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Goal orientations of teachers for teaching are significant in shaping educational settings such that they are linked to some school concepts. The curriculum and teaching autonomy of teachers is related to the teachers' goal orientation. Further, school climate that is shaped by behaviors of teachers, students, and administrators affects the goal orientations for teaching. The aim of this study is to reveal the role of teachers' autonomy behavior and perceptions of school climate on their goal orientations for teaching. The study was designed as correlational study. The participants consisted of 284 teachers in public and private schools. Instruments were "goal orientation scale for teaching", "school climate scale", and "teacher autonomy scale". Multiple Hierarchical Regression Analysis method was used to analyze data. The results indicated that school climate was more powerful of and significant predictor of goal orientation than teacher autonomy. Further, teacher autonomy and school climate predicted dimensions of goal orientation for teaching, which were mastery, performance-approach, work-avoidance, and student relations. The study made a contribution to teacher education and school effectiveness literature by revealing factors related to teachers' goal orientations for teaching.

Abstract Recently, biometric recognition has become a significant field of research. The concept of cancelable biometrics (CB) has been introduced to address security concerns related to the handling of sensitive data. In this paper, we address unconstrained face verification by proposing a deep cancelable framework called BiometricNet+ that employs random projections (RP) to conceal face images and compressive sensing (CS) to reconstruct measurements in the original domain. Our lightweight design enforces the properties of unlinkability, revocability, and non-invertibility of the templates while preserving face recognition accuracy. We compare facial features by learning a regularized metric: at training time, we jointly learn facial features and the metric such that matching and non-matching pairs are mapped onto latent target distributions; then, for biometric verification, features are randomly projected via random matrices changed at every enrollment and query and reconstructed before the latent space mapping is computed. We assess the face recognition accuracy of our framework on challenging datasets such as LFW, CALFW, CPLFW, AgeDB, YTF, CFP, and RFW, showing notable improvements over state-of-the-art techniques while meeting the criteria for secure cancelable template design. Since our method requires no fine-tuning of the learned features, it can be applied to pre-trained networks to increase sensitive data protection.

The aim of this study is to investigate the effect of Universal Design for Learning on students' academic achievement and attitudes towards mathematics course, and to reveal the opinions of the students about Universal Design for Learning. In this study, the concurrent embedded strategy was used. The participants consisted of 33 primary school students from two different 4th grade classes at a primary school. In the quantitative part of the study, non-equivalent control groups design was utilized. Data were collected through an academic achievement test and an attitude scale. The findings indicated that Universal Design for Learning had a large effect on academic achievement and attitudes towards mathematics course. In the qualitative part of the study, case study was used and data were collected through a focus group interview. The students participating in the interview were identified with a maximum variation sampling method. The students stated that Universal Design for Learning improved their attention, interest, cooperation, and self-regulation skills. They also stated that Universal Design for Learning supported their retention of knowledge, multiple representations of knowledge, and active participation to lesson. As a result, Universal Design for Learning is thought to be one of the effective methods in mathematic courses and its use is recommended. This study was derived from the master's thesis named "The Effect of Universal Design for Learning on Students Academic Achievement and Attitude towards Mathematic Course".

The purpose of the study is to reveal the effect of cooperative learning on the attitudes and reflective thinking skills of the students' in English course. The study was carried out for 5 weeks with 66 students studying at the 10th grade at an Anatolian high school in the district of Karadeniz Eregli in Zonguldak during the fall term of the 2015-2016 academic year. The design of the study was 'nonequivalent control groups pre-test post-test' which is one of quasi-experimental designs. The scale of attitude towards English course and the scale of reflective thinking were used as instruments of the study which were applied as a pre-test and as a post-test. According to the results, it was concluded that cooperative learning is significantly and positively more effective on students' attitudes and reflective thinking skills than traditional method. This study was produced from master thesis of written by first author under the supervision of second author.

The purpose of this study was to investigate a predictive power of prospective teachers' self-efficacy sources on their teaching self-efficacy and attitude towards the teaching profession. Design of the study was the correlational research. The study was conducted on 315 prospective teachers studying pedagogical formation education in a 2017-18 academic year. Instruments were "Prospective Teachers' Self-Efficacy Sources Scale", "Teaching Self-Efficacy Scale" and "Attitude Scale of Teaching Profession". Pearson Product Moment Correlation Coefficient and PATH analysis methods were used to analyze the data. Results indicated that emotional states, mastery experience, and verbal persuasion were significant predictors of prospective teachers' teaching self-efficacy and attitudes towards teaching profession.

