Welcome to Sana's Insights Metrics guide! This article will help you understand the various metrics available in our platform, ensuring you can effectively track and analyze user and content activity. Whether you're an admin, manager, or content creator, these insights will empower you to make data-driven decisions and enhance the learning experience.
Sana's analytics tool is called Insights and can be found under the Manage section in the navigation sidebar. It's a widget-based customizable dashboard solution.
What is a widget?
Widgets are powerful tools that render specific metrics in various visual formats, such as bar charts, line graphs, or tables. They form the building blocks of a dashboard, allowing you to choose how you visualize and analyze your data, whether through aggregated metrics or raw data.
Creating widgets and dashboards helps you get a comprehensive overview of the learning environment by analyzing key metrics, allowing for data driven decision making, visualized insights and performance monitoring.
The widget builder
When creating a new widget, you first choose between looking at either Metrics or Raw data.
Metrics are quantitative metrics and can be aggregated across dimensions, e.g. for a whole group. In contrast, raw data displays only the lowest level of data, which is especially useful for qualitative and text-based data that can't be aggregated or summarized, such as reflections and course feedback. Learn more about how to create widgets and a step-by-step guide for both here.
Metrics and data breakdown
Chart type
You can choose between different types of charts to display your data. While Raw data is consistently presented in a table format, Metrics can be visualized using six different chart types:
Table
Use case: Presenting detailed data that can be easily scanned and compared row by row, such as average score by question or number of users per department.
Pivot table
Use case: When you want to display 2 different dimensions on the x-axis and y-axis respectively. E.g. presenting an overview of progress throughout programs or multiple courses across users or groups.Bar chart
Use case: Comparing quantities across different dimensions, making it easy to see which dimensions are larger or smaller such as time spent per group.
Line chart
Use case: Showing trends and changes over time, providing a clear view of data progression such as time spent learning or active users.
Simple metric
Use case: Highlighting key figures and providing an at-a-glance view of critical data points such as total number of learners or daily/weekly/monthly active users.
Progress bar
Use case: Showing progress towards a specific target or goal, making it easy to see how much has been completed and how much is left, such as start rate for assigned courses or course completion rates.
Metrics
Metrics are used for examining quantitative data, either for individual users or aggregated across an entire group. They help you quickly grasp trends and patterns at a higher level.
User Activity Metrics
Users
Number of User Accounts: The total number of user accounts registered on the platform.
Active Users
Daily Active Users (DAU): The number of unique users who use the platform in a single day, calculated based on the UTC timezone of our database.
Weekly Active Users (WAU): The number of unique users who use the platform over a rolling 7-day period from the current date, based on the UTC timezone.
Monthly Active Users (MAU): The number of unique users who use the platform over a rolling 30-day period from the current date, using UTC as the standard timezone.
Quarterly Active Users (QAU): The number of unique users who use the platform over a rolling 90-day period from the current date, aligned with the UTC timezone.
Time Spent
Total Time Spent: The cumulative time users spend on courses in the platform
Average Time Spent Per User: The average time each users spend on courses in the platform.
How is Time Spent Calculated?
Courses | The time is tracked from the moment a user opens a course until they close it. If a user leaves the course open but becomes inactive, the system will continue to count the time for an additional 15 minutes before stopping. |
Live sessions | The time tracked is based the sessions actual duration. When a user is marked as attended on a session (either automatically or manually) we give the user time spend of the whole duration of the session.
Meaning, time spend calculation in live sessions uses:
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In person event | When a user is marked as attended for the event, the sessions total duration will be counted towards the user's time spent. |
Content Activity Metrics
Progress
The estimated percentage progress of courses by users, helping track how far along they are in their learning journey.
Skills
Subscriptions: The total number of skills a user is following.
Badges Achieved: The total number of skill levels a user has completed.
Certificates
Active certificates: The number of active certificates currently issued to learners.
Expired certificates: The number of certificates that have expired.
Revoked certificates: The number of certificates that have been revoked.
Total certificates: Sum of all certificates.
