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ABSTRACTStudents’ approaches to learning are central to the process of learning. Previous research has revealed that influencing students’ approaches towards deep learning is a complex process and seems much more difficult than expected, even in student-activating learning environments. There is evidence that learning approaches are impacted not only by the learning environment, but also by how students perceive it. However the nature of the links between the environment itself, the way in which it is perceived by students and students’ learning approaches is poorly understood. This study aimed at investigating the relationships between students’ perception of their educational context and learning approaches in three learning environments differing by their teaching formats (lecture or problem-based-learning PBL) and integration level of the curriculum (traditional or integrated). We tested the hypothesis that a PBL format and an integrated curriculum are associated to deeper approaches to learning and that this is mediated by student perception. The study sample was constituted of 1394 medical students trained respectively in a traditional lecture-based (n = 295), in an integrated lecture-based (n = 612) and in an integrated PBL-based (n = 487) curricula. They completed a survey including the Dundee-Ready-Educational-Environment-Measure (students’ perceptions of the educational environment) and the Revised-Study-Process-Questionnaire (learning approaches). Data were analysed by path analysis. The model showed that the learning environment was related to students’ learning approaches by two paths, one direct and one mediated via students’ perception of their educational context. In the lecture-based curricula students’ used deeper approaches when it was integrated and both paths were cumulative. In the PBL-based curriculum students’ did not use deeper approaches than with lectures, due to opposite effects of both paths. This study suggested that an integrated lecture-based curriculum was as effective as a PBL curriculum in promoting students’ deep learning approaches, reinforcing the importance of integrating the curriculum before choosing the teaching format.
22Investigating the learning approaches of students in nursing education
Investigating the learning approaches of students in nursing education
47Deep Ensemble Learning Approaches in Healthcare to Enhance the Prediction and Diagnosing Performance: The Workflows, Deployments, and Surveys on the Statistical, Image-Based, and Sequential Datasets.
With the development of information and technology, especially with the boom in big data, healthcare support systems are becoming much better. Patient data can be collected, retrieved, and stored in real time. These data are valuable and meaningful for monitoring, diagnosing, and further applications in data analysis and decision-making. Essentially, the data can be divided into three types, namely, statistical, image-based, and sequential data. Each type has a different method of retrieval, processing, and deployment. Additionally, the application of machine learning (ML) and deep learning (DL) in healthcare support systems is growing more rapidly than ever. Numerous high-performance architectures are proposed to optimize decision-making. As reliability and stability are the most important factors in the healthcare support system, enhancing the predicted performance and maintaining the stability of the model are always the top priority. The main idea of our study comes from ensemble techniques. Numerous studies and data science competitions show that by combining several weak models into one, ensemble models can attain outstanding performance and reliability. We propose three deep ensemble learning (DEL) approaches, each with stable and reliable performance, that are workable on the above-mentioned data types. These are deep-stacked generalization ensemble learning, gradient deep learning boosting, and deep aggregation learning. The experiment results show that our proposed approaches achieve more vigorous and reliable performance than traditional ML and DL techniques on statistical, image-based, and sequential benchmark datasets. In particular, on the Heart Disease UCI dataset, representing the statistical type, the gradient deep learning boosting approach dominates the others with accuracy, recall, F1-score, Matthews correlation coefficient, and area under the curve values of 0.87, 0.81, 0.83, 0.73, and 0.91, respectively. On the X-ray dataset, representing the image-based type, the deep aggregation learning approach shows the highest performance with values of 0.91, 0.97, 0.93, 0.80, and 0.94, respectively. On the Depresjon dataset, representing the sequence type, the deep-stacked generalization ensemble learning approach outperforms the others with values of 0.91, 0.84, 0.86, 0.8, and 0.94, respectively. Overall, we conclude that applying DL models using our proposed approaches is a promising method for the healthcare support system to enhance prediction and diagnosis performance. Furthermore, our study reveals that these approaches are flexible and easy to apply to achieve optimal performance.
