- Eklemek veya çıkarmak istediğiniz kriterleriniz için 'Dahil' / 'Hariç' seçeneğini kullanabilirsiniz. Sorgu satırları birbirine 'VE' bağlacı ile bağlıdır. - İptal tuşuna basarak normal aramaya dönebilirsiniz.
The main goal of this thesis is to investigate the effect of two vocabulary learning approaches: 1) Computer-assisted Language Learning (CALL) with a computer-based flashcards program, Quizlet, 2) Keeping vocabulary notebooks on high school EFL learners' vocabulary learning. The vocabulary targeted for the study is determined from three sequential units of the textbook. The units are determined randomly considering the starting date of the study. Eighty-nine students in four beginner EFL classes were randomly assigned as control or treatment groups. A computer-based flashcard program (Quizlet) and a vocabulary notebook program was i . . .mplemented in two different classes over a 3 week period. The remaining two classes acting as control groups followed the same curriculum with the same materials without using Quizlet or keeping vocabulary notebooks. Vocabulary acquisition was measured by pre, post and delayed post-tests of unannounced vocabulary tests including orthography, grammatical accuracy and use adapted from  Laufer & Goldstein (2004) and, meaning and form, adapted from  Webb (2009). Pre, post and delayed post-test scores of students were analyzed to evaluate the effectiveness of the learning process. Learners' perceptions about the training are also investigated by means of interviews. The results show that participants who learned the target words through Quizlet Flashcard Software Program outperformed those who completed the tasks by means of vocabulary notebook and control group. The analysis of the differences between the two tasks (receptive and productive) for each aspect (orthography, meaning and form, grammar) shows that the tasks measuring receptive knowledge led to significantly higher gains than tasks measuring productive knowledge of all three groups
Automatic extraction of bare-Earth LiDAR points to generate Digital Terrain Model (DTM) is still an ongoing problem. Even though there are several methods for ground filtering, automatic and adaptive methods are still a need due to the complexity of the environment. In this study, we address the ground filtering problem by applying Empirical Mode Decomposition (EMD) to the airborne LiDAR data. EMD is a data-driven method that adapts to the local characteristics of the signal. We benefit from EMD to extract the local trend of the LiDAR height data. This way, can extract a local adaptive threshold to filter ground and non-ground objec . . .ts. We tested our method using the ISPRS LiDAR reference dataset and obtained promising results. We also compared the filtering results with the ones in the literature to show the improvements obtained