Search results for: teaching and learning empathy
3373 Fourier Transform and Machine Learning Techniques for Fault Detection and Diagnosis of Induction Motors
Authors: Duc V. Nguyen
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Induction motors are widely used in different industry areas and can experience various kinds of faults in stators and rotors. In general, fault detection and diagnosis techniques for induction motors can be supervised by measuring quantities such as noise, vibration, and temperature. The installation of mechanical sensors in order to assess the health conditions of a machine is typically only done for expensive or load-critical machines, where the high cost of a continuous monitoring system can be Justified. Nevertheless, induced current monitoring can be implemented inexpensively on machines with arbitrary sizes by using current transformers. In this regard, effective and low-cost fault detection techniques can be implemented, hence reducing the maintenance and downtime costs of motors. This work proposes a method for fault detection and diagnosis of induction motors, which combines classical fast Fourier transform and modern/advanced machine learning techniques. The proposed method is validated on real-world data and achieves a precision of 99.7% for fault detection and 100% for fault classification with minimal expert knowledge requirement. In addition, this approach allows users to be able to optimize/balance risks and maintenance costs to achieve the highest benet based on their requirements. These are the key requirements of a robust prognostics and health management system.Keywords: fault detection, FFT, induction motor, predictive maintenance
Procedia PDF Downloads 1733372 Embedded Hybrid Intuition: A Deep Learning and Fuzzy Logic Approach to Collective Creation and Computational Assisted Narratives
Authors: Roberto Cabezas H
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The current work shows the methodology developed to create narrative lighting spaces for the multimedia performance piece 'cluster: the vanished paradise.' This empirical research is focused on exploring unconventional roles for machines in subjective creative processes, by delving into the semantics of data and machine intelligence algorithms in hybrid technological, creative contexts to expand epistemic domains trough human-machine cooperation. The creative process in scenic and performing arts is guided mostly by intuition; from that idea, we developed an approach to embed collective intuition in computational creative systems, by joining the properties of Generative Adversarial Networks (GAN’s) and Fuzzy Clustering based on a semi-supervised data creation and analysis pipeline. The model makes use of GAN’s to learn from phenomenological data (data generated from experience with lighting scenography) and algorithmic design data (augmented data by procedural design methods), fuzzy logic clustering is then applied to artificially created data from GAN’s to define narrative transitions built on membership index; this process allowed for the creation of simple and complex spaces with expressive capabilities based on position and light intensity as the parameters to guide the narrative. Hybridization comes not only from the human-machine symbiosis but also on the integration of different techniques for the implementation of the aided design system. Machine intelligence tools as proposed in this work are well suited to redefine collaborative creation by learning to express and expand a conglomerate of ideas and a wide range of opinions for the creation of sensory experiences. We found in GAN’s and Fuzzy Logic an ideal tool to develop new computational models based on interaction, learning, emotion and imagination to expand the traditional algorithmic model of computation.Keywords: fuzzy clustering, generative adversarial networks, human-machine cooperation, hybrid collective data, multimedia performance
Procedia PDF Downloads 1433371 Error Analysis: Examining Written Errors of English as a Second Language (ESL) Spanish Speaking Learners
Authors: Maria Torres
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After the acknowledgment of contrastive analysis, Pit Coder’s establishment of error analysis revolutionized the way instructors analyze and examine students’ writing errors. One question that relates to error analysis with speakers of a first language, in this case, Spanish, who are learning a second language (English), is the type of errors that these learners make along with the causes of these errors. Many studies have looked at the way the native tongue influences second language acquisition, but this method does not take into account other possible sources of students’ errors. This paper examines writing samples from an advanced ESL class whose first language is Spanish at non-profit organization, Learning Quest Stanislaus Literacy Center. Through error analysis, errors in the students’ writing were identified, described, and classified. The purpose of this paper was to discover the type and origin of their errors which generated appropriate treatments. The results in this paper show that the most frequent errors in the advanced ESL students’ writing pertain to interlanguage and a small percentage from an intralanguage source. Lastly, the least type of errors were ones that originate from negative transfer. The results further solidify the idea that there are other errors and sources of errors to account for rather than solely focusing on the difference between the students’ mother and target language. This presentation will bring to light some strategies and techniques that address the issues found in this research. Taking into account the amount of error pertaining to interlanguage, an ESL teacher should provide metalinguistic awareness of the students’ errors.Keywords: error analysis, ESL, interlanguage, intralangauge
Procedia PDF Downloads 2983370 Systems Intelligence in Management (High Performing Organizations and People Score High in Systems Intelligence)
Authors: Raimo P. Hämäläinen, Juha Törmänen, Esa Saarinen
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Systems thinking has been acknowledged as an important approach in the strategy and management literature ever since the seminal works of Ackhoff in the 1970´s and Senge in the 1990´s. The early literature was very much focused on structures and organizational dynamics. Understanding systems is important but making improvements also needs ways to understand human behavior in systems. Peter Senge´s book The Fifth Discipline gave the inspiration to the development of the concept of Systems Intelligence. The concept integrates the concepts of personal mastery and systems thinking. SI refers to intelligent behavior in the context of complex systems involving interaction and feedback. It is a competence related to the skills needed in strategy and the environment of modern industrial engineering and management where people skills and systems are in an increasingly important role. The eight factors of Systems Intelligence have been identified from extensive surveys and the factors relate to perceiving, attitude, thinking and acting. The personal self-evaluation test developed consists of 32 items which can also be applied in a peer evaluation mode. The concept and test extend to organizations too. One can talk about organizational systems intelligence. This paper reports the results of an extensive survey based on peer evaluation. The results show that systems intelligence correlates positively with professional performance. People in a managerial role score higher in SI than others. Age improves the SI score but there is no gender difference. Top organizations score higher in all SI factors than lower ranked ones. The SI-tests can also be used as leadership and management development tools helping self-reflection and learning. Finding ways of enhancing learning organizational development is important. Today gamification is a new promising approach. The items in the SI test have been used to develop an interactive card game following the Topaasia game approach. It is an easy way of engaging people in a process which both helps participants see and approach problems in their organization. It also helps individuals in identifying challenges in their own behavior and in improving in their SI.Keywords: gamification, management competence, organizational learning, systems thinking
Procedia PDF Downloads 983369 Interpretable Deep Learning Models for Medical Condition Identification
Authors: Dongping Fang, Lian Duan, Xiaojing Yuan, Mike Xu, Allyn Klunder, Kevin Tan, Suiting Cao, Yeqing Ji