Read more103Improving students' learning approaches through intervention in an information systems learning environment
A study is reported which aimed to improve the proportion of students using aspects of deep learning approaches in an undergraduate information systems (IS) subject. Underlying the study was relational learning research, which identified learning environment factors more likely to be perceived by students who adopted deep learning approaches. These factors were used to design, and refine over five years, small‐scale interventions to the IS subject’s learning environment. To investigate the impact of the interventions students’ learning approaches were evaluated each year on the basis of responses to short written answer and Likert‐scale questionnaire items. In the fifth year of the study a statistically significant increase in the proportion of students using aspects of deep learning approaches was identified. Among a number of important learning environment factors, perception of workload appeared to be a key to encouraging the use of deep learning approaches. Through gradually decreasing the workload in the subject each year, a point was reached where enough educationally critical content was covered to satisfy the subject aims, but significantly more students perceived they had enough time to apply deep learning approaches.
Read more29Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study.
Nursing education presents unique challenges, including high levels of academic stress and varied learning approaches among students. Understanding the relationship between academic stress and learning approaches is crucial for enhancing nursing education effectiveness and student well-being. This study aimed to investigate the prevalence of academic stress and its correlation with learning approaches among nursing students. A cross-sectional descriptive correlation research design was employed. A convenient sample of 1010 nursing students participated, completing socio-demographic data, the Perceived Stress Scale (PSS), and the Revised Study Process Questionnaire (R-SPQ-2F). Most nursing students experienced moderate academic stress (56.3%) and exhibited moderate levels of deep learning approaches (55.0%). Stress from a lack of professional knowledge and skills negatively correlates with deep learning approaches (r = -0.392) and positively correlates with surface learning approaches (r = 0.365). Female students showed higher deep learning approach scores, while male students exhibited higher surface learning approach scores. Age, gender, educational level, and academic stress significantly influenced learning approaches. Academic stress significantly impacts learning approaches among nursing students. Strategies addressing stressors and promoting healthy learning approaches are essential for enhancing nursing education and student well-being. Understanding academic stress's impact on nursing students' learning approaches enables tailored interventions. Recognizing stressors informs strategies for promoting adaptive coping, fostering deep learning, and creating supportive environments. Integrating stress management, mentorship, and counseling enhances student well-being and nursing education quality.
Read moreApproaches to Learning and Academic Performance among Undergraduate Students in Organic Chemistry
The relationship between students’ learning approaches and academic performance is a critical factor in higher education. This study examined the association between learning approaches and academic achievement among undergraduate students enrolled in an organic chemistry course. A cross-sectional survey design was employed, involving a purposive sample of 36 Level 200 undergraduate chemistry students (28 males, 8 females). Data were collected using the Revised Two-Factor Study Process Questionnaire (R-SPQ-2F). Pearson’s correlation analysis revealed a weak, negative, and non-significant relationship between deep learning approaches and academic performance (r = –0.132), as well as between surface approaches and performance (r = –0.273). These findings suggest that neither deep nor surface learning approaches significantly predict academic outcomes in organic chemistry. The study highlights the need for pedagogical practices that promote reflective learning, intrinsic motivation, and deep engagement with course content.
Read more1Students’ Learning Motivation in Mathematics Learning: It is Effects on Students’ Learning Approaches on Surface Approach of Learning and Deep Approach of Learning
This quasi-experimental study investigates the impact of learning motivation on students’ learning approaches in mathematics education, focusing on intrinsic motivation, extrinsic motivation, and task value. In the study, divided into experimental group (N = 56) and control groups (N = 47). The experimental group received targeted interventions designed to enhance intrinsic motivation and emphasize the task value of mathematics, while the control group followed standard instructional methods. Data were collected through pre- and post-intervention surveys measuring motivation levels and learning approaches, categorized as surface learning and deep learning. The findings reveal that students in the experimental group exhibited a significant increase in intrinsic motivation and perceived task value, leading to a marked shift from surface learning approaches to deep learning strategies. Specifically, the experimental group demonstrated increase in deep learning engagement compared to increase in the control group. These results underscore the critical role of learning motivation in shaping effective learning approaches in mathematics education. The study highlights the importance of fostering intrinsic motivation and emphasizing task value to enhance students’ learning experiences and outcomes in mathematics.
