Welcome to Sana's Insight 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.
Widgets can be found in the Insights section, where they help you create comprehensive and customizable dashboards.
What is a widget?
Widgets are powerful tools that render specific metrics in various visual formats, such as bar charts, time series, scatter plots, numbers, 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.
You can preview the widgets from the dashboard, and optionally download the data.
Creating widgets and composing dashboards can help 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 have the option to choose to visualize data through either Metrics or Raw data.
While Metrics can be used for looking at quantitative metrics, and to look at aggregated numbers, e.g. for a whole group. Raw data is for digging into the lowest level of data, especially on qualitative and text-based data 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 a specific user attributes e.g department, country or group.
Pivot table
Use case: Presenting an overview of progress throughout programs or multiple courses across users or groups.Bar chart
Use case: Comparing quantities across different categories, making it easy to see which categories 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 spend 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 ideal for examining quantitative data and aggregated numbers, providing a broad overview, such as the performance of 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.
Weekly Active Users (WAU): The number of unique users who use the platform in a single week.
Monthly Active Users (MAU): The number of unique users who use the platform in a single month.
Quarterly Active Users (QAU): The number of unique users who use the platform in a single quarter.
Time Spent
Total Time Spent: The total time users spend on courses in the platform
Content Activity Metrics
Progress
Estimated Progress: The estimated progress of a course by a user, 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.
Courses Accessible
Total Number of Courses Available: The total number of courses a user can access on the platform.
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.
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.
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.
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.
Poll
Votes: The total number of votes on a poll.
Unique Responders: The number of unique users who voted on a poll.
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 like reflections and rating comments. This type of data allows for in-depth analysis and insights, offering a detailed and authentic view of the underlying information since it hasn't been aggregated or summarized.
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.
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
FAQ
How is Time Spent Calculated?
The calculation of "time spent" is based on user interaction with a course. 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.