Feedback and Assessment
Machine learning can empower applications to provide more advanced feedback techniques – delivering automated, timely, relevant, individualised feedback to learners informed by real-time data processing by ML algorithms. An ML application can process aggregate learner data to predict the optimum occasions to provide feedback and identify the format feedback should take. ML techniques can allow feedback to take place when learners are in the flow of learning, when it may be most effective. ML techniques enable systems to deliver intelligent automated feedback, such as advice on skills gaps, directly to learners.
ML can be a driving force behind more efficient online assessment strategies. By processing large amounts of user data, applications can create more intuitive, intelligent tests and quizzes for learners. Some systems can automatically formulate appropriate test questions in reaction to learner activity. This can be delivered when needed to provide dynamic and effective assessment strategies within learning platforms.
Personalisation and Recommendation
Personalisation and recommendation of learning resources is a common current use of ML in learning platforms. In this context, ML interprets large volumes of data to match appropriate content to learners’ needs based on their, and other users’, previous activity. This allows more targeted planning and development using learning behaviours, performance indicators, and emerging patterns to personalise online training.
ML is already implemented in some of the large learning platforms to drive more intelligent content recommendation for learners. This helps users find more relevant content from the increasingly large volumes of content available, both in internal knowledge repositories and on the web. ML enables systems to offer personalised training paths and adaptable online training resources based on data about employees’ learning experiences and assessments.
Identifying Learner Groups
Corporates, higher education institutions and schools are all focused on the early identification of learners who are at risk of disengaging and failing to complete their learning objectives. Learning analytics and machine learning techniques can identify trends in learner data that empower organisations to identify at-risk learners earlier and with greater accuracy.
Identification of at-risk learners improves the learner experience by providing necessary support earlier in the learner’s journey. As well as supporting at-risk learners, ML can help organisations identify high-performing learners or other learner groups. This type of insight can enhance the organisation’s learning strategy and increase learning effectiveness by tailoring learning to the needs of specific learner cohorts.
Chatbots and Virtual Assistants
We are already familiar with chatbots and virtual assistants that provide contextually valuable information to help us complete tasks. Machine learning is the introverted superpower that has contributed to the significant progress of these chatbots and intelligent agents in recent years. While not commonly applied in learning environments, they are likely to become integral learning tools in time. Chatbot and virtual assistant platforms driven by ML and NLP techniques boast features that would be valuable in a learning context. Chatbots can be built and trained for learning without much technical development because other services provide much of the underlying platform. Chatbots also rely on much simpler user interface (UI) elements that do not require significant design and development. As they deliver large amounts of learning data, chatbots and intelligent agents offer extensive scope for additional integration of ML in learning.
There is no question that machine learning has the capacity to extensively enhance the HR function and, in particular, corporate learning and performance. When properly applied, ML can provide significant benefits across a wide range of learning and performance areas. It has the potential to provide more informed, reactive and effective learning platforms that can support the needs of today’s learners and drive a more data-centric approach to the delivery of learning, and learning strategy, within organisations.
While it is likely that an increased reliance on intelligent machines will make many current jobs redundant, their increasing integration into business workflows will create new jobs characterised by less manual effort – freeing humans to spend more time using their skills in critical thinking, analysis and creativity.