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# Enhancing Learner Engagement and Attention in XR Environments: Metrics and Strategies

Carlos Lievano Taborda

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Carlos Lievano Taborda. Enhancing Learner Engagement and Attention in XR Environments: Metrics and Strategies. 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Mar 2025, Saint Malo, France. 10.1109/VRW66409.2025.00429. hal-05006503

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Submitted on 9 Jul 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Enhancing Learner Engagement and Attention in XR Environments:
Metrics and Strategies

Carlos Liévano Taborda*
VENISE Team, LISN, CNRS
University Paris-Saclay

## ABSTRACT

This study explores the roles of Engagement and Attention (E/A) in eXtended Reality (XR) learning environments, emphasizing their importance in enhancing educational outcomes. Through a comprehensive review, we identify prevalent methods for measuring E/A, including post-test evaluations, summative assessments, and real-time monitoring techniques. Our findings reveal a gap in the integration of these cognitive states and highlight the limited focus on collaborative learning scenarios, emphasizing the need for multi-method approaches to assess E/A in XR. To address these gaps, we propose research directions that combine user-centered design and pedagogical strategies to enhance E/A in XR learning. By incorporating gamification, structured lesson plans, and interactive content, future XR learning environments can mitigate distractions and foster immersive and personalized learning experiences. This paper sets the foundation for optimizing E/A measurement and improvement strategies in diverse educational contexts.

**Index Terms:** Engagement, Attention, Learning, Virtual Reality.

## 1 INTRODUCTION

Engagement and Attention (E/A) are crucial cognitive states in learning, particularly in immersive eXtended Reality (XR) environments where interactivity and immersion redefine educational experiences [9, 5]. These states influence learners' focus, motivation, and overall knowledge retention. Despite their centrality to effective learning, current research often examines engagement and attention separately [7, 2], leaving a gap in understanding their combined dynamics in XR scenarios. To sustain learner focus within immersive environments, XR enables the use of user-centered designs such as attention paradigms, including dynamic cueing techniques, which help maintain E/A throughout the experience [8, 4]. Theoretical foundations for this interplay can be drawn from cognitive models such as dual process theory of attention [1], which distinguishes between focused and divided attention in complex learning environments.

Through a comprehensive literature review [6], we analyzed existing methods for measuring and enhancing E/A in XR learning. Our findings reveal a predominant reliance on post-test evaluations and summative assessments, with limited use of real-time monitoring. Collaborative scenarios are notably underrepresented, likely due to challenges in designing multi-user studies and measuring E/A during interactions [3]. To address these gaps, we integrate learning strategies such as gamified tasks, comparing their impact in two conditions: individual learning, focused on personal engagement, and collaborative learning, emphasizing shared task completion. This approach aims to clarify the interplay between E/A and learning outcomes across diverse pedagogical settings.

This review is the scientific foundation for an ongoing experiment to address these research gaps. The methodology leverages

real-time E/A measurement to understand learner behavior and to refine and evaluate learning strategies under individual and collaborative conditions. By integrating these aspects, the research shows that real-time metrics are critical for both assessing engagement and tailoring effective strategies. This dual focus bridges user-centered design, pedagogy, and advanced measurement, optimizing XR learning environments across diverse contexts.

## 2 METHODOLOGY

This study addresses the challenge of assessing the effectiveness of XR learning environments by integrating E/A metrics into educational experiments. While XR has demonstrated potential in enhancing remote learning, there is limited research on how collaborative versus individual learning strategies and attention paradigms influence learning outcomes. Our research aims to bridge this gap, focusing on understanding the correlations between E/A metrics and learning performance.

### 2.1 Experimental Design

The experiment tests two main dimensions:

#### 1. Learning Strategies:

* **C1: Individual learning:** Participants interact individually with a virtual representation of the DNA double helix, performing sequence identification and transcription activities.

* **C2: Collaborative learning:** Participants collaborate to decode mRNA into protein sequences, fostering teamwork and discussion.

#### 2. Attention Paradigms:

* **C3: Without Attention Paradigms:** The tasks are completed without additional guidance, requiring the participants to independently manage their focus and navigate the learning environment.