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Accurate prediction of a medical condition with straight clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community is still, to a certain degree, suspicious about the model's accuracy and interpretability. This paper presents an innovative hierarchical attention deep learning model to achieve good prediction and clear interpretability that can be easily understood by medical professionals. This deep learning model uses a hierarchical attention structure that matches naturally with the medical history data structure and reflects the member’s encounter (date of service) sequence. The model attention structure consists of 3 levels: (1) attention on the medical code types (diagnosis codes, procedure codes, lab test results, and prescription drugs), (2) attention on the sequential medical encounters within a type, (3) attention on the medical codes within an encounter and type. This model is applied to predict the occurrence of stage 3 chronic kidney disease (CKD3), using three years’ medical history of Medicare Advantage (MA) members from a top health insurance company. The model takes members’ medical events, both claims and electronic medical record (EMR) data, as input, makes a prediction of CKD3 and calculates the contribution from individual events to the predicted outcome. The model outcome can be easily explained with the clinical evidence identified by the model algorithm. Here are examples: Member A had 36 medical encounters in the past three years: multiple office visits, lab tests and medications. The model predicts member A has a high risk of CKD3 with the following well-contributed clinical events - multiple high ‘Creatinine in Serum or Plasma’ tests and multiple low kidneys functioning ‘Glomerular filtration rate’ tests. Among the abnormal lab tests, more recent results contributed more to the prediction. The model also indicates regular office visits, no abnormal findings of medical examinations, and taking proper medications decreased the CKD3 risk. Member B had 104 medical encounters in the past 3 years and was predicted to have a low risk of CKD3, because the model didn’t identify diagnoses, procedures, or medications related to kidney disease, and many lab test results, including ‘Glomerular filtration rate’ were within the normal range. The model accurately predicts members A and B and provides interpretable clinical evidence that is validated by clinicians. Without extra effort, the interpretation is generated directly from the model and presented together with the occurrence date. Our model uses the medical data in its most raw format without any further data aggregation, transformation, or mapping. This greatly simplifies the data preparation process, mitigates the chance for error and eliminates post-modeling work needed for traditional model explanation. To our knowledge, this is the first paper on an interpretable deep-learning model using a 3-level attention structure, sourcing both EMR and claim data, including all 4 types of medical data, on the entire Medicare population of a big insurance company, and more importantly, directly generating model interpretation to support user decision. In the future, we plan to enrich the model input by adding patients’ demographics and information from free-texted physician notes.Keywords: deep learning, interpretability, attention, big data, medical conditions
Procedia PDF Downloads 913368 Metadiscourse in Chinese and Thai Request Emails: Analysis and Pedagogical Application
Authors: Chia-Ling Hsieh, Kankanit Potikit
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Metadiscourse refers to linguistic resources employed by writers to organize text and interact with readers. While metadiscourse has received considerable attention within the field of discourse analysis, few studies have explored the use of metadiscourse in email, one of the most popular forms of computer-mediated communication. Furthermore, the diversity of cross-linguistic research required to uncover the influence of cultural factors on metadiscourse use is lacking. The present study compares metadiscourse markers employed in Chinese and Thai-language request emails with the purpose of discovering cross-cultural similarities and differences that are meaningful and applicable to foreign language teaching. The analysis is based on a corpus of 200 request emails: 100 composed in Chinese and 100 in Thai, with half of the emails from each language data set addressed to professors and the other half addressed to classmates. Adopting Hyland’s model as an analytical framework, two primary categories of metadiscourse are identified. Textual metadiscourse helps to create text coherence, while interpersonal metadiscourse functions to convey authorial stance. Results of the study make clear that both Chinese and Thai-language emails use significantly more interpersonal markers than textual markers, indicating that email, as a unique communicative medium, is characterized by high degrees of concision and interactivity. Users of both languages further deploy similar patterns in writing emails to recipients of different social statuses. Compared with emails addressed to classmates, emails addressed to professors are notably longer and include more transition and engagement markers. Nevertheless, cultural factors do play a role. Emails composed in Thai, for example, include more textual markers than those in Chinese, as Thai favors formal expressions and detailed explanations, while in contrast, emails composed in Chinese employ more interpersonal markers than those in Thai, since Chinese tends to emphasize recipient involvement and attitudinal warmth. These findings thereby demonstrate the combined effects of email as a communicative medium, social status, and cultural values on metadiscourse usage. The study concludes by applying these findings to pedagogical suggestions for teaching email writing to Chinese and Thai language learners based on similarities and differences in metadiscourse strategy between the two languages.Keywords: discourse analysis, email, metadiscourse, writing instruction
Procedia PDF Downloads 1283367 Student Feedback of a Major Curricular Reform Based on Course Integration and Continuous Assessment in Electrical Engineering
Authors: Heikki Valmu, Eero Kupila, Raisa Vartia
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A major curricular reform was implemented in Metropolia UAS in 2014. The teaching was to be based on larger course entities and collaborative pedagogy. The most thorough reform was conducted in the department of electrical engineering and automation technology. It has been already shown that the reform has been extremely successful with respect to student progression and drop-out rate. The improvement of the results has been much more significant in this department compared to the other engineering departments making only minor pedagogical changes. In the beginning of the spring term of 2017, a thorough student feedback project was conducted in the department. The study consisted of thirty questions about the implementation of the curriculum, the student workload and other matters related to student satisfaction. The reply rate was more than 40%. The students were divided to four different categories: first year students [cat.1] and students of all the three different majors [categories 2-4]. These categories were found valid since all the students have the same course structure in the first two semesters after which they may freely select the major. All staff members are divided into four teams respectively. The curriculum consists of consecutive 15 credit (ECTS) courses each taught by a group of teachers (3-5). There are to be no end exams and continuous assessment is to be employed. In 2014 the different teacher groups were encouraged to employ innovatively different assessment methods within the given specs. One of these methods has been since used in categories 1 and 2. These students have to complete a number of compulsory tasks each week to pass the course and the actual grade is defined by a smaller number of tests throughout the course. The tasks vary from homework assignments, reports and laboratory exercises to larger projects and the actual smaller tests are usually organized during the regular lecture hours. The teachers of the other two majors have been pedagogically more conservative. The student progression has been better in categories 1 and 2 compared to categories 3 and 4. One of the main goals of this survey was to analyze the reasons for the difference and the assessment methods in detail besides the general student satisfaction. The results show that in the categories following more strictly the specified assessment model much more versatile assessment methods are used and the basic spirit of the new pedagogy is followed. Also, the student satisfaction is significantly better in categories 1 and 2. It may be clearly stated that continuous assessment and teacher cooperation improve the learning outcomes, student progression as well as student satisfaction. Too much academic freedom seems to lead to worse results [cat 3 and 4]. A standardized assessment model is launched for all students in autumn 2017. This model is different from the one used so far in categories 1 and 2 allowing more flexibility to teacher groups, but it will force all the teacher groups to follow the general rules in order to improve the results and the student satisfaction further.Keywords: continuous assessment, course integration, curricular reform, student feedback
Procedia PDF Downloads 2033366 The Influence of Argumentation Strategy on Student’s Web-Based Argumentation in Different Scientific Concepts
Authors: Xinyue Jiao, Yu-Ren Lin
