The widespread use of technologies is increasing exponentially in various sectors including education. In relation to this, Open and Distance Learning (ODL) is one of the methods in delivering lectures through the use of internet. ODL has been proposed years back but the implementation is getting obvious lately. Due to unforeseen circumstances, ODL is the best medium to ensure the effectiveness of the deliverable. An instructional model is used as a method to guide teaching process. This method would be more useful when it can integrate with learning styles as well. This paper aims to integrate instructional models with learning styles for the ODL environment. Based on the previous research, classifying the instructional models that fit best to the learning styles would help in enhancing student performance. This integration will also give benefits towards educators significantly. To conclude, a well-designed instructional model that is align with learning styles will give a great impact on teaching and learning process.
<p>COVID-19 is a pandemic crisis that has introduced new norm to the world where we are not encouraged to be in 3C areas, namely crowded place, confined space, and close conservation. We must also ensure that we are at least one meter apart from one another at all time even while queuing. The queuing process can be seen at any organization that offer services. Adhering to the new norm can be a challenge for organization such as banks, hospitals, and government offices when the number of clients waiting in queue increases while in confined space. On the client’s side, they must go through the queue process of obtaining a queue number ticket and then wait to be served in confined and sometimes crowded space every time they require a service. Thequeue process will be repeated at different premise. This study proposes real-time multi-organizationsC19-SmartQ system which use predictive modelling to generate single or consecutive queue number tickets for any client requiring services from two different organizations located within the same building. C19-SmartQsystemmanages queues thus administer social distancing and streamline queues to reduce waiting periods and improve service efficiency. To ensure operability of C19-SmartQ system, itwas tested on the functionality and web server speed performance. The web server speed performance results show that data transfer and web loading were stable since there was only an increase of 0.2 seconds or 0.08% as the number of users per session increases. In the future, the system can be designed to accommodate queuing for more organizations located within the same building. Machine learning can also be integrated in the system to improve the predictive modelling based on current environment at each organization.</p>
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