A phenomenographic study to explore tutors' perceptions of the role of written feedback in promoting self-regulated learning at Durham University.

This thesis explored academics’ perceptions of self-regulated learning and their perceptions of how their written feedback helps students develop self-regulated learning skills. Semi-structured interviews were used to collect data from thirty-seven academics from three academic faculties at Durham University; the Faculty of Science, the Faculty of Arts and Humanities and the Faculty of Social Sciences and Health. As the purpose of the study was to identify the variances in the perceptions and beliefs of the academics interviewed, the data were analysed according to phenomenographic principles. According to the data analysis, four different categories emerged in relation to academics’ perceptions of self-regulated learners: ‘a self-regulated learner is a student who tries to understand concepts introduced in the degree program’, ‘a self-regulated learner is a student who connects concepts with each other to develop their own meanings’, ‘a self-regulated learner is a student who develops their content knowledge to be able to critically evaluate the evidence to develop their own perspective', and ‘a self-regulated learner is a student who develops learning skills to change as a person to become a life-long learner'. As we move from the first to the fourth category, conceptions become increasingly sophisticated. That is, whilst the conceptions of self-regulated learners described by academics in the first category are the simplest, those described in the fourth category are the most sophisticated. In the four categories, conceptions of self-regulated learners described by academics in the fourth category seem to be the most in line with traditional definitions of self-regulated learning as found in the academic literature. The findings in this study also indicate that there are important differences in academics’ use of written feedback that are strongly related to their perceptions of what self-regulated learning is and how it might be developed. That is, whilst academics in the first category use their written feedback to help their students understand concepts, academics in the second category use their written feedback to help their students connect concepts with each other to develop their own meanings. While academics in the third category use their written feedback to support the development of students’ critical thinking skills, academics in the fourth category use their written feedback to help develop their students’ motivation because they think that students who have sufficient motivation are likely to take more responsibility for their own learning. Academics in the fourth category believe that students who have taken responsibility for their own learning can develop their learning skills so that they are likely to become life-long learners. The thesis concludes that while some academics’ perceptions seem to align with the definition of self-regulated learning presented in the literature, most academics’ perceptions do not seem to be in line with this definition. Such findings indicate that there are discrepancies between theory and academics’ perceptions. The strong associations between academics’ perceptions of self-regulated learning and beliefs about their use of written feedback have important implications for teaching and learning. Therefore, it is likely that only academics in category 4, and possibly some academics in category 3 are using their written feedback in ways that will actually support the development of self-regulated learning. Academics who hold category 1, 2 and 3 perceptions are likely to be promoting some forms of learning behaviour and skills but whether they are fully supporting self-regulated learning is unclear. The implications of such a finding are that academics see self-regulated learning as more complex in practice and there are variances in their perceptions about where self-regulated learning starts from. Thus, academics’ perceptions present us with a more nuanced understanding of how self-regulated learning is viewed in practice. The findings also show that written feedback is used differently in all categories. We, therefore, need to acknowledge different functions and formats of written feedback and how these relate to different aspects of self-regulated learning

PSEUDO-SLANT SUBMANIFOLD IN KENMOTSU SPACE FORMS

In this paper, we study geometry of the pseudo-slant submanifold of a Kenmotsu space form. Necessary and suffcient conditions are given for a submanifold to be a pseudo-slant submanifold in Kenmotsu manifolds. Finally, we give some results for totally umbilical pseudo-slant submanifold in a Kenmotsu manifold and Kenmotsu space form

Enhancing aircraft safety through advanced engine health monitoring with long short-term memory

Predictive maintenance holds a crucial role in various industries such as the automotive, aviation and factory automation industries when it comes to expensive engine upkeep. Predicting engine maintenance intervals is vital for devising effective business management strategies, enhancing occupational safety and optimising efficiency. To achieve predictive maintenance, engine sensor data are harnessed to assess the wear and tear of engines. In this research, a Long Short-Term Memory (LSTM) architecture was employed to forecast the remaining lifespan of aircraft engines. The LSTM model was evaluated using the NASA Turbofan Engine Corruption Simulation dataset and its performance was benchmarked against alternative methodologies. The results of these applications demonstrated exceptional outcomes, with the LSTM model achieving the highest classification accuracy at 98.916% and the lowest mean average absolute error at 1.284%

Autonomous ground refuelling approach for civil aircrafts using computer vision and robotics

3D visual servoing systems need to detect the object and its pose in order to perform. As a result accurate, fast object detection and pose estimation play a vital role. Most visual servoing methods use low-level object detection and pose estimation algorithms. However, many approaches detect objects in 2D RGB sequences for servoing, which lacks reliability when estimating the object’s pose in 3D space. To cope with these problems, firstly, a joint feature extractor is employed to fuse the object’s 2D RGB image and 3D point cloud data. At this point, a novel method called PosEst is proposed to exploit the correlation between 2D and 3D features. Here are the results of the custom model using test data; precision: 0,9756, recall: 0.9876, F1 Score(beta=1): 0.9815, F1 Score(beta=2): 0.9779. The method used in this study can be easily implemented to 3D grasping and 3D tracking problems to make the solutions faster and more accurate. In a period where electric vehicles and autonomous systems are gradually becoming a part of our lives, this study offers a safer, more efficient and more comfortable environment

