Resumen El estudio actual investigó el nivel de Conocimiento Tecnológico Pedagógico del Contenido (TPACK) y las necesidades de los futuros docentes a través de un diseño paralelo convergente. Los participantes incluyeron 120 futuros maestros de Inglés como Lengua Extranjera (ILE), 79 mujeres y 41 hombres, que estudiaban en diferentes grados en una universidad estatal en Turquía. La escala TPACK-Deep desarrollada por Kabakçı-Yurdakul et al. (2012) con respecto a diferentes aspectos de TPACK sirvió como instrumento de recopilación de datos cuantitativos y se utilizó un cuestionario abierto para recopilar datos cualitativos sobre la opinión y las necesidades de los estudiantes de magisterio. En cuanto al análisis de los datos cuantitativos, se utilizó estadística descriptiva. Por otro lado, se implementó un análisis temático para los datos cualitativos. Los hallazgos indicaron que los futuros maestros de ILE generalmente tenían un alto nivel de competencia en TPACK. Además, las necesidades de TPACK de los estudiantes de magisterio estaban relacionadas principalmente con problemas basados en tecnología (TK, TCK y TPK) y el acceso a herramientas tecnológicas. Finalmente, al final del artículo, se brindan más sugerencias e implicaciones para estudios prospectivos y académicos sobre los temas mencionados anteriormente.

Abstract The current study investigated the Technological Pedagogical Content Knowledge (TPACK) level and needs of pre-service teachers through a convergent parallel design. The participants included 120 English as a Foreign Language (EFL) pre-service teachers, 79 females and 41 males, studying at a state university in Turkey. The TPACK-Deep scale developed by Kabakçı-Yurdakul et al. (2012) served as the quantitative data collection instrument and an open-ended questionnaire was used to collect qualitative data on the opinion and needs of the participants. As for the analysis of quantitative data, the authors employed descriptive statistics. On the other hand, a thematic analysis was implemented for the qualitative data. The findings indicated that EFL pre-service teachers generally had a high level of TPACK proficiency. Moreover, the TPACK needs of student teachers were mainly related to technology-based problems (TK, TCK, and TPK) and access to technological tools. Finally, at the end of the article, further suggestions and implications are provided for prospective studies and scholars over the above-mentioned issues.

RESUMO Objetivo: Comparar as medidas realizadas usando análise cefalométrica digital-manual (DM) e análise cefalométrica automatizada por meio de uma plataforma online de inteligência artificial (IA), segundo as diferentes más oclusões esqueléticas sagitais. Métodos: Foram incluídas radiografias cefalométricas de 105 indivíduos selecionados aleatoriamente (idade média: 17,25 ± 1,87 anos). O software Dolphin Imaging foi utilizado para análise cefalométrica DM, e a plataforma WebCeph foi utilizada para análise cefalométrica baseada em IA. No total, foram avaliadas 10 medidas lineares e 12 angulares. O testet pareado, a ANOVA de uma via e o coeficiente de correlação intraclasse foram utilizados para avaliar as diferenças entre os dois métodos. O nível de significância foi estabelecido em p<0,05. Resultados: Com exceção dos parâmetros SNB, NPog, U1.SN, U1.NA, L1-APog, I/I e LIE, todos os outros parâmetros apresentaram valores significativamente diferentes entre os dois métodos (p<0,05). Enquanto não foi encontrada diferença (p>0,05) nas medidas SNA e SNB entre os dois métodos no grupo má oclusão de Classe I, foi encontrada diferença entre os métodos no grupo má oclusão de Classe II. Entretanto, no grupo má oclusão de Classe III somente o SNA foi diferente (p<0,05). O ângulo ANB diferiu significativamente em todos os três grupos de más oclusões. Foi encontrada boa correlação entre os dois métodos para todos os parâmetros, exceto para CoA e CoGn. Conclusão: Embora para algumas medidas tenham sido detectadas diferenças significativas entre os dois métodos de análise cefalométrica, nem todas as diferenças foram clinicamente significativas. O método de análise cefalométrica baseado em IA precisa ser aperfeiçoado, com maior especificidade para cada má oclusão.

ABSTRACT Objective: The aim of this study was to compare the measurements performed with digital manual (DM) cephalometric analysis and automatic cephalometric analysis obtained from an online artificial intelligence (AI) platform, according to different sagittal skeletal malocclusions. Methods: Cephalometric radiographs of 105 randomly selected individuals (mean age: 17.25 ± 1.87 years) were included in this study. Dolphin Imaging software was used for DM cephalometric analysis, and the WebCeph platform was used for AI-based cephalometric analysis. In total, 10 linear and 12 angular measurements were evaluated. The paired t-test, one-way ANOVA test, and intraclass correlation coefficient tests were used to evaluate the differences between the two methods. The level of statistical significance was set at p< 0.05. Results: Except for SNB, NPog, U1.SN, U1.NA, L1-APog, I/I, and LLE parameters, all other parameters presented significant differences between the two methods (p< 0.05). While there was no difference (p> 0.05) in both SNA and SNB measurements between the two methods in the Class I malocclusion group, there was a difference between both methods in the Class II malocclusion group. Meanwhile, only the SNA in the Class III malocclusion group was different (p< 0.05). The ANB angle differed significantly in all three malocclusion groups. For both methods, all parameters except CoA and CoGn were found to have good correlation. Conclusion: Although significant differences were detected in some measurements between the two cephalometric analysis methods, not all differences were clinically significant. The AI-based cephalometric analysis method needs to be developed for more specific malocclusions.

Abstract: This paper focuses on serving twofold benefits for the environment by providing not only recycling of a waste material but also improving rutting performance of sand subgrade under cyclic traffic loads. In this context, a series of laboratory experiments have been conducted to benchmark the performance of commercially manufactured geotextile and palm tree pruning waste (PTPW) as soil improvement agents. Experimental results of the study were evaluated based on permanent (plastic), total, and elastic deformation, rut depth reduction (RDR), traffic benefit ratio (TBR), percentage of elastic deformation, and resilient modulus (MR). In the view of experimental results, geotextile and PTPW-reinforced sand subgrades demonstrated well performance in the sense of permanent and elastic deformations when compared to unreinforced case. It is also realized that the most satisfactory performance was obtained when geotextile or PTPW are located at a burial depth of both 50 mm and 100 mm. In that case, TBR values of geotextile and PTPW-reinforced subgrades were almost the same at 20 mm permanent deformation (i.e., 6.71 and 6.76, respectively). Furthermore, when the results were evaluated based on RDR, it is observed that geotextile and PTPW reinforcements reduced the rut depth at the rate of 49.31 % and 37.15 % at the end of 5000 load cycle, respectively.