Search results for: learning table

Authors: Mahmut Yildirim

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This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM.

Keywords: bidirectional long short-term memory, deep learning, maximum likelihood, OFDM with all index modulation, signal detection

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Authors: Anandita Mitro, Archana Srivastava, Bidisha Banerjee

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The present paper attempts to analyze the hierarchy and interconnection of factors responsible for the uptake of BCG vaccination in India. The study uses National Family Health Survey (NFHS-5) data which was conducted during 2019-21. The univariate logistic regression method is used to understand the univariate effects while the interconnection effects have been studied using the Categorical Inference Tree (CIT) which is a non-parametric Machine Learning (ML) model. The hierarchy of the factors is further established using Conditional Inference Forest which is an extension of the CIT approach. The results suggest that BCG vaccination coverage was influenced more by system-level factors and awareness than education or socio-economic status. Factors such as place of delivery, antenatal care, and postnatal care were crucial, with variations based on delivery location. Region-specific differences were also observed which could be explained by the factors. Awareness of the disease was less impactful along with the factor of wealth and urban or rural residence, although awareness did appear to substitute for inadequate ANC. Thus, from the policy point of view, it is revealed that certain subpopulations have less prevalence of vaccination which implies that there is a need for population-specific policy action to achieve a hundred percent coverage.

Keywords: vaccination, NFHS, machine learning, public health

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Authors: Eftah Bte Moh, Hj Abdullah Izazol Binti Idris, Abd. Aziz Bin Abd. Shukor

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This study aimed to identify the alignment of understanding and assessment practices among secondary school teachers. The study was carried out using quantitative descriptive study. The sample consisted of 164 teachers who taught Form 1 and 2 from 11 secondary schools in the district of North Kinta, Perak, Malaysia. Data were obtained from 164 respondents who answered Expectation Alignment Understanding and Practices of School Assessment (PEKDAPS) questionnaire. The data were analysed using SPSS 17.0 +. The Cronbach alpha value obtained through PEKDAPS questionnaire pilot study was 0.86. The results showed that teachers' performance in PEKDAPS based on the mean value was less than 3, which means that perfect alignment does not occur between the understanding and practices of school assessment. Two major PEKDAPS sub-constructs of articulation across grade and age and usability of the system were higher than the moderate alignment of the understanding and practices of school assessment (Min=2.0). The content was focused on PEKDAPs sub-constructs which showed lower than the moderate alignment of the understanding and practices of school assessment (Min=2.0). Another two PEKDAPS sub-constructs of transparency and fairness and the pedagogical implications showed moderate alignment (2.0). The implications of the study is that teachers need to fully understand the importance of alignment among components of assessment, learning and teaching and learning objectives as strategies to achieve quality assessment process.

Keywords: school based assessment, alignment, understanding, assessment practices

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Authors: Margareth S. Zanchetta, Kateryna Metersky, Valerie Tan, Charissa Cordon, Stephanie Lucchese, Yana Siganevich, Prasha Sivasundaram, Truong Binh Nguyen, Imran Qureshi

