How to Make Decisions That Work: Three Things L&D Teams Need to Know about Machine Learning
By Arijit Banerjee
Automation of mundane processes in departments such as Human Resources and Learning & Development, is old news. However, as companies allocate larger budgets, and assign a seat at the table to L&D departments as strategic partner, it is time for a technology upgrade. Machine Learning has shown a lot of promise with respect to the role it can play in L&D. For instance, it can be used to boost employee engagement, create custom learning content, measure effectiveness of learning programs and determine ROI.
Nevertheless, for a department that has traditionally leaned on human acumen to assess employees and their needs, embracing data driven technologies like Machine Learning (ML) can be perplexing. As a technological disruption that caters to the requirements of the present and the foreseeable future, transitioning to Machine Learning is imperative for L&D. In fact, a recent study indicated that 76% of decision makers believed AI to be a crucial component to overall strategy.
So, how should L&D teams equip themselves to effectively leverage ML technology?
Knowledge about the machine learning process
A survey conducted among professionals across Europe and UAE, indicated skepticism among the workforce towards AI adoption. Less than 20% of them were comfortable with accepting an AI based decision, over one made after human judgement. Moreover, 66% of the respondents believed their organization was unprepared to adopt AI based technology. These findings point to the need for L&D teams to have an extensive understanding of the Machine Learning technology that their organization’s use.
An in-depth understanding about the intricacies of ML can help L&D professionals leverage the technology to devise efficient and creative solutions. Furthermore, L&D teams can use this knowledge to address the apprehensions of the workforce regarding the technology, and encourage them to adopt and engage with the new platform.
Understanding the different algorithms
Analyzing copious amounts of data may be cumbersome for humans, but Machine Learning platforms thrive on large volumes of clean data. It is thereby essential for L&D departments to choose the optimal algorithm that can furnish actionable insights, while minimizing burden on the teams.
One of the more commonly used ML algorithms is ‘supervised learning’ which involves human intervention. Here the platform is supplied with data, and is trained to search for, and recognize particular patterns. The performance of the algorithm is then adjusted until it delivers accurate results. This type is often used to forecast future trends based on historic data. On the other hand in ‘unsupervised machine learning’ the application sifts through all the data, instead of being fed select information. With this type of algorithm, the ML platform has the potential to discover latent data trends and other significant information. However, both the algorithms require human intervention to decipher the results.
Supervised learning is a better option for those that want to evaluate the efficacy of the algorithm since the desired outcome is predefined. However, the effectiveness of unsupervised learning algorithm is more difficult to comprehend, since the value of the outcomes could vary.
Drawing insights from data
Usually, L&D teams collect and sift through a barrage of data generated from HR tools and learning and management systems (LMS). However, the real challenge is in analyzing the data and gaining insights that foster a more efficient and productive L&D program. Machine Learning platforms enable organizations to gain a deeper understanding of learners, facilitating the creation of personalized training modules with adaptive learning capabilities.
In upskilling, ML led programs help L&D teams build augmented content that drives better engagement. For instance, by studying the pattern of pausing, skipping and stopping of video content in particular sections, teams can understand effectiveness, identify the types of information that the learner seeks, as well as determine the optimal format.
Moreover, current learning platforms leverage Machine Learning to obtain predictive analytics driven insights that forecast outcomes based on past data. L&D teams can build detailed reports based on these results, to gauge efficacy and assess return on investment (ROI). They can also use these reports to ascertain individual training needs, and leverage their understanding of trends to avert issues in time. Furthermore, L&D teams can derive useful metrics that help quantify success, and recommend training paths.
When it comes to Learning & Development in organizations, the culture of continuous learning and upskilling is becoming part of the company culture. So it’s essential for L&D teams to meet the demand through optimal channels. Studies show that the completion rate of MOOC course is only 10-15%. Equipped with in-depth understanding of Machine Learning, L&D teams can have better success by designing custom programs with relevant content that are disseminated in the right channels at the right time.