Using Artificial Neural Network in the Prediction of Metacognitive Thinking Skills: Performance Evaluation of Network Structure and Training Methods

Sibel Barin Özkan; Yasin Özkan

Makale detayları

Using Artificial Neural Network in the Prediction of Metacognitive Thinking Skills: Performance Evaluation of Network Structure and Training Methods

Yazarlar:

Sibel Barin Özkan | Yasin Özkan

Yayıncı:

Zonguldak Bulent Ecevit University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Karaelmas Fen ve Mühendislik Dergisi Vol.15 Issue 1, pp.107-118

Belge Tipi:

Makale

Yayın Tarihi:

4/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001345567

pISSN:

2146-7277

Anahtar kelimeler:

Özet:

 Metacognitive thinking skill is one of the most important skills that individuals should have today. In recent years, technology has been frequently utilized in the acquisition of this skill. The development and transformation of technology, especially robots in recent years, has affected societies both directly and indirectly. The predictions of scientists have started to find a response in the education community for many years. Therefore, it has become unthinkable that robots do not affect the field of education. In this study, a feed-forward back-propagation artificial neural network was used to predict metacognition training and planning, monitoring and evaluation skills. In this context, information on 250 instructors working in universities affiliated to the Council of Higher Education was used as the input of the artificial neural network. A suitable network design was obtained by making metacognition training prediction for different artificial neural network designs. Then, the network was trained using different training algorithms for this appropriate network design and the most appropriate training method was determined among these algorithms. The performances of different network designs and training algorithms were analyzed in terms of correlation coefficient and mean squared error. In terms of evaluating the parameters affecting the performance, the designs with 1 and 2 hidden layers were run 10 times, and the network structure with 20 and 10 hidden neurons in each layer, respectively, was determined as the most appropriate design. For this network structure, the best results in terms of performance parameters were observed when Conjugate Gradient Fletcher-Reeves and Levenberg-Marquardt training algorithm were used.

 Metacognitive thinking skill is one of the most important skills that individuals should have today. In recent years, technology has been frequently utilized in the acquisition of this skill. The development and transformation of technology, especially robots in recent years, has affected societies both directly and indirectly. The predictions of scientists have started to find a response in the education community for many years. Therefore, it has become unthinkable that robots do not affect the field of education. In this study, a feed-forward back-propagation artificial neural network was used to predict metacognition training and planning, monitoring and evaluation skills. In this context, information on 250 instructors working in universities affiliated to the Council of Higher Education was used as the input of the artificial neural network. A suitable network design was obtained by making metacognition training prediction for different artificial neural network designs. Then, the network was trained using different training algorithms for this appropriate network design and the most appropriate training method was determined among these algorithms. The performances of different network designs and training algorithms were analyzed in terms of correlation coefficient and mean squared error. In terms of evaluating the parameters affecting the performance, the designs with 1 and 2 hidden layers were run 10 times, and the network structure with 20 and 10 hidden neurons in each layer, respectively, was determined as the most appropriate design. For this network structure, the best results in terms of performance parameters were observed when Conjugate Gradient Fletcher-Reeves and Levenberg-Marquardt training algorithm were used.

 Metacognitive thinking skill is one of the most important skills that individuals should have today. In recent years, technology has been frequently utilized in the acquisition of this skill. The development and transformation of technology, especially robots in recent years, has affected societies both directly and indirectly. The predictions of scientists have started to find a response in the education community for many years. Therefore, it has become unthinkable that robots do not affect the field of education. In this study, a feed-forward back-propagation artificial neural network was used to predict metacognition training and planning, monitoring and evaluation skills. In this context, information on 250 instructors working in universities affiliated to the Council of Higher Education was used as the input of the artificial neural network. A suitable network design was obtained by making metacognition training prediction for different artificial neural network designs. Then, the network was trained using different training algorithms for this appropriate network design and the most appropriate training method was determined among these algorithms. The performances of different network designs and training algorithms were analyzed in terms of correlation coefficient and mean squared error. In terms of evaluating the parameters affecting the performance, the designs with 1 and 2 hidden layers were run 10 times, and the network structure with 20 and 10 hidden neurons in each layer, respectively, was determined as the most appropriate design. For this network structure, the best results in terms of performance parameters were observed when Conjugate Gradient Fletcher-Reeves and Levenberg-Marquardt training algorithm were used.