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Due to a new area of practice, Canadian nurses possess knowledge gaps regarding the use of cannabis-based therapies by clients/patients. Education related to medical cannabis (MC) and recreational cannabis (RC) is required to promote nurses’ competency and confidence in supporting clients/patients using MC/RC toward the improvement of health outcomes. A team composed of nursing researchers and undergraduate/graduate students implemented a national survey to explore this theme with the population of undergraduate, graduate (MN and NP), and Post-Diploma (RN Bridging) nursing students enrolled in Canadian Universities Nursing Programs. Upon Research Ethics Board approval, survey recruitment was supported by major nursing stakeholders. The research questions were : (a) Which are the most preferred sources of information on MC/RC for nursing students? (b) Which are the factors and preferred learning modalities that could increase interest in learning about MC/RC, and (c) What are the future career plans among nursing students, and how would they consider the prospective use of cannabis in their practice? The survey was available from Sept. 2022 to Feb. 2023, hosted by a remote platform. An original questionnaire (English-French) was composed of 18 multiple choice questions and 2 open-ended questions. Sociodemographic information and closed-ended responses were compiled as descriptive statistics, while narrative accounts will be analysed through thematic analysis. Respondents (n=153) were from 7 Canadian provinces, national (99%) and international students (1%); the majority of respondents (61%) were in the age range of 21-30 years old. Results indicated that respondents perceive a gap in the undergraduate curriculum on the topics of MC/RC (91%) and that their learning needs include regulations (90%), data on effectiveness (88%), dosing best practices (86%), contraindications (83%), and clinical and medical indications (76%). Respondents reported motivation to learn more about MC/RC through online lectures/videos (65%), e-learning modules or online interactive training (61%), workshops (51%), webinars (36%), and social media (35%). Their primary career-related motivations regarding MC/RC knowledge include enhancing nursing practice (76%), learning about this growing scope of practice (61%), keeping up-to-date responding to scientific curiosity (59%), learning about evidence-based practice (59%), and utilizing alternative forms of medical treatment (37%). Respondents indicated that the integration of topics on cannabis in any course in the undergraduate and/or graduate curriculum would increase their desire to learn about MC/RC as equally as exposure within a clinical setting (75%). The emerging trend in the set of narrative responses (n=130) suggests that respondents believe educational MC/RC content should be integrated into core nursing courses. Respondents also urged educators to be well-informed about evidence-based practice related to MC/RC and to reflect upon stigma and biases surrounding its use. Future knowledge dissemination and translation activities include scholarly products and presentations to stimulate discussion amongst nursing faculty and students, as well as nurses in clinical settings. The goal is to mobilise talents and build collaboration for the development of a socially responsive curriculum on MC/RC competency to address the education-related expectations of all these social actors.

Keywords: Canada, medical cannabis, nursing education, nursing graduate student, nursing undergraduate student, online survey, recreational cannabis

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Authors: Davut Disci

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Objective: to measure the factors affecting expectations and intentions of using mobile phone in educational contexts by university students, using advanced equations and modeling techniques. Design and Methodology: According to the literature, Mobile Addiction, Parental Surveillance- Safety/Security, Social Relations, and Mobile Behavior are most used terms of defining mobile use of people. Therefore these variables are tried to be measured to find and estimate their effects on expectations and intentions of using mobile phone in educational context. 421 university students participated in this study and there are 229 Female and 192 Male students. For the purpose of examining the mobile behavior and educational expectations and intentions, a questionnaire is prepared and applied to the participants who had to answer all the questions online. Furthermore, responses to close-ended questions are analyzed by using The Statistical Package for Social Sciences(SPSS) software, reliabilities are measured by Cronbach’s Alpha analysis and hypothesis are examined via using Multiple Regression and Linear Regression analysis and the model is tested with Structural Equation Modeling(SEM) technique which is important for testing the model scientifically. Besides these responses, open-ended questions are taken into consideration. Results: When analyzing data gathered from close-ended questions, it is found that Mobile Addiction, Parental Surveillance, Social Relations and Frequency of Using Mobile Phone Applications are affecting the mobile behavior of the participants in different levels, helping them to use mobile phone in educational context. Moreover, as for open-ended questions, participants stated that they use many mobile applications in their learning environment in terms of contacting with friends, watching educational videos, finding course material via internet. They also agree in that mobile phone brings greater flexibility to their lives. According to the SEM results the model is not evaluated and it can be said that it may be improved to show in SEM besides in multiple regression. Conclusion: This study shows that the specified model can be used by educationalist, school authorities to improve their learning environment.