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Argumentation is an essential aspect of scientific thinking which has been widely concerned in recent reform of science education. The purpose of the present studies was to explore the influences of two variables termed ‘the argumentation strategy’ and ‘the kind of science concept’ on student’s web-based argumentation. The first variable was divided into either monological (which refers to individual’s internal discourse and inner chain reasoning) or dialectical (which refers to dialogue interaction between/among people). The other one was also divided into either descriptive (i.e., macro-level concept, such as phenomenon can be observed and tested directly) or theoretical (i.e., micro-level concept which is abstract, and cannot be tested directly in nature). The present study applied the quasi-experimental design in which 138 7th grade students were invited and then assigned to either monological group (N=70) or dialectical group (N=68) randomly. An argumentation learning program called ‘the PWAL’ was developed to improve their scientific argumentation abilities, such as arguing from multiple perspectives and based on scientific evidence. There were two versions of PWAL created. For the individual version, students can propose argument only through knowledge recall and self-reflecting process. On the other hand, the students were allowed to construct arguments through peers’ communication in the collaborative version. The PWAL involved three descriptive science concept-based topics (unit 1, 3 and 5) and three theoretical concept-based topics (unit 2, 4 and 6). Three kinds of scaffoldings were embedded into the PWAL: a) argument template, which was used for constructing evidence-based argument; b) the model of the Toulmin’s TAP, which shows the structure and elements of a sound argument; c) the discussion block, which enabled the students to review what had been proposed during the argumentation. Both quantitative and qualitative data were collected and analyzed. An analytical framework for coding students’ arguments proposed in the PWAL was constructed. The results showed that the argumentation approach has a significant effect on argumentation only in theoretical topics (f(1, 136)=48.2, p < .001, η2=2.62). The post-hoc analysis showed the students in the collaborative group perform significantly better than the students in the individual group (mean difference=2.27). However, there is no significant difference between the two groups regarding their argumentation in descriptive topics. Secondly, the students made significant progress in the PWAL from the earlier descriptive or theoretical topic to the later one. The results enabled us to conclude that the PWAL was effective for students’ argumentation. And the students’ peers’ interaction was essential for students to argue scientifically especially for the theoretical topic. The follow-up qualitative analysis showed student tended to generate arguments through critical dialogue interactions in the theoretical topic which promoted them to use more critiques and to evaluate and co-construct each other’s arguments. More explanations regarding the students’ web-based argumentation and the suggestions for the development of web-based science learning were proposed in our discussions.Keywords: argumentation, collaborative learning, scientific concepts, web-based learning
Procedia PDF Downloads 1053365 A Comparative Asessment of Some Algorithms for Modeling and Forecasting Horizontal Displacement of Ialy Dam, Vietnam
Authors: Kien-Trinh Thi Bui, Cuong Manh Nguyen
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In order to simulate and reproduce the operational characteristics of a dam visually, it is necessary to capture the displacement at different measurement points and analyze the observed movement data promptly to forecast the dam safety. The accuracy of forecasts is further improved by applying machine learning methods to data analysis progress. In this study, the horizontal displacement monitoring data of the Ialy hydroelectric dam was applied to machine learning algorithms: Gaussian processes, multi-layer perceptron neural networks, and the M5-rules algorithm for modelling and forecasting of horizontal displacement of the Ialy hydropower dam (Vietnam), respectively, for analysing. The database which used in this research was built by collecting time series of data from 2006 to 2021 and divided into two parts: training dataset and validating dataset. The final results show all three algorithms have high performance for both training and model validation, but the MLPs is the best model. The usability of them are further investigated by comparison with a benchmark models created by multi-linear regression. The result show the performance which obtained from all the GP model, the MLPs model and the M5-Rules model are much better, therefore these three models should be used to analyze and predict the horizontal displacement of the dam.Keywords: Gaussian processes, horizontal displacement, hydropower dam, Ialy dam, M5-Rules, multi-layer perception neural networks
Procedia PDF Downloads 2133364 Predicting Subsurface Abnormalities Growth Using Physics-Informed Neural Networks
Authors: Mehrdad Shafiei Dizaji, Hoda Azari
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The research explores the pioneering integration of Physics-Informed Neural Networks (PINNs) into the domain of Ground-Penetrating Radar (GPR) data prediction, akin to advancements in medical imaging for tracking tumor progression in the human body. This research presents a detailed development framework for a specialized PINN model proficient at interpreting and forecasting GPR data, much like how medical imaging models predict tumor behavior. By harnessing the synergy between deep learning algorithms and the physical laws governing subsurface structures—or, in medical terms, human tissues—the model effectively embeds the physics of electromagnetic wave propagation into its architecture. This ensures that predictions not only align with fundamental physical principles but also mirror the precision needed in medical diagnostics for detecting and monitoring tumors. The suggested deep learning structure comprises three components: a CNN, a spatial feature channel attention (SFCA) mechanism, and ConvLSTM, along with temporal feature frame attention (TFFA) modules. The attention mechanism computes channel attention and temporal attention weights using self-adaptation, thereby fine-tuning the visual and temporal feature responses to extract the most pertinent and significant visual and temporal features. By integrating physics directly into the neural network, our model has shown enhanced accuracy in forecasting GPR data. This improvement is vital for conducting effective assessments of bridge deck conditions and other evaluations related to civil infrastructure. The use of Physics-Informed Neural Networks (PINNs) has demonstrated the potential to transform the field of Non-Destructive Evaluation (NDE) by enhancing the precision of infrastructure deterioration predictions. Moreover, it offers a deeper insight into the fundamental mechanisms of deterioration, viewed through the prism of physics-based models.Keywords: physics-informed neural networks, deep learning, ground-penetrating radar (GPR), NDE, ConvLSTM, physics, data driven
Procedia PDF Downloads 433363 Determine Causal Factors Affecting the Responsiveness and Productivity of Non-Governmental Universities
Authors: Davoud Maleki
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Today, education and investment in human capital is a long-term investment without which the economy will be stagnant Stayed. Higher education represents a type of investment in human resources by providing and improving knowledge, skills and Attitudes help economic development. Providing efficient human resources by increasing the efficiency and productivity of people and on the other hand with Expanding the boundaries of knowledge and technology and promoting technology such as the responsibility of training human resources and increasing productivity and efficiency in High specialized levels are the responsibility of universities. Therefore, the university plays an infrastructural role in economic development and growth because education by creating skills and expertise in people and improving their ability.In recent decades, Iran's higher education system has been faced with many problems, therefore, scholars have looked for it is to identify and validate the causal factors affecting the responsiveness and productivity of non-governmental universities. The data in the qualitative part is the result of semi-structured interviews with 25 senior and middle managers working in the units It was Islamic Azad University of Tehran province, which was selected by theoretical sampling method. In data analysis, stepwise method and Analytical techniques of Strauss and Corbin (1992) were used. After determining the central category (answering for the sake of the beneficiaries) and using it in order to bring the categories, expressions and ideas that express the relationships between the main categories and In the end, six main categories were identified as causal factors affecting the university's responsiveness and productivity.They are: 1- Scientism 2- Human resources 3- Creating motivation in the university 4- Development based on needs assessment 5- Teaching process and Learning 6- University quality evaluation. In order to validate the response model obtained from the qualitative stage, a questionnaire The questionnaire was prepared and the answers of 146 students of Master's degree and Doctorate of Islamic Azad University located in Tehran province were received. Quantitative data in the form of descriptive data analysis, first and second stage factor analysis using SPSS and Amos23 software were analyzed. The findings of the research indicated the relationship between the central category and the causal factors affecting the response The results of the model test in the quantitative stage confirmed the generality of the conceptual model.Keywords: accountability, productivity, non-governmental, universities, foundation data theory
Procedia PDF Downloads 623362 MEET (Maximise the Erasmus Experience Together): Gains, Challenges and Proposals
Authors: Susana Olmos, Catherine Spencer
