Analyze

Admin Area > Analyze

In the LOL Online Learning Platform, the "Analyze" feature is a set of tools that helps teachers and administrative teams monitor student learning situations and system usage. Through this function, you can check:

• Are students regularly logging in for online classes?
• Is anyone encountering problems while using the system?
• Is the system operating normally?

This information assists teachers in adjusting course content and pacing and enables administrative teams to detect abnormalities promptly, increasing overall management efficiency.

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Student Abnormal Viewing Records

Admin Area > Analyze > Student Abnormal Viewing Records

This section is dedicated to recording abnormal student viewing behavior detected by the system. It is a key audit tool to ensure platform content security and prevent cheating or account sharing.

The system automatically records abnormal situations while students are watching videos, such as disconnections, repeated logins, or device anomalies, helping administrators quickly troubleshoot problems. The main reasons for abnormalities are:

1. Multiple viewing sessions (logging in on multiple devices simultaneously, resulting in forced logout by the system)
2. Abnormal login (such as IP switching or account sharing behavior)
3. Logout due to prolonged inactivity

Filter and Search

Provides a date range selector, allowing staff to filter abnormal records within a specific time period. There is also a keyword input box to search the content description of records.

Abnormal Record List

Abnormal activities flagged by the system are listed in a table in reverse chronological order (latest first).

Time

The exact date and time when the abnormal activity occurred.

Content

A detailed description of the abnormal event.

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System Analysis

Admin Area > Analyze > System Analysis

This is a data visualization dashboard, presenting platform user sources and viewing traffic statistics in chart form.

The system analysis feature uses charts and data statistics to help administrators fully grasp users' device environments, usage habits, and browsing patterns. This information not only helps determine system compatibility and performance bottlenecks but also serves as an important basis for future development and upgrades.

This module is mainly divided into three sections: Operating System Analysis, Browser Version Analysis, and Concurrent Video Viewing Trend Analysis.

Operating System Analysis

This section presents the distribution ratio of user device operating systems currently logged into the platform in a pie chart. By analyzing this chart, you can quickly identify which device types users mainly use to log in, such as mobile devices or desktop computers, and adjust platform functionality support and design direction accordingly.

• iOS APP: Indicates users logging in and studying through official apps installed on Apple devices, such as iPhones or iPads.
• Android: Refers to users logging in with Android phones or tablets.
• Windows: Desktop or laptop computer users logging in via web browsers.
• Mac, Linux, Chrome OS, Blueeyes OS, and other systems: Although the ratio is relatively small, they still provide references for device diversity and help the system maintain good cross-platform support.

This data helps the engineering team estimate resource allocation priorities and can serve as a strategic indicator for marketing or user experience design.

Browser Analysis

This chart analyzes the types and versions of browsers users use to log in to the platform, including both desktop browsers and app versions. This is useful for understanding device operating environments and version upgrades.

• LOLAPP: The institution’s own branded learning application, mainly used for video viewing or interactive tasks.
• APP Versions (e.g., APP 1.2.6, 2.00, etc.): Helps analyze the proportion of different versions and identify inconsistent upgrades or users not updating.
• Firefox, Microsoft Edge, Safari, Internet Explorer: Provides analysis for web-based logins and helps system administrators detect which browsers may require additional support or compatibility fixes.

By comprehensively evaluating the usage of various browsers and versions, the system can perform more targeted version testing and deployment.

Concurrent Video Viewing (24 Hours / Week / Month / Year)

This function provides analysis in four time dimensions, from real-time traffic to long-term usage trends, facilitating system resource allocation and capacity expansion decisions.

• 24-Hour Chart: Shows the number of users watching videos simultaneously for each hour, helping to observe peak and off-peak periods and optimize online support or bandwidth allocation.
• Weekly / Monthly Charts: Analyze mid-term usage trends, such as peaks during weekends, holidays, or midterms, which can be used for promotional planning.
• Annual Chart: Shows changes in annual user activity to understand overall growth or low seasons, assisting in annual resource and budget planning.

