With the evolution of Web 2.0, the user is no longer treated like a simple consumer, but also a producer of web content. This shift has put the user at the center of the data collecting process. Consequently, an enormous volume of data is produced every second and from various sources (social networks, search engines, various services, videos, images, text). Big Data refers to the analysis and exploitation of this vast amount of information. However, These data do not exist in a perfectly ordered form and are not susceptible to traditional analytical operations. They no longer fall within the structures which are easy to use and go through, rather they are semi-structured or unstructured. Also, in the educational context, the social tools of web 2.0 allow us to create and publish any type of educational content such as lectures, exercises, assignments or tests, and resources for informal and collaborative learning. However, collected data cannot always be efficiently analyzed and used in order to meet learner needs and expectations. This poses a question of how to deal with and process these unstructured data to make them consumable by human users and applications?
According to the International Journal of Advanced Computer Science and Applications, currently, 2.5 terabytes of data are produced every day in the world. By the year 2020, it is estimated that this number will be multiplied by 50. Google receives 40,000 requests for information every second, 72 movies are set to YouTube every minute, and 217 new Smartphone users are counted every minute. Today the information is coming from all sides: geolocation sensors, data from smartphones (connection logs, appeals, etc.), data posted on social networks, video and satellite images, transactions, sensor forms of movement or connected objects, etc.
In the area of data management for purposes of e-learning platforms; the rapid increase in the number of types of information makes it extremely difficult to deal with it through traditional data management tools. Indeed, for educational purposes and especially in distance learning, the user is left with course materials in all kinds of formats (text, video, image), quizzes, and other types of heterogeneous data. This requires a specialized system that can manage a wide variety of data and provide flexibility and consistency so that the user can have the best learning experience.
Structured Data vs Unstructured Data
For the most part, structured data refers to identifiable and highly organized information that is smoothly integrated into relational databases and is easy to navigate through with simple search engine algorithms. The original underlying benefit of structured data focused on the separation of content and format. The separation enables content to be created independent of the delivery platform or rendering device. The evolution of structured data has led to more benefits beyond the original separation of content from format concept. These include improved metadata, linking, configuration, content management, and data exchange. On the other hand, unstructured data is the exact opposite. The lack of structure makes the compilation an inefficient and laborious process.
Structured data makes it much easier to deal with information by using computers. However, users usually don’t interact with information in database format, and unstructured data presents information in forms that are more agreeable with humans. This blog is an example of unstructured data. While articles might be arranged by date and time, if it were truly structured, they would also be arranged by exact subject and content, with no deviation or spread. This would be impractical for writers and readers both; because even in highly focused articles, people tend to touch on different subjects and make various references.
The problem with unstructured data is that of volume. It requires a huge investment of resources to sift through and extract the necessary elements, as in a web-based search engine. Current data management techniques often miss a considerable amount of information. As a result, the potentially game-changing data is wasted because of inefficient analysis.
Benefits for Elearning
According to I/ITSEC, two out of three changes to curricula in the U.S. Navy are driven by equipment changes. They suggest that the percentage is similar in the other services, although specific analyses are unknown. The single largest cost for training content and technical manuals is lifecycle maintenance. It follows that structured data reduces ownership costs through the ability to maintain the training content simultaneously rather than subsequently to system updates.
The relationship between governance, acquisition, design, development, and lifecycle management of learning content used in a VLE must include a coherent and consistent approach to handling data on a system-wide level. Efficient content management and development in a learning environment must be based on a quantitative set of expectations and requirements. The use of structured content based on technical data specifications is fundamental to facilitate content management in learning. This approach enables the mapping of metadata between acquisition, development, and lifecycle management that results in learning content being ensured, traceable, and aligned to the systems and to the learners.
YouTestMe eLearning solutions have been designed with all the benefits of structured data in mind. The system infrastructure enables the hierarchical organization of learner groups and their respective courses. For example structuring a college department with all its courses, lessons, materials, exams, marking criteria, results, statistics, and classes in a neatly organized hierarchy. And all of these categories can be further organized into sub-levels. Each category, each learner, each item has a specific place in the structure. It allows for efficient navigation and a high degree of user-friendliness. Everything can be located easily and nothing ever gets lost.
Source article: LinkedIn