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Every year our School in DIT (Dublin Institute of Technology) hosts approximately 80 Erasmus students from partner universities across Europe. Our own students are required to spend a compulsory 3rd year abroad on study and/or work placements. This is an extremely rewarding experience for all of the students, however, it can also be a challenging one. With this in mind, we started a project which aimed to make this transition as easy and productive as possible. The project, which is called MEET: Maximise the Erasmus Experience Together, focuses on the students’ own active engagement in learning and preparation – outside of the classroom –and their own self-directed pursuit of opportunities to develop their confidence and preparedness, which would work as an important foundation for the transformative learning that study abroad implies. We focussed on creating more structured opportunities where Erasmus students from our partner universities (currently studying at DIT) and our second-year students could interact and learn from each other, and in so doing improve both their language and intercultural skills. Our experience so far has been quite positive and we have seen how students taking part in this project have developed as autonomous learners as well as enhanced both their linguistic and intercultural knowledge. As the linguistic element of our project was one of our main priorities, we asked the students to keep a reflective diary on the activities that were organised by the group in the TL. Also, we use questionnaires as well as personal interviews to assess their development. However, there are challenges and proposals we would make to bring this project forward for the near future.Keywords: erasmus, intercultural competence, linguistic competence, extra curriculum activities
Procedia PDF Downloads 3783361 Non-intrusive Hand Control of Drone Using an Inexpensive and Streamlined Convolutional Neural Network Approach
Authors: Evan Lowhorn, Rocio Alba-Flores
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The purpose of this work is to develop a method for classifying hand signals and using the output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNN) were applied. CNN's are a subset of deep learning, which allows grid-like inputs to be processed and passed through a neural network to be trained for classification. This type of neural network allows for classification via imaging, which is less intrusive than previous methods using biosensors, such as EMG sensors. Classification CNN's operate purely from the pixel values in an image; therefore they can be used without additional exteroceptive sensors. A development bench was constructed using a desktop computer connected to a high-definition webcam mounted on a scissor arm. This allowed the camera to be pointed downwards at the desk to provide a constant solid background for the dataset and a clear detection area for the user. A MATLAB script was created to automate dataset image capture at the development bench and save the images to the desktop. This allowed the user to create their own dataset of 12,000 images within three hours. These images were evenly distributed among seven classes. The defined classes include forward, backward, left, right, idle, and land. The drone has a popular flip function which was also included as an additional class. To simplify control, the corresponding hand signals chosen were the numerical hand signs for one through five for movements, a fist for land, and the universal “ok” sign for the flip command. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18) to retrain the network for custom classification. An algorithm was created to interpret the classification and send encoded messages to a Ryze Tello drone over its 2.4 GHz Wi-Fi connection. The drone’s movements were performed in half-meter distance increments at a constant speed. When combined with the drone control algorithm, the classification performed as desired with negligible latency when compared to the delay in the drone’s movement commands.Keywords: classification, computer vision, convolutional neural networks, drone control
Procedia PDF Downloads 2123360 Improving Climate Awareness and the Knowledge Related to Climate Change's Health Impacts on Medical Schools
Authors: Abram Zoltan
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Over the past hundred years, human activities, particularly the burning of fossil fuels, have released enough carbon dioxide and other greenhouse gases to dissipate additional heat into the lower atmosphere and affect the global climate. Climate change affects many social and environmental determinants of health: clean air, safe drinking water, and adequate food. Our aim is to draw attention to the effects of climate change on the health and health care system. Improving climate awareness and the knowledge related to climate change's health impacts are essential among medical students and practicing medical doctors. Therefore, in their everyday practice, they also need some assistance and up-to-date knowledge of how climate change can endanger human health and deal with these novel health problems. Our activity, based on the cooperation of more universities, aims to develop new curriculum outlines and learning materials on climate change's health impacts for medical schools. Special attention is intended to pay to the possible preventative measures against these impacts. For all of this, the project plans to create new curriculum outlines and learning materials for medical students, elaborate methodological guidelines and create training materials for medical doctors' postgraduate learning programs. The target groups of the project are medical students, educational staff of medical schools and universities, practicing medical doctors with special attention to the general practitioners and family doctors. We had searched various surveys, domestic and international studies about the effects of climate change and statistical estimation of the possible consequences. The health effects of climate change can be measured only approximately by considering only a fraction of the potential health effects and assuming continued economic growth and health progress. We can estimate that climate change is expected to cause about 250,000 more deaths. We conclude that climate change is one of the most serious problems of the 21st century, affecting all populations. In the short- to medium-term, the health effects of climate change will be determined mainly by human vulnerability. In the longer term, the effects depend increasingly on the extent to which transformational action is taken now to reduce emissions. We can contribute to reducing environmental pollution by raising awareness and by educating the population.Keywords: climate change, health impacts, medical students, education
Procedia PDF Downloads 1273359 Towards Automatic Calibration of In-Line Machine Processes
Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales
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In this presentation, preliminary results are given for the modeling and calibration of two different industrial winding MIMO (Multiple Input Multiple Output) processes using machine learning techniques. In contrast to previous approaches which have typically used ‘black-box’ linear statistical methods together with a definition of the mechanical behavior of the process, we use non-linear machine learning algorithms together with a ‘white-box’ rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process in order to control de-tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case. Case 1, Tension Control of a Winding Process. A plastic web is unwound from a first reel, goes over a traction reel and is rewound on a third reel. The objectives are: (i) to train a model to predict the web tension and (ii) calibration to find the input values which result in a given tension. Case 2, Friction Force Control of a Micro-Pullwinding Process. A core+resin passes through a first die, then two winding units wind an outer layer around the core, and a final pass through a second die. The objectives are: (i) to train a model to predict the friction on die2; (ii) calibration to find the input values which result in a given friction on die2. Different machine learning approaches are tested to build models, Kernel Ridge Regression, Support Vector Regression (with a Radial Basis Function Kernel) and MPART (Rule Induction with continuous value as output). As a previous step, the MPART rule induction algorithm was used to build an explicative model of the error (the difference between expected and real friction on die2). The modeling of the error behavior using explicative rules is used to help improve the overall process model. Once the models are built, the inputs are calibrated by generating Gaussian random numbers for each input (taking into account its mean and standard deviation) and comparing the output to a target (desired) output until a closest fit is found. The results of empirical testing show that a high precision is obtained for the trained models and for the calibration process. The learning step is the slowest part of the process (max. 5 minutes for this data), but this can be done offline just once. The calibration step is much faster and in under one minute obtained a precision error of less than 1x10-3 for both outputs. To summarize, in the present work two processes have been modeled and calibrated. A fast processing time and high precision has been achieved, which can be further improved by using heuristics to guide the Gaussian calibration. Error behavior has been modeled to help improve the overall process understanding. This has relevance for the quick optimal set up of many different industrial processes which use a pull-winding type process to manufacture fibre reinforced plastic parts. Acknowledgements to the Openmind project which is funded by Horizon 2020 European Union funding for Research & Innovation, Grant Agreement number 680820Keywords: data model, machine learning, industrial winding, calibration
Procedia PDF Downloads 2423358 Corporate Social Responsibility and Career Education: An International Case Study
