How Will Learning Analytics Change Education in the Future? Essay Example

Learning Analytics is the best option in education transformation in the future as people try to embrace the benefits associated with electronic learning. This is achieved through virtual learning environment by the teachers, the students as well as the administrators. The virtue environment contributes significantly in identifying the most relevant information in virtual learning environment. When Learning Analytics is used with the required effectiveness, it will facilitate in the achievement of enhanced experiences among the students through offering a personalized learning that will benefit the students. Students retention can also be enhanced through for example identification of areas requiring additional support at early stages.

Learning Analytics is also an important source of insight in to the current as well as the future performance of the student. The online instructors and other professionals in e-learning can custom tailor the experiences of learning analytics to address the needs of individual learners. Learning Analytics improves the learning outcomes and the entire process of learning through the use of computer aided learning and use of virtual classrooms. This offers an opportunity for virtual interactions that plays a crucial role in academic performance of the students (Reimann, Markauskaite & Bannert, 2014).

Learning Analytics applies data mining in resolving issues of interaction between the teachers and the students and also the interactions amongst the students themselves. This improves the effectiveness of learning as a result of intellectual stimulation and exchange of ideas.

Learning Analytics is also anticipated to transform the traditional face-to-face type of in-class learning which a conventionally teacher-centric type of learning is. In this situation, learning was done through direct interaction with the teacher in explaining and interpreting the learning content. Learning Analytics thus fosters transference in decentralizing the process of learning thus attributing much learning efforts in creation of content and improving the course structure. This in turn will translate in to reaching higher audience and time reduction thus accelerating the level of interaction among the students, the course agents and elements (Blikstein, et al., 2014).

Learning Analytics facilitates for empowering multiple interactions among the agents that are involved in the process of learning such as the tutors, course designers, the learners, administrative staff, interfaces, content, the environment, codes among others. Through learning analytics, the raw data in the system of education is converted to useful information, thus improving the impact of such information in the practice of education. Learning Analytics applies similar principles of data mining as in other fields such as medicine, genetics and business among others (Romero & Ventura, 2010).

The use of learning analytics facilitates for the discovery of innovative knowledge. This plays a vital role in enhancement of particular quality aspects in education and enhances the effectiveness of the learning process. Since the concept has been successful in other fields such as e-commerce, learning analytics will definitely have positive implications in the future of learning (Agudo-Peregrina, et al., 2014).  Although its progress in education has been slow, there are some notable changes in its progress and an increase in the use of learning analytics has been notable in the education system. It is however important to identify the different applications of data mining in other fields that may not be applicable in the learning environment.

References

Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542-550.

Blikstein, P., Worsley, M., Piech, C., Sahami, M., Cooper, S., & Koller, D. (2014). Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming. Journal of the Learning Sciences, 23(4), 561-599.

Reimann, P., Markauskaite, L., & Bannert, M. (2014). e-Research and learning theory: What do sequence and process mining methods contribute? British Journal of Educational Technology, 45(3), 528-540.

Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40(6), 601-618.