Read more3Deep Learning Approach in Improving Critical Thinking Skills of Elementary School Students
A deep learning approach in education that creates deep, meaningful, and exhilarating learning experiences, as well as developing students' critical thinking skills. The implementation of this approach has great challenges, especially in terms of teacher competence, curriculum, and supporting facilities. The main objective of this learning is to equip students with critical thinking skills that will not only be useful in school, but also in their daily lives. The research method used is a literature study, which aims to review and analyze various relevant previous research. Data were obtained through searching, identification, and selection of literature indexed in Scholler, and Scopus. Relevant literature was selected based on topic relevance and publication quality, with data analysis using qualitative techniques based on content analysis, including categorization, synthesis of findings, and narrative analysis. The results of the study show that the epistemological gap in teacher competence is the main obstacle in implementing deep learning effectively. Teachers who lack reflective understanding and critical literacy can hinder the goal of deep learning. In addition, a curriculum that does not support active learning based on 21st century skills is also a challenge. Project-based, collaborative, and problem-based learning can improve students' critical thinking skills, but it requires adjustments in the education system. Based on these findings, strategic measures such as improved teacher training, flexible curriculum changes, and the provision of adequate support facilities are essential to optimize the application of deep learning in education. With the right application, deep learning can produce students who are more critical, creative, and ready to face global challenges.
Read more45A Hybrid Deep Learning and Handcrafted Feature Approach for Cervical Cancer Digital Histology Image Classification
Cervical cancer is the second most common cancer affecting women worldwide but is curable if diagnosed early. Routinely, expert pathologists visually examine histology slides for assessing cervix tissue abnormalities. A localized, fusion-based, hybrid imaging and deep learning approach is explored to classify squamous epithelium into cervical intraepithelial neoplasia (CIN) grades for a dataset of 83 digitized histology images. Partitioning the epithelium region into 10 vertical segments, 27 handcrafted image features and rectangular patch, sliding window-based convolutional neural network features are computed for each segment. The imaging and deep learning patch features are combined and used as inputs to a secondary classifier for individual segment and whole epithelium classification. The hybrid method achieved a 15.51% and 11.66% improvement over the deep learning and imaging approaches alone, respectively, with a 80.72% whole epithelium CIN classification accuracy, showing the enhanced epithelium CIN classification potential of fusing image and deep learning features.
Read more4A Hybrid Deep Learning and Handcrafted Feature Approach for Cervical Cancer Digital Histology Image Classification
Cervical cancer is the second most common cancer affecting women worldwide but is curable if diagnosed early. Routinely, expert pathologists visually examine histology slides for assessing cervix tissue abnormalities. A localized, fusion-based, hybrid imaging and deep learning approach is explored to classify squamous epithelium into cervical intraepithelial neoplasia (CIN) grades for a dataset of 83 digitized histology images. Partitioning the epithelium region into 10 vertical segments, 27 handcrafted image features and rectangular patch, sliding window-based convolutional neural network features are computed for each segment. The imaging and deep learning patch features are combined and used as inputs to a secondary classifier for individual segment and whole epithelium classification. The hybrid method achieved a 15.51% and 11.66% improvement over the deep learning and imaging approaches alone, respectively, with a 80.72% whole epithelium CIN classification accuracy, showing the enhanced epithelium CIN classification potential of fusing image and deep learning features.
Read more136Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment
Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment
Read more9Multi-Modal Analysis and Federated Learning Approach for Classification and Personalized Prognostic Assessment in Myeloid Neoplasms
Multi-Modal Analysis and Federated Learning Approach for Classification and Personalized Prognostic Assessment in Myeloid Neoplasms
Read more1The Relationship between Erciyes, Selçuk, and Akdeniz Medical School Third-Year Students’ Learning Approaches and Their Non-attendance Attitude and Tendencies
Background: Non-attendance is an undesirable student behavior. Although some studies about the factors for non-attendance behavior have been carried out at medical schools, the learning approach of a student has not been studied and it can also be a factor for non-attendance. We aimed to assess the relationship between learning approaches and non-attendance attitude and tendency.<br />Methods: This is a correlational study. 644 students registered in three medical schools were enrolled. Data were collected during May 2015. “The Revised Two Factor Learning Approach Scale”, “Non-attendance<br />Attitude Scale” and “Non-attendance Tendency Scale” were used as data collection tools.<br />Results: Out of 478 studied students, 10.3% mentioned that they never missed theoretical classes and 71.3% mentioned that they never missed practical classes. Sleeplessness was the most common reason for<br />non-attendance. 45.6% of all students thought that non-attendance affected student success. The students’ mean score for deep learning was 29.5±6.1 and for superficial learning was 30.5±5.6. The mean score<br />for non-attendance attitude scale was 54.4±12.8 and from non-attendance tendency scale was 90.5±19.6. Conclusion: Learning approach is an effective factor for attendance. As deep learning approach is adopted, tendency for non-attendance decreases and the attitude becomes positive.<br />Keywords: LEARNING, STUDENTS, MEDICAL, ATTENDANCE, ATTITUDE
Read more9Respiratory signal estimation for cardiac perfusion SPECT using deep learning.
