Privacy Concerns of Student Data Shared with Instructors in an Online Learning Management System

Document Title

Privacy Concerns of Student Data Shared with Instructors in an Online Learning Management System

Document Information

• Subject Area: Research on Higher Education Technology and Privacy Issues
• Keywords: Higher Education, Educational Technology, Privacy, Data Monitoring, Student Data, Learning Management System, Canvas Platform, Learning Analytics, Data Sharing, Teacher-Student Relationship

Research Background and Issues

• Identified Problems and Challenges:
  Learning Management Systems (LMS), such as Canvas, are widely used for course management and online teaching in higher education. These systems collect a large amount of student data and share it with instructors, potentially impacting classroom experiences and outcomes without explicit student consent. This data sharing raises significant privacy concerns, yet previous research has primarily focused on external data threats rather than internal platform privacy issues.

• Significance:
  Privacy concerns are directly related to student equity, risks of misrepresentation, and the negative psychological effects of surveillance. These issues affect students' academic performance and the power dynamics in teacher-student relationships.

• Research Motivation and Related Work:
  While Learning Analytics (LA) plays a role in optimizing teaching, LMS platforms share detailed "raw" student behavior data with instructors, often lacking transparency and clear purpose. Excessive surveillance has been likened to a "digital panopticon"—a metaphor for exacerbated imbalances in power dynamics. The adoption of LMS during the pandemic for remote teaching further accelerated these issues, yet their privacy implications remain underexplored.

Solutions

• Methods or Solutions:
  The authors investigated privacy concerns through a study of 31 undergraduate students at a public university in the United States, focusing on the impact of data sharing within the Canvas platform. Semi-structured interviews and thematic analysis were used to explore students' concerns about data visibility to instructors.

• Innovations:

  • Identified under-researched privacy issues related to "raw" student data, such as detailed login times, submission behaviors, and browsing activities.
  • Differentiated privacy concerns in LMS from those in Learning Analytics, highlighting the unique worries stemming from direct data sharing.
  • Proposed design and policy recommendations to enhance transparency and student privacy autonomy.

• Implementation Steps and Key Techniques:
  The study conducted interviews in three stages:

  • Introduction and Background: Understanding students' general perceptions of privacy.
  • Exploration Phase: Discussing the potential privacy implications of specific shared data (e.g., login times, submission times) on the Canvas platform.
  • Reflection and Suggestions: Inviting students to propose recommendations for improving privacy protections. Inductive thematic analysis was used to code and summarize the interview content.

Research Findings

• Specific Findings:

  • Identified key student concerns: Misrepresentation, the necessity of data collection (Not Necessary), feelings of surveillance (Vulnerability), and the need for informed consent.
  • Found that some students acknowledged the utility of data sharing (Genuine Need), recognizing its potential benefits for classroom management and stress alleviation.

• Strengths:

  • Provided a nuanced understanding of students' contextualized privacy anxieties and their specific expressions of concern regarding surveillance and power dynamics.
  • Distinguished the privacy risks of "raw" student data from the systemic privacy issues in Learning Analytics, offering a more direct view of teacher-student interactions.

• Experimental or Evaluation Results:
  The data revealed:

  • Different types of information (e.g., login times, submission times) elicited varying levels of privacy concern. A lack of transparency and purpose in data usage contributed to students' feelings of surveillance.
  • Some students expressed indifference to privacy concerns, stating that they "have nothing to hide." However, this attitude may stem from an adaptation to asymmetrical authority rather than a genuine lack of privacy awareness.

• Limitations and Future Directions:

  • Limitations:

    • The data was limited to the Canvas system and may not capture privacy characteristics of other LMS platforms.
    • The sample size was small, predominantly female, and did not focus on the unique privacy needs of marginalized groups.
    • Semi-structured interviews may introduce subjectivity and variability in participant responses.

  • Future Directions:

    • Investigate additional types of shared information (e.g., online exam data) to explore privacy concerns distinct from those already studied.
    • Evaluate the feasibility of privacy control designs, such as implementing privacy dashboards in LMS platforms.
    • Further analyze the context of "nothing to hide" attitudes and their implications for student behavior and privacy education.

Conclusion

This study reveals the complex impact of data sharing within Learning Management Systems on student privacy, particularly the role of power dynamics and feelings of surveillance in shaping student behavior and teacher-student relationships. By enhancing transparency, designing privacy protection features, and supporting policy measures, the research aims to alleviate students' privacy anxieties and promote the practice of privacy autonomy. The paper also highlights the need for broader empirical validation and theoretical modeling to optimize LMS design and policies.