The aim of this study is to develop the English language skills self-efficacy belief scale for higher education students. For this purpose, the trial form has got 48 items. In the fall semester of the 2017-2018 academic year, the trial form was applied to a total of 305 university students. Explanatory factor analysis was conducted to prove the construct validity of the developed scale. For the factor analysis of the data, it is shown that the data set is suitable for factor analysis as the KMO value is 0,96 and the Barlett test significance value is 0,000. The communality values to select items, factor loadings and item total correlations were examined in item selection. The scale is formed as four factors; reading, writing, speaking and listening, and a total of 29 items. In order to determine the appropriateness of the scale to the students of the same level, the scale was applied to 301 preparatory students in School of Foreign Languages of a university. As a result of confirmatory factor analysis, the scale of 22 items, the high item total correlations of the items forming the scale factors show the high structural validity of these scale items. In the confirmation study, the Cronbach Alpha internal consistency coefficients of the scales were 0,82 to 0,91. These values show that the scale provides reliable results, in other words, the scale correctly measures the students' self-efficacy beliefs in 4 basic language skills.

Positive learning environment enhance students' motivation. Student-teacher rapport is a way of building positive classroom environment. The student-teacher rapport has been identified in the literature as a significant factor influencing learning outcomes. Research on student-teacher rapport examined together with the concepts of attitude and self-efficacy is limited. However, to consider student-teacher rapport, attitudes and self-efficacy beliefs towards teaching profession is important to uncover the factors affecting quality in the teaching profession. Thus, to determine the predictive power of student-teacher rapport on prospective teachers' attitudes and self-efficacy beliefs towards teaching profession was the aim of the study. This study was in a correlational design. The study was conducted on 499 prospective teachers who studying in a university located in the western part of Turkey and participating voluntarily in the study, during the 2015-2016 spring term. Data were collected using three instruments, the Teacher-Student Rapport Scale, the Teachers' Sense of Efficacy Scale, and the Attitude Scale of Teaching Profession. Pearson product-moment correlation coefficient (Pearson's r) and stepwise-regression analysis were conducted to analyze the data. Results revealed that the student-teacher rapport and the prospective teachers' self-efficacy beliefs and attitudes towards teaching profession were positively and significantly related to each other. Also, the prospective teacher-lecturer rapport had a significant effect on prospective teachers' attitudes and self-efficacy beliefs towards the teaching profession. While prospective teacher-lecturer rapport predicted 8% of the prospective teachers' attitudes towards the teaching profession, the rapport between prospective teacher and lecturer predicted 10% of the prospective teachers' self-efficacy beliefs towards the teaching profession.

This study aims to search the effect of flipped classroom on students' learning a new grammar structure and their attitudes towards the English course. The study was carried out for 6 weeks with 56 students studying in five different preparatory classes at a black sea region university's foreign languages school in the fall term of 2015-2016 academic year. The design of the study was 'non-equivalent control group design' which is one of quasi-experimental designs. Data were gathered with help of an achievement test and an attitude scale. Flipped classroom model was applied in the experimental group while traditional instruction was applied in the control group. At the end of the study, it was concluded that flipped classroom had medium effect on students' academic achievements and attitudes comparing to traditional instruction.

The purpose of the study is to reveal the effect of cooperative learning on language skills in an English course. The study was carried out for 5 weeks with 66 students studying at the 10th grade at an Anatolian high school in the district of Karade niz Eregli in Zonguldak during the fall term of the 2015-2016 academic year. The design of the study was 'nonequivalent control groups pre-test post-test' which is one of quasi-experimental designs. Data were gathered using an achievement test measuring students' vocabulary knowledge, grammar knowledge, reading comprehension and listening skills. Results showed that cooperative learning had a larger effect on vocabulary knowledge, grammar, listening and reading skills compared to traditional method.