Keywords: education, mobile behavior, mobile learning, technology, Turkey

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Authors: Hao Huang

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INTRODUCTION: The Association of American Colleges and Universities (AACU) recommends the development of ‘high-impact practices,’ in an effort to increase rates of student retention and student engagement at undergraduate institutions. To achieve these goals, the Scripps College Humanities Institute and HI Fellows Seminar not only featured distinguished academics presenting their scholarship about current immigration policy and its consequences in the USA and around the world but integrated socially significant community leaders and creative activists/artivists in public talks, student workshops and collaborative art events. Students participated in experiential learning that involved guest personal presentations and discussions, oral history interviews that applied standard oral history methodologies, detailed cultural documentation, collaborative artistic interventions, and weekly posts in Internet Digital Learning Environment Sakai collaborative course forums and regular responses to other students’ comments. Our teaching pedagogies addressed the four learning styles outlined in Kolb’s Learning Style Inventory. PROJECT DESCRIPTION: Over the academic year 2017-18, the Scripps College Humanities Institute and HI Fellows Seminar presented a Fall 2017 topic, ‘The World at Our Doorsteps: Immigration and Deportation in Los Angeles’. Our purpose was to address how current federal government anti-immigration measures have affected many students of color, some of whom are immigrants, many of whom are related to and are friends with people who are impacted by the attitudes as well as the practices of the U.S. Citizenship and Immigration Services. In Spring 2018, we followed with the topic, ‘Exclusive Nationalisms: Global Migration and Immigration’. This addresses the rise of white supremacists who have ascended to position of power worldwide, in America, Europe, Russia, and xenophobic nationalisms in China, Myanmar and the Philippines. Recent scholarship has suggested the existence of categories of refugees beyond the political or social, who fit into the more inclusive category of migrants. ASSESSMENT METHODOLOGIES: Assessment methodologies not only included qualitative student interviews and quantitative student evaluations in standard rubric format, but also Outcome Assessments, Formative Evaluations, and Outside Guest Teacher feedback. These indicated that the most effective educational practices involved collaborative inquiry in undergraduate research, community-based learning, and capstone projects. Assessments of E-portfolios, written and oral coursework, and final creative projects with associated 10-12 page analytic paper revealed that students developed their understanding of how government and social organizations work; they developed communication skills that enhanced working with others from different backgrounds; they developed their ability to thoughtfully evaluate their course performance by adopting reflective practices; they gained analytic and interpretive skills that encouraged self-confidence and self- initiative not only academically, but also with regards to independent projects. CONCLUSION: Most importantly, the Scripps Humanities Institute experiential learning project spurred on real-world actions by our students, such as a public symposium on how to cope with bigots, a student tutoring program for immigrant staff children, student negotiations with the administration to establish meaningful, sustainable diversity and inclusion programs on-campus. Activism is not only to be taught to and for our students– it has to be enacted by our students.

Keywords: immigration, migration, human rights, learning assessment

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Authors: Maria Ezziani, Rabie Zine, Amine Amar, Ilhame Kissani

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This paper introduces a predictive model for urban transportation engineering, which is vital for efficient traffic management. Utilizing comprehensive datasets and advanced statistical techniques, the model accurately forecasts travel times by considering temporal variations and traffic dynamics. Machine learning algorithms, including regression trees and neural networks, are employed to capture sequential dependencies. Results indicate significant improvements in predictive accuracy, particularly during peak hours and holidays, with the incorporation of traffic flow and speed variables. Future enhancements may integrate weather conditions and traffic incidents. The model's applications range from adaptive traffic management systems to route optimization algorithms, facilitating congestion reduction and enhancing journey reliability. Overall, this research extends beyond travel time estimation, offering insights into broader transportation planning and policy-making realms, empowering stakeholders to optimize infrastructure utilization and improve network efficiency.

Keywords: predictive analytics, traffic flow, travel time estimation, urban transportation, machine learning, traffic management

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Authors: Clifford Irikefe Gbeyonron

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Applied linguists concur that there is low-level achievement in English language use among Nigerian secondary school students. One of the factors that exacerbate this is classroom feature of which large class size is obvious. This study investigated the impact of large classes on learning English as a second language (ESL) at junior secondary school (JSS) in Yobe State. To achieve this, Solomon four-group experimental design was used. 382 subjects were divided into four groups and taught ESL for thirteen weeks. 356 subjects wrote the post-test. Data from the systematic observation and post-test were analyzed via chi square and ANOVA. Results indicated that learners in large classes (LLC) attain lower linguistic progress than learners in small classes (LSC). Furthermore, LSC have more chances to access teacher evaluation and participate actively in classroom discourse than LLC. In consequence, large classes have adverse effects on learning ESL in Yobe State. This is inimical to English language education given that each learner of ESL has their individual peculiarity within each class. It is recommended that strategies that prioritize individualization, grouping, use of language teaching aides, and theorization of innovative models in respect of large classes be considered.