Courses Accessible
Total Number of Courses Available: The total number of courses each user can access on the platform. Note: when aggregated across groups, this metric will count each User-Course combination as one data-point.
Assigned
Courses Assigned: The total number of courses assigned to a user.
Paths Assigned: The total number of learning paths assigned to a user.
Programs Assigned: The total number of learning programs assigned to a user.
Starts
Course Starts: The total number of courses started by a user.
Path Starts: The total number of learning paths started by a user.
Program Starts: The total number of learning programs started by a user.
Start Rate
Course Start Rate: The percentage of started courses out of all accessible courses (assigned and non-assigned).
Path Start Rate: The percentage of started paths out of all assigned or started paths.
Program Start Rate: The percentage of started programs out of all assigned programs.
Completions
Course Completions: The total number of courses completed by a user.
Path Completions: The total number of learning paths completed by a user.
Program Completions: The total number of learning programs completed by a user.
Completion Rate
Course Completion Rate: The percentage of completed courses out of all accessible courses (assigned and non-assigned).
Path Completion Rate: The percentage of completed paths out of all assigned or started paths.
Program Completion Rate: The percentage of completed programs out of all assigned programs.
Course Feedback
Course Feedback Count: The total number of feedback entries submitted for a course.
Average Course Rating: The average rating given to a course by users.
Interactive Card Metrics
Question
Attempts: The total number of attempts on a question.
Unique Responders: The number of unique users who responded to a question.
Correct Rate: The percentage of correct answers out of all attempts.
First Attempt Correct Rate: The percentage of correct answers on the first attempt.
Exercises
Submissions: The total number of submissions for an exercise.
Pass rate: The percentage of learners who passed the exercise.
First time pass rate: The percentage of learners who passed the exercise on their first attempt.
Assessment
Attempts: The total number of attempts on an assessment.
Average Attempts per Responder: The average number of attempts per user who attempted the assessment.
Unique Responders: The number of unique users who attempted an assessment.
Score: The percentage of correct answers out of all attempts.
First Attempt Score: The percentage of correct answers on the first attempt.
Last Attempt Score: The percentage of correct answers on the last attempt.
Poll
Votes: The total number of votes on a poll.
Unique Responders: The number of unique users who voted on a poll.
Breakdown
Breakdowns allows users to segment and filter data precisely to track progress and analyze specific metrics. By breaking down data into columns like time, user attributes, content, skills, and interactive components, users can focus on the most relevant details for evaluating learning outcomes and engagement.
Time
Year | Breakdown information by calendar year for annual progress tracking |
Quarter | Breakdown information by quarters to monitor seasonal or quarterly trends |
Month | Breakdown information by month to observe monthly activity or performance |
Week | View data week by week to track short-term progress |
Date | Breakdown information by each date |
User
User | Breakdown metrics by individual users to assess personal engagement |
Group
Group | Segment data by groups to analyze collective progress within teams or cohorts |
User Attributes
Is Manager | Filter users based on managerial status to compare manager vs. non-manager engagement. |
Role | Segment by role to observe trends and performance across different user roles |
Language | Break down data by language preference for language-specific insights |
Origin | Breakdown users by their origin |
Registration Step | Breakdown by registration stage to monitor onboarding progress. |
User Created At | Breakdown by account creation date to view cohorts of new users. |
User Disabled Date | Breakdown of data for accounts based on their disablement date. |
User Activation Date | Segment data by initial activation dates for insights on new user activity. |
Direct Manager | Breakdown data by direct manager. |
Indirect Manager | Breakdown data for users based on higher-level management influence. |
User Status | Breakdown by active, inactive, or other statuses for real-time insights on engagement. |
User Type | Breakdown data by user type to distinguish patterns among different user groups. |
Last Active Date | Track users’ last active date to monitor recent engagement. |
Custom Attributes
Custom Attributes | Segment data based on specific custom attributes available in your organization. |
Content
All Content | Breakdown and Analyze engagement across all content available to users. |
Course | Breakdown data by individual courses for detailed course-level insights. |
Path | Segment data by learning paths to track progress across structured learning sequences. |
Program | Breakdown by programs to understand performance across broader learning initiatives. |