Abstract In this work, we present the Gaussian Class-Conditional Simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that improves over the state-of-the-art in terms of classification accuracy and adversarial robustness, with little extra cost for network training. The proposed method learns a mapping of the input classes onto Gaussian target distributions in a latent space such that a hyperplane can be used as the optimal decision surface. Instead of maximizing the likelihood of target labels for individual samples, our loss function pushes the network to produce feature distributions yielding high inter-class separation and low intra-class separation. The mean values of the learned distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy. Moreover, GCCS provides improved robustness against adversarial perturbations, outperforming models trained with conventional adversarial training (AT). In particular, our model learns a decision space that minimizes the presence of short paths toward neighboring decision regions. We provide a comprehensive empirical evaluation that shows how GCCS outperforms state-of-the-art approaches over challenging datasets for targeted and untargeted gradient-based, as well as gradient-free adversarial attacks, both in terms of classification accuracy and adversarial robustness.

We explored how beginning-level interior architecture students develop skills to create mental visualizations of three-dimensional objects and environments, how they develop their technical drawing skills, and whether or not physical and computer generated models aid this design process. We used interviews and observations to collect data. The findings provide an insight on what kind of difficulties students experience during their learning process and how they overcome those difficulties. The results of the study indicate that the students' lack of skills in technical drawing and in creating 2D and 3D mental visualizations negatively influenced their design process. Using the existing body of literature, we discussed the findings and suggested teaching strategies to improve the learning process for the beginning-level interior architecture students. The findings of this study allowed us to have a better understanding of the student design and learning process.

The purpose of this study is to compare the predictive power of prospective teachers' basic needs on self-actualization. This is a correlational research which is one of the descriptive research methods. The study was conducted on 1033 prospective teachers studying in Bulent Ecevit University Eregli Faculty of Education in the spring term of the academic year of 2013-2014. Data were collected with "self-actualization scale" which is subscale of "Healthy Life Style Behaviour Scale" and "University Students' Basic Needs Scale". Pearson product-moment correlation coefficient technique and stepwise-regression analysis were used to analyze the data. As a result of the study, it has been identified that the most effective factor on prospective teachers' self-actualization is the fun need which is one of the sub-dimension of basic needs.

Metabolomics has been used as a powerful tool for the analysis, and drug-lead identification in medicinal plants and herbal medicines. For the coverage of a broader range of plant-based metabolites using LC-MS, one of the important parameters is the selection of analysis mode and data processing for identification. This paper describes the utility of two distinctive acquisition modes in combination, a classic data-dependent acquisition (DDA) mode and a data-independent acquisition (DIA) mode for obtaining the mass spectrometry data of plant extracts using LC-ESI-QTOF/MS. Tribulus terrestris plant was used as a case study. We have applied three-step data analysis pipeline 1-annotation and putative identification of metabolites, 2-validation, and relative quantification, and 3-multivariate analysis using open-access MS-DIAL, Skyline, and Perseus software. A total of four samples of T. terrestris (aqueous extracts), two fruits, and two whole plant samples, from two different regions, were used. By combining data analysis results of plant fruit samples from two different regions, a total of 95 and 77 metabolites were identified in positive and negative ionization modes, respectively. Similarly, in the analysis of the whole plant from two different regions, 75 and 76 metabolites were identified in positive and negative ionization modes, respectively. We suggested the use of DDA mode for annotation, identification of metabolites, and generation of transition lists in MS-DIAL, furthermore, the use of DIA acquisition mode for enhancing metabolites sensitivity in complex samples, deconvolute MS1/MS2 spectra in Skyline for the quantitative performance and analytical reliability. The developed protocol can be used for the broader coverage of plant-based metabolites.

Teacher training is regarded as a fundamental mechanism for developing pedagogical content knowledge of teachers' and emerging training applications to reach high standards. The purpose of this study is to investigate views of faculty of education students' and pedagogical formation training certificate program students' about perceived instrumentality of educational sciences courses. The cross-sectional study was used to investigate the research questions. Data were collected from 415 teacher candidates using the Perceived Instrumentality of Educational Sciences Courses Survey. Results revealed that faculty of education students' opinions towards the functionality of educational sciences courses were more positive than pedagogical formation training certificate program students' opinions.

This study aims to determine the effect of self-regulated jigsaw IV upon university students' learning a new grammar structure within EFL learning process and also their attitudes towards the English course. The research was carried out with 40 students studying in two different prep classes at Bulent Ecevit University Foreign Languages College in the spring term of 2011-2012 academic year. During the courses, while self-regulated jigsaw IV was carried out with the experimental group, a traditional method was performed in the control group. As quantitative data suggest; self-regulated jigsaw IV has significantly increased students' academic achievement compared to traditional method; however, it is slightly effective on their attitudes towards English. As qualitative data suggest; the students in the experimental group feel self-satisfied with their learning and they can use self-regulation skills in their autonomous studies.

