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Journals / EMO Bilimsel Dergi / 2026 / Cilt: 16 Sayı: 1

Time Series Clustering of Student Response Times in Adaptive Tests: Exploring Their Relationship with Academic Success

Adaptif Testlerde Öğrenci Yanıt Sürelerinin Zaman Serisi Kümelemesi: Akademik Başari İle İlişkilerinin İncelenmesi

Ahmet Hakan İnce(GAZIANTEP ISLAM SCIENCE AND TECHNOLOGY UNIVERSITY)
Serkan Özbay(GAZIANTEP UNIVERSITY)0000-0001-5973-8243
Pages
7–19
Publication Date
2026-01-31
DOI
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AI Summary & Key Takeaways

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

Keywords: Detect Algorithm, K-means, Hierarchical clustering, computerized adaptive testing.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study explores time-series clustering to analyze student response time patterns in computerized adaptive testing and their link to academic success.
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Key Findings & Data:A novel algorithm, Proposed DETECT (Detection of Educational Trends Elicited by Clustering Time-series data), integrates temporal dynamics into behavioral profiling.
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Conclusion & Impact:Keywords: Detect Algorithm, K-means, Hierarchical clustering, computerized adaptive testing.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Detect Algoritması · K-ortalamalar algoritması · Hiyerarşik kümeleme

Abstract

This study explores time-series clustering to analyze student response time patterns in computerized adaptive testing and their link to academic success. A novel algorithm, Proposed DETECT (Detection of Educational Trends Elicited by Clustering Time-series data), integrates temporal dynamics into behavioral profiling. Unlike traditional methods, DETECT applies change-point detection and segment-level feature extraction to reveal latent response patterns, offering a time-aware view of test-taking behaviors. Data from 150 students completing a 30-item mathematics assessment were preprocessed with outlier removal and z-score normalization. DETECT was compared to K-means and Hierarchical clustering. One-way ANOVA and Tukey HSD tests showed significant group differences for K-means and Hierarchical clustering (p < 0.001), but not for DETECT (p = 0.1737), highlighting its focus on temporal behavior over static performance. Correlation analysis found no significant link between average response time and scores. DETECT presents a promising tool for nuanced, personalized, and diagnostically rich educational assessments. Keywords: Detect Algorithm, K-means, Hierarchical clustering, computerized adaptive testing.

Özet

Bu çalışma, bilgisayarlı uyarlanabilir testlerde öğrenci yanıt süresi kalıplarını analiz etmek ve akademik başarı ile ilişkilerini incelemek için zaman serisi kümeleme tekniklerini araştırmaktadır. Önerilen DETECT (Detection of Educational Trends Elicited by Clustering Time-series data) algoritması, zamansal dinamikleri davranışsal profillemeye entegre ederek klasik yöntemlerden ayrılır. DETECT, değişim noktası tespiti ve segment düzeyinde özellik çıkarımı ile gizli yanıt kalıplarını ortaya çıkarır ve sınav süreçlerine zamana duyarlı bir bakış sunar. 150 öğrencinin 30 maddelik matematik değerlendirmesinden elde edilen veriler, aykırı değerlerin çıkarılması ve z-skoru normalizasyonu ile ön işleme tabi tutulmuştur. DETECT, K-ortalamalar ve Hiyerarşik kümeleme yöntemleriyle karşılaştırılmıştır. ANOVA ve Tukey HSD testleri, K-ortalamalar ve Hiyerarşik kümeleme için anlamlı grup farklılıkları (p < 0.001), ancak DETECT için anlamlı olmayan sonuçlar (p = 0.1737) ortaya koymuştur. DETECT, yanıt süresi kalıplarını analiz ederek daha kişiselleştirilmiş ve tanısal açıdan zengin eğitim değerlendirmeleri için umut vadetmektedir

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Cite This Article

İnce, A. H., Özbay, S. (2026). Time Series Clustering of Student Response Times in Adaptive Tests: Exploring Their Relationship with Academic Success. EMO Bilimsel Dergi, Cilt: 16 Sayı: 1, 7–19. https://ajindex.com/articles/1390255

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