What do Teens Make of Personal Informatics? Young People's Responses to Self-Tracking Practices for Self-Determined Motives

Title of the Paper

What do Teens Make of Personal Informatics? Young People’s Responses to Self-Tracking Practices for Self-Determined Motives

Bibliographic Information

• Subject Areas: Human-Computer Interaction, Human Behavior Data Analysis, Educational Technology
• Keywords: Personal Informatics, Teenage Users, Self-Tracking, Data Interpretation, Self-Determined Motives, Social Contexts, Data Reflection

Research Background and Issues

• What problems or challenges did the authors identify?

  • Teenagers are increasingly exposed to and using Personal Informatics (PI) tools in their daily lives. However, existing research primarily focuses on adult users, lacking an in-depth understanding of teenagers’ tracking motivations and practices.
  • In current educational environments, PI tools are often used to achieve specific goals (e.g., increasing physical activity levels) or integrated into STEM education. However, there is insufficient attention to teenagers’ autonomous social contexts and diverse motivations.

• Why is this issue important?

  • Personal Informatics, as a tool, can provide teenagers with opportunities for self-knowledge and reflection, supporting them in exploring personal values, emotional states, and life habits.
  • Investigating how teenagers autonomously use PI tools and assign meaning to them can offer new perspectives for educational interventions and technology design, potentially improving teenagers’ well-being and learning outcomes.

• Research Motivation and Related Work

  • Early PI research primarily focused on behavior change models, but subsequent work has shifted to exploring how PI data can be used for self-knowledge and understanding.
  • Existing studies demonstrate that PI tools are effective in supporting STEM education, particularly in student-driven learning. However, these scenarios are still dominated by adult-defined goals, with limited focus on the significance and practical application of autonomous data tracking by teenagers.
  • The authors aim to explore how teenage users autonomously track data, interpret their data, and what implications these practices have for social interactions and personal development.

Solutions

• What methods or solutions did the authors propose?

  • The authors designed a qualitative research framework combining a learning phase and an exploration phase, allowing teenagers to autonomously select PI tools and use them to track personal life factors.
  • The learning phase included scaffolded classroom sessions to help teenagers understand the potential of PI data and guide their exploration.
  • The exploration phase enabled teenagers to independently use PI tools in their daily lives, setting tracking goals based on personal interests.

• What is innovative about this solution?

  • It moves beyond behavior change or STEM education goals, allowing teenagers to design tracking methods based on their own motivations.
  • The scaffolded design within social contexts enables teenagers to autonomously interpret and reflect on PI data, assigning it more personalized and socialized meanings.

• What are the implementation steps? What key technologies were used?

  • Learning Phase: Conducted four scaffolded classroom sessions (introducing tracking practices, collaborative discussions, tool setup guidance, and sharing data insights) to help participants build an understanding of PI.
  • Exploration Phase: Teenagers independently used PI tools daily to track goals of interest, recorded data, and reflected on their understanding and insights through follow-up interviews.
  • Tools and Data: Provided various PI tools (e.g., Fitbit, Daylio, RescueTime) to support different types of data tracking (e.g., emotions, physical activity, study time).

Research Findings

• What specific findings were achieved?

  • Teenage users demonstrated adaptability to PI tools, autonomously tracking life factors they cared about (e.g., anxiety levels, physical activity, emotional states) and deriving meaning from the data.
  • PI data helped participants reflect on personal habits and feelings, such as the impact of emotional fluctuations on learning or the significance of exercise intensity for health.
  • PI practices were found to support well-being (e.g., externalizing data to alleviate emotional stress) and improve time and emotion management skills.

• What advantages does it have compared to existing solutions?

  • This study emphasizes teenagers’ autonomy rather than restricting them to predefined goals.
  • The scaffolded and social-context design deepened the application of PI tools, making data reflection more socially meaningful.
  • Teenagers were able to derive personally valuable insights from the data, surpassing the simple behavior change model.

• What were the experimental or evaluation results?

  • Most participants were able to establish sustained PI practices and interpret their data, while some chose to discontinue due to conflicts between practice and expectations.
  • Emotion tracking emerged as the most popular practice area, with many teenagers reporting that the data helped them enhance emotional control and reflective abilities.
  • The scaffolding design proved effective in supporting personally relevant learning and constructing meaning from data.

• Limitations and Future Directions

  • Limitations: Some participants abandoned PI practices due to perceived lack of meaningful data or excessive control by the tools; teenagers require more technical support and guidance in data interpretation.
  • Future Directions: Research on optimizing PI tool design to balance user autonomy and structure; exploring the potential of PI in supporting teenagers’ emotional management and personal value formation; expanding PI applications to non-STEM contexts such as health education and time management.

Conclusion

This study deepens our understanding of how teenagers perceive and use Personal Informatics tools, highlighting the potential of PI data to promote self-reflection and support personal development. The authors suggest that future designs and educational interventions should focus more on teenagers’ autonomous needs, supporting them in assigning personalized and socialized meanings to data within everyday social contexts.