A deep learning approach to estimating interaction levels in face-to-face lessons

There are minimal published data on the relationship between personality traits and learning approaches among medical students. This study explored the causal-effect relationship of personality traits and learning approaches among medical students. A cross-sectional study was conducted on medical students and they responded to the Learning Approach Inventory and USM Personality Inventory to measure personality traits and learning approaches, respectively. A structural equation modelling was performed by AMOS 24 to test the causal-effect relationship of personality traits and learning approaches. Conscientiousness had a positive direct effect on deep learning approach, while neuroticism had negative direct effect on deep and strategic learning approaches. Extraversion, openness, and agreeableness had no significant link or effect on any learning approaches. Strategic learning approach had positive direct effect on deep learning approach and a mediator for surface learners on deep learning approach. Surface learning approach had a negative direct effect on deep learning approach. There was a significant relationship of specific personality traits and learning approaches. Conscientiousness and neuroticism had significant relationships with deep and strategic learning approaches. These findings enables medical educators to have a better understanding of the influence of personality traits on medical students’ learning approaches to learning tasks and their implications on instructional strategies.

Investigating the learning approaches of students in nursing education

This study aimed to compare the impact of the partially flipped physiology classroom (PFC) and the traditional lecture-based classroom (TLC) on students' learning approaches. The study was conducted over 5 mo at Xiangya School of Medicine from February to July 2022 and comprised 71 students majoring in clinical medicine. The experimental group (n = 32) received PFC teaching, whereas the control group (n = 39) received TLC. The Revised Two-Factor Study Process Questionnaire (R-SPQ-2F) was used to assess the impact of different teaching methods on students' learning approaches. After the PFC, students got significantly higher scores on deep learning approach (Z = -3.133, P < 0.05). Conversely, after the TLC students showed significantly higher scores on surface learning approach (Z = -2.259, P < 0.05). After the course, students in the PFC group scored significantly higher in deep learning strategy than those in the TLC group (Z = -2.196, P < 0.05). The PFC model had a positive impact on deep learning motive and strategy, leading to an improvement in the deep approach, which is beneficial for the long-term development of students. In contrast, the TLC model only improved the surface learning approach. The study implies that educators should consider implementing PFC to enhance students' learning approaches.NEW & NOTEWORTHY In this article, we compare the impact of the partially flipped classroom (PFC) and the traditional lecture classroom (TLC) in a physiology course on medical students' learning approaches. We found that the PFC benefited students by significantly enhancing their deep learning motive, strategy, and approach, which was good for them. However, the TLC model only improved the surface learning motive and approach.

Person re-identification (ReID) aims to find the person of interest across multiple non-overlapping cameras. It is considered an essential step for person tracking applications which is vital for surveillance. Person ReID could be investigated either using image-based or video-based. Video-based person ReID is considered more discriminating and realistic than image-based ReID due to the massive information extracted for each person. Different deep-learning techniques have been used for video-based ReID. In this survey, recently published articles are reviewed according to video-based ReID system pipeline: deep features learning, deep metric learning, and deep learning approaches. The deep feature learning approaches are categorized into spatial and temporal approaches, while deep metric learning is divided into metric and metric learning approaches. The deep learning approaches are differentiated into: supervised, unsupervised, weakly-supervised, and one-shot learning. A detailed analysis is held for the architectures of the state-of-the-art deep learning approaches. And their performance on four benchmark datasets is compared.

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

The deep and surface learning approaches are closely related to the students' interaction with learning content and learning outcomes. While students with a surface approach have a tendency to acquire knowledge without questioning and to try to pass courses with minimum effort, students with a deep learning approach tend to use more skills such as problem-solving, questioning, and research. Studies show that learning approaches of students can change depending on subject, task and time. Therefore, it is important to identify students with a surface learning approach in online learning environments and to plan interventions that encourage them to use deep learning approaches. In this study, video viewing behaviors of students with deep and surface learning approaches are analyzed using video analytics. Video viewing patterns of students with different learning approaches are also compared. For this purpose, students (N=31) are asked to study a 10-minutes-long video material related to Computer Hardware course. Video interactions in this process were also recorded using video player developed by the authors. At the end of the lab session, students were asked to fill in the Learning Approach Scale by taking into account their learning approaches to the course. As a result of the study, it was observed that the students with surface approach made a statistically significant forward seek over to the students used deep learning approach while watching the video. Moreover, an investigation on the time series graphs of two groups revealed that surface learners watched the video more linearly and had fewer interactions with it. These interaction data can be modeled with machine learning techniques to predict students with surface approach and can be used to identify design problems in video materials.

