Öğrenme Analitiklerinde Etik, Gizlilik ve Adalet: Ortak Atıf ve Bibliyografik Eşleşme Analizleriyle Bilim Haritalama Çalışması (2012–2025)

Öz

Öğrenme analitikleri, öğrencilere ilişkin verinin hacmi ve çeşitliliği arttıkça giderek daha karmaşık etik sorunlar doğurmaktadır. Bu tartışma on yılı aşkın süredir sürmesine karşın, alanın etik literatürünün kendi bilgi yapısı sistematik olarak haritalanmamıştır. Bu çalışma, öğrenme analitiklerinde etik, gizlilik ve adalet temalarının bilimsel yapısını bibliyometrik yöntemlerle ortaya koymayı amaçlamaktadır. Web of Science Core Collection ve Scopus veri tabanlarından elde edilen ve tekilleştirme sonrası 2012–2025 dönemini kapsayan 770 doküman analiz edilmiştir. Performans analizinin yanı sıra ortak atıf, bibliyografik eşleşme, anahtar kelime ortak-oluşum ve Kleinberg atılım tespiti analizleri uygulanmıştır. Alan, %36,96'lık yıllık bileşik büyüme oranıyla üç evreli bir gelişim göstermektedir. Ortak atıf ağı dokuz küme ortaya koymuş, kümeler arası bağlantı yoğunluğunun yüksek olması alanın ayrı düşünce okullarına bölünmediğini, ortak bir kanon etrafında örgütlendiğini göstermiştir. Algoritmik adalet kümesi yapısal olarak ayrıksıdır ve kavramsal araçlarını büyük ölçüde makine öğrenmesi literatüründen devralmaktadır. Bibliyografik eşleşme analizi sekiz güncel araştırma cephesi belirlemiş; çok kipli analitikler ile yapay zekâ etiği çerçevelerinin kesişimindeki cephe, ortak atıf ağında marjinal konumda olmasına karşın en güncel yönelimi oluşturmuştur. Tematik haritada etik kavramları yüksek yoğunluk fakat düşük merkeziyetle niş çeyrekte konumlanmakta, bu da etik tartışmasının gelişmiş fakat alanın uygulama temalarından yalıtılmış kaldığına işaret etmektedir. Atılım analizi büyük veri odaklı erken dönemden yapay zekâ odaklı güncel döneme geçişi belgelemiş, ayrıca gizlilik tartışmasının genel etik tartışmasından yaklaşık beş yıl sonra yoğunlaştığını göstermiştir. Çalışma, ağ eşiklerinin duyarlılık analiziyle gerekçelendirilmesi ve küme kararlılığının düzeltilmiş Rand indeksiyle belgelenmesi yoluyla bibliyometrik uygulamaya yöntemsel bir katkı da sunmaktadır.

Anahtar Kelimeler

• Öğrenme analitikleri
• Etik
• Öğrenci gizliliği
• Algoritmik adalet
• Bibliyometrik analiz
• Ortak atıf analizi

Destekleyen Kurum

Bu çalışmanın yürütülmesi için herhangi bir finansman alınmamıştır.

Etik Beyan

Bu çalışma, kamuya açık bibliyografik veri tabanlarından elde edilen üstveri üzerinde yürütülmüştür. İnsan ya da hayvan katılımcı içermediğinden etik kurul onayı gerektirmemektedir. Araştırma etik standartlara uygun olarak yapılmıştır.

Ethics, Privacy, and Fairness in Learning Analytics: A Science Mapping Study Using Co-Citation and Bibliographic Coupling Analyses (2012–2025)

Öz

Learning analytics raises increasingly complex ethical questions as the volume and variety of student data expand. Although this debate has continued for more than a decade, the knowledge structure of the field's own ethics literature has not been systematically mapped. This study aims to reveal the scientific structure of ethics, privacy, and fairness themes in learning analytics through bibliometric methods. A total of 770 documents covering the period 2012–2025 were analysed after deduplication of records retrieved from the Web of Science Core Collection and Scopus. In addition to performance analysis, co-citation, bibliographic coupling, keyword co-occurrence, and Kleinberg burst detection analyses were conducted. The field displays a three-phase development pattern with a compound annual growth rate of 36.96%. The co-citation network revealed nine clusters; the high density of between-cluster connections indicates that the field is not divided into separate schools of thought but is instead organised around a shared canon. The algorithmic fairness cluster is structurally distinct and derives its conceptual tools largely from the machine learning literature. Bibliographic coupling identified eight current research fronts; the front situated at the intersection of multimodal analytics and artificial intelligence ethics frameworks constitutes the most recent orientation despite occupying a marginal position in the co-citation network. On the thematic map, ethics-related concepts fall into the niche quadrant with high density but low centrality, indicating that the ethical debate is well developed yet isolated from the field's application themes. Burst detection documented the transition from an early big-data-oriented period to a current artificial-intelligence-oriented one and showed that the privacy debate intensified approximately five years after the general ethics debate. The study also contributes methodologically by justifying network thresholds through sensitivity analysis and documenting cluster stability with the adjusted Rand index.

Anahtar Kelimeler

• Learning analytics
• Ethics
• Student privacy
• Algorithmic fairness
• Bibliometric analysis
• Co-citation analysis

Destekleyen Kurum

No funding was received for conducting this study.

Etik Beyan

This study was conducted on metadata obtained from publicly available bibliographic databases. It does not involve human or animal participants and therefore does not require ethics committee approval. All procedures followed were in accordance with the ethical standards.

Kaynakça

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