Through cross-analysis at multiple time scales, potential system bottlenecks and expansion needs can be effectively detected.

"System Analysis" not only provides visual chart interfaces for operation but is also an important basis for platform technical maintenance and strategic planning. Through this data, institutions can better understand the actual usage environment and behavior patterns, enabling evidence-based upgrade planning, resource allocation, and user experience optimization.

Operating System Analysis

This tab uses a pie chart to analyze the percentage of operating systems used by all users logging into the platform.

Browser Analysis

This tab uses a pie chart to analyze the percentage of browsers used by users.

Concurrent Video Viewing (24 hours) / (week) / (month) / (year)

All four tabs have the same function: to show the trend of concurrent online viewers with a line chart, with only the time dimension being different.

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Viewing Analysis

Admin Area > Analyze > Viewing Analysis

A data analysis tool allowing staff to analyze and count video viewing data from different dimensions (students or staff).

It provides viewing counts, total viewing duration, and video point consumption for each student or staff member and allows filtering by class or account, making it easy to check the learning status of specific individuals. This helps track student learning participation and assess the effectiveness of teachers’ video usage.

Student Viewing Analysis

An interface used to analyze "student" viewing behavior.

Filter and Search

Provides a complete date picker and quick date ranges such as Today, Yesterday, This Week, Last Week, This Month, Last Month, and also allows filtering by "class" for specific student groups.

Only data within the past six months is available for query; remind staff of the time range limit when querying.

Export to Excel

You can export the analysis report below.

Student Viewing List

Lists the viewing data of students in table form.

Name

Displays the student's name.

Account (Student ID)

Displays the student's account (student ID).

Viewing Count

The total number of videos watched by the student within the selected time period.

Points Deducted

The total number of points deducted for watching videos.

Expiry Date

The validity period of the student's account.

Staff Viewing Analysis

An interface for analyzing "staff" viewing.

Filter and Search

Similar to the student tab, but mainly filters by "teacher."

List Content

Displays the teacher’s viewing data in table form.

Teacher

Lists by teacher.

Course

Name of the course the teacher watched.

Unit

Name of the unit the teacher watched.

Class

Name of the class the teacher watched.

Viewing Count

Total number of videos watched by the teacher within the selected time period.

Points Deducted

Total number of points deducted by the teacher for watching videos.

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How to Use the Analysis Function?

1. Log in to the platform using an administrator account.

2. Click "Analyze" on the left menu.

3. Select the analysis item you want to view:

   • Viewing Analysis
   • Abnormal Records
   • System Analysis
   • System Trend Analysis (week / month / year)

4. The system will automatically load the corresponding data.

5. If further processing is needed, you can export the data as an Excel file for storage or analysis.

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Application Examples of the Analysis Function

• Academic directors can analyze the viewing status of each class at the end of the semester to confirm whether students continuously participated in courses.
• When students report video playback issues, administrative staff can check the abnormal records for more details.
• Teachers can observe peak viewing times and adjust video release schedules to increase viewership.
• Before issuing course certificates, check whether students meet the required viewing hours and points thresholds.
• Customer service staff can suggest students use more stable devices or browsers based on device analysis results.
• The technical team can observe login devices and platform load status to adjust server configuration or support strategies.

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Frequently Asked Questions

Q1: Why do students get disconnected in the middle of watching a video?

A: Possible reasons include unstable internet, device anomalies, system logout, exceeding seat limit, etc. It is recommended to check the "Abnormal Records" page for details.

Q2: Can I query data for a specific student or class?

A: Yes. In "Viewing Analysis," you can filter by account or class, and data can be exported.

Q3: Is point consumption equal to video viewing time?

A: Not exactly. Some videos require reaching a specific viewing time to deduct points. It is recommended to check both viewing duration and points records.

Q4: Is it normal for students to play videos for a long time without interaction?

A: The system records device, IP, time, etc. If there are unusual usage situations, it is recommended to proactively contact the student.

Q5: Can I download analysis data?

