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FATE in MMLA: A Student-Centred Exploration of Fairness, Accountability, Transparency, and Ethics in Multimodal Learning Analytics

Yueqiao Jin, Vanessa Echeverria, Lixiang Yan, Linxuan Zhao, Riordan Alfredo, Yi-Shan Tsai, Dragan Gašević, Roberto Martinez-Maldonado

TL;DR

FATE in MMLA investigates how students perceive fairness, accountability, transparency, and ethics in multimodal learning analytics within authentic collaborative learning. Using semi-structured interviews with 14 undergraduates who used MMLA visualisations during post-activity reflection in healthcare simulations, the study employs a deductive-inductive thematic analysis to reveal concerns about data accuracy, representation, and incomplete data, as well as opportunities for multi-level data access, continuous consent, and clearer data-processing explanations. The findings identify concrete FATE issues across four dimensions and offer design and governance recommendations to ensure fair, accountable, transparent, and ethically robust MMLA deployments in real classrooms. The work highlights the importance of student-centered governance, targeted consent mechanisms, and transparent data practices to maximize educational benefits while safeguarding privacy and reducing bias in data-driven learning analytics.

Abstract

Multimodal Learning Analytics (MMLA) integrates novel sensing technologies and artificial intelligence algorithms, providing opportunities to enhance student reflection during complex, collaborative learning experiences. Although recent advancements in MMLA have shown its capability to generate insights into diverse learning behaviours across various learning settings, little research has been conducted to evaluate these systems in authentic learning contexts, particularly regarding students' perceived fairness, accountability, transparency, and ethics (FATE). Understanding these perceptions is essential to using MMLA effectively without introducing ethical complications or negatively affecting how students learn. This study aimed to address this gap by assessing the FATE of MMLA in an authentic, collaborative learning context. We conducted semi-structured interviews with 14 undergraduate students who used MMLA visualisations for post-activity reflection. The findings highlighted the significance of accurate and comprehensive data representation to ensure visualisation fairness, the need for different levels of data access to foster accountability, the imperative of measuring and cultivating transparency with students, and the necessity of transforming informed consent from dichotomous to continuous and measurable scales. While students value the benefits of MMLA, they also emphasise the importance of ethical considerations, highlighting a pressing need for the LA and MMLA community to investigate and address FATE issues actively.

FATE in MMLA: A Student-Centred Exploration of Fairness, Accountability, Transparency, and Ethics in Multimodal Learning Analytics

TL;DR

FATE in MMLA investigates how students perceive fairness, accountability, transparency, and ethics in multimodal learning analytics within authentic collaborative learning. Using semi-structured interviews with 14 undergraduates who used MMLA visualisations during post-activity reflection in healthcare simulations, the study employs a deductive-inductive thematic analysis to reveal concerns about data accuracy, representation, and incomplete data, as well as opportunities for multi-level data access, continuous consent, and clearer data-processing explanations. The findings identify concrete FATE issues across four dimensions and offer design and governance recommendations to ensure fair, accountable, transparent, and ethically robust MMLA deployments in real classrooms. The work highlights the importance of student-centered governance, targeted consent mechanisms, and transparent data practices to maximize educational benefits while safeguarding privacy and reducing bias in data-driven learning analytics.

Abstract

Multimodal Learning Analytics (MMLA) integrates novel sensing technologies and artificial intelligence algorithms, providing opportunities to enhance student reflection during complex, collaborative learning experiences. Although recent advancements in MMLA have shown its capability to generate insights into diverse learning behaviours across various learning settings, little research has been conducted to evaluate these systems in authentic learning contexts, particularly regarding students' perceived fairness, accountability, transparency, and ethics (FATE). Understanding these perceptions is essential to using MMLA effectively without introducing ethical complications or negatively affecting how students learn. This study aimed to address this gap by assessing the FATE of MMLA in an authentic, collaborative learning context. We conducted semi-structured interviews with 14 undergraduate students who used MMLA visualisations for post-activity reflection. The findings highlighted the significance of accurate and comprehensive data representation to ensure visualisation fairness, the need for different levels of data access to foster accountability, the imperative of measuring and cultivating transparency with students, and the necessity of transforming informed consent from dichotomous to continuous and measurable scales. While students value the benefits of MMLA, they also emphasise the importance of ethical considerations, highlighting a pressing need for the LA and MMLA community to investigate and address FATE issues actively.
Paper Structure (37 sections, 1 figure)

This paper contains 37 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Background
  3. Fairness, Accountability, Transparency and Ethics (FATE)
  4. Fairness
  5. Accountability
  6. Transparency
  7. Ethics
  8. FATE in Multimodal Learning Analytics
  9. Methods
  10. The Authentic Learning Context
  11. The MMLA System
  12. Multimodal Data Collection
  13. Visualisations
  14. Study Design
  15. Participant Recruitment
  16. ...and 22 more sections

Figures (1)

  • Figure 1: A set of four visualisations used by teachers to guide reflection on collaboration dynamics in the classroom based on a combination of positioning (A, B, C and D) and audio (B, C and D) data.