The purpose of this study was to examine the impact of self-regulated learning on students' reading comprehension and attitude towards Turkish course and metacognitive thinking skills. For this purpose, the study was carried out with the 5th graders in Zonguldak Province. In this study, one of the classes was designated as the experimental group and the other class as the control group. While self-regulated learning was applied to the experimental group, a traditional method was applied to the control group. The study lasted for 8 weeks totally. In the study, non-equivalent control group's pretest-posttest design was used. The data were obtained with the help of a reading comprehension test, an attitude scale, a metacognitive thinking skills scale and an interview. At the end of the study, depending on the quantitative data, it was found that self-regulated learning significantly increased the reading comprehension and metacognitive thinking skills of the students in the experimental group. By depending on the qualitative data, it was found that students in the experimental group used self-regulated learning skills while studying.

The purpose of this study is to investigate the relation between prospective teachers' goal orientations and academic e-dishonesty behaviors, and also the effects of their goal orientations on academic e-dishonesty behaviors. This research was conducted with correlational method. Participants of the study were 669 prospective teachers attending at Bulent Ecevit University, Eregli Faculty of Education. The data were collected with goal orientation scale and academic e-dishonesty scale. Mean and standard deviation were used for data analysis, and Pearson product-moment correlation was used for revealing the relations between variables. Additionally, stepwise regression analysis was conducted to reveal the prospective teachers' goal orientation scores' predictive power on their academic e-dishonesty scores. Finally, negative, weak, and significant relation was found between learning goal orientation and plagiarism, falsification, delinquency and unauthorized help which are sub-dimensions of the academic e-dishonesty scale. In addition positive, weak and significant relation was found between performance avoidance and all of the sub-dimensions of the academic e-dishonesty scale. The prospective teachers' performance-avoidance orientation became the most powerful and significant predictor of all of the sub-dimensions of the academic e-dishonesty scale.

Due to the numerous real-world applications of the crowd counting job, it has become a popular research topic. Modern crowd counting systems have a sophisticated structure and employ a filter on a big image size, making them difficult to use. Because these technologies are computationally intensive and difficult to implement in small surveillance systems, they are not appropriate for use in small surveillance systems. They also function poorly in a variety of sizes and densities, as well. Transfer learning and deep convolutional neural network architecture are used to create a modest but efficient network, which we describe herein. We named the proposed crowd counting architecture deep crowd transfer network (DCTNets) since it incorporates both deep learning and transfer learning principles into a single system. Among DCTNets’ key components are a detection module that is based on mask R-CNNs and an estimate module that is based on deep convolutional neural networks. In the first step, we apply transfer learning to the Mask R-CNN model using the datasets ShanghaiTech, JHU-CROWD++, and UCF-QNRF. After that, we train and evaluate the complete architecture on these datasets using the transfer learning results. Input images are sent through a Mask R-CNN, which counts individuals and segments the counted region, then through an estimation network, which estimates the population size, and finally through a merge of the outputs from the two models. According to the findings of comparative tests, the proposed model outperforms existing state-of-the-art approaches on the ShanghaiTech, JHU-CROWD++, and UCF-QNRF datasets.

Asthma is a major non-communicable disease (NCD) affecting adults and children, among whom it is the most common chronic disease reported all around the globe1 Asthma has been a significant cause of hospitalizations and financial burdens for low and lower- middle -income countries like Pakistan. According to the latest WHO data published in 2020, asthma deaths in Pakistan reached a staggering 20,750 or 1.42% of total deaths.2 Many etiologies including social and environmental factors, genetic predispositions, and ambient air pollution, influence asthma morbidity and mortality. In particular, the worsening air quality index in the country needs to be a point of focus as a significant cause of asthma related morbidity. The rapid decline in air quality levels in Pakistan, particularly in major industrial cities such as Lahore and Karachi, is a cause of grave concern. For example, air quality in Lahore has declined over the last two decades: in 1998, its pollution concentration was 33 µg/m³. By 2016, it had doubled to 64 µg/m³—more than six times the WHO guideline.3 Similarly, Karachi, the largest city in Pakistan, has a PM2.5 concentrations, which is 3.4 times the WHO annual air quality guideline value, an alarming scenario for a city where environmental policies and infrastructure is not in place.4 A study in Lahore noted the role of ambient air pollution among various population groups of the city. The research showed a positive Pearson correlation between environmental air pollutants and the number of asthma patients visiting the OPD.5 However, a similar, comprehensive study has not been conducted in Karachi on the same level. As in most cases, the first step toward solving a problem is to recognize it. Hence there’s a need for more research to be conducted to determine the number of hospitalizations of asthmatic patients caused by low air quality as it will give us a better understanding of the true extent of the issue at hand and allow more appropriate prioritization, in the context of public health by the government. Once this is done, resources can be allocated more efficiently towards areas and populations suffering more acutely to relieve the suffering of the majority. In addition, educational campaigns can be designed considering the population’s educational demographics and general knowledge regarding the exact reasons for the worsening air quality in those areas. ---Continue

