Do you want to improve your training analysis process? As the first stage of the widely adopted ADDIE model, analysis plays an imperative role in the success of a training program. The training analysis determines training objectives, targeted learners and delivery methods, helping to ensure that time and resources are invested well.
Now, with the advancement of data and machine learning technologies, learning and development (L&D) professionals can reinvent the training analysis process from two perspectives: redefining the source of data and turbocharging the analysis process.
Redefining the Source of Data
Ideally, the training analysis should use data collected from multiple sources to avoid bias or oversights. However, the conventional approaches — namely, surveys, questionnaires or interviews — are labor-intensive and time-consuming. Relying on these approaches, most L&D departments, especially in large organizations, struggle to reach a diverse sample. In the past, training analysis often settled with data collected from a small number of employees that reflected only snapshots of the organizations. This type of compromise raises risks that the final training program might fail to meet business needs.
L&D departments aspiring to more and better data should reconsider where they collect it. In fact, companies have a plethora of data that points to training needs; L&D professionals just need to know where to look. Terabytes of data are generated continuously in company digital platforms, such as search engines (internal and external), learning management systems (LMSs), talent management systems and enterprise communication tools.
Besides the data generated in a digital format, conversations from offline meetings, such as performance review meetings, can be easily transcribed and converted to a digital text format. Compared to data collected through traditional methods, this type of data is generated in a more authentic context and represents a larger number of individuals across the organization.
Turbocharging the Analysis Process
Acquiring the learning data is only the first step. The next, equally important, step is to harness it. People relying on manual processes or tools developed for small data will soon find themselves overwhelmed by big data. Computational algorithms provide a more efficient way to transform data into insights.
What insights can machine learning algorithms bring to L&D professionals? Here are a few examples:
Reveal Knowledge and Skill Gaps
Text mining algorithms can reveal the knowledge and skills employees most need while performing their daily tasks. While employees across the organization may use different terminologies to refer to the same thing, ask different questions relating to a same problem and carry out lengthy discussions revolving around one topic, text mining algorithms are capable of abstracting the essence of this complexity. In addition, comparing the data generated by a novice and with the data generated by an expert can provide insights about the training required to convert the former to the latter.
Identify Target Audience
Clustering algorithms help L&D professionals identify groups of employees with similar knowledge levels and learning needs. Instead of giving the whole company the same training, L&D professionals can give employees training content that is tailored to their needs.
Determine Delivery Method
Algorithms can also leverage employees’ learning-related behavior data. Drawing from this type of analysis, L&D professionals can choose an effective delivery method based on employees’ learning habits, such as the preferred time slot for learning, the preferred format of information and the average attention span.
As these examples suggest, big data and machine learning algorithms can greatly improve the learning analysis process. It’s time for L&D professionals to leverage these new technologies and develop more targeted, effective training solutions.
