Research and Development

Instructional Design, Crowdsourcing and Quality Control

Instructional Design

Instructional design is the theory and practice of designing instructional experiences and encompasses the design of educational resources such as hypertext, images, animations, charts, graphs, infographics, audio, video, 3D graphics, computer simulations, games, learning objects, courses, textbooks and the contents of intelligent tutoring systems.


Diagrams are symbolic representations of information according to visualization techniques. Diagrams, conveying information visually, are a powerful and expressive medium. Diagram authoring software applications allow individuals without computer programming expertise to participate in the design of resources, the entry of data and the development of software.

Educational resources such as learning objects, courses, textbooks and the contents of intelligent tutoring systems can be represented as diagrams.


Crowdsourcing leverages the intelligence and wisdom of crowds toward the collaborative design of resources and the solving of problems.

The crowdsourcing of instructional design is facilitated by software applications for the collaborative authoring of extensible diagrams.

Relevant software systems include wiki software, collaborative document authoring software, integrated development environments, extensible workflow and diagram authoring software and version control systems.

Quality Control

Quality control is essential to crowdsourced endeavors. Learning management systems may interoperate with crowdsourced diagrams. Intelligent tutoring systems may draw dialogue, behaviors and other contents from crowdsourced diagrams.

With a model of quality – a definition of quality – quality control can be described as matters of quality assessment and quality assurance.

The quality of crowdsourced instructional design relates to the quality of other crowdsourced endeavors, such as encyclopedias, insofar as the veracity and factual accuracy of the resultant information is paramount. Also important to the quality of instructional design is its pedagogical efficacy. As education science is considered to be the study of improving the teaching process, quality with respect to crowdsourced instructional design can be phrased as a moving target.

The assessment of quality can utilize educational data mining, learning analytics, learner feedback and other techniques for obtaining educational metrics.

The assurance of quality is approached as a matter of computer-aided design. Software can apprise users of pertinent design constraints, educational standards and recommendations. Software can provide users with insights into what is working and why with respect to design elements, structures and patterns in the contexts of learning objectives, plans and strategies. Software can provide users with information, warnings and errors with respect to courses being designed and with respect to existing courses. Software bots can process collections of educational resources, for example performing verification and validation services.


We need to standardize models for the diagrams of educational resources such as learning objects, courses, textbooks and tutorial contents. Diagrams of such educational resources should interoperate with both learning management systems and intelligent tutoring systems.

We can envision educators and instructional designers utilizing diagram authoring software. Both real-time collaboration and larger-scale crowdsourcing scenarios should be considered with respect to such software.

One promising area of research and development is computer-aided crowdsourced instructional design which leverages the intelligence and wisdom of crowds toward the design of diagrams of educational resources while the recommendations of software tools and the insights of education science are brought to bear for quality assurance.

Another promising area of research and development is computer-automated instructional design which encompasses the synthesis, selection and sequencing of multimedia educational resources per learning objectives, plans and strategies. Approaches may utilize machine learning and discovery upon collections of educational resources, such algorithms informed by educational metrics.