Background: In problem-based learning (PBL) curricula implemented around the world, it is assumed that students adopt a deep learning approach to studying and aim to gain a profound understanding of the subjects being studied. However, it is not clear which PBL components initiate or deter deep learning and to what extent this happens and why. Aim: This study explored to which extent students used a deep or surface learning approach in PBL and whether this differs across years. We also investigated which PBL components students perceived to be hindrances to deep or surface learning. Methods: The study took place at Sulaiman Al Rajhi Medical College, Qassim, Kingdom of Saudi Arabia. A mixed-methods approach was applied. A validated questionnaire and semi-structured focus group interviews were conducted sequentially. Results: First-, second-, and third-year students reported, in scale 1–5, for deep learning scores, respectively, with mean (M) = 3.55, M = 3.41, and M = 3.55. First-, second-, and third-year students reported, in scale 1–5, for surface learning scores, respectively, with M = 2.88, M = 2.78, and M = 2.89. The differences for both deep and surface learning across the years were statistically nonsignificant. According to students, they study deeply on main learning objectives and superficially on minor objectives as indicated by tutors, they are stimulated toward deep learning through interesting topics during self-study, and examinations drive them toward deep or surface learning depending on the question format and necessity to pass. Conclusions: The results of this study confirm that students' perceptions of PBL components affect their approaches to deep and surface learning. These effects are not entirely negative or positive. Students seem to frequently employ a deep learning approach in PBL throughout the 3 years. These conclusions will allow program administrators/educationalists to constructively design curricula around the perceptions of learners of PBL tutors, topics, and examinations.

The purpose of this study is to describe the effectiveness, readiness and suitability of the learning approach framework using the deep learning approach in elementary schools, especially the readiness of resources, both teachers and supporting facilities for learning activities in elementary schools in Bima. The method used in this study is library research, by systematically reviewing literature to collect, evaluate, and synthesize information from various sources relevant to the research topic. The study result indicate that the deep learning approach in learning consists of 3 main pillars, namely understanding the differentiation of student learning, inviting students to think critically in solving problems, and learning with fun, so that students can easily remember what has been learned. The application of deep learning in elementary schools emphasizes contextual learning that can be applied to learning science, mathematics, Indonesian, PPKn, PAI, and other non-curricular learning. It's just that this approach can be. Applied chose the synonim if the teacher is able to contextualize the latest issues, with the subject matter delivered. For teachers who have participated in the school mover program or teacher practice/movers, they can easily apply and combine deep learning as a learning approach, but in one elementary school not all teachers follow the program. This means that there needs to be mentoring or training from driving teachers or GMP group groups and the like, for all elementary school teachers, and this does not rule out the possibility of being implemented well, as a deep, detailed and enjoyable learning approach.

Through the power of new sensing technologies, we are increasingly digitizing the real world. However, instruments produce unstructured data, mainly in the form of point clouds for 3D data and images for 2D data. Nevertheless, many applications (such as navigation, survey, infrastructure analysis) need structured data containing objects and their geometry. Various computer vision approaches have thus been developed to structure the data and identify objects therein. They can be separated into model-driven, data-driven, and knowledge-based approaches. Model-driven approaches mainly use the information on the objects contained in the data and are thus limited to objects and context. Among data-driven approaches, we increasingly find deep learning strategies because of their autonomy in detecting objects. They identify reliable patterns in the data and connect these to the object of interest. Deep learning approaches have to learn these patterns in a training stage. Knowledge-based approaches use characteristic knowledge from different domains allowing the detection and classification of objects. The knowledge must be formalized and substitutes the training for deep learning. Semantic web technologies allow the management of such human knowledge. Deep learning and knowledge-based approaches have already shown good results for semantic segmentation in various examples. The common goal but the different strategies of the two approaches engaged our interest in doing a comparison to get an idea of their strengths and weaknesses. To fill this knowledge gap, we applied two implementations of such approaches to a mobile mapping point cloud. The detected object categories are car, bush, tree, ground, streetlight and building. The deep learning approach uses a convolutional neural network, whereas the knowledge-based approach uses standard semantic web technologies such as SPARQL and OWL2to guide the data processing and the subsequent classification as well. The LiDAR point cloud used was acquired by a mobile mapping system in an urban environment and presents various complex scenes, allowing us to show the advantages and disadvantages of these two types of approaches. The deep learning and knowledge-based approaches produce a semantic segmentation with an average F1 score of 0.66 and 0.78, respectively. Further details are given by analyzing individual object categories allowing us to characterize specific properties of both types of approaches.

