# The Usefulness of Video Learning Analytics in Small Scale E-Learning Scenarios
## 5 Discussion  
Both research questions have been answered with positive results: learning analytics data collected from students watching educational videos are perceived as useful by teachers, even in small scale learning scenarios, and the minimum amount of data required to achieve that perception of usefulness can be achieved in small scale scenarios. While more research is needed in order to present more conclusive results, it appears that in every learning scenario in which we expect at least 225 playbacks for a video (which may be aggregated over time), it is useful to set up an infrastructure to collect clickstream data for educational videos, to process the collected data and present the resulting information to teachers and other stakeholders. It is to be expected that in many cases, that 225 limit can be significantly lower, especially for shorter videos. One question that remains unanswered is whether data obtained from similar scenarios (say, similar courses in different schools, for example), can be aggregated together, which may reduce the impact of that lower bound. In any case, these data are at least an order of magnitude lower than those present in the literature: for those studies presenting the number of playback sessions analysed or equivalent data, [PERSON] et al. [26], [PERSON] et al. [38] and [PERSON] et al. [16] talk about \"millions of clicks\"; [PERSON] et al. [24] use thousands of playbacks; [PERSON] and [PERSON] [8] analyse thousands of events. According to the definition of perceived usefulness by [PERSON] [17], the results we have obtained regarding both perceived usefulness and ease of use and presentation of our data seem promising and granting further work: the interviewed teachers universally felt that the presented data gave them information about how students watch their videos that was not previously available to them, such as the frequency with which students skip to certain segments in the video or review other segments, and they were unanimous in their desire to have the infrastructure for data collection and analysis deployed to their videosin the future and the obtained information presented to them. Thus, the previously little explored application of learning analytics to educational videos in small scale learning scenarios seem deserving of further attention. More work is needed on the presentation of the data so that the presented information is easier to interpret, and to analyse how possible improvements are decided upon, brought to the videos and their results are analysed. In the interviews that were conducted, teachers were proactive in pointing out possible data patterns that might be indicative of potential problems associated with the educational video itself or with the design of the courses in which the videos are used and which could lead to, firstly, detecting the occurrence of those problems and, secondly, taking measures to eliminate them or minimize their effects. Some examples of such patterns are a predominance of skipping forwards being indicative of a video containing redundant segments that could be eliminated (potentially just for some students, according to their previous navigation patterns), or the existence of segments that are reviewed frequently, which could be complemented with more detailed explanations for some students, again as a result of their navigation patterns. This leads us to think that there is a big opportunity to extend the scope of the research so that it is taken to more small scale scenarios, especially those in which the use of educational videos has not been the focus until very recently, where it is to be expected that more easily detectable problems may be occurring. Regarding the limitations of our work, firstly, we must be aware we are dealing with possibly incomplete data. Collected data are not necessarily representative of the attention patterns by students: e.g., interruptions in their environment do not necessarily map to collected data.