A Corpus-driven Scoping Systematic Review of Four Decades of Teacher Professional Development Research: Exploring Research Foci, Content Areas, Designs Methods and Trends

This study represents the findings of a systematic review (SR) of literature in the teacher professional development (TPD) domain to outline the research patterns through content examination of 199 research articles (RAs) in the area of TPD over the previous 40 years (1982 -2021). RAs were investigated and their research content areas, utilized research methods, data collection procedures, and findings were analyzed and coded. The broad investigation of the RAs showed a wide variety of themes that corresponded to 22 research areas. TPD program effects, TPD & technology, and TPD & Sociolinguistics were the most searched content areas. It was also found that the qualitative method with 52.26% of occurrences appeared to be the dominant research method used in RAs. Exploring data collection procedures, it was uncovered that interview, questionnaire and observation were the main data collection strategies utilized within the TPD RAs. Analyzing the findings, changes in teacher practices, attitudes and knowledge, learner achievements, and determining priorities for TPD programs were the most reported findings in TPD RAs. This corpus-driven SR underpins the notion that TPD makes a difference in altering teachers’ practices and attitudes and improves learner abilities if specific characteristics are taken into account in the planning and administration of TPD programs

On evaluation of thermophysical properties of transformer oil-based nanofluids: A comprehensive modeling and experimental study

Transformer oil-based nanofluids are known to have higher thermal conductivity and heat transfer performance compared to conventional transformer oils. In this study, four different types of transformer oil-based nanofluids are synthesized using the well-known two-step method. The first nanofluid contains pure multi-walled carbon nanotubes (MWCNTs), while other samples consist of 20 Vol% of MWCNTs and 80 Vol% of different oxide nanoparticles (i.e., Al2O3, TiO2, and SiO2). The dynamic viscosity and thermal conductivity of prepared samples are investigated in seven different volume fractions of 0.001, 0.0025, 0.005, 0.01, 0.025, 0.05, and 0.1%. Besides, the breakdown voltage of the pure transformer oil and nanofluids containing 0.05 and 0.1 Vol% of nanoparticles is investigated. The outcomes show that dielectric properties of hybrid carbon-based nanofluids are far better compared to those properties of the pure MWCNTs nanofluids. Finally, eight different soft computing approaches, including group method of data handling (GMDH), support vector machine (SVM), radial basis function (RBF) neural network, multilayer perceptron (MLP), and MLP and RBF models optimized with bat and grasshopper optimization algorithm (GOA), are used to model the viscosity and thermal conductivity of synthesized nanofluids. The outcomes show that the GMDH approach significantly outperforms all other models in terms of predicting the thermal conductivity and dynamic viscosity of transformer oil-based nanofluids. (C) 2019 Elsevier B.V. All rights reserved