Authors: Cristina Costa-Lobo, Ana Martins, Maria Das Dores Formosinho, Ana Campina, Filomena Ponte
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This paper is a report on the findings of a study conducted at an international leading food group. Documentary analysis and discourse analysis techniques were used to examine how corporate social responsibility and career education are valued by this international group. The Survey on Corporate Social Responsibility and Career Education was used, with 18 open-ended questions, the first six related to Corporate Social Responsibility and the last 12 related to Education for the Career. The Survey on the Social Emergency Fund was made up of 16 open-ended questions. The Social Welfare Survey was used to investigate the contribution of social workers in this area, as well as to understand their status. The sample of this investigation is composed by the Director of the development area, by the Coordinator and two Social Assistants of the Social Emergency Fund. Their collaboration was the provision of information in the form of an interview where the two main axes of this study were explored: Corporate Social Responsibility and Career Education. With regard to the analysis of data obtained from interviews, it was accomplished through the content analysis according to the Bardin's method (2004), through the pre-analytical, exploratory and qualitative treatment and interpretation of responses. Critical review of documents was also used. The success and effectiveness of this international group are marked by ambition, ability to resist difficulties, sharing of values, spirit of unity and team sense that is shared in its different companies, its leadership position is also due to the concern to see reinforced and developed values of work, discipline, rigor and competence, its management is geared towards responding to immediate challenges from a Corporate Social Responsibility perspective that is characteristic of it, incorporating concerns about impacts both in the medium and long term. In addition to internal training, it directs investments for external training by promoting actions such as participation in seminars and congresses worldwide and the creation of partnerships in various areas of management with prestigious teaching entities. Findings indicate the creation of a training school, with initiatives for internal and external training, in partnerships with prestigious teaching entities. Of particular note is the Management Trainees Program, developed for more than 25 years, characterized by building a career by obtaining knowledge and skills acquired in the combination of on-the-job experience and a training program.Keywords: career education, corporate social responsibility, training school, management trainees program
Procedia PDF Downloads 2293357 Land Cover Remote Sensing Classification Advanced Neural Networks Supervised Learning
Authors: Eiman Kattan
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This study aims to evaluate the impact of classifying labelled remote sensing images conventional neural network (CNN) architecture, i.e., AlexNet on different land cover scenarios based on two remotely sensed datasets from different point of views such as the computational time and performance. Thus, a set of experiments were conducted to specify the effectiveness of the selected convolutional neural network using two implementing approaches, named fully trained and fine-tuned. For validation purposes, two remote sensing datasets, AID, and RSSCN7 which are publicly available and have different land covers features were used in the experiments. These datasets have a wide diversity of input data, number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in training, validation, and testing. As a result, the fully trained approach has achieved a trivial result for both of the two data sets, AID and RSSCN7 by 73.346% and 71.857% within 24 min, 1 sec and 8 min, 3 sec respectively. However, dramatic improvement of the classification performance using the fine-tuning approach has been recorded by 92.5% and 91% respectively within 24min, 44 secs and 8 min 41 sec respectively. The represented conclusion opens the opportunities for a better classification performance in various applications such as agriculture and crops remote sensing.Keywords: conventional neural network, remote sensing, land cover, land use
Procedia PDF Downloads 3723356 A Self-Built Corpus-Based Study of Four-Word Lexical Bundles in Native English Teachers’ EFL Classroom Discourse in Northeast China: The Significance of Stance
Authors: Fang Tan
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This research focuses on the appropriate use of lexical bundles in spoken discourse, particularly in English as a Foreign Language (EFL) classrooms in Northeast China. While previous studies have mainly examined lexical bundles in written discourse, there is a need to investigate their usage in spoken discourse due to the limited availability of spoken discourse corpora. English teachers’ use of lexical bundles is crucial for effective teaching and communication in the EFL classroom. The aim of this study is to investigate the functions of four-word lexical bundles in native English teachers’ EFL oral English classes in Northeast China. Specifically, the research focuses on the usage of stance bundles, which were found to be the most significant type of bundle in the analyzed corpus. By comparing the self-built university spoken English classroom discourse corpus with the other self-built university English for General Purposes (EGP) corpus, the study aims to highlight the difference in bundle usage between native and non-native teachers in EFL classrooms. The research employs a corpus-based study. The observed corpus consists of more than 300,000 tokens, in which the data has been collected in the past five years. The reference corpus is composed of over 800,000 tokens, in which the data has been collected over 12 years. All the primary data collection involved transcribing and annotating spoken English classes taught by native English teachers. The analysis procedures included identifying and categorizing four-word lexical bundles, with specific emphasis on stance bundles. Frequency counts, and comparisons with the Chinese English teachers’ corpus were conducted to identify patterns and differences in bundle usage. The research addresses the following questions: 1) What are the functions of four-word lexical bundles in native English teachers’ EFL oral English classes? 2) How do stance bundles differ in usage between native and non-native English teachers’ classes? 3) What implications can be drawn for English teachers’ professional development based on the findings? In conclusion, this study provides valuable insights into the usage of four-word lexical bundles, particularly stance bundles, in native English teachers’ EFL oral English classes in Northeast China. The research highlights the difference in bundle usage between native and non-native English teachers’ classes and provides implications for English teachers’ professional development. The findings contribute to the understanding of lexical bundle usage in EFL classroom discourse and have theoretical importance for language teaching methodologies. The self-built university English classroom discourse corpus used in this research is a valuable resource for future studies in this field.Keywords: EFL classroom discourse, four-word lexical bundles, stance, implication
Procedia PDF Downloads 663355 Data-Driven Insights Into Juvenile Recidivism: Leveraging Machine Learning for Rehabilitation Strategies
Authors: Saiakhil Chilaka
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Juvenile recidivism presents a significant challenge to the criminal justice system, impacting both the individuals involved and broader societal safety. This study aims to identify the key factors influencing recidivism and successful rehabilitation outcomes by utilizing a dataset of over 25,000 individuals from the NIJ Recidivism Challenge. We employed machine learning techniques, particularly Random Forest Classification, combined with SHAP (SHapley Additive exPlanations) for model interpretability. Our findings indicate that supervision risk score, percent days employed, and education level are critical factors affecting recidivism, with higher levels of supervision, successful employment, and education contributing to lower recidivism rates. Conversely, Gang Affiliation emerged as a significant risk factor for reoffending. The model achieved an accuracy of 68.8%, highlighting its utility in identifying high-risk individuals and informing targeted interventions. These results suggest that a comprehensive approach involving personalized supervision, vocational training, educational support, and anti-gang initiatives can significantly reduce recidivism and enhance rehabilitation outcomes for juveniles, providing critical insights for policymakers and juvenile justice practitioners.Keywords: juvenile, justice system, data analysis, SHAP
Procedia PDF Downloads 253354 The Participation of Graduates and Students of Social Work in the Erasmus Program: a Case Study in the Portuguese context – the Polytechnic of Leiria
Authors: Cezarina da Conceição Santinho Maurício, José Duque Vicente
