# The Usefulness of Video Learning Analytics in Small Scale E-Learning Scenarios
## 3 Methods and Materials
### Data Analysis
The presented session corresponds to a student watching the first 130 s of the video (1 in the figure), then pausing for 11 s (2), watching the video until the 212\({}^{\prime\prime}\) mark (3), skipping backwards 6\({}^{\prime\prime}\) (4), etc. Lastly, pausing behaviour has also been visualized with the use of scatter plots (see Figure 4 for an example). In this case, again, the horizontal axis has been chosen to represent \"video time\" (the moment in the video at which the student pauses). The vertical axis has been chosen to represent the length of the pause. As pause durations have a very wide range, with many short and few longer pauses, a logarithmic scale can be chosen for the vertical axis. In order to better represent at what points in a video pauses take place, a histogram can be employed too. We can observe different kinds of events by their grouping: \"pause and follow\", \"pause and skip backwards\", \"pause and skip forwards\", \"pause and leave\", \"skip back  
Figure 4: A scatter plot representing pausing behaviour for a typical video. Figure 3: A scatter plot representing skipping behaviour for a typical educational video. Two jumps have been highlighted. Jump 1 represents a short backwards jump. Jump 2 represents a jump from the beginning of the video to the 16:04 mark. wards while playing\" and \"skip forwards while playing\". It is beyond the scope of this article to provide an exhaustive study of the different kinds of events that can be found in the scatter plots and to analyse whether there are significant differences among them, although we consider it an interesting line of future work. Semistructured interviews were carried out with the teachers for the courses. In order to answer the first research question, sets of visualizations of types 2 and 3, and, where appropriate, a subset of visualizations of type 1 (the five single playbacks with the most events, and then five examples corresponding to the most common behaviour patterns, when visual inspection has shown common patterns) for each course were presented to the involved teachers. A block of four identical questions was presented regarding each graph (for brevity, we only present the block regarding playbacks with the most events):  
1. Have you found the graphs corresponding to the five single playbacks with the most events useful? 2. Would you be interested in having those graphs for videos you use in your teaching activity? 3. Is there any information in those graphs that will lead you to take any action related to your teaching activity related to those videos useful? If so, please state which information and which actions. 4. On a scale from 1 (very hard to read) to 5 (very easy to read), how easy to read are these graphs? Questions 1 and 4, in particular, were informed by the technology acceptance model [17]. Finally, an open question was presented to collect feedback from teachers:
5. Can you think of any improvements we could make to the graphs you have been shown? If so, which? In order to answer the second research question, we have presented subsets of the same data. As courses are organized in semesters, we chose semesters as the unit: for a particular course, visualizations 2 and 3 are presented for the data corresponding to the first semester. Then, the same visualizations are presented for the first two semesters, and so on. Teachers were asked which of the graphs would be the first one to be useful to them.