Keywords: large classes, achievement, classroom discourse

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Authors: Zhao Luqian, Wang Yafei

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Participation in music activities is beneficial for enhancing wellbeing, especially for aged people (Creech, 2013). Looking at percussion group in particular, it can facilitate a sense of belonging, relaxation, energy, and productivity, learning, enhanced mood, humanising, seems of accomplishment, escape from trauma, and emotional expression (Newman, 2015). In health literatures, group drumming is effective in reducing stress and improving multiple domains of social-motional behaviors (Ho et al., 2011; Maschi et al., 2010) because it offers a creative and mutual learning space that allows patients to establish a positive peer interaction (Mungas et al., 2014; Perkins, 2016). However, very few studies have investigated the effect of group drumming from the aspect of patients’ needs. Therefore, this study focuses on the discussion of patients' specific needs within mental health and explores how group percussion may meet their needs. Seligman’s (2011) five core elements of mental health were applied as patients’ needs in this study: (1) Positive emotions; (2) Engagement; (3) Relationships; (4) Meaning and (5) Accomplishment. 12 participants aged 57- 80 years were interviewed individually. The researcher also had observation in four drumming groups simultaneously. The results reveal that group drumming could improve participants’ mental wellbeing. First, it created a therapeutic health care environment extending beyond the elimination of boredom, and patients could focus on positive emotions during the session of group drumming. Secondly, it was effective in satisfying patients’ level of engagement. Thirdly, this study found that joining a percussion group would require patients to work on skills such as turn-taking and sharing. This equal relationship is helpful for releasing patients’ negative mood and thus forming tighter relationships between and among them. Fourthly, group drumming was found to meet patients’ meaning needs through offering them a place of belonging and a place for sharing. Its leaner-oriented approach engaged patients by a sense of belonging, accepting, connecting, and ownership. Finally, group drumming could meet patients’ needs for accomplishment through the learning process. The inclusive learning process, which indicates there is no right or wrong throughout the process, allowed patients to make their own decisions. In conclusion, it is difficult for patients to achieve positive emotions, engagement, relationships, meanings, and accomplishments in a hospital setting. Drumming can be practiced for enhancement in terms of reducing patients’ negative emotions and improving their experiences in a hospital through enriched social interaction and sense of accomplishment. Also, it can help patients to enhance social skills in a controlled environment.

Keywords: group drumming, hospital, mental health, music psychology

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Authors: Lianzhong Zhang, Chao Huang

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Sea-land segmentation is a basic step in many tasks such as sea surface monitoring and ship detection. The existing sea-land segmentation algorithms have poor segmentation accuracy, and the parameter adjustments are cumbersome and difficult to meet actual needs. Also, the current sea-land segmentation adopts traditional deep learning models that use Convolutional Neural Networks (CNN). At present, the transformer architecture has achieved great success in the field of natural images, but its application in the field of radar images is less studied. Therefore, this paper proposes a sea-land segmentation method based on the transformer architecture to strengthen edge supervision. It uses a self-attention mechanism with a gating strategy to better learn relative position bias. Meanwhile, an additional edge supervision branch is introduced. The decoder stage allows the feature information of the two branches to interact, thereby improving the edge precision of the sea-land segmentation. Based on the Gaofen-3 satellite image dataset, the experimental results show that the method proposed in this paper can effectively improve the accuracy of sea-land segmentation, especially the accuracy of sea-land edges. The mean IoU (Intersection over Union), edge precision, overall precision, and F1 scores respectively reach 96.36%, 84.54%, 99.74%, and 98.05%, which are superior to those of the mainstream segmentation models and have high practical application values.

