Technological innovations offer immense opportunities for Kydon to make learning better, aligned with our mandate to make learning scalable, sustainable, and adaptive. Our in-house R&D team develops independently or works with global strategic partners to create new products for our clients.
Learning Content Technology
Learning content technology has stalled since the establishment of SCORM (Shareable Content Object Reference Model) standard in 2000 and the final update in 2009.
While Experience API (xAPI) was established between 2010 and 2013, it is not a content technology but focuses on recording the context in which learning experiences take place in various software platforms.
Kydon started developing its learning content technology in 2017 for template-based content development. Focused on layman users, we aim to move beyond SCORM-based authoring capabilities that only professionals can use.
The tool aims to enable more significant proliferation and “speed to market” in content creation.
In the world of Big Data, Kydon develops learning analytics capabilities where we design technology database strategies to collect, analyze, and report user learning behavioral data to enhance learning design.
Going beyond data mining, Kydon is working on leveraging Learning Records Store (LRS) technology that serves as a repository for learning records from connected systems beyond learning platforms alone.
We will also be introducing learning analytics to administrators and learners so that each user can make independent decisions to optimize their learning and make early intervention decisions.
Our platforms will analyze course or content learning and collaborative interactions and engagements.
A breakthrough application of artificial intelligence, machine learning allows our software engineers to code our computer systems to ‘think’ and ‘learn’ like humans rather than ‘teach’ them every single task.
Kydon’s R&D interests involve incorporating machine learning into our learning platforms to extract meaningful patterns of learner behavior from masses of user data.
We develop specific algorithms that are data-driven to enable our platform to make intelligent recommendations or personalize a student’s learning path by responding to his or her learning activities on the platform more effectively.
The benefit of this is learners spend less time finding what they need or want and more time learning at their interest and level. The machine learning algorithms and strategy our in-house development team examines include course recommendations and outcome prediction (e.g., Decision Trees).
The blockchain is a distributed ledger whereby transactions would have to be verified before being added to the blockchain. This ensures that, unlike traditional servers, there is no single point of failure should the server be compromised.
Malicious attackers would need to control 51% of the computing power to add false blocks of information onto the blockchain. This would promote trust in the information provided by the platform being built on the blockchain.
This is crucial as we design learning platforms that function as an education provider that people trust, including smart contract transactions and acting as a hub for certificate storage.
Blockchain can also allow course creators and learners to verify their identities online and give them more control over who can access their identity information.
Users can be reassured that they interact with legitimate parties and ensure that user identity information would not be stored on the platform. Blockchain technology can also allow for an education ledger where learners’ course history would be documented on the blockchain.