* **C4: With Attention Paradigms (featuring visual cues such as arrows and highlights):** Dynamic visual cues, such as arrows and highlights, are introduced to guide participants' focus on key elements of the tasks and improve task clarity.

The experiment is structured around molecular biology learning tasks to assess these dimensions, with metrics captured for engagement, attention, and learning performance.

### 2.2 Metrics for Assessment:

* **Attention Metrics:** Eye-tracking data (fixation duration, saccade patterns, and AOI analysis) and self-reported attention scores.

* **Engagement Metrics:** User Engagement Scale, task time, interaction frequency, and self-reported scores.

<footnote>*e-mail: Carlos.lievano@universite-paris-saclay.fr</footnote>
* **Learning Performance:** Test scores, task completion rates and quality of task results.

## 2.3 Contribution and Approach

This research provides an innovative methodology for assessing the interplay between E/A metrics and learning outcomes in XR environments. It introduces a comprehensive framework that combines cognitive data with learning performance analysis to optimize XR learning strategies. By comparing individual versus collaborative strategies and exploring the effects of attention paradigms, the study identifies best practices for immersive learning design. Additionally, this methodology was designed with scalability in mind, emphasizing its applicability across diverse XR educational scenarios.

## 3 PROGRESS AND CURRENT FINDINGS

The experimental design and implementation are completed and we are on the completion of a pilot study. This phase focuses on ensuring robust data collection pipelines and refining real-time analytics to support reliable metric validation Scenarios for the Individual (C1) and Collaborative (C2) tasks are fully developed, incorporating immersive 3D molecular biology simulations. Gamification and interactive elements were integrated to maintain engagement and support learning.

Preliminary tests confirm the usability of the design, including task clarity, user interaction flow, and the feasibility of E/A data collection (e.g., eye-tracking and questionnaires). Participant feedback has been invaluable in refining the interface and activities, leading to clearer task instructions and a more intuitive user interface.

### Preliminary hypothesis

Although formal data analysis is pending, initial observations include:

1. **Collaborative learning tasks (C2)** obtain more active engagement and teamwork compared to individual tasks (C1).

2. **Attention paradigms (C4)** increased focus, as indicated by longer fixation durations and fewer saccadic movements compared to the absence of paradigms (C3).

3. **Engagement levels** were higher in tasks that combined collaboration and attention paradigms. These findings suggest the potential of attention paradigms to enhance cognitive focus, especially in collaborative learning environments.

### Challenges and Limitations

Several challenges emerged during the early stages:

* Ensuring the reliability of real-time eye-tracking across diverse user setups.

* Managing cognitive load in collaborative tasks to maintain focus without overwhelming participants.

* Synchronizing engagement and attention data collection in dynamic collaborative settings.

The pilot study validated the experimental setup, fine-tuned the data collection methods, and confirmed the experiment's timing, ensuring an efficient and accurate execution phase.

## 4 CONCLUSION

This research investigates the relationship between engagement, attention metrics, and learning performance in XR environments. The experiment compares individual versus collaborative learning strategies and examines the impact of attention paradigms. Early findings suggest that combining these factors can improve learning outcomes and user engagement, supporting the potential of XR for enhancing remote learning experiences.

Looking ahead, key questions to explore during the doctoral consultation include:

1. **Optimization of Metrics Collection:** How can we improve the real-time collection and reliability of E/A data in dynamic XR settings?

2. **Scalability of Experimental Design:** What approaches can ensure the scalability and generalizability of the experiment across XR and diverse educational contexts?

3. **Leveraging Data for Adaptive Learning:** How can the data collected from E/A metrics inform adaptive learning systems to respond dynamically to students' cognitive states during XR-based tasks?

These discussions will be instrumental in refining the hypotheses and shaping the conclusions drawn from the experiment's outcomes and data analysis, ensuring robust contributions to XR learning research.

## ACKNOWLEDGMENTS

This work was supported by French government funding managed by the National Research Agency (ANR) under the Investments for the Future program (PIA) with the grant ANR-21-ESRE-0030 (CONTINUUM project). I would like to thank my supervisor, Patrick Bourdot, for their invaluable guidance, my co-supervisor, Jeanne Vezien, for their support, and Nicolas Ferey for their valuable contributions to this research.

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