As new eLearning tools are developed and existing tools are optimized, we are collecting more and more data that will hopefully allow us to more systematically approach digital learning. In the early era of digital education, our analysis of the field has been limited due to the complexity of the relevant research and the number of variables involved. Many of the pros and cons of tools like massive open online courses (MOOCs) are purely anecdotal and thus offer little value in the development of evidence-based educational strategies. As the technical obstacles of MOOCs will likely be overcome quickly, new challenges regarding the efficacy and efficiency of MOOCs will likely become a major focus of digital education.
If evidence-based MOOCs are in our future, we will have to start approaching the factors and outcomes involved in these platforms more scientifically. One approach that is already highly used for other purposes, particularly by leading companies is the well known A/B testing method. A/B testing allows you to use two different versions of the same product initially so you can determine which product is better. For MOOCs, this may mean developing two of the same courses in different formats and measuring student performance to determine which course format to use in the future.
One of the main aspects of education that is debated is how to best keep students’ attention. Some argue that interspersing interactive activities or problem solving can make it easier for students to maintain high levels of attention throughout a lecture. A/B testing can help us determine the best ways to maintain attention by providing different versions of course that involve different intervals of straight didactic lecture interspersed with other activities.
There are a number of factors in addition to how structure the lesson itself that will be studied as eLearning tools evolve. Ways to increase engagement and interest will, for example, be highly valuable. While we can design MOOCs in ways that are conducive to systematic study of learning processes, we can also leverage pre-existing data to help us optimize MOOCs. Data from academic areas such as education, psychology, and neuroscience can be analyzed in new ways to inform the eLearning space. Subject matter experts will always be critical for the content creation aspect of course development. However, educators, psychologists, and scientists can work together with these experts to determine the best ways to present that content so that it is easily digested and remembered.
MOOCs themselves are sure to be powerful research tools that not only draw upon pre-existing data from a number of disciplines and industries but that will also help further those same industries. Millions of people use MOOCs from individual companies, and the numbers are growing. Each person who participates in a MOOC provides a wealth of data that can be used to better understand learning processes and educational techniques. As these data grow exponentially, so too will our ability to convey complex information in ways that are more and more conducive to learning. This reciprocal relationship between these academic areas of study and MOOCs has the potential to revolutionize education at all levels and in all fields. As MOOCs become invaluable for training students, we will become experts in the optimization of these platforms and will enable strategic use and development of these types of eLearning tools.
The Tips Will Help You Leverage Data to Create, Deliver, and Track Content:
- Develop goals for your content and relevant outcome measures so that you can track those goals.
- Identify a method that you think will help you meet those goals.
- Employ that method with one group and do not employ the method with another. Look at the outcome measure you developed and determine if you’ve figured out a way to meet your objective.
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