Based on these findings, we've developed a comprehensive SRL analysis framework for esports coaches that:

Collects and analyzes player performance data
Calculates SRL metrics (learning rate, consistency, section mastery, metacognitive ratio)
Generates personalized improvement recommendations based on SRL profile

Targeted Interventions Include:
- Section-specific practice protocols
- Structured metacognitive activities
- Calibrated goal-setting tools
- Performance visualization and tracking
- Adaptive challenge progression

Learning Analytics in Trackmania

Project Background

Context: Solo Research Project for Seminar on Learning Analytics

This research project explores how Self-Regulated Learning (SRL) principles manifest in competitive gaming environments, specifically within Trackmania—a time-trial racing game where players continuously refine their performance across various tracks. By analyzing player data from racing sessions, we can identify patterns that distinguish effective from ineffective learners, providing insights into optimizing skill development in performance-driven contexts.

Research Context

Learning Environment: Digital competitive gaming platform (Trackmania)

Participants: Esports players between 16-25 years of age

Activity Structure: Players engage in time-trial racing across different maps, aiming to optimize their lap times through:

Goal setting (targeting medal times or personal bests)
Repeated attempts with strategic adjustments
Self-monitoring of progress
Analysis of performance data

Theoretical Framework

This project draws on three key theoretical perspectives:

1. Self-Regulated Learning Theory examines how learners strategically enhance performance through goal setting, self-monitoring, and adaptation.

2. Transfer of Learning Theory explores how skills acquired in one context can be applied to new situations, facilitating adaptability.

3. GIFT Framework (Generalized Intelligent Framework for Tutoring) assesses learner states, predicts progress, and delivers personalized guidance to optimize learning outcomes.

SRL Phases in Trackmania

Our analysis identifies four key phases of self-regulated learning that can be observed through player data:

1. Goal Setting

Players establish performance targets ranging from general (beat threshold times) to specific (improve particular sections). Effective SRL involves progressive, calibrated goals focused on both overall time and section-specific improvements.

2. Monitoring

Indicated by patterns in checkpoint and finish times. Strong SRL players demonstrate targeted attention to problematic sections with systematic comparison to previous attempts.

3. Metacognition

Quantified through map preview activities and replay analysis. The metacognitive ratio (time spent analyzing versus playing) decreases as expertise develops while remaining focused on specific challenges.

4. Strategic Enactment

Measured through Learning Rate (seconds improved per attempt) and Learning Consistency (standard deviation of improvement percentages). Effective SRL shows both substantial improvement and consistency with focused enhancement of problematic sections.

Key Findings

Our synthetic data analysis revealed noticeable differences between players with strong SRL skills and those with poor SRL skills.

Top performers demonstrated remarkable self-regulated learning patterns, averaging 1.77 seconds improvement per attempt with consistent progress (consistency index of 2.87). These players methodically reduced their times across all checkpoints by extensively studying tracks before racing, strategically analyzing replays, and progressing through clear developmental phases: first exploring the track, then refining specific sections, and finally optimizing their overall performance.

In contrast, struggling players showed minimal improvement (just 0.38 seconds per attempt) with highly inconsistent results (consistency index of 4.87). Their performance was characterized by erratic checkpoint times without clear progress patterns, minimal use of preview and replay features, frequent abandonment of challenging sessions, and persistent difficulty with technically demanding sections. These stark differences highlight how effective learning strategies—not just practice time—separate elite performers from casual players.

Logic Diagram

Applications & Interventions

Significance

This research demonstrates how principles of self-regulated learning can be quantified and applied in performance-driven digital environments. The findings have implications beyond esports, potentially informing approaches to skill development in education, professional training, and other domains where continuous improvement and adaptation are essential.

By understanding the patterns that distinguish effective from ineffective learners, we can develop more targeted interventions that accelerate skill acquisition and optimize performance improvement. There also lies potential in understanding transferable skillsets to environments beyond Trackmania.