percentages from all five studies results in an average of 60%. the 2| See their job. To further highlight the magnitude of the problem, consider the effect scrap learning has on time and money. According to the 2018 ATD State of the Industry research report, the average per employee organization training expenditure in 2018 was $1299, and the average number of training hours consumed per employee was 34. Table 1 shows how much scrap learning costs the average organization. the program as something 3| View that will enhance their career. How to Combat Scrap Learning personally motivated to use the 5| Are new information. A possible new solution to combat scrap learning is predictive learning analytics™ (PLA). PLA provides L&D professionals with a systematic and credible process for optimizing the value of corporate L&D investments by measuring and monitoring the amount of scrap learning associated. Unlike other training transfer solutions, which focus almost exclusively on training delivery and design, PLA provides a holistic approach to increasing training transfer. The methodology is founded on three research-based training transfer components and 12 researchbased training transfer factors. Learning Program Design LEARNERS: 1| Acquire new information. program as relevant to improvement in a critical 4| See department business metric if new information is applied. Learner Attributes an immediate opportunity to 12| Have use the new information learned. Predictive Learning Analytics Methodology The PLA methodology consists of three phases and nine steps, and it provides L&D professionals with insight on actions required to maximize training transfer (see Figure 1 on page 43). LEARNERS: confident in their ability to apply 6| Are the new knowledge learned. on lessons learned and how 7| Reflect they can improve their performance. the program as an opportunity 8| View to learn new things. Learner Work Environment LEARNERS: actively engaged by 9| Are manager before attending their the training to discuss how the program will improve their performance. actively engaged 10| Are manager post-program by their regarding how learning will be applied. 11| Are supported by colleagues. Phase 1 | Data Collection and Analysis The objective of Phase 1 is to identify the underlying causes of scrap learning associated with a training program. During this phase, five specific data sets are identified. Two of these are predictive, and three are data driven. The two predictive data sets pinpoint include: › Which participants are most and least likely to apply what they learned back on the job. 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 | 41https://www.trainingindustry.com/magazine https://www.trainingindustry.com/magazine