Skills
Skill | Breakdown data by specific skills for skill-level insights and development. |
Skill Level | Segment data by proficiency levels to assess progress at each skill stage. |
Certificates
Certificate | Segment by certificates to monitor completion and certification status. |
Status | Breakdown data by certification status (e.g., active, expired) for compliance tracking. |
Expiry Date | Breakdown by certificate expiry dates for timely renewals. |
Issue Date | Breakdown by the date certificates were issued to monitor progress over time. |
Revoked Date | Breakdown certificates based on revocation dates for compliance and review. |
Interactive Cards
Card | Breakdown data by interactive cards to monitor user interactions. |
Card Type | Segment information by type of card (e.g., quiz, poll) to analyze specific interactive elements. |
Questions |
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Exercises |
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Assessment |
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Polls |
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Content Attributes
Is Assigned | Breakdown by assigned content to track completion of specific assignments. |
Course Type | Breakdown by course type for content type-specific engagement insights, for example SCORM courses. |
Course Edition | Breakdown by edition to compare updates or versions of the same course. |
Course Visibility | Segment by visibility status to see which content is accessible to users. |
Tags | Breakdown content by tags for targeted content analysis. |
Course Duration | Breakdown data by course duration for insights on time commitment. |
Content Assignment Date | Breakdown content based on when it was assigned, with breakdowns by year, quarter, month, and week. |
Content Start Date | Breakdown data by start date to monitor when content engagement begins. |
Content Completion Date | Breakdown data with respective to content completion data to track completion dates with breakdowns for timing insights. |
Due Date | Breakdown data by due date to monitor timeliness of content completion. |
Is Overdue | Breakdown data by if the user is overdue or not (Yes/No) |
Last Progress Date | Segment data by the last date users made progress to track recent engagement. |
Course Version | Which version of a course a user completed, to check if learners completed the most recent or an older version of the course. |
Course Feedback |
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Reflections |
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Raw Data
Raw Data refers to the unprocessed, original information collected directly from sources. It is particularly useful for delving into the most granular level of data, especially when dealing with qualitative or text-based information that can't be aggregated or summarized, such as reflections and rating comments. This type of data allows for in-depth analysis and insights, offering a detailed view of the underlying information.
Data type
Users: All users
Questions: All question answers
Course Feedback: All course feedback
Polls: All poll votes
Reflections: All reflection responses
Exercises: All exercise submissions
Columns
When a data type is selected, it will automatically populate the table with pre-determined relevant column dimensions. However, you can edit these to display the relevant information you need. Keep in mind, that some columns can only be used with certain data types.
Time: Year, Quarter, Month, Week or Date
User: All users
Group: All groups
User attributes: Choose between the available user attributes
Content: All, Courses, Path or Programs
Skills: Use
metricsfor skills insightsInteractive cards: Choose between interactive cards, e.g. card, question, exercise
Content attributes: Choose between content attributes cards, e.g. type, edition, visibility
💡 By leveraging both metrics and raw data, widgets enable you to gain comprehensive insights and make data-driven decisions with ease.
Filtering
Data filtering allows you to refine and break down your data further by focusing on specific users, groups, content, or other criteria. Whether you're looking at aggregated metrics or detailed raw data, filtering helps ensure that only the most relevant information is displayed. Simply choose the data you want to visualize and apply filters to highlight the specific insights you need.
SCORM Courses
In SCORM courses, users often encounter questions and quizzes that require passing scores. The SCORM course provider sends these scores via the SCORM API. This information is stored in a table and made accessible in Insights, similar to the approach used for Assessments. This setup enhances data visibility and analysis, offering more granular insights. Results can be broken down and filtered based on course type (SCORM) - directly in the widget builder.
Natural language filtering in the Widget builder
You can also leverage the use of natural language when adding filters in the widget builder. This can be a real time saver in the natural flow of work. You can combine it with @tagging to look at specific content, users, or groups.
You find this through +Add filter > Generate filter