This paper describes the application of micro-Doppler radar (MDR) to gait classification based on fall risk-related differences using deep learning and gait parameter-based approaches. Two classification problems were considered in this study: elderly non-fallers and multiple fallers were classified to investigate the detection of fall risk-related gait differences, and middle-aged (50s) and elderly (70s) adults were classified to detect aging-related gait differences. The MDR signal data of the participants were simulated using an open motion capture gait dataset. The classification results obtained using the deep learning and gait parameter-based approaches showed that the classification accuracy achieved using a support vector machine with the gait parameters extracted from the MDR signals was better than that resulting from the deep learning of spectrogram (time-velocity distribution) images of the MDR signals for both classification problems. The gait parameter-based approach achieved the classification rates of 79 % for faller/non-faller classification and 82 % for 50s/70s classification, whereas the corresponding accuracies were 73 % and 76 %, respectively, using the deep learning approach. These results reveal that the gait parameters extracted via MDR measurements include sufficient information on gait to detect individuals with a high risk of falls and the gait parameter-based approaches are thus effective for both classification problems.

Deep learning, a state-of-the-art machine learning approach, has shown outstanding performance over traditional machine learning in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vision. The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention, as rapid progress in neuroimaging techniques has generated large-scale multimodal neuroimaging data. A systematic review of publications using deep learning approaches and neuroimaging data for diagnostic classification of AD was performed. A PubMed and Google Scholar search was used to identify deep learning papers on AD published between January 2013 and July 2018. These papers were reviewed, evaluated, and classified by algorithm and neuroimaging type, and the findings were summarized. Of 16 studies meeting full inclusion criteria, 4 used a combination of deep learning and traditional machine learning approaches, and 12 used only deep learning approaches. The combination of traditional machine learning for classification and stacked auto-encoder (SAE) for feature selection produced accuracies of up to 98.8% for AD classification and 83.7% for prediction of conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to AD. Deep learning approaches, such as convolutional neural network (CNN) or recurrent neural network (RNN), that use neuroimaging data without pre-processing for feature selection have yielded accuracies of up to 96.0% for AD classification and 84.2% for MCI conversion prediction. The best classification performance was obtained when multimodal neuroimaging and fluid biomarkers were combined. Deep learning approaches continue to improve in performance and appear to hold promise for diagnostic classification of AD using multimodal neuroimaging data. AD research that uses deep learning is still evolving, improving performance by incorporating additional hybrid data types, such as—omics data, increasing transparency with explainable approaches that add knowledge of specific disease-related features and mechanisms.

Can online discussions facilitate deep learning for students in General Education?

Deep learning approaches (discovering, exploring, and understanding core problem) is essential for university students because this approach helps students to achieve good competencies. However, recently a large number of students have surface learning approaches that make students’ capacities can not meet career demands. As a result, it needs to adjust the instructional design to encourage students to have deep learning approaches. Therefore, to have a database for the adjustment, this study aims to evaluate students’ learning approaches and figure out impact factors. Through the survey of 653 students, the data were analyzed Cronbach Alpha, Mean, Variance, ANOVA, and qualitative analysis from the interview, the result showed that students have deep and strategic learning approaches in a quite good level (Mean = 3.45). The study figured out learning approaches were impacted by recognization of subject significance, learning depression, the unconfidence of students. Also, the study recommended some adjustments in instructional design to engage students to have deep learning.

Purpose: This study aims to investigate university students’ cognitive flexibility level, learning approaches and strategies they use as well as the relations between these approaches and strategies.Instrument and Method: In this study, exploratory design mixed research method was applied. In the quantitative part of the study, students’ cognitive flexibility levels, learning approaches, and strategies they use were investigated and the relations among them were determined. In the qualitative part of the study, 12 student-centered interviews, of both semesters, were conducted with those who had high and low grades from cognitive flexibility, learning approaches and learning strategies scale. In the quantitative part of the study, 626 students of medical school from I. year to the VI and in the qualitative part, the 12 students who were in the focus group formed the study group. The data were collected via cognitive flexibility, learning approaches and learning strategies scale.Findings: In the study it was revealed that medical faculty students’ cognitive flexibility level was high; students had both deep and surface learning approaches while deep learning approaches were higher than surface ones to a certain extent, students benefited from each of socio-emotional, sense-making, repetition and attention learning strategies. Male students’ surface learning tendency is higher than that of the female. The female students use attention learning strategy more than the male ones do. The students that took part in the study claimed that the examinations did not measure their learning degree and forced them to towards “memorizing information”.Results: While using deep learning approach and surface learning approach at a high quantity may seem as a contradiction, focus group interviews have shown that education system gives some messages to students: “If you study deeply, it is not certain to pass; but if you memorize, it is clear: Success!”. When cognitive flexibility increases, the usage of socio-emotional learning strategy also increases. When deep learning approach increases, the use of socio-emotional learning strategy, sense-making learning strategy and repetition learning strategy increases.

Deep reinforcement learning for stock portfolio optimization by connecting with modern portfolio theory