percent of the program material 1| What are you applying back on the job? confident are you 2| How estimate is accurate? 3| that your What obstacles prevented you from utilizing all that you learned? Waiting 30 days post-program is critical because it allows for the "forgetting curve" effect to take place and provides more accurate data. The scrap learning percentage score provides a baseline against which follow-up scrap learning scores can be compared. These comparisons serve as a way to monitor the effect targeted corrective actions had on increasing training transfer. The obstacles data identifies barriers participants encountered that prevented them from applying what they learned. Waiting 30 days to collect the data allows for the full range of training transfer obstacles to emerge since some are likely to happen almost immediately while others will occur later. Frequently mentioned obstacles are candidates for targeted corrective actions to increase training transfer. Phase 2 | Solution Implementation While pinpointing the underlying causes of scrap learning is valuable, being able to monitor and manage the targeted corrective actions taken to address them is of even greater significance. It is the focus of Phase 2 and this is where the "rubber meets the road." It's where you can be strategic and use data to connect a training program with job application. It's also an opportunity to demonstrate Phase 1 | Data Collection and Analysis SELECT learning program and identify Calibration Cohort creative problem-solving and the ability to manage critical business issues to a successful conclusion. Phase 3 | Report Your Results The objective of the third phase is to share your results with senior executives. Deliver the data as a story and take the executives on a journey of discovery. Start with a hook, tell the truth without bias and provide context. In summary, scrap learning has been around forever. However, there is now a way to measure, monitor and manage it using predictive learning analytics.™ Ken Phillips is the founder and CEO of Phillips Associates and the creator and chief architect of the predictive learning analytics™ learning evaluation methodology. Email Ken. 30 DAYS after previous step BUILD your PLA survey and COLLECT data CALCULATE: * LAI * MTSI * TTCI CALCULATE scrap learning percentage and identify obstacles to training t ransfer Phase 2 | Solution Implementation TARGET "at risk" and "least likely" to apply learners for reinforcement TARGET managers with low or negative MTSI scores for help and support DEVELOP targeted corrective actions to mitigate or eliminate the underlying causes of scrap learning TARGET training transfer components and factors with low TTCI scores for improvement TARGET obstacles to training transfer for elimination CONDUCT Level 2 and 3 evaluations to validate accuracy of PLA algorithm RECALCULATE scrap learning percentage following implementation of targeted corrective actions Phase 3 | Report Your Results REPORT results to key business executive stakeholders ADD data from LMS/HRIS systems T R A I N I N G I N DUSTR Y MAGAZ INE -DATA FLUENCY IN LEARNI NG 2 02 0 I WWW. T RAI NINGINDU S T RY . C OM/ MAGAZ I NE | 43https://www.trainingindustry.com/magazine https://www.trainingindustry.com/magazine