LITERATURE REVIEW ON ASSESSMENT MODELS AND THEIR RELATIONSHIP TO LEARNING ANALYTICS
Current trends increasingly evidence the importance of the use of processed data supported by technological tools that offer support to the teacher for an effective intervention, and with it an improvement of the student's real situation. This article presents a literature review on the relationship of learning analytics to a teacher evaluation model in blended learning environments in higher education. The main objective is focused on identifying the relationship between the use of data through the analysis of students' results for the design of an evaluation model that allows adapting the contents of a course through recommendations from analytics. A literature review is developed, following a methodology based on the logic of systematic mapping with the PRISMA model. Three processes are developed according to the protocols of data search planning, search execution and results analysis. 105 scientific papers of greater relevance are identified from a set of 942 of the most cited in the Web of Science, from 2020 to 2022 and according to the inclusion and exclusion criteria for the terms of the metadata search string. The studies gather different ways of using technologies derived from learning analytics, as well as their support, making it possible to apply changes in the curriculum to generate an improvement in the evaluation process. The models identified are grouped into three categories. The first focuses on the learning process for the student that involves the technological infrastructure and its information, a second category that emphasizes the support of learning analytics as information to teachers, and a third category that addresses learning from the self-regulated model and approaches, which give a vision for the support of the technological infrastructure, standardized resources, organizational and instructional designs, as well as the strategies duly proposed by the students that allow the development of the learning process. The different proposals found pose challenges to improve the evaluation process of university students. The results show the great potential impact of learning analytics on existing assessment models. The development of tools to support analytics according to a learning context opens the possibilities of inducing others to adopt increasingly promising developments. Among the limitations, the lack of transversal skills in the teacher to achieve effectiveness in the learning process and the proper interpretation of the data tools is evidenced. This review contributes to clarify the fundamentals of the relationship between learning analytics and assessment models.