Visual Narrative Explorer

Table 4-1 outlined the functionality of the Visual Narrative Viewer sub-component, the Tailoring Engine, and the Usage Pattern Store, which use the published visual narratives and the Derived Data Visualisation Model to support consumers view, navigate, explore, and understand the communicated message. The following requirements are supported by the Visual Narrative explorer:

 present published visual narratives to consumers and support viewing, navigation and visual interactions,

 support consumer exploration by presenting derived data explorations,

 tailor the ordering of derived data explorations by inferring consumer preferences,  minimise disruption to the flow of the story by separating the analysis of the derived

data explorations from the visual narrative by presenting the derived data explorations as links (derived data explorations links) beside each narrative slice.

113 4.9.1 Visual Narrative Viewer

The objective of the Visual Narrative Viewer is to enable consumers to view, navigate, interact with and explore published visual narratives. It consists of an interface presenting the titles of published visual narratives that can be selected by the consumer to load and navigate. The interface also presents the data used by the derived data explorations to the consumers to enable them to specify which explorations they are interested in. For example, if a published visual narrative presents course engagement and learning resource usage for a student, the derived data exploration options that can be selected by the consumer can include engagement and resource usage. Selecting course engagement as the exploration option will include derived data exploration of engagement, such as engagement breakdown per activity, engagement comparisons between class students and top engagement levels.

Selecting a visual narrative through the Visual Narrative Viewer causes all the narrative slices to appear individually within the interface, allowing the consumer to navigate across them to view the entire visual narrative, reading the descriptions and interacting with the visualisations (step three in Figure 4.11). The visual interactions include details-on-demand revealing element values, data series filtering to include or remove a series of values and zooming to view details of clustered values. The visual interactions come as part of the sourced visualisation techniques and are not included in the design of the Visual Narrative Viewer.

The Derived Data Visualisation Model (discussed in section 4.7.3), constructs derived data objects that are visualised by the Visualisation Engine. Once a visual narrative is selected (step one of Figure 4.11) by the consumer, the Visual Narrative Viewer extracts the narrative slices and presents links to the derived data explorations. It does this by calling the Derived Data Visualisation Model passing all the narrative slices (step two of Figure 4.11), which returns a set of derived data explorations (via the Visualisation Engine) for each narrative slice. Consumers can explore the derived data explorations by selecting the corresponding links to load the visualisations separately to the visual narrative. The consumer can analyse the derived data explorations individually and then return to the visual narrative.

4.9.2 Usage Pattern Store

The objective of the Usage Pattern Store is to monitor consumer activities and record the interaction patterns of derived data exploration usage. The Usage Pattern Store monitors consumer interactions with the derived data and the visualisations corresponding derived data

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exploration by logging events related to both the derived data and the corresponding visualisations. It is important that both data and visualisation logs are recorded to handle scenarios where certain data, for example a course engagement narrative may consist of derived data explorations showing the engagement level of a student and a category of students. In this scenario, there are two derived data explorations for course engagement data, resulting in two sets of logs for the visualisations and one set of logs for the data (course engagement). In this scenario, the Usage Pattern Store will record interactions for the course engagement data and the interactions with the visualisations. The events recorded include, the time spent viewing the data and visualisations, the number of times visualisations using the data were visited and the number of interactions carried out by the consumer against these visualisations. Consider an example where a narrative slice consists of a visualisation displaying a student’s learning resources usage patterns. In this example, the derived data explorations can present:

 Visualisation 1: the student’s course engagement patterns,  Visualisation 2: the student’s completed learning activities,  Visualisation 3: all students’ learning resource usage patterns,  Visualisation 4: the best students’ learning resource usage patterns.

In this scenario, the Usage Pattern Store will monitor and record activities against course engagement, learning activities, and learning resource data. Interactions, viewing times and the number of visits for visualisations 3 and 4 in the example are logged against the learning resource data and the two visualisations. Similarly interactions, viewing times and the number of visits for visualisations 1 and 2 are logged against course engagement and learning activities respectively and the two visualisations.

The Usage Pattern Store records data using maps with the string representation of the data and visualisation names as the keys and interactions, viewing times, and the number of visits as properties to objects mapping to the keys.

4.9.3 Tailoring Engine

The objective of the Tailoring Engine is to order the derived data explorations links presented with each narrative slice by inferring consumer preferences. The engine uses the three logs (interactions, viewing times and the number of visualisation revisits) per data and visualisations corresponding to each derived data exploration recorded by the Usage Pattern Store, to order

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the derived data exploration links accordingly. Consumer preferences are determined by assigning weightings to the three recorded logs and calculating user interests. The three recorded logs are assigned weightings as follows:

1. the number of exploration revisits has the highest weighting assigned to it as these repeat visits indicate that the consumer is interested in the related data,

2. visual interactions present consumer interest in the data, but this is not as strong as revisiting an exploration, hence the weighting is lower,

3. viewing times also indicates consumer interest in the data, however, an exploration can be loaded but the consumer’s attention may be elsewhere and this needs to be taken into consideration, hence the weighting assigned to this log is the lowest.

Both the exploration and data scores are considered in the process to determine the ordering of the links, as this indicates the data that is of most interest to the consumer, and the data including any filters or configurations set by Derived Data Model. The Tailoring Engine iterates through the list of derived data exploration links, extracting the data and filter fields and applies these weightings. The derived data exploration links are displayed on the Visual Narrative Interaction and Explorer interface and their ordering corresponds to the calculated score. The derived data exploration link with the highest score is presented at the start of the list followed by the remaining links in descending score order.