Keywords: SAR, sea-land segmentation, deep learning, transformer

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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Authors: Majed A. Alsalem

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Universal Design for Learning (UDL) is common theme in education across the US and an influential model and framework that enables students in general and particularly students who are deaf and hard of hearing (DHH) to access the general education curriculum. UDL helps teachers determine how information will be presented to students and how to keep students engaged. Moreover, UDL helps students to express their understanding and knowledge to others. UDL relies on technology to promote students' interaction with content and their communication of knowledge. This study included 120 DHH students who received daily instruction based on UDL principles. This study presents the results of the study and discusses its implications for the integration of UDL in day-to-day practice as well as in the country's education policy. UDL is a Western concept that began and grew in the US, and it has just begun to transfer to other countries such as Saudi Arabia. It will be very important to researchers, practitioners, and educators to see how UDL is being implemented in a new place with a different culture. UDL is a framework that is built to provide multiple means of engagement, representation, and action and expression that should be part of curricula and lessons for all students. The purpose of this study is to investigate the variables associated with the implementation of UDL in Saudi Arabian schools and identify the barriers that could prevent the implementation of UDL. Therefore, this study used a mixed methods design that use both quantitative and qualitative methods. More insights will be gained by including both quantitative and qualitative rather than using a single method. By having methods that different concepts and approaches, the databases will be enriched. This study uses levels of collecting date through two stages in order to insure that the data comes from multiple ways to mitigate validity threats and establishing trustworthiness in the findings. The rationale and significance of this study is that it will be the first known research that targets UDL in Saudi Arabia. Furthermore, it will deal with UDL in depth to set the path for further studies in the Middle East. From a perspective of content, this study considers teachers’ implementation knowledge, skills, and concerns of implementation. This study deals with effective instructional designs that have not been presented in any conferences, workshops, teacher preparation and professional development programs in Saudi Arabia. Specifically, Saudi Arabian schools are challenged to design inclusive schools and practices as well as to support all students’ academic skills development. The total participants in stage one were 336 teachers of DHH students. The results of the intervention indicated significant differences among teachers before and after taking the training sessions associated with their understanding and level of concern. Teachers have indicated interest in knowing more about UDL and adopting it into their practices; they reported that UDL has benefits that will enhance their performance for supporting student learning.

Keywords: deaf and hard of hearing, professional development, Saudi Arabia, universal design for learning

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Authors: Ana Martins, Isabel Martins

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The purpose of this research is to ascertain whether students at HEIs cultivate distributed leadership and higher-level skills to inspire knowledge creation. Critical reflection of extant literature illustrates the need for a culture of innovation in organizational sustainability. New age leadership behaviors harmonize innovation. The leadership self-efficacy construct supports organizational learning. This exploratory study applies the pragmatic paradigm methodology using the survey research method for primary data collection. A questionnaire was distributed to a sample of university students based in the Southern Anatolian region of Turkey, from both under and postgraduate Business degree programs. An analysis of the findings reveals a greater connection in influencing behavior relying more on the task-centered perspective rather than with the people perspective. These results reveal the need for HEIs to instill a humanistic perspective in curricula enabling graduates to be capable leaders with the awareness soft skills to energize creativity and innovation. A limitation of this research is that one university makes it difficult to generalize to a broader population. This study is of added value for scholars and organizations in the current knowledge and innovation economy.

Keywords: distributed leadership, exploration, higher education institutions, innovation, knowledge creation, learning, self-efficacy

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Authors: Sakinah Idris, Femke Ten Hoeve, Kirstin Greaves-Lord