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Established in 1987, the Erasmus Programme is a program for the exchange of higher education students. Its purposes are several. The mobility developed has contributed to the promotion of multiple learning, the internalization the feeling of belonging to a community, and the consolidation of cooperation between entities or universities. It also allows the experience of a European experience, considering multilingualism one of the bases of the European project and vehicle to achieve the union in diversity. The program has progressed and introduced changes Erasmus+ currently offers a wide range of opportunities for higher education, vocational education and training, school education, adult education, youth, and sport. These opportunities are open to students and other stakeholders, such as teachers. Portugal was one of the countries that readily adhered to this program, assuming itself as an instrument of internationalization of polytechnic and university higher education. Students and social work teachers have been involved in this mobility of learning and multicultural interactions. The presence and activation of this program was made possible by Portugal's joining the European Union. This event was reflected in the field of portuguese social work and contributes to its approach to the reality of european social work. Historically, the Portuguese social work has built a close connection with the Latin American world and, in particular, with Brazil. There are several examples that can be identified in the different historical stages. This is the case of the post-revolution period of 1974 and the presence of the reconceptualization movement, the struggle for enrollment in the higher education circuit, the process of winning a bachelor's degree, and postgraduate training (the first doctorates of social work were carried out in Brazilian universities). This influence is also found in the scope of the authors and the theoretical references used. This study examines the participation of graduates and students of social work in the Erasmus program. The following specific goals were outlined: to identify the host countries and universities; to investigate the dimension and type of mobility made, understand the learning and experiences acquired, identify the difficulties felt, capture their perspectives on social work and the contribution of this experience in training. In the methodological field, the option fell on a qualitative methodology, with the application of semi-structured interviews to graduates and students of social work with Erasmus mobility experience. Once the graduates agreed, the interviews were recorded and transcribed, analyzed according to the previously defined analysis categories. The findings emphasize the importance of this experience for students and graduates in informal and formal learning. The authors conclude with recommendations to reinforce this mobility, either at the individual level or as a project built for the group or collective.Keywords: erasmus programme, graduates and students of social work, participation, social work
Procedia PDF Downloads 1493353 Creativity in the Dark: A Qualitative Study of Cult’s Members Battle between True and False Self in Heterotopia
Authors: Shirly Bar-Lev, Michal Morag
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Cults are usually thought of as suppressive organizations, where creativity is systematically stifled. Except for few scholars, creativity in cults remains an uncharted terrain (Boeri and Pressley, 2010). This paperfocuses on how cult members sought real and imaginary spaces to express themselves and even used their bodies as canvases on which to assert their individuality, resistance, devotion, pain, and anguish. We contend that cult members’ creativity paves their way out of the cult. This paper is part of a larger study into the experiences of former members of cults and cult-like NewReligiousMmovements (NRM). The research is based on in-depth interviews conducted with thirtyIsraeli men and women, aged 24 to 50, who either joined an NRM or were born into one. Their stories reveal that creativity is both emplaced and embedded in power relations. That is why Foucault’s idea of Heterotopia and Winnicott’s idea of the battle between True and False self canbenefit our understanding of how cult members creatively assert their autonomy over their bodies and thoughts while in the cult. Cults’ operate on a complex tension between submission and autonomy. On the one hand, they act as heterotopias byallowing for a ‘simultaneousmythic and real contestation of the space in which we live. Ascounter-hegemonic sites, they serve as‘the greatest reserve of theimagination’, to use Foucault’s words. Cults definitely possesselements of mystery, danger, and transgression where an alternative social ordering can emerge. On the other hand, cults are set up to format alternative identities. Often, the individuals who inhibit these spaces look for spiritual growth, self-reflection, and self-actualization. They might willingly relinquish autonomy over vast aspects of their lives in pursuit of self-improvement. In any case, cultsclaim the totality of their members’ identities and absolute commitment and compliance with the cult’s regimes. It, therefore, begs the question how the paradox between autonomy and submissioncan spur instances of creativity. How can cult members escape processes of performative regulation to assert their creative self? Both Foucault and Winnicott recognize the possibility of an authentic self – one that is spontaneous and creative. Both recognize that only the true self can feel real andmust never comply. Both note the disciplinary regimes that push the true self into hiding, as well as the social and psychological mechanisms that individuals develop to protect their true self. But while Foucault spoke of the power of critic as a way of salvaging the true self, Winnicott spoke of recognition and empathy - feeling known by others. Invitinga dialogue between the two theorists can yield a productive discussion on how cult members assert their ‘true self’ to cultivate a creative self within the confines of the cult.Keywords: cults, creativity, heterotopia, true and false self
Procedia PDF Downloads 893352 Cognitive Linguistic Features Underlying Spelling Development in a Second Language: A Case Study of L2 Spellers in South Africa
Authors: A. Van Staden, A. Tolmie, E. Vorster
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Research confirms the multifaceted nature of spelling development and underscores the importance of both cognitive and linguistic skills that affect sound spelling development such as working and long-term memory, phonological and orthographic awareness, mental orthographic images, semantic knowledge and morphological awareness. This has clear implications for many South African English second language spellers (L2) who attempt to become proficient spellers. Since English has an opaque orthography, with irregular spelling patterns and insufficient sound/grapheme correspondences, L2 spellers can neither rely, nor draw on the phonological awareness skills of their first language (for example Sesotho and many other African languages), to assist them to spell the majority of English words. Epistemologically, this research is informed by social constructivism. In addition the researchers also hypothesized that the principles of the Overlapping Waves Theory was an appropriate lens through which to investigate whether L2 spellers could significantly improve their spelling skills via the implementation of an alternative route to spelling development, namely the orthographic route, and more specifically via the application of visual imagery. Post-test results confirmed the results of previous research that argues for the interactive nature of different cognitive and linguistic systems such as working memory and its subsystems and long-term memory, as learners were systematically guided to store visual orthographic images of words in their long-term lexicons. Moreover, the results have shown that L2 spellers in the experimental group (n = 9) significantly outperformed L2 spellers (n = 9) in the control group whose intervention involved phonological awareness (and coding) including the teaching of spelling rules. Consequently, L2 learners in the experimental group significantly improved in all the post-test measures included in this investigation, namely the four sub-tests of short-term memory; as well as two spelling measures (i.e. diagnostic and standardized measures). Against this background, the findings of this study look promising and have shown that, within a social-constructivist learning environment, learners can be systematically guided to apply higher-order thinking processes such as visual imagery to successfully store and retrieve mental images of spelling words from their output lexicons. Moreover, results from the present study could play an important role in directing research into this under-researched aspect of L2 literacy development within the South African education context.Keywords: English second language spellers, phonological and orthographic coding, social constructivism, visual imagery as spelling strategy
Procedia PDF Downloads 3613351 Interactive Virtual Patient Simulation Enhances Pharmacology Education and Clinical Practice
Authors: Lyndsee Baumann-Birkbeck, Sohil A. Khan, Shailendra Anoopkumar-Dukie, Gary D. Grant
