Bridge the Gap from Training to Application with Predictive Learning Analytics

by Ken Phillips, CEO, Phillips Associates

In my previous blog post, I discussed the concept of scrap learning and how it is arguably the number one issue confronting the L&D profession today. I also provided a formula you could use to estimate the cost of scrap learning associated with your training programs.

In this post, I’ll share with you a revolutionary methodology I’ve been working on for the past several years called Predictive Learning Analyticts™ (PLA). The method enables you to pinpoint the underlying causes of scrap learning associated with a training program. It consists of three phases and nine steps to provide you with the data you need to take targeted corrective actions to maximize training transfer (see figure below). 

While the specific questions and formulae for the scores are proprietary, I hope you can apply the concepts in your organization using your own survey questions and your own weighting for the indexes. Even if you adopt a simpler process, the concepts will guide you and the article will give you an idea of what is possible.

Phase 1: Data Collection and Analysis

Unlike other training transfer approaches which focus mostly on the design and delivery of training, PLA offers a holistic approach to increasing training transfer. Built on a foundation of three research-based training transfer components and 12 research-based training transfer factors (see chart below), PLA targets the critical connection among all these elements. In short, PLA provides L&D professionals with a systematic, credible and repeatable process for optimizing the value of corporate learning and development investments by measuring, monitoring, and managing the amount of scrap learning associated with those investments.

Training Transfer Components & Training Transfer Factors

Phase 1: Data Collection & Analysis

The objective of phase one, Data Collection & Analysis, is to pinpoint the underlying causes of scrap learning associated with a training program using predictive analytics and data. Five metrics are produced and provide L&D professionals with both direction and insight as to where corrective actions should be targeted to maximize training transfer. The five measures are:

  • Learner Application Index™ (LAI) scores
  • Manager Training Support Index™ (MTSI) scores
  • Training Transfer Component Index™ (TTCI) scores
  • A scrap learning percentage score
  • Obstacles preventing training transfer

Data for calculating the first three measures: LAI, MTSI, and TTCI scores, is collected from program participants immediately following a learning program using a survey. The survey consists of 12 questions based on the 12 training transfer factors mentioned earlier. Data for calculating the final two measures are collected from participants 30 days post program using either a survey or focus groups and consists of the following three questions:

  1. What percent of the program material are you applying back on the job?
  2. How confident are you that your estimate is accurate?
  3. What obstacles prevented you from utilizing all that you learned if you’re not applying 100%?

Waiting 30 days post program is critical because it allows for the “forgetting curve” effect—the decline of memory retention over time—to take place and provides more accurate data.

LAI Scores

LAI scores predict which participants attending a training program are most likely to apply, at risk of not applying and least likely to apply what they learned in the program back on the job. Participants who fall into the at-risk and least likely to apply categories are prime candidates for follow-up and reinforcement activities. Examples include email reminders, micro-learning or review modules, and coaching or mentoring to try and move them into the most likely to apply category.

MTSI Scores

MTSI scores predict which managers of the program participants are likely to do a good or poor job of supporting the training they directed their employees to attend. Managers identified as likely to do a poor job of supporting the training are prime candidates for help and support in improving their approach. This help might take the form of one-on-one coaching; a job aid explaining what a manager should do before, during, and after sending an employee to training; or creating a training program which teaches managers how to conduct pre- and post-training discussions with employees.

TTCI Scores

TTCI scores identify which of the three training transfer components and the 12 training transfer factors affiliated with them are contributing the most and least to training transfer. Any components or factors identified as impeding or not contributing to training transfer are prime candidates for corrective action.

Scrap Learning Percentage

The scrap learning percentage score identifies the amount of scrap learning associated with a training program. It provides a baseline score against when follow-up scrap learning scores can be compared to determine the effect targeted corrective actions had on increasing training transfer.

The obstacles data identifies barriers participants encountered in the 30 days since attending the training program that prevented them from applying what they learned back on the job. Waiting 30 days to collect the data allows for the full range of training transfer obstacles to emerge. For example, some are likely to occur almost immediately—I forgot the things I learned—while others are likely to occur laters—I never had an opportunity to apply what I learned. Frequently mentioned obstacles are prime candidates for corrective actions to mitigate or eliminate them.

Phase 2: Solution Implementation

The objective of phase two: Solution Implementation, is to identify, implement, and monitor the effectiveness of corrective actions taken to mitigate or eliminate the underlying causes of scrap learning identified during phase one. Here is where the “rubber meets the road,” and you have an opportunity to demonstrate your creative problem-solving skills and ability to manage a critical business issue to a successful conclusion. Following the implementation of the corrective actions, it is now time to recalculate the amount of scrap learning associated with the training program. You can then compare the results to the baseline scrap learning percentage calculated during phase one.

Phase 3: Report Your Results

The objective of the third phase: Report Your Results, is to share your results with senior executives. Using the data you collected during phases one and two, it is time to show that you know how to manage the scrap learning problem to a successful conclusion.

In Sum

Scrap learning has been around forever, however what is different today is that there are now ways to measure, monitor, and manage it. One of those ways is through Predictive Learning Analytics™. Alternatively, you might employ the concepts to build your own simpler model. Either way, we an an opportunity to reduce scrap learning.

If you would like more information about the Predictive Learning Analytics™ methodology, email me at: ken@phillipsassociates.com. I have an ebook that covers the method and a case study illustrating how a client used the process to improve the training transfer of a leadership development program.

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