An increasing number of organizations across the globe are showing an interest in learning analytics. The reason behind this is that learning analytics can help an organization understand how best to train its employees, making the investment in training programs more worthwhile.
But, applying learning analytics comes with its own set of challenges, and ignoring these challenges will cost you time and money. Here is a list of challenges that L&D personnel face during the implementation and application of learning analytics.
Capability Gaps in Data Handling
When compared to financial or operational analytics, learning analytics is still in a nascent stage.
Frequently, L&D personnel are unclear as to how capturing data will help align training programs with organizational goals. They often also do not understand what data should be measured, or why.
It is essential to have a process in place for handling data and for reporting the metrics to stakeholders in the business. There is no point in analyzing data for the sake of analysis. For example, if there is a new curriculum for training the sales force, check whether it has had an impact on increasing sales revenue. A thorough analysis of the course data will help you understand how to improve the training program in such a way as to better align with the goals of the organization. This can make it easier to offer recommendations that will help stakeholders make informed decisions.
To implement learning analytics, you must first have the infrastructure in place to do so. The LMS in your organization is a powerful system that captures and stores an extensive quantity of raw data. Learning analytics can be applied to this data to gain powerful insights that can then help the L&D department to develop even better training programs.
Dealing with Analysis Paralysis
It is true that learning analytics can help training programs to become more effective, and thus profitable. But, at the same time, having too much data on hand can lead to a situation in which you are so data-driven that you become hesitant to make even the smallest of decisions. This is termed analysis paralysis.
The process of data gathering, and the subsequent analysis of data sets that are irrelevant or unnecessary for the decision-making process, often amounts to nothing more than a waste of time and money.
The solution lies in deciding how much data is enough to make a decision. It is better to set a timeframe for the decision-making process. Having access to the right data, at the right time, can help you make the right decision – avoiding analysis paralysis.
Flawed Analysis of Data
When the analysis of learning data is flawed, it leads to faulty decisions. For instance, if a report generated by applying learning analytics through an LMS reveals that the average assessment score is currently 95%, then you cannot conclude that all the employees in the organization have fared well in the assessment. You will have to get data on the number of learners who were supposed to take the online training program, and their progress in the course. A skewed data analysis results in an incorrect report.
Presentation of Data
Learning analytics can give you an insight into the status of training programs within your organization, and the way they have contributed to meeting the organization’s goals. The collection of data is not enough; you also need to present it in the right format. You may have collected important data, but if you do not present it properly, you will be unable to impress the decision makers. Sort, and group data in order to present the essential information clearly and effectively.
For example, consider the results of a survey in which learners have requested more information on a certain part of the e-Learning course. You have the data to show that the online training program requires some rework, but how do you convince stakeholders? Data gathered through learning analytics can be presented in the form of graphs or charts to make it easier for decision makers to understand. You can always get a report on learners’ scores by topic, and present a comparison chart of learners’ performance in each section.
Data Protection Laws
Learning analytics makes extensive use of data and data analysis to improve the learning process for learners. When you collect personal data, there are certain data protection laws that you must comply with, depending on the location of the learner.
Your organization may not have the rights to transfer personal data of learners to jurisdictions that are not recognized as having adequate data protection measures in place. This may also include companies in the same group located in different geographical areas. For example, countries in the European Union are covered by a data protection law. If data is shared to countries outside of this jurisdiction, it can result in a fine of up to 4% of the annual global turnover.
Sharing Learning Analytics Information
HR departments in organizations are concerned about sharing data gathered through learning analytics. Displaying data on dashboards or sending email alerts to learners who have not performed well in the online course assessment can have negative consequences. Learners with poor scores may feel demotivated to continue with the training.
Also, if learners have given a poor rating for a particular online course and this is displayed online, this may act as a deterrent for other learners; they may see that the course is difficult, or has been rated poorly, and lack motivation to complete the course.
When you decide to implement learning analytics in your organization, remember to plan ahead so that adequate measures can be put in place to address the challenges listed above. Ignoring these challenges can cost you – both in time and in the organization’s money.
For those who are already using learning analytics within the workplace, use the Comments section below to share your experiences on dealing with these challenges.