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Adaptive Learning Paths: Tutor View and Learning Analytics

Page Overview

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1 Initial Problem

Administrators and tutors currently lack specialized tools to manage complex, non-linear learning sequences. Without automated checks for "dead ends" (objects that do not lead anywhere), there is a risk of creating broken learning paths. Additionally, there is a lack of integrated analytics to monitor how students interact with adaptive branches.

2 Conceptual Summary

The tutor's management interface and introduces basic learning analytics for adaptive paths. Key features include:

  • Enhanced Object Pool GUI: Improved management of objects within the learning sequence.
  • Dead End Detection: Automatic identification of objects that lack outgoing conditions or do not lead to a sequence exit. These are displayed in the object table.
  • Entry and Exit Point Definition: Explicitly defining which object(s) serve as the starting point and which indicate the successful completion of the sequence.
  • Online Status Validation: The Learning Sequence Object (LSO) cannot be set to "Online" if:
    • No start object is defined.
    • At least one unintended dead end exists (unless marked as an optional end-node).
  • Learning Behavior Analytics:
    • Frequency: Tracking how often each object is accessed.
    • Success Rate: Tracking how often users fail or pass specific objects/conditions.

3 User Interface Modifications

3.1 List of Affected Views

  • Tutor's Object List (Content tab of the Learning Sequence).
  • Settings tab (Online/Offline toggle).
  • New Analytics tab (or sub-view within Statistics).

3.2 User Interface Details

  • New Table Column: "Path Integrity" or "Dead End" indicator in the object list.
  • Validation Feedback: Clear error messages in the Settings tab explaining why the LSO cannot be set online.
  • Analytics Dashboard: Simple charts or tables showing access frequency and failure rates per object.

3.3 New User Interface Concepts

none

3.4 Accessibility Implications

The implementation utilizes Kitchen Sink elements to ensure accessibilty.

4 Additional Information

4.1 Involved Authorities

4.2 Technical Aspects

  • Implementation of a graph traversal algorithm to detect unreachable nodes or nodes without exits (Dead Ends).
  • Extension of the persistence layer to store analytics data (counters for access and failure).
  • Integration of validation logic into the status-change workflow.

4.3 Privacy

Learning progress data is processed to evaluate the conditions. No new categories of personal data are introduced.

4.4 Security

No special security-relevant changes beyond the standard.

4.5 Contact

Person to be contacted in case of questions about the feature or for funding offers:  Auerbach, Jeanine [jeanine.auerbach], Clausen, Keven [keven.clausen]

4.6 Funding

Funding status and funding parties are listed in the block 'Status of Feature' in the right column of this page.

If you are interested to give funding for this feature, please get into contact with the person mentioned above as 'Contact'.

Implementation requires funding of 9,000 Euro.

5 Discussion

6 Implementation

Feature has been implemented by {Please add related profile link of this person}

6.1 Description and Screenshots

{ Description of the final implementation and screenshots if possible. }

6.2 Test Cases

Test cases completed at {date} by {user}

  • {Test case number linked to Testrail} : {test case title}

6.3 Privacy

Information in privacy.md of component: updated at {date} by {user} | no change required

6.4 Approval

Approved at {date} by {user}.

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Last edited: 5. May 2026, 12:24, Auerbach, Jeanine [jeanine.auerbach]