# The Usefulness of Video Learning Analytics in Small Scale E-Learning Scenarios
## 2 Literature Review
### Learning Analytics and Educational Data Mining to Enhance the Teaching/Learning Process  
The fields of learning analytics and educational data mining have put considerable effort into studying how students interact with learning resources. Naturally, this is hampered by the fact that one can only analyse interactions leaving a trace. Moreover, with textual resources, even if they are electronic in nature (a website, a PDF file), it is very hard to know where a student is in the text without intrusive, cumbersome equipment: it is only through the use of eye tracking that one can follow the gaze of students at a given point. This has had an effect on granularity: many of the research papers found in the literature (see [1, 21, 22]) are more concerned with student navigation through sequences of learning materials than the interaction with a single learning resource as a venue for the teaching and learning process. Thus, even when an infrastructure to collect learning analytics data has been put into place, in most cases its objective is not to get back some of the information that teachers are expected to obtain in traditional, face-to-face settings. Online video, as we have seen, provides a big opportunity to dig down and operate at a lower level of granularity. Most research papers found in the literature focus on the context of MOOCs (massive online open courses) [16, 24, 25], which often use collections of educational videos as the main learning material. This context has given place to a lot of very interesting research on large scale information for a video: it is relatively easy, for example, to observe patterns such as student dropout or navigation patterns (say, students reviewing a given segment of a video at a given point in the course, or repeated viewing of worked-out examples) [26], and this can provide teachers with actionable information that can be used to detect problematic points in a video, segments that need to be explained in more detail, etc. That research has, understandably, been more concerned with providing information for the improvement of an educational video than with analysing how a single student--or small groups of students showing common behaviour--watches that video and trying to detect patterns and intervene on her learning. That is especially justifiable in the MOOC context, where the student teacher ratio is very high and student expectations regarding teacher interaction are low. However, in many educational contexts, in higher or secondary education, student teacher ratios are much lower, and teachers are expected (and able) to give individual students feedback on their learning activities. In our experience, when teachers are offered a peek into the trace left by students watching educational videos, they are often surprised at how a significant number of students do not watch the video from beginning to end without pausing or skipping backwards or forwards: for most educational videos we have analysed, independently of the subject or its learning objectives, there will be a significant number of students who will pause and skip, and go backwards and forwards, significantly more than lecturers had anticipated. Additionally, most of the research has focused on either giving the student some feedback to improve her learning or trying to predict student success or failure. Our focus is on detecting segments in the video that could be improved in some way (by reshooting it, editing it or by adding some interactivity to the video in response to student behaviour patterns, such as suggesting additional learning resources for some students, for example) to improve the teaching and learning process. The use of blended and virtual learning is currently increasing at a very fast pace, with pre-pandemic expectations of annual growth at a yearly rate of 15.5% until 2025 [27]. Due to the pandemic, higher educational institutions are adding additional tools and resources to support learners across their learning process.