Abstract A potent napin protein has been thoroughly characterized from seeds of rocket salad (Eruca sativa). Eruca sativa napin (EsNap) was purified by ammonium sulfate precipitation (70%) and size-exclusion chromatography. Single intact 16 kDa EsNap band was reduced to 11 and 5 kDa bands respectively on SDS-PAGE. Nano LC–MS/MS yielded two fragments comprising of 26 residues which showed 100% sequence identity with napin-3 of Brassica napus. CD spectroscopy indicated a dominant α-helical structure of EsNap. Monodispersity of EsNap was verified by dynamic light scattering, which also confirmed the monomeric status with a corresponding hydrodynamic radius of 2.4 ± 0.2 nm. An elongated ab initio shape of EsNap was calculated based on SAXS data, with an Rg of 1.96 ± 0.1 nm. The ab initio model calculated by DAMMIF with P1 symmetry and a volume of approx. 31,100 nm3, which corresponded to a molecular weight of approximately 15.5 kDa. The comparison of the SAXS and ab initio modeling showed a minimized χ2-value of 1.87, confirming a similar molecular structure. A homology model was predicted using the coordinate information of Brassica napus rproBnIb (PDB ID: 1SM7). EsNap exhibited strong antifungal activity by significantly inhibiting the growth of Fusarium graminearum. EsNap also showed cytotoxicity against the hepatic cell line Huh7 and the obtained IC50 value was 20.49 µM. Further, strong entomotoxic activity was experienced against different life stages of stored grain insect pest T. castaneum. The result of this study shows insights that can be used in developing potential antifungal, anti-cancerous and insect resistance agents in the future using EsNap from E. sativa.

Individuals' subjective beliefs about knowledge is defined as an epistemology. Epistemological beliefs of individuals has the potential in developing the curriculum. A motivational approach that attempts to explain the purpose of learners' participation in teaching activities is called goal orientation. Learners' goals of participating in teaching activities affect ways of participation to learning activities, participation levels and maintaining level of participation. The purpose of this study is to examine the relation between teacher candidates' goal orientations and the belief that learning depends on effort. In this study, correlation method was used. The achievement goal orientation scale and the scale of belief that learning depends on effort, which is the subscale of the epistemological belief scale. The data are collected from 161 teacher candidates. Pearson Product Moment Correlation and stepwise regression analysis method were used in data analysis. The results of the study showed that there was positive and significant correlation between belief that learning depends on effort, learning orientation and performance-orientation approach. On the other hand, there is no significant correlation between performance-avoidence orientation and belief that learning depends on effort. [This paper was presented at INTE 2015.]

Several applications of artificially modeled drivers, such as autonomous vehicles (AVs) or surrounding traffic in driving simulations, aim to provide not only functional but also human-like behavior. The development of human-like AVs is expected to improve the interaction between AVs and humans in traffic, whereas, in a driving simulation, the objective is to create realistic replicas of real driving scenarios to investigate various research questions under safe and reproducible conditions. In urban traffic, driving behavior strongly depends on the situational context, which introduces new challenges not only for modeling but also for the evaluation of such models. However, current objective assessment strategies rarely consider situational context and human similarity, whereas subjective approaches are not suitable for iterative development processes. In this paper, we present a first attempt to make the plausibility and human-likeness of vehicles’ trajectories objectively measurable. A multidimensional quality function is presented that incorporates various parameters characterizing human-like driving behavior and compares each of those parameters to human driving behavior under similar conditions. Among other things, our validation results show that the presented evaluation methodology is scalable to a wide range of situations has the ability to identify model weaknesses, and is able to reflect the way people distinguish between artificial and human behavior.