# The Usefulness of Video Learning Analytics in Small Scale E-Learning Scenarios
###### Abstract  
A variety of tools are available to collect, process and analyse learning data obtained from the clickstream generated by students watching learning resources in video format. There is also some literature on the uses of such data in order to better understand and improve the teaching-learning process. Most of the literature focuses on large scale learning scenarios, such as MOCCs, where videos are watched hundreds or thousands of times. We have developed a solution to collect clickstream analytics data applicable to smaller scenarios, much more common in primary, secondary and higher education, where videos are watched tens or hundreds of times, and to analyse whether the solution is useful to teachers to improve the learning process. We have deployed it in a real scenario and collected real data. Furthermore, we have processed and presented the data visually to teachers for those scenarios and have collected and analysed their perception of their usefulness. We conclude that the collected data are perceived as useful by teachers to improve the teaching and learning process. learning analytics; learning from video; small scale learning scenarios; online learning; blended learning +
Footnote †: journal: Medical  
example [13]. [PERSON] et al. [14] take a similar approach using xAPI [15]. These collected data are usually called clickstream data [13; 16]. Moreover, given the linearity of video, one can be sure to a high degree of where in a video a student is, something that, in the case of text, would require cumbersome eye tracking technology. Could these clickstream data make it possible to give teachers actionable information regarding the learning process? Could we, as an example, detect patterns indicative of problems or opportunities in the learning process? Additionally, it has become possible to add some level of interactivity to video players, beyond basic playing, pausing and skipping forwards and backwards. Some examples of such interactivity are: (i) a video can be made to stop automatically at a certain point, (ii) some text or figure can be shown on demand, or (iii) a certain action can be triggered by a sequence of events (say, we can show text if a student pauses for a certain amount of time in a given segment of the video and then skips backwards to another given segment). Could these features be used to intervene in a student's learning process in real time? It is our objective to determine whether the use of video learning analytics is perceived as useful for the improvement of the teaching process in small scale learning scenarios. We use the term \"small scale\" to refer to all kinds of learning scenarios in which educational videos are watched by students individually (fully remote learning, flipped classroom environments, face-to-face learning scenarios in which students are given videos to watch as feedback or reinforcement, etc.) in which the student-to-teacher ratio allows for individual intervention: at most a few hundred students per teacher, and usually tens of students per teacher. This results in videos used in playbacks for a single video being at most a few hundred times per course collectively among all students, and, in many cases, fewer than one hundred times. Thus, in the present work, \"small scale\" refers to scenarios in which we have collected information for less than one thousand playbacks for each video. We are interested in small scale learning scenarios because, to our knowledge, there is no research in the field that applies to these scenarios, which are more common than the big scale learning scenarios commonly found in the literature. We use the definition of \"perceived usefulness\" given by [PERSON] in the framework of the technology acceptance model (TAM) [17]. That is, perceived usefulness is \"the degree to which a person believes that using a particular system would enhance his or her job performance\".