The basis of personalization is focusing on the features of the learners to achieve better outcomes. Thus, a suitable method to achieve personalization is to use learning analytics in a personalized learning system. In other words, an example would be specialized schools, where one can find the most suitable group according to one’s special needs (Burgos, 2020). Moreover, according to the information obtained in the process of analytics, the learning process will also be formatted depending on the features.
Furthermore, specialized schools develop an example in which learning analytics uses a personalized learning system to provide additional information to learners and/or instructors. This can be articulated by special surveys or works aimed at obtaining results about the characteristics of the group. Depending on the results obtained, the educational process can be formatted according to the special directions of most of the members of the group. In addition, the groups themselves can also be modified to achieve the greatest efficiency. Finally, in my opinion, these two scenarios are both relevant because they formulate the use of the most effective tools in terms of achieving positive outcomes.
In other words, personalization and learning analytics complement each other due to different principles of working for the same purpose, namely the collection of information. In addition, due to some differences, these two processes can use each other’s features to fill gaps in their principles. For example, in case of need in the analytical aspect, it is necessary to resort to the principles of learning analytics. Finally, it should also be noted that these processes are important in terms of the harmonious development of students, as they formulate the adjustment of the environment to the person, and not conversely.
Reference
Burgos, D. (2020). Radical solutions and learning analytics: Personalised learning and teaching through big data. Springer Nature.