 Üstbilişsel düşünme becerisi günümüzde bireylerin sahip olması gereken en önemli becerilerden biridir. Son yıllarda bu becerinin kazanılmasında teknolojiden sıklıkla yararlanılmaktadır. Teknolojinin özellikle de robotların son yıllardaki gelişimi ve dönüşümü toplumları hem doğrudan hem de dolaylı olarak etkilemiştir. Bilim insanlarının öngörüleri uzun yıllardır eğitim camiasında karşılık bulmaya başladı. Dolayısıyla robotların eğitim alanını etkilememesi düşünülemez hale geldi. Bu çalışmada üstbiliş eğitimi ve planlama, izleme ve değerlendirme becerilerini tahmin etmek için ileri beslemeli geri yayılımlı yapay sinir ağı kullanılmıştır. Bu kapsamda Yüksek Öğretim Kuruluna bağlı üniversitelerde görev yapan 250 öğretim elemanına ait veriler yapay sinir ağlarına giriş olarak verilmiştir. Farklı yapay sinir ağı tasarımları için üstbiliş eğitimi tahmini yapılarak uygun bir ağ tasarımı elde edilmiştir. Daha sonra bu uygun ağ tasarımı için farklı eğitim algoritmaları kullanılarak ağ eğitilmiş ve bu algoritmalar arasından en uygun eğitim yöntemi belirlenmiştir. Farklı ağ tasarımlarının ve eğitim algoritmalarının performansları, korelasyon katsayısı ve ortalama hata karesi açısından analiz edilmiştir. Performansı etkileyen parametrelerin değerlendirilmesi açısından 1 ve 2 gizli katmanlı tasarımlar 10 kez çalıştırılmış ve her katmanda sırasıyla 20 ve 10 gizli nöronlu ağ yapısı en uygun tasarım olarak belirlenmiştir. Bu ağ yapısı için performans parametreleri açısından en iyi sonuçlar Eşlenik Gradyan Fletcher-Reeves ve Levenberg-Marquardt eğitim algoritması kullanıldığında gözlemlenmiştir.

Makale detayları

Using Artificial Neural Network in the Prediction of Metacognitive Thinking Skills: Performance Evaluation of Network Structure and Training Methods

Yazarlar:

Sibel Barin Özkan | Yasin Özkan

Yayıncı:

Zonguldak Bulent Ecevit University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Karaelmas Fen ve Mühendislik Dergisi Vol.15 Issue 1, pp.107-118

Belge Tipi:

Makale

Yayın Tarihi:

4/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001345567

pISSN:

2146-7277

Artificial neural networks

machine learning

metacognitionMakine öğrenmesi

yapay sinir ağları

üstbiliş.

Sayı

Makale detayları

Using Artificial Neural Network in the Prediction of Metacognitive Thinking Skills: Performance Evaluation of Network Structure and Training Methods

Yazarlar: Sibel Barin Özkan | Yasin Özkan

Yayıncı: Zonguldak Bulent Ecevit University

Ana Başlıklar: Bilgisayar Yazılımı

Dergi: Karaelmas Fen ve Mühendislik Dergisi Vol.15 Issue 1, pp.107-118

Belge Tipi: Makale

Yayın Tarihi: 4/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001345567

pISSN: 2146-7277

Artificial neural networks

machine learning

metacognitionMakine öğrenmesi

yapay sinir ağları

üstbiliş.

Sayı

Yazarlar:

Sibel Barin Özkan | Yasin Özkan

Yayıncı:

Zonguldak Bulent Ecevit University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Karaelmas Fen ve Mühendislik Dergisi Vol.15 Issue 1, pp.107-118

Belge Tipi:

Makale

Yayın Tarihi:

4/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001345567

pISSN:

2146-7277