Designing a Draft for a Metaheuristic Curriculum Evaluation Model (MCEM) Based on the Examination of Various Metaheuristic Artificial Intelligence Optimization Applications

Yıl 2024, Cilt: 12 Sayı: 2, 989 - 1055, 29.07.2024

Volkan Duran Gülay Ekici

https://doi.org/10.46778/goputeb.1463058

Öz

This paper explores the integration of metaheuristic artificial intelligence (AI) optimization algorithms into the process of curriculum evaluation, proposing a novel approach that could enhance educational outcomes. A meta-synthesis of the existing literature on the application of AI optimization techniques—such as tabu search, simulated annealing, genetic algorithms, and ant colony optimization—in educational contexts was conducted. This study revealed a scarcity of direct applications of these algorithms in curriculum evaluation, thus identifying a gap in research and an opportunity for exploration. We proposed detailed models for adapting various metaheuristic optimization algorithms to assess curriculum components, including objectives, content, teaching methodologies, and assessment strategies. Our paper synthesizes insights from the literature review and suggests avenues for experimental studies to assess the effectiveness of AI optimization algorithms across diverse educational levels and curricula. Furthermore, we introduce a draft of the Metaheuristic Curriculum Evaluation Model (MCEM), synthesized from the reviewed optimization models and curriculum evaluation processes. This exploration into the integration of metaheuristic AI optimization algorithms within curriculum evaluation highlights a promising frontier in educational research. By detailing potential applications, addressing methodological rigor, and considering context-specific nuances, this paper lays the groundwork for future studies that could evaluate how curricula are developed, evaluated, and optimized from a different perspective.

Anahtar Kelimeler

Optimization Algorithms, Curriculum Evaluation, Artificial Intelligence, Meta-Synthesis

Çeşitli Meta-Sezgisel Yapay Zekâ Optimizasyon Uygulamalarının İncelenmesine Dayalı Bir Meta-Sezgisel Eğitim Programı Değerlendirme Modeli Taslağı Tasarlanması

Öz

Bu araştırma, metasezgisel yapay zeka (AI) optimizasyon algoritmalarının eğitim programı değerlendirme sürecine entegrasyonunu araştırmaktadır ve bunun eğitsel sonuçlarını artırabilecek yeni bir yaklaşım önermektedir. Çalışmada tabu arama, benzetilmiş tavlama, genetik algoritmalar ve karınca kolonisi optimizasyonu gibi yapay zeka optimizasyon tekniklerinin eğitim bağlamlarında uygulanmasına ilişkin mevcut literatürün bir meta sentezi gerçekleştirilmiştir. Bu çalışma, eğitimde program değerlendirmede bu algoritmaların doğrudan uygulanmasının literatürdeki azlığını ortaya çıkarmıştır, böylece literatürde bir boşluk ve bir keşif fırsatı olduğunu göstermiştir. Hedefler, içerik, öğretim yöntemleri ve değerlendirme stratejileri dahil olmak üzere eğitim programı bileşenlerini değerlendirmek için çeşitli meta-sezgisel optimizasyon algoritmalarını uyarlamak için ayrıntılı modeller önerilmiştir. Makale, literatür taramasından elde edilen bilgileri sentezlemiş ve çeşitli eğitim seviyeleri ve programlarıyla yapay zeka optimizasyon algoritmalarının etkinliğini değerlendirmek için deneysel çalışmalar için yollar önermiştir. Ayrıca, incelenen optimizasyon modellerinden ve eğitim programı değerlendirme süreçlerinden sentezlenen Metasezgisel Eğitim Programı Değerlendirme Modeli'nin bir taslağı sunulmuştur. Meta-sezgisel yapay zeka optimizasyon algoritmalarının eğitimde program değerlendirmesine entegrasyonuna yönelik bu araştırma, eğitim araştırmalarında yeni bir alana vurgu yapmaktadır. Potansiyel uygulamaları detaylandırarak, metodolojik titizliği ele alarak ve bağlama özgü nüansları göz önünde bulundurarak bu çalışmada, eğitimde program geliştirilme, değerlendirilme ve optimize edilme şekline farklı bakacak olan gelecekteki çalışmalara zemin hazırlanmaktadır.

Anahtar Kelimeler

Optimizasyon Algoritmaları, Eğitimde Program Değerlendirme, Yapay Zekâ, Meta-Sentez

Kaynakça

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