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Technology-enhanced education tools are being rapidly integrated into health programs globally. These tools provide an interactive platform for students and can be used to deliver topics in various modes including games and simulations. Simulations are of particular interest to healthcare education, where they are employed to enhance clinical knowledge and help to bridge the gap between theory and practice. Simulations will often assess competencies for practical tasks, yet limited research examines the effects of simulation on student perceptions of their learning. The aim of this study was to determine the effects of an interactive virtual patient simulation for pharmacology education and clinical practice on student knowledge, skills and confidence. Ethics approval for the study was obtained from Griffith University Research Ethics Committee (PHM/11/14/HREC). The simulation was intended to replicate the pharmacy environment and patient interaction. The content was designed to enhance knowledge of proton-pump inhibitor pharmacology, role in therapeutics and safe supply to patients. The tool was deployed into a third-year clinical pharmacology and therapeutics course. A number of core practice areas were examined including the competency domains of questioning, counselling, referral and product provision. Baseline measures of student self-reported knowledge, skills and confidence were taken prior to the simulation using a specifically designed questionnaire. A more extensive questionnaire was deployed following the virtual patient simulation, which also included measures of student engagement with the activity. A quiz assessing student factual and conceptual knowledge of proton-pump inhibitor pharmacology and related counselling information was also included in both questionnaires. Sixty-one students (response rate >95%) from two cohorts (2014 and 2015) participated in the study. Chi-square analyses were performed and data analysed using Fishers exact test. Results demonstrate that student knowledge, skills and confidence within the competency domains of questioning, counselling, referral and product provision, show improvement following the implementation of the virtual patient simulation. Statistically significant (p<0.05) improvement occurred in ten of the possible twelve self-reported measurement areas. Greatest magnitude of improvement occurred in the area of counselling (student confidence p<0.0001). Student confidence in all domains (questioning, counselling, referral and product provision) showed a marked increase. Student performance in the quiz also improved, demonstrating a 10% improvement overall for pharmacology knowledge and clinical practice following the simulation. Overall, 85% of students reported the simulation to be engaging and 93% of students felt the virtual patient simulation enhanced learning. The data suggests that the interactive virtual patient simulation developed for clinical pharmacology and therapeutics education enhanced students knowledge, skill and confidence, with respect to the competency domains of questioning, counselling, referral and product provision. These self-reported measures appear to translate to learning outcomes, as demonstrated by the improved student performance in the quiz assessment item. Future research of education using virtual simulation should seek to incorporate modern quantitative measures of student learning and engagement, such as eye tracking.Keywords: clinical simulation, education, pharmacology, simulation, virtual learning
Procedia PDF Downloads 3423350 Gamification Beyond Competition: the Case of DPG Lab Collaborative Learning Program for High-School Girls by GameLab KBTU and UNICEF in Kazakhstan
Authors: Nazym Zhumabayeva, Aleksandr Mezin, Alexandra Knysheva
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Women's underrepresentation in STEM is critical, worsened by ineffective engagement in educational practices. UNICEF Kazakhstan and GameLab KBTU's collaborative initiatives aim to enhance female STEM participation by fostering an inclusive environment. Learning from LEVEL UP's 2023 program, which featured a hackathon, the 2024 strategy pivots towards non-competitive gamification. Although the data from last year's project showed higher than average student engagement, observations and in-depth interviews with participants showed that the format was stressful for the girls, making them focus on points rather than on other values. This study presents a gamified educational system, DPG Lab, aimed at incentivizing young women's participation in STEM through the development of digital public goods (DPGs). By prioritizing collaborative gamification elements, the project seeks to create an inclusive learning environment that increases engagement and interest in STEM among young women. The DPG Lab aims to find a solution to minimize competition and support collaboration. The project is designed to motivate female participants towards the development of digital solutions through an introduction to the concept of DPGs. It consists of a short online course, a simulation videogame, and a real-time online quest with an offline finale at the KBTU campus. The online course offers short video lectures on open-source development and DPG standards. The game facilitates the practical application of theoretical knowledge, enriching the learning experience. Learners can also participate in a quest that encourages participants to develop DPG ideas in teams by choosing missions throughout the quest path. At the offline quest finale, the participants will meet in person to exchange experiences and accomplishments without engaging in comparative assessments: the quest ensures that each team’s trajectory is distinct by design. This marks a shift from competitive hackathons to a collaborative format, recognizing the unique contributions and achievements of each participant. The pilot batch of students is scheduled to commence in April 2024, with the finale anticipated in June. It is projected that this group will comprise 50 female high-school students from various regions across Kazakhstan. Expected outcomes include increased engagement and interest in STEM fields among young female participants, positive emotional and psychological impact through an emphasis on collaborative learning environments, and improved understanding and skills in DPG development. GameLab KBTU intends to undertake a hypothesis evaluation, employing a methodology similar to that utilized in the preceding LEVEL UP project. This approach will encompass the compilation of quantitative metrics (conversion funnels, test results, and surveys) and qualitative data from in-depth interviews and observational studies. For comparative analysis, a select group of participants from the previous year's project will be recruited to engage in the DPG Lab. By developing and implementing a gamified framework that emphasizes inclusion, engagement, and collaboration, the study seeks to provide practical knowledge about effective gamification strategies for promoting gender diversity in STEM. The expected outcomes of this initiative can contribute to the broader discussion on gamification in education and gender equality in STEM by offering a replicable and scalable model for similar interventions around the world.Keywords: collaborative learning, competitive learning, digital public goods, educational gamification, emerging regions, STEM, underprivileged groups
Procedia PDF Downloads 643349 Applying Biosensors’ Electromyography Signals through an Artificial Neural Network to Control a Small Unmanned Aerial Vehicle
Authors: Mylena McCoggle, Shyra Wilson, Andrea Rivera, Rocio Alba-Flores
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This work introduces the use of EMGs (electromyography) from muscle sensors to develop an Artificial Neural Network (ANN) for pattern recognition to control a small unmanned aerial vehicle. The objective of this endeavor exhibits interfacing drone applications beyond manual control directly. MyoWare Muscle sensor contains three EMG electrodes (dual and single type) used to collect signals from the posterior (extensor) and anterior (flexor) forearm and the bicep. Collection of raw voltages from each sensor were connected to an Arduino Uno and a data processing algorithm was developed with the purpose of interpreting the voltage signals given when performing flexing, resting, and motion of the arm. Each sensor collected eight values over a two-second period for the duration of one minute, per assessment. During each two-second interval, the movements were alternating between a resting reference class and an active motion class, resulting in controlling the motion of the drone with left and right movements. This paper further investigated adding up to three sensors to differentiate between hand gestures to control the principal motions of the drone (left, right, up, and land). The hand gestures chosen to execute these movements were: a resting position, a thumbs up, a hand swipe right motion, and a flexing position. The MATLAB software was utilized to collect, process, and analyze the signals from the sensors. The protocol (machine learning tool) was used to classify the hand gestures. To generate the input vector to the ANN, the mean, root means squared, and standard deviation was processed for every two-second interval of the hand gestures. The neuromuscular information was then trained using an artificial neural network with one hidden layer of 10 neurons to categorize the four targets, one for each hand gesture. Once the machine learning training was completed, the resulting network interpreted the processed inputs and returned the probabilities of each class. Based on the resultant probability of the application process, once an output was greater or equal to 80% of matching a specific target class, the drone would perform the motion expected. Afterward, each movement was sent from the computer to the drone through a Wi-Fi network connection. These procedures have been successfully tested and integrated into trial flights, where the drone has responded successfully in real-time to predefined command inputs with the machine learning algorithm through the MyoWare sensor interface. The full paper will describe in detail the database of the hand gestures, the details of the ANN architecture, and confusion matrices results.Keywords: artificial neural network, biosensors, electromyography, machine learning, MyoWare muscle sensors, Arduino