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Background: Social skills interventions are considered to be efficacious if social skills are improved as a result of an intervention. Nevertheless, the objective assessment of social skills is hindered by a lack of sensitive and validated measures. To measure the change in social skills after an intervention, questionnaires reported by parents, clinicians and/or teachers are commonly used. Observations are the most ecologically valid method of assessing improvements in social skills after an intervention. For this purpose, The Program for the Educational and Enrichment of Relational Skills (PEERS) was developed for adolescents, in order to teach them the age-appropriate skills needed to participate in society. It is an evidence-based intervention for adolescents with ASD that taught ecologically valid social skills techniques. Objectives: The current study aims to describe the development and psychometric evaluation of the Dutch Contextual Assessment of Social Skills (CASS), an observational outcome measure of social skills for adolescents with Autism Spectrum Disorder (ASD). Methods: 64 adolescents (M = 14.68, SD = 1.41, 71% boys) with ASD performed the CASS before and after a social skills intervention (i.e. PEERS or the active control condition). Each adolescent completed a 3-minute conversation with a confederate. The conversation was prompt as a natural introduction between two-unfamiliar, similar ages, opposite-sex peers who meet for the first time. The adolescent and the confederate completed a brief questionnaire about the conversation (Conversation Rating Scale). Results: Results indicated sufficient psychometric properties. The Dutch CASS has a high level of internal consistency (Cronbach's α coefficients = 0.84). Data supported the convergent validity (i.e., significant correlated with the Social Skills Improvement System (SSiS). The Dutch CASS did not significantly correlate with the autistic mannerism subscale from Social Responsiveness Scale (SRS), thus proved the divergent validity. Based on scorings made by raters who were kept blind to the time points, reliable change index was computed to assess the change in social skills. With regard to the content validity, only the learning objectives of the first two meetings of PEERS about conversational skills relatively matched with rating domains of the CASS. Due to this underrepresentation, we found an existing observational measure (TOPICC) that covers some of the other learning objectives of PEERS. TOPICC covers 22% of the learning objectives of PEERS about conversational skills, meanwhile, CASS is 45%. Unfortunately, 33% of the learning objectives of PEERS was not covered by CASS or TOPICC. Conclusion: Recommendations are made to improve the psychometric properties and content validity of the Dutch CASS.

Keywords: autism spectrum disorder, observational, PEERS, social skills

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Authors: Khanita Limhan

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Due to the fact that the instructional media is a very crucial implement in English as Foreign Language (EFL) teaching and learning because it simply motivates or demotivates the learners to learn English. Furthermore, it could enormously involve the learners in the real language. The mixed-method research investigates undergraduate student teachers at the Faculty of Education in aspects of the preferences and attitudes towards the use of instructional media in a Thai EFL classroom. Therefore, there were 21 female and 4 male students, who are being educated to be secondary English teachers in Thai educational system, participated in this study. Moreover, the data was gathered from six open-ended questions; obviously, all were given at least 30 - 45 minutes to express their preferences and thoughts in their native tongue at the end of the English for English teacher course. The results of this study indicated that 64 % of student teachers preferred to study English grammar through songs and music; 54% of them desire to learn English grammar through English movies; and 40% of them want to acquire English grammar by watching short documentaries. Since, the participants illustrated that they feel neither anxious nor bored; however, they feel very excited and fun while studying. In addition, they pointed out that they could improve their listening proficiency; obtain new vocabulary; and comprehend the cultural content authentically from the instructional media. It can be concluded that the use of instructional media affects students and teachers’ motivations and attitudes on English teaching and learning.

Keywords: attitudes, preferences, student teachers, instructional media

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Authors: Niloofar Yousefi, Marie Alaghband, Ivan Garibay

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With the increase of credit card usage, the volume of credit card misuse also has significantly increased, which may cause appreciable financial losses for both credit card holders and financial organizations issuing credit cards. As a result, financial organizations are working hard on developing and deploying credit card fraud detection methods, in order to adapt to ever-evolving, increasingly sophisticated defrauding strategies and identifying illicit transactions as quickly as possible to protect themselves and their customers. Compounding on the complex nature of such adverse strategies, credit card fraudulent activities are rare events compared to the number of legitimate transactions. Hence, the challenge to develop fraud detection that are accurate and efficient is substantially intensified and, as a consequence, credit card fraud detection has lately become a very active area of research. In this work, we provide a survey of current techniques most relevant to the problem of credit card fraud detection. We carry out our survey in two main parts. In the first part, we focus on studies utilizing classical machine learning models, which mostly employ traditional transnational features to make fraud predictions. These models typically rely on some static physical characteristics, such as what the user knows (knowledge-based method), or what he/she has access to (object-based method). In the second part of our survey, we review more advanced techniques of user authentication, which use behavioral biometrics to identify an individual based on his/her unique behavior while he/she is interacting with his/her electronic devices. These approaches rely on how people behave (instead of what they do), which cannot be easily forged. By providing an overview of current approaches and the results reported in the literature, this survey aims to drive the future research agenda for the community in order to develop more accurate, reliable and scalable models of credit card fraud detection.

