Enhancing science learning through computational thinking and modeling in middle school classrooms: A mixed methods study
Başlık çevirisi mevcut değil.
- Tez No: 626138
- Danışmanlar: PROF. DANIŞMAN YOK
- Tez Türü: Doktora
- Konular: Eğitim ve Öğretim, Education and Training
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2018
- Dil: İngilizce
- Üniversite: North Carolina A&T State University
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 175
Özet
Computational thinking and modeling are authentic practices that scientists and engineers use frequently in their daily work. Advances in modern computing technologies over the past couple of decades have further emphasized the centrality of modeling in science by making computationally-enabled model use and construction more accessible to scientists. For this reason, it is important for students to get exposed to these practices in K-12 science classrooms. This mixed-methods study investigated how a one-week intervention in regular middle school science classrooms that introduced computational thinking practices and simulation-based model building through a visual block-based programming environment influenced students' understanding of computational thinking concepts and practices as well as their conceptual understanding of a physical science topic, force and motion. The study also examined whether and how seventh-grade students' attitudes towards programming changed as a result of participating in the intervention course. Eighty-two seventh-grade students from a public middle school in the southeastern United States participated in the study. Quantitative data sources included pre- and post-tests of students' knowledge of computational thinking concepts and practices, force and motion concepts, and their attitudes towards programming. Qualitative data sources included classroom observation notes, interviews with students, and reflection statement responses. During the classroom intervention, students were introduced to computational thinking concepts and practices through a block-based programming environment called Scratch and constructed simulationbased computational models of physical phenomena. The findings of the study indicated that engaging in building computational models through Scratch programming environment in regular science classrooms resulted in significant conceptual learning gains for the sample of this study. The affordances of the dynamic nature of computational models let students both“observe”and“interact”with the target phenomenon in real time while the generative dimension of model construction promoted a rich classroom discourse facilitating conceptual learning. The results also indicated that students had more favorable attitudes towards programming on the post-test compared to the pre-test. This study contributes to the nascent literature on integrating computational modeling into K-12 science curricula by emphasizing the affordances and generative dimension of model construction through block-based programming.
Özet (Çeviri)
Özet çevirisi mevcut değil.
Benzer Tezler
- A new volterra neural network layer lıbrary usıng tensorflow
Başlık çevirisi yok
ZAKARIA FAYEZ ABD ALYAFAWI
Yüksek Lisans
İngilizce
2023
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolSakarya ÜniversitesiBilgisayar ve Enformatik Mühendisliği Ana Bilim Dalı
PROF. DR. DEVRİM AKGÜN
- Efficient deep learning approaches for signal and image analysis applications
Sinyal ve görüntü analizi uygulamaları için verimli derin öğrenme yaklaşımları
ONUR CAN KOYUN
Doktora
İngilizce
2024
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrolİstanbul Teknik ÜniversitesiBilgisayar Bilimleri Ana Bilim Dalı
PROF. DR. BEHÇET UĞUR TÖREYİN
- Yüksek çözünürlüklü görüntülerde derin öğrenme tabanlı nesne tespiti için yeni bir önişleme yöntemi geliştirilmesi
Development of a new preprocessing method for deep learning based object detection in high resolution images
MUHAMMED TELÇEKEN
Doktora
Türkçe
2024
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolSakarya ÜniversitesiBilgisayar Mühendisliği Ana Bilim Dalı
PROF. DR. DEVRİM AKGÜN
PROF. DR. SEZGİN KAÇAR
- Havayolu yolculuk deneyimini iyileştirmek için makine öğrenmesi yöntemleriyle uçuş gecikmesi tahmini
Machine learning techniques for enhancing airline passenger experience through flight delay prediction
ESMA ERGÜN
Yüksek Lisans
Türkçe
2024
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrolİstanbul Teknik ÜniversitesiBilişim Uygulamaları Ana Bilim Dalı
DR. ÖĞR. ÜYESİ SÜHA TUNA
- Enhancing grouping-scoring-modeling (G-S-M) approach through a statistical pre-scoring component: A case study for high-dimensional transcriptomic data analysis
Istatistiksel ön puanlama bileşeni ile gruplama puanlama modellemesi (GSM) yaklaşımın geliştirilmesi: Yüksek boyutlu transkriptomik veri analizi için bir vaka çalışması
MAHAM KHOKHAR
Yüksek Lisans
İngilizce
2024
Bilim ve TeknolojiAbdullah Gül ÜniversitesiVeri Bilimi Ana Bilim Dalı
DOÇ. DR. BURCU GÜNGÖR