Higher education is undergoing significant changes. The demand of changing the instructional approach from a traditional teacher-centred approach to a project-based or problem-based student-centred approach is widely discussed [Schreurs & Dumbraveanu, 2014]. Also, digitalization leads to an increase in digital tools, including online interaction, which open new opportunities for increasing the individualization of education in terms of location, time, content, and pace of learning. All these significantly raises the demand for the quality of teaching and instructional design [Educause, 2021]. 

At the same time, due to the digitalization of education, learning analytics is becoming an essential growing field of study and practice in education worldwide. The data on the instructor’s work and the students’ learning activities could be analyzed to enhance learning outcomes and the educational environment. Learning data is used in primary, secondary, and tertiary education and lifelong learning. The exampling areas of its application are: learner`s motivation, learning styles detection, performance prediction, etc. [Bravo-Agapito et al., 2018]. Data for analysis may be extracted from various digital environments, such as LMS [Schumacher, et al., 2018], MOOCs [Feng et al., 2019], applications. Learning data may be analyzed through different methods, like factor analysis, regression, correlation mining, causal data mining, path mining, clustering, text mining, classification [Bravo-Agapito et al., 2018]. Importantly, data-driven decision-making in education may provide feedback to instructors, informing them on the possible way of the course improvement [Schifter et al., 2014]. 

However, two problems can be stated. First, the problem of the current instructional design models is that course design improvements are usually implemented at the end of the course, which does not benefit the current learning group. It is difficult for teachers to get and interpret current data and plan timely interventions. Considering this, learning analytics can provide new opportunities to gain valuable insights into student learning behaviour. But there are limited ways of using learning analytics for instructional design improvement now, not many cases were described in the research literature, and no systematic literature review on this topic currently exists [Schmitz et al., 2017]. At the same time, there is an apparent demand among instructional designers and teachers to use learning analytics in the design and/or redesign of courses [Muljana & Luo, 2021].

The other side of the problem is the challenge posed by learning analytics. Despite learning analytics providing data on specific, observable behaviour in real time, there is a lack of clear contextual frameworks that lead teachers and instructional designers to interpret the information. Instructional design can be seen as a framework allowing to capture pedagogical intent that can provide context for understanding different sets of data [Lockyer et al., 2013].

Thus, despite the great possibilities of learning analytics, its resources have not yet been used efficiently in instructional design improvement. There is a severe gap between the theory and practice of instructional design and the possibilities of learning analytics. This gap should be bridged through the development of the theory and practice of instructional design that could contribute to the constant improvement of educational products through their redesign based on evidence-based approaches, including learning analytics. 

The first step to bridge this gap is to map the existing research literature in the intersection of learning analytics and instructional design. The research question for the proposed scoping literature review is whether this field provides suggestions to course quality enhancement, how elaborated this field is, what results it came to and what gaps are looking forward to being addressed by education researchers in the future.

The research will be performed as a scoping review aiming at the following aspects, formulated for current research basing on (Munn at al.): 1) identifying the types of available evidence of learning analytics applications for informing and improving instructional design; 2) examining how research is conducted in such a field, which methods are applied, which theories are discussed and which data is used; 3) discovering the gaps in the knowledge base. At the first stage of the review research, the original papers and reviews satisfying the search request “learning analytics AND instructional design” in article title, abstract and keywords will be derived from the major bibliometric databases Scopus and Web of Science. At the second stage, those papers that do not have an open full text will be extracted from the sample. Next, two scholars will independently read these papers to manually extract the authors’ name and affiliation, research questions, applied methods and data, research results, practical implications, as well as limitations. Finally, the authors will discuss their results to elaborate the decision on the review results.

The suggested review will result in a map of the research in the intersection of learning analytics and instructional design. It will deliver the scholars and institutions working in this field, theories, methods and data that are used, and the outcomes that have been achieved by those scholars so far. In general, this review will answer the question of whether this field provides suggestions to course quality enhancement, how elaborated this field is, what results it came to and what gaps are looking forward to being addressed by education researchers in the future. It will help to state new research questions in this field knowing that they are original and haven’t been answered yet. The newcomers to the field of learning analytics for informing instructional design will get a map of the research centres and leading scholars they might consider for potential cooperation.

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