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A Sample Report is as shown below: The Report is divided into a Filter (left) pane and Data (right) pane.

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Filter Pane

The Filter Pane has three controls that lets the user filter the records that will be displayed on the right pane.
  • the first filter [Training Dates] helps to filter data using a date range.
  • the second filter [Nodes] helps to filter based on Nodes.
  • the third filter [Username] lets the user select one or more trainees.
  • the fourth filter [Session Mode] helps to filter trainings based on how they were run, if it was run in Practice mode or an Assessment mode.

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Data Pane

The Data Pane can be divided into three interconnected panels. Top Panel The top panel consists of cards that display summary information: a. Nodes - This card in the report displays the total number of active nodes that are present in the experience. b. Node Runs – This card displays the total number of times the node was executed as part of the various sessions that were run for the selected filters in the left pane. c. Nodes Total Time – This card displays the total time that was spent by the trainees on the execution of nodes. d. Average Time per Node – This card displays the average time taken per node. Center PanelThe Center panel helps visualize information related to every node. Here, the number of times that a particular node was executed as part of the various sessions is displayed. This pane can help understand the time taken for every node in the training and hence give an idea of the complexity of the node. By selecting an individual node, users can see in how many sessions the node was executed from the data in the bottom panel. The red line that runs across the Nodes denotes the average time taken for the execution of the node across all the sessions. The yellow line indicates the Standard Deviation of the time taken for that node.

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Notes:

Nodes with higher average time are the complex nodes and Nodes with a low average time are the simpler nodes. Nodes that have higher std deviation are the ones wherein there was a large variation in results. The following maybe deduced using the average time and standard deviation data:
  1. High Avg Time + Low Std Deviation means everybody struggled on that node.
  2. High Avg Time + High Std Deviation means it was a complex node, but few users struggled more than the others.
  3. Low Avg Time + Low Std Deviation means it was a simple node and all users breezed thru that node.
  4. Low Avg Time + High Std Deviation means it was a simple node, but few users struggled with it.
Bottom Panel The bottom panel shows trainee performance per node. This panel displays all the nodes that were run as part of the session, the time taken for each node along with the Training Date and the User information. The Sequence ID in this table can be used to sort the nodes in the order that they were executed. When no node is selected, this panel will display data across all users and all sessions. Once the user selects a node, this panel will display data only for the selected node. This panel could be used to better understand why some users took longer to complete the training than others, by identifying the nodes they took more time on.

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Summary

This section allows the user to see overall information across all nodes. Users can further filter and see only trainings within a certain date range or only for certain users by using the filters in the left pane. This visualization provides various insights, including the following: • Nodes with a high “Avg Time Taken” are the complex nodes and Nodes with a lower value are the simpler nodes. • Using the two metrics “Avg Time Taken” and “Standard Deviation” for a node the complexity of the node and performance of users with respect to the node can be deduced. For e.g., if the High average time is coupled with a Low Std Deviation then it means that all trainees struggled with that node. However, if the Std Deviation was high then that means only a few trainees struggled more than others. • The “Node Session Details” table in the visualization will then help figure out which trainees struggled on that node. Cross-referencing this data with the trainee report data can provide insights on whether the issue was with the trainee or the node.