Instructional designers rarely consider themselves to be data scientists. Still, instructional designers and other content creators are expected to incorporate analytics into their processes, especially when conducting learner evaluation and analysis. The idea of applying analytics to learning data isn’t new, and the benefits of doing so have been covered extensively in recent industry literature. However, most learning and development professionals haven’t been exposed to much data analysis, and lack the skillset to measure and report powerfully.
The (Unfortunate) Norm in Instructional Design
Most learning and development organizations have implemented content authoring tools and learning management systems. These tools generate valuable data points such as enrolled students, time spent online, and final test scores. Some companies may have even invested in the latest technologies like xAPI that allow instruction designers to store and retrieve records about learners and share this data across platforms.
Canned reports and dashboards that these tools provide don’t tell a complete data story; additional tools for collaboration, storing, cleaning, analyzing, and visualizing data are needed. At organizations that are not data-driven, instructional designers may not be privy to what relevant data is collected, how it is stored and secured, who owns it, and its level of quality. This is especially true if useful data from other departments or an external entity is collected.
For designing courses, our data scientists built a bespoke content authoring tool that automates code snippet updates and standardizes formatting within a specified set of relevant instructional materials.
The Data Society Difference in Instructional Design
At Data Society, data informs every aspect of our work, and we practice a data-driven approach to drive innovative instructional content. Our instructional designers create and deploy highly customized courses that teach complex data science concepts and techniques. From R, to Python, and beyond, our courses combine hands-on skill-building with using real-time data to directly address challenges within students’ teams and organizations.
In the past, this approach posed a considerable challenge for our content creators, with dynamic programming languages and datasets continuously being updated to meet client needs rather than manually updating our training materials with fresh screen prints of code. Fortunately, our well-rounded team allows our instructional designers to collaborate with our data scientists, who jump at the challenge of solving scenarios like these. For instance, when determining the best syllabus for an executive-level course, we use text mining methods to analyze previous course evaluations’ open response sections to guide the refinement of each module into truly audience-based learning.
Data-Driven Content Authoring Tool
For designing courses, our data scientists built a bespoke content authoring tool that automates code snippet updates and standardizes formatting within a specified set of relevant instructional materials. An innovative feature built into the authoring tool provides access to all our elements exercises, slides, code, and instructor notes within one file. Now, anytime course material is updated, instructors can adjust the code quickly to dynamically revise slides without worrying about version control conflicts. The team reduced its creation time for an hour’s worth of content from 10 hours, including debugging, topic alignment, and formatting, down to fewer than 5 hours, cutting working time in half!
Digital transformation is more than just higher ROIs; it’s having the ability to transform your industry and society rapidly using tools that make your job easier, freeing you and your experts to focus on what matters. Data science revolutionizes how instructional designers create content and assess student transformation. Need help starting a data evolution at your company? Get in touch with our Solutions team at hello@datasociety.com.
