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Journals / Journal of Educational Technology and Online Learning / 2026 / Volume: 9 Issue: 1

Evaluating the effectiveness of a collaborative robotic maintenance training platform in extended reality (XR): A comprehensive validation study

Batur Alp Akdoğn(ESKİŞEHİR TEKNİK ÜNİVERSİTESİ)0009-0004-4897-0935
Yunus Emre Esen(ESKİŞEHİR TEKNİK ÜNİVERSİTESİ)0000-0001-8319-5605
Kübra Yayan(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ)0000-0001-7003-6437
Uğur Yayan(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ)0000-0003-1394-5209
Pages
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Publication Date
2026-01-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results indicated high usability (average SUS score of 78) and a balanced cognitive workload (average NASA-TLX score of 4.3).

📌 3 Key Takeaways

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Objective & Hypothesis:Maintenance training for industrial robotic systems involves challenges such as high costs, safety risks, and hardware limitations.
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Key Findings & Data:A two-phase research design was adopted, involving 25 undergraduate students (Phase 1) and 6 experienced robotics experts (Phase 2).
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Conclusion & Impact:Results indicated high usability (average SUS score of 78) and a balanced cognitive workload (average NASA-TLX score of 4.3).
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Extended Reality (XR) · Robotic Maintenance · Training Effectiveness

Abstract

Maintenance training for industrial robotic systems involves challenges such as high costs, safety risks, and hardware limitations. This study presents the validation outcomes of the Multiplayer No-Code Mixed Reality Editor solution developed to provide immersive, interactive, and scalable training. The platform features a no-code scenario creation tool that allows users to design detailed training scenarios without programming knowledge, offering a more hands-on experience than traditional formats. A two-phase research design was adopted, involving 25 undergraduate students (Phase 1) and 6 experienced robotics experts (Phase 2). Participant performance was evaluated using task completion times, pre/post-tests, the System Usability Scale (SUS), and the NASA Task Load Index (NASA- TLX). The findings showed significant advantages over traditional methods. Results indicated high usability (average SUS score of 78) and a balanced cognitive workload (average NASA-TLX score of 4.3). XR- trained experts completed virtual scenarios up to 76.8% faster and real- world tasks 28.7% faster than their traditionally trained counterparts, reporting higher clarity and applicability. These results prove that editör is an effective, efficient, and user-friendly training solution for both novice and expert users in industrial robotic maintenance.

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Cite This Article

Akdoğn, B. A., Esen, Y. E., Yayan, K., Yayan, U. (2026). Evaluating the effectiveness of a collaborative robotic maintenance training platform in extended reality (XR): A comprehensive validation study. Journal of Educational Technology and Online Learning, Volume: 9 Issue: 1. https://doi.org/10.31681/jetol.1772552

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