A: Yes. All analysis reports can be exported as Excel files for backup or advanced analysis.

Q6: What is the main function of the "Student Abnormal Viewing Records" interface? What types of abnormal behavior information does the system record here, and how is this information presented?

A: The "Student Abnormal Viewing Records" interface is dedicated to recording abnormal student viewing behavior detected by the system, serving as a key audit tool to ensure platform content security and prevent cheating or account sharing. This interface lists each abnormal activity flagged by the system in a table, in reverse chronological order (latest first), including the exact date and time and detailed description of each abnormal event.

Q7: What filtering and search features does the "Student Viewing Analysis" interface provide? What are its highlights regarding report export?

A: The "Student Viewing Analysis" interface is a tool for analyzing "student" viewing behavior. It provides a complete date picker and preset quick date ranges (such as Today, Yesterday, This Week, Last Week, This Month, Last Month) for fast filtering. Staff can also filter by "class" for specific student groups. Note that this interface only provides data for queries within the past six months. The analysis report below can be exported as an Excel file.

Q8: What key student viewing data is displayed in the "Student Viewing List" of "Student Viewing Analysis"?

A: In the "Student Viewing List" of "Student Viewing Analysis," the data is presented in a table, including: student’s name, student’s account (student ID), number of videos watched by the student in the selected time period, points deducted while watching videos, and the validity period of the student’s account.

Q9: What are the main differences between "Staff Viewing Analysis" and "Student Viewing Analysis" in terms of analysis targets, filtering focus, and list content?

A: "Staff Viewing Analysis" analyzes "staff" viewing data, while "Student Viewing Analysis" analyzes "student" data. Both provide similar filter and search features, but "Staff Viewing Analysis" focuses on "teacher" as the main filter. In terms of list content, "Staff Viewing Analysis" presents tables by teacher, listing teacher, course name, unit name, class name, viewing count (in the selected period), and points deducted. In comparison, the "Student Viewing List" shows student name, account, viewing count, points deducted, and expiry date.

Q10: What is the main function of the "System Analysis" interface? What two chart forms does it use to present user source statistics?

A: "System Analysis" is a data visualization dashboard that mainly presents user source and viewing traffic statistics in chart form. It uses pie charts to analyze the operating system and browser percentages used by users logging into the platform.

Q11: Explain the function of the "Concurrent Video Viewing" series of tabs in the "System Analysis" interface and clarify the similarities and differences of the time dimensions.

A: The "Concurrent Video Viewing" series of tabs in the "System Analysis" interface includes (24 hours), (week), (month), (year). All these tabs have the same function, displaying the trend of concurrent online viewers with line charts. The only difference is the time dimension, using daily, weekly, monthly, or yearly axes to show the trend in viewer numbers.

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Suggestions and Reminders

The analysis module is not only for viewing usage data but is also an important tool for optimizing teaching and management. Regular use of the analysis function can:

• Check teaching effectiveness and student engagement
• Prevent system operation problems
• Identify usage trends and learning pain points
• Improve platform usage efficiency and stability

It is recommended that academic, customer service, and administrative departments review analysis reports weekly to keep track of learning trends and platform status, ensuring stable teaching and service quality.

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Extended Resources and Integration Suggestions

To maximize the effectiveness of the analysis function, it is recommended to integrate with the following modules:

• Scoring Module: Compare viewing behavior with test results to analyze learning performance gaps.
• Course Assignment Module: Observe the correlation between assignment completion and video viewing.
• Cloud Message Module: Push reminders or make-up class notifications to abnormal users.
• Certificate Module: Confirm if viewing thresholds are met before automatically issuing certificates.

These integrations will help establish a more complete data-driven teaching management mechanism.

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This manual strives for accuracy and completeness, but we do not assume any liability for errors, omissions, or updates. The content may be modified at any time without prior notice. We are not responsible for any damages arising from the use of this manual or downloading its contents, including but not limited to system failures, data loss, or infringement of rights. Users assume full responsibility and risk.