Procedia PDF Downloads 1743348 Sociocultural and Critical Approach for Summer Study Abroad Program in Higher Education
Authors: Magda Silva
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This paper presents the empirical and the theoretical principles associated with the Duke in Brazil Summer Program. Using a sociocultural model and critical theory, this study abroad maximizes students’ ability to enrich language competence, intercultural skills, and critical thinking. The fourteen-year implementation of this project demonstrates the global importance of foreign language teaching as the program unfolds into real life scenarios within the cultures of distinct regions of Brazil; Cosmopolitan Rio, in the southeast, and rural Belém, northern Amazon region.Keywords: study abroad, critical thinking, sociocultural theory, foreign language, empirical, theoretical
Procedia PDF Downloads 4113347 Examining Geometric Thinking Behaviours of Undergraduates in Online Geometry Course
Authors: Peter Akayuure
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Geometry is considered an important strand in mathematics due to its wide-ranging utilitarian value and because it serves as a building block for understanding other aspects of undergraduate mathematics, including algebra and calculus. Matters regarding students’ geometric thinking have therefore long been pursued by mathematics researchers and educators globally via different theoretical lenses, curriculum reform efforts, and innovative instructional practices. However, so far, studies remain inconclusive about the instructional platforms that effectively promote geometric thinking. At the University of Education, Winneba, an undergraduate geometry course was designed and delivered on UEW Learning Management System (LMS) using Moodle platform. This study utilizes van Hiele’s theoretical lens to examine the entry and exit’s geometric thinking behaviours of prospective teachers who took the undergraduate geometry course in the LMS platform. The study was a descriptive survey that involved an intact class of 280 first-year students enrolled to pursue a bachelor's in mathematics education at the university. The van Hiele’s Geometric thinking test was used to assess participants’ entry and exit behaviours, while semi-structured interviews were used to obtain data for triangulation. Data were analysed descriptively and displayed in tables and charts. An Independent t-test was used to test for significant differences in geometric thinking behaviours between those who entered the university with a diploma certificate and with senior high certificate. The results show that on entry, more than 70% of the prospective teachers operated within the visualization level of van Hiele’s geometric thinking. Less than 20% reached analysis and abstraction levels, and no participant reached deduction and rigor levels. On exit, participants’ geometric thinking levels increased markedly across levels, but the difference from entry was not significant and might have occurred by chance. The geometric thinking behaviours of those enrolled with diploma certificates did not differ significant from those enrolled directly from senior high school. The study recommends that the design principles and delivery of undergraduate geometry course via LMS should be structured and tackled using van Hiele’s geometric thinking levels to serve as means of bridging the existing learning gaps of undergraduate students.Keywords: geometric thinking, van Hiele’s, UEW learning management system, undergraduate geometry
Procedia PDF Downloads 1303346 Digital Literacy Landscape of Islamic Boarding Schools in Indonesia
Authors: Zainuddin Abuhamid Muhammad Ghozali, Andrew Whitworth
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Islamic boarding school or pesantren is a distinctive education institution in Indonesia focusing on religious teachings. Its stance in restricting access to the internet raises a question about its students’ development of digital literacy. Inspired by Luckin’s ecology of resource model, this study aims to map out the digital literacy situation of the institution based on the availability of learning resources, such as digital facilities, digital accessibility, and digital competence. This study was carried out through a survey method involving 50 teachers from pesantrens across the nation. The result shows that pesantrens have provided students with digital facilities at a moderate level, yet the accessibility to using them is still limited. They also incorporated digital competencies into their curriculum, with an emphasis on digital ethics. The study also identifies different patterns of pesantrens’ behavior based on types and educational levels, where certain school types and educational levels tend to give a stricter policy compared to others or vice versa. The restriction of digital resources in pesantren indicated that they had done a filtration process to design their learning environment. The filtration was mainly motivated by sociocultural factors, where they drew concern for the negative impact of the internet. Notably, this restriction also contributes to students’ poor development of digital literacy.Keywords: digital literacy, ecology of resources, Indonesia, Islamic boarding school
Procedia PDF Downloads 713345 The Applications and Effects of the Career Courses of Taiwanese College Students with LEGO® SERIOUS PLAY®
Authors: Payling Harn
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LEGO® SERIOUS PLAY® is a kind of facilitated workshop of thinking and problem-solving approach. Participants built symbolic and metaphorical brick models in response to tasks given by the facilitator and presented these models to other participants. LEGO® SERIOUS PLAY® applied the positive psychological mechanism of Flow and positive emotions to help participants perceiving self-experience and unknown fact and increasing the happiness of life by building bricks and narrating story. At present, LEGO® SERIOUS PLAY® is often utilized for facilitating professional identity and strategy development to assist workers in career development. The researcher desires to apply LEGO® SERIOUS PLAY® to the career courses of college students in order to promote their career ability. This study aimed to use the facilitative method of LEGO® SERIOUS PLAY® to develop the career courses of college students, then explore the effects of Taiwanese college students' positive and negative emotions, career adaptabilities, and career sense of hope by LEGO® SERIOUS PLAY® career courses. The researcher regarded strength as the core concept and use the facilitative mode of LEGO® SERIOUS PLAY® to develop the 8 weeks’ career courses, which including ‘emotion of college life’ ‘career highlights’, ‘career strengths’, ‘professional identity’, ‘business model’, ‘career coping’, ‘strength guiding principles’, ‘career visions’,’ career hope’, etc. The researcher will adopt problem-oriented teaching method to give tasks which according to the weekly theme, use the facilitative mode of LEGO® SERIOUS PLAY® to guide participants to respond tasks by building bricks. Then participants will conduct group discussions, reports, and writing reflection journals weekly. Participants will be 24 second-grade college students. They will attend LEGO® SERIOUS PLAY® career courses for 2 hours a week. The researcher used’ ‘Career Adaptability Scale’ and ‘Career Hope Scale’ to conduct pre-test and post-test. The time points of implementation testing will be one week before courses starting, one day after courses ending respectively. Then the researcher will adopt repeated measures one-way ANOVA for analyzing data. The results revealed that the participants significantly presented immediate positive effect in career adaptability and career hope. The researcher hopes to construct the mode of LEGO® SERIOUS PLAY® career courses by this study and to make a substantial contribution to the future career teaching and researches of LEGO® SERIOUS PLAY®.Keywords: LEGO® SERIOUS PLAY®, career courses, strength, positive and negative affect, career hope
Procedia PDF Downloads 2533344 Ethical Utility of Artificial Intelligence in Education
Authors: Layan Kateb, Jawaher Ragban, Rawia Alamri, Rewaa Alhazmi
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This paper evaluates user satisfaction with the AI functionality of Coconote, an innovative app designed to transform raw input -documents, voice recordings, and video recordings- into structured study materials such as flashcards, study guides, and transcripts. Leveraging AI-driven natural language processing and summarization algorithms, Coconote aims to streamline the study process by providing personalized, accessible, and accurate learning tools. To measure user satisfaction, we conducted a survey among 20 participants, including students, educators, and professionals, who tested the app’s core features over a two-week period. The survey assessed criteria such as ease of use, accuracy of generated materials, and perceived value in enhancing productivity. Results indicate high satisfaction levels with the app’s ability to produce coherent and concise outputs, with 83% of respondents reporting improved study efficiency. However, feedback highlighted areas for improvement, including occasional inaccuracies in transcription and customization limitations in flashcard formatting. This study demonstrates the potential of AI to transform learning methods and identifies key areas for refinement in user-centric design. Coconote represents a step forward in leveraging AI to enhance educational technologyKeywords: tools for students, artificial intelligence in education, AI study tool, ethical AI tools
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