Respiratory motion induces artifacts in reconstructed cardiac perfusion SPECT images. Correction for respiratory motion often relies on a respiratory signal describing the heart displacements during breathing. However, using external tracking devices to estimate respiratory signals can add cost and operational complications in a clinical setting. We aim to develop a deep learning (DL) approach that uses only SPECT projection data for respiratory signal estimation. A modified U-Net was implemented that takes temporally finely sampled SPECT sub-projection data (100ms) as input. These sub-projections are obtained by reframing the 20-s list-mode data, resulting in 200 sub-projections, at each projection angle for each SPECT camera head. The network outputs a 200-time-point motion signal for each projection angle, which was later aggregated over all angles to give a full respiratory signal. The target signal for DL model training was from an external stereo-camera visual tracking system (VTS). In addition to comparing DL and VTS, we also included a data-driven approach based on the center-of-mass (CoM) strategy. This CoM method estimates respiratory signals by monitoring the axial changes of CoM for counts in the heart region of the sub-projections. We utilized 900 subjects with stress cardiac perfusion SPECT studies, with 302 subjects for testing and the remaining 598 subjects for training and validation. The Pearson's correlation coefficient between the DL respiratory signal and the reference VTS signal was 0.90, compared to 0.70 between the CoM signal and the reference. For respiratory motion correction on SPECT images, all VTS, DL, and CoM approaches partially de-blured the heart wall, resulting in a thinner wall thickness and increased recovered maximal image intensity within the wall, with VTS reducing blurring the most followed by the DL approach. Uptake quantification for the combined anterior and inferior segments of polar maps showed a mean absolute difference from the reference VTS of 1.7% for the DL method for patients with motion >12mm, compared to 2.6% for the CoM method and 8.5% for no correction. We demonstrate the capability of a DL approach to estimate respiratory signal from SPECT projection data for cardiac perfusion imaging. Our results show that the DL based respiratory motion correction reduces artefacts and achieves similar regional quantification to that obtained using the stereo-camera VTS signals. This may enable fully automatic data-driven respiratory motion correction without relying on external motion tracking devices.
Read more20Dose image prediction for range and width verifications from carbon ion-induced secondary electron bremsstrahlung x-rays using deep learning workflow.
Imaging of the secondary electron bremsstrahlung (SEB) x rays emitted during particle-ion irradiation is a promising method for beam range estimation. However, the SEB x-ray images are not directly correlated to the dose images. In addition, limited spatial resolution of the x-ray camera and low-count situation may impede correctly estimating the beam range and width in SEB x-ray images. To overcome these limitations of the SEB x-ray images measured by the x-ray camera, a deep learning (DL) approach was proposed in this work to predict the dose images for estimating the range and width of the carbon ion beam on the measured SEB x-ray images. To prepare enough data for the DL training efficiently, 10,000 simulated SEB x-ray and dose image pairs were generated by our in-house developed model function for different carbon ion beam energies and doses. The proposed DL neural network consists of two U-nets for SEB x ray to dose image conversion and super resolution. After the network being trained with these simulated x-ray and dose image pairs, the dose images were predicted from simulated and measured SEB x-ray testing images for performance evaluation. For the 500 simulated testing images, the average mean squared error (MSE) was 2.5×10-5 and average structural similarity index (SSIM) was 0.997 while the error of both beam range and width was within 1mm FWHM. For the three measured SEB x-ray images, the MSE was no worse than 5.5×10-3 and SSIM was no worse than 0.980 while the error of the beam range and width was 2mm and 5mm FWHM, respectively. We have demonstrated the advantages of predicting dose images from not only simulated data but also measured data using our deep learning approach.
Read more8Improving Energy Efficiency Fairness of Wireless Networks: A Deep Learning Approach
Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.
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