Development of vision guided real-time trajectory planning system for autonomous ground refuelling operations using hybrid dataset

Accurate and rapid object localisation and pose estimation are playing key roles during some of the real-time robotic operations such as object grasping and object manipulating. To do so, high-level robotic vision solutions need to be adopted. Computer vision approaches require a large amount of data to be able to create a perception pipeline robustly. Preparing such dataset to train the deep neural network could be challenging as the collection and manual annotation of huge amounts of data can take long hours and the development of the dataset needs to cover different conditions in weather and lighting. To ease this process, generating a synthetic dataset could be used. Due to the limitations of the synthetic dataset which will be described further down, instead of using a sole synthetic dataset, a hybrid dataset can be developed with the real dataset to overcome the limitations of both datasets. Even though the main objective of this study is to fulfil an autonomous nozzle insertion process for the ground refuelling operation of civil aircraft, the proposed approach is generic and can be adapted to any 3D visual robotic manipulation operation. This study is also offered to be the first visual trajectory planning control mechanism depending on the hybrid dataset to this date

The influence of micro-expressions on deception detection

Facial micro-expressions are universal symbols of emotions that provide cohesion to interpersonal communication. At the same time, the changes in micro-expressions are considered to be the most important hints in the psychology of emotion. Furthermore, analysis and recognition of these micro-expressions have pervaded in various areas such as security and psychology. In security-related matters, micro-expressions are widely used to detect deception. In this research, a deep learning model that interprets the changes in the face into meaningful information has been trained using The Facial Expression Recognition 2013 dataset. Necessary data is also obtained through live stream or video stream by detecting via computer vision and evaluating with the trained model. Finally, the data obtained is transformed into graphic and interpreted to determine whether the people are trying to deceive or not. The deception classification accuracy of the custom trained model is 74.17% and the detection of the face with high precision using the computer vision methods increased the accuracy of the obtained data and provided it to be interpreted correctly. In this respect, the study differs from other studies using the same dataset. In addition, it is aimed to facilitate the deception detection which is performed in a complex and expensive way, by making it simple and understandable

Evaluation of Body Composition and Quality of Life of University Students

The aim of this study is to evaluate the physical activity levels and the quality of life of the university students. In this purpose, the SF-36 life quality scale was used to examine the quality of life of the participants and physical activity levels were also determined through IPAQ. One-way ANOVA was used to compare the quality of life based on the physical activity levels of the participants. Moreover, the Bonferroni Correction were used to determine which physical activity level causes the difference by keeping the type I error rate fixed at 0.05. Statistically significant results were observed between social function and physical activity levels for the female group. In addition, statistically significant results were found between vitality and physical activity levels for the male group. The results show that there is a significant relationship between physical activity and quality of life. The individuals who feel more fit and social tend to have an active life. Given that it is crucial to intensify the studies on this topic for university students to encourage them for taking up regular physical activity as a part of life style

Functional Neural Networks Stratify Parkinson’s Disease Patients Across the Spectrum of Cognitive Impairment

Introduction: Cognitive impairment (CI) is a significant non-motor symptoms inParkinson’s disease (PD) that often precedes the emergence of motor symptoms by several years. Patients with PD hypothetically progress from stages without CI (PD-normal cognition [NC]) to stageswithMild CI (PD-MCI) and PDdementia (PDD). CI symptoms in PD are linked to different brain regions and neural pathways, in addition to being the result of dysfunctional subcortical regions. However, it is still unknown how functional dysregulation correlates to progression during the CI. Neuroimaging techniques hold promise in discriminating CI stages of PD and further contribute to the biomarker formation of CI in PD. In this study, we explore disparities in the clinical assessments and resting-state functional connectivity (FC) among three CI stages of PD. Methods: We enrolled 88 patients with PD and 26 healthy controls (HC) for a cross sectional clinical study and performed intra- and inter-network FC analysis in conjunction with comprehensive clinical cognitive assessment. Results: Our findings underscore the significance of several neural networks, namely, the default mode network (DMN), frontoparietal network (FPN), dorsal attention network, and visual network (VN) and their inter–intra-network FC in differentiating between PD-MCI and PDD. Additionally, our results showed the importance of sensory motor network, VN,DMN, and salience network (SN) in the discriminating PD-NC from PDD. Finally, in comparison to HC, we found DMN, FPN, VN, and SN as pivotal networks for further differential diagnosis of CI stages of PD. Conclusion:We propose that resting-state networks (RSN) can be a discriminating factor in distinguishing the CI stages of PD and progressing from PD-NC toMCI or PDD. The integration of clinical and neuroimaging data may enhance the early detection of PD in clinical settings and potentially prevent the disease from advancing to more severe stages