over time, your number of answer choices will increase from three to six.
  • Fast Lane – Drive to various destinations by answering 7 answers correctly at each level, choosing between two answer options.  Avoid road hazards and increase your driving speed – and points – by answering questions correctly.
  • Ice Breaker – Choose the correct answer (from 2 options) before the ice blocks stack up.  Once you have a stack of five ice blocks, the game will end, but if you answer five questions correctly in a row, you’ll be surprised with some help.

blooms taxonomy understand   Understand

This domain tests the learner’s ability to explain ideas and concepts.  Does the learner comprehend the meaning, translation, and interpretation of the topic?

  • Academy – This activity allows the learner to gain an understanding of the topic through examining knowledge, answering questions, and receiving real-time feedback on their responses.
  • Fast Lane – The learner is given two answer options and needs to determine the best choice by understanding both.
  • Ice Breaker – Choose the correct answer from two options before the ice blocks stack up.  Once again, the learner will need to understand both answer options to make the best choice.  If they answer correctly enough times, they are rewarded!

blooms taxonomy apply   Apply

In this level of Bloom’s Taxonomy, the learner is challenged with solving problems by applying previously acquired knowledge, techniques, and rules.  An example of this would be choosing attributes that make up a product or process.

  • Coordinates – Select facts/attributes that make up a product or process.
  • Path Finder – Navigate your way through a series of events using the arrows to move forward and backward within the game.  When presented with a decision, select the best possible course of action.
  • Task – Set up an out-of-game task for your learners to complete, applying the knowledge they’ve already acquired.

blooms taxonomy analyze   Analyze

This level of Bloom’s Taxonomy tests whether the learner can distinguish between different facts and inferences.  In this domain, the learner should be finding evidence that supports generalizations.

  • Coordinates – Determine the meaning of knowledge and analyze which piece of information belongs where.
  • Path Finder – Determine the best choice possible by analyzing potential outcomes for each decision.

blooms taxonomy evaluate   Evaluate

In this level of Bloom’s Taxonomy, you’re testing whether the learner is able to make judgments about the value of the information.  An example of this includes being able to select the most effective solution.

  • Academy – Real-time feedback and a progress summary allows the learner to reflect and evaluate how they did, and the adaptive questioning allows them to automatically adapt their learning on the fly.
  • External Link – When used with a third-party survey tool, the External Link activity can be used to Evaluate under the Blooms taxonomy.

blooms taxonomy create   Create

In this level of Bloom’s Taxonomy, the learner puts ideas together, building a holistic structure of diverse elements.  An example of this would be writing an operations or processes manual.

  • Task – Use the out of game task to instruct the learner to create something, using all of the previous knowledge they’ve acquired from the other learning domains.

By involving different methods of learning in workplace training, your learners will be able to retain and apply knowledge in a more efficient and affective way.  Next time you create a training program, try leveraging Bloom’s Taxonomy to deliver content in a way that will challenge your learners with a variety of learning methodologies.

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Empowering learners Together

We’re excited to announce that we are now a part of the Axonify family. To all of those who have supported the MLevel journey, we thank you!  Here’s to the next chapter and to furthering data-driven learning.