Journals / Uygulamalı Bilimler Fakültesi Dergisi / 2025 / Cilt: 7 Sayı: 1-2

LEARNMATE: A GAMIFIED APPROACH IN COGNITIVE SKILL MEASUREMENT AND DEVELOPMENT

LEARNMATE: BİLİŞSEL BECERİ ÖLÇME VE GELİŞTİRMEDE OYUNLAŞTIRILMIŞ YAKLAŞIM

İbrahim Kuru(AKDENİZ ÜNİVERSİTESİ)0000-0003-3362-3725
Hatice Küpeli(AKDENİZ ÜNİVERSİTESİ)0009-0005-2872-5841
Kerim Kürşat Çevik(AKDENİZ ÜNİVERSİTESİ)0000-0002-2921-506X
Pages
1–13
Publication Date
2025-12-19
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

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

In conclusion, Learnmate was identified as a valid and applicable digital tool for measuring cognitive processes.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study introduces "Learnmate," an AI-supported, gamified cognitive measurement and development platform featuring a Turkish interface.
📊
Key Findings & Data:In each task, the system records the student's accuracy rate, reaction time at the millisecond level, and focus stability.
💡
Conclusion & Impact:In conclusion, Learnmate was identified as a valid and applicable digital tool for measuring cognitive processes.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Learnmate · Bilişsel Değerlendirme · Oyunlaştırma

Abstract

This study introduces "Learnmate," an AI-supported, gamified cognitive measurement and development platform featuring a Turkish interface. Stemming from the lack of comprehensive, native-language digital assessment tools in Turkey, the primary aim of this research is to measure and support the development of students' fundamental cognitive skills, such as attention, memory, logical reasoning, and visual-spatial perception. As a methodology, the Learnmate platform utilizes thirty-eight different gamified modules. The application's architecture is composed of four main layers: User Interface, Data Collection, Assessment, and Reporting. In each task, the system records the student's accuracy rate, reaction time at the millisecond level, and focus stability. This data is processed by an AI-based analysis engine and converted into individual performance reports. In conclusion, Learnmate was identified as a valid and applicable digital tool for measuring cognitive processes. With features such as reaction time analysis, error pattern review, and multi-dimensional reporting, the application constitutes a unique example within Turkey's educational technology ecosystem.

Özet

Bu çalışma, yapay zekâ destekli, oyunlaştırılmış ve Türkçe arayüzlü bir bilişsel ölçme-gelişim platformu olan "Learnmate" uygulamasını tanıtmaktadır. Türkiye'de yerli dilde kapsamlı dijital değerlendirme araçlarının eksikliğinden yola çıkan bu araştırmanın temel amacı, öğrencilerin dikkat, hafıza, mantıksal düşünme ve görsel-uzamsal algı gibi temel bilişsel becerilerini ölçmek ve bu becerilerin gelişimini desteklemektir. Yöntem olarak, Learnmate platformu otuz sekiz farklı oyunlaştırılmış modül kullanmaktadır. Uygulamanın mimarisi; Kullanıcı Arayüzü, Veri Toplama, Değerlendirme ve Raporlama olmak üzere dört ana katmandan oluşmaktadır. Sistem, her görevde öğrencinin doğruluk oranını, milisaniye düzeyinde tepki süresini ve odaklanma stabilitesini kaydetmektedir. Bu veriler, yapay zekâ tabanlı bir analiz motoru tarafından işlenerek bireysel performans raporlarına dönüştürülmektedir. Sonuç olarak, Learnmate'in bilişsel süreçleri ölçmede geçerli ve uygulanabilir bir dijital araç olduğu saptanmıştır. Tepki süresi analizi, hata örüntüsü incelemesi ve çok boyutlu raporlama gibi özellikleriyle uygulama, Türkiye’deki eğitim teknolojileri ekosisteminde özgün bir örnek teşkil etmektedir.

🔖

Cite This Article

Kuru, İ., Küpeli, H., Çevik, K. K. (2025). LEARNMATE: A GAMIFIED APPROACH IN COGNITIVE SKILL MEASUREMENT AND DEVELOPMENT. Uygulamalı Bilimler Fakültesi Dergisi, Cilt: 7 Sayı: 1-2, 1–13. https://ajindex.com/articles/1153509

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (11)

  1. Anderson, M., & Reiser, R. (2019). Learning analytics and motivation in digital learning systems. Computers & Education.
  2. Brusilovsky, P. (2001). Adaptive educational hypermedia. User Modeling and User-Adapted Interaction, 11(1–2), 87–110.
  3. Çetin, B., & Çakır, R. (2021). Gamification in Turkish educational technologies: Opportunities and challenges. Turkish Online Journal of Distance Education, 22(4), 175–191.
  4. Çetin, B., & Çakır, R. (2021). Gamification in Turkish educational technologies. Turkish Online Journal of Distance Education, 22(4), 175–191.
  5. Hamari, J., Koivisto, J., & Sarsa, H. (2019). Does gamification work? Hawaii International Conference on System Sciences.
  6. Katz, B., Jones, M. R., & Shin, J. C. (2018). The efficacy of computerized cognitive training in children. Journal of Applied Research in Memory and Cognition, 7(4), 475–489.
  7. Koçyiğit, D., Kılınç, M. E., Yılmaz, R. N., Mutlu, E., & Ayhan, Y. (2025). Psychometric Properties of Turkish Instruments Assessing Social Cognition in Psychiatry and Neurology: A Systematic Review. Turkish Journal of Psychiatry, 36, 414.
  8. Melby-Lervåg, M., & Hulme, C. (2013). Is working memory training effective? Developmental Psychology, 49(2), 270–291.
  9. Morris, C., & MacGlashan, H. (2020). AI-based personalization in educational measurement. Frontiers in Psychology, 11, 1586.
  10. Weisz, J. R., Ng, M. Y., & Bearman, S. K. (2019). Measurement-based digital care in youth mental health. JMIR Pediatrics and Parenting, 2(2), e12458.
  11. Zhao, S., Chen, Z., & Lu, J. (2023). Gamified learning in cognitive skill development: A systematic review. Computers in Human Behavior Reports, 10, 100263.