Keywords: Credit Card Fraud Detection, User Authentication, Behavioral Biometrics, Machine Learning, Literature Survey

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Authors: Michal Wolk, Bat-Sheva Hadad, Orly Rubinsten

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Objective: The visuospatial perception system process that allows us to decompose and recompose small quantities into a whole is often called “groupitizing.” Previous studies have been found that adults use groupitizing processes in quantity estimation tasks and link this ability of subgroups recognition to arithmetic proficiency. This pilot study examined if adults with math difficulties benefit from visuospatial grouping cues when asked to estimate the quantity of a given set. It also compared the tipping point in which a significant improvement occurs in adults with typical development compared to adults with math difficulties. Method: In this pilot research, we recruited adults with low arithmetic abilities and matched controls. Participants were asked to estimate the quantity of a given set. Different grouping cues were displayed (space, color, or none) with different visual configurations (different quantities-different shapes, same quantities- different shapes, same quantities- same shapes). Results: Both groups showed significant performance improvement when grouping cues appeared. However, adults with low arithmetic abilities benefited from the grouping cues already in very small quantities as four. Conclusion: impaired perceptual groupitizing abilities may be a characteristic of low arithmetic abilities.

Keywords: groupitizing, math learning disability, quantity estimation, visual perception system

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Authors: Sheila Santharamohana, Susan Bennett

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Historically, the attrition rates in secondary schools of Indigenous people or Orang Asli of Malaysia have been a cause for nationwide concern. Efforts to increase student engagement focusing on curriculum re-design and aid have not had the targeted impact. The scope of the research explored a change model incorporating project-based learning and wrap-around support through school-community partnerships to increase Orang Asli engagement, student outcomes and improve cultural connectedness. The evaluation methodology was mixed-method comprising a student questionnaire, interviews, and document analysis. Data and evidence were gathered from school staff, community, the Orang Asli governmental authority (JAKOA) and external agencies. Findings from the year-long research suggests shared values and goals in school-community partnerships foster responsive leadership and is key to safeguarding vulnerable Orang Asli, resulting in improved student outcomes. The research highlighted the barriers to the recognition and distinct needs and unique values of the Orang Asli that impact their educational equity and outcomes.

Keywords: Indigenous Education, Cultural Connectedness, School-Community Partnership, Student Outcomes

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Authors: Lily Parm, Irma Nool, Liina Männiksaar, Mare Tupits, Ivi Prits, Merilin Kuhi, Valentina Raudsepp

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Background: The results of previous clinical satisfaction surveys show that nursing students swhounderw entinternships in the pediatricwardhadthelowestsatisfactioncomparedtootherwards, but the quality of students' practicaltrainingexperienceisanimportant determinant in nursing education. The aim of theresearchwastodescribenursingstudents` assessment to the clinical learning environment and supervision in pediatric wards Method: Theresearchisquantitative. All studentswhohadpracticaltraining in the pediatric ward participated in the study (N = 39). FordatacollectionClinicalLearningEnvironment, Supervision, and NurseTeacher (CLES + T) evaluationscalewasused, wherethescalewasanswered on a 5-point Likert scale. In addition, 10 backgroundvariableswereused in the questionnaire. IBM SPSS Statistics 28.0 wasusedfordataanalysis. Descriptive statistics and Spearmanncorrelationanalysiswasusedtofindcorrelatinsbetweenbackgroundvariables and satisfaction with supervision.Permissiontoconductthestudy (No 695) hasbeenobtainedbytheEthicsCommittee of theInstituteforHealthDevelopment. Results: Of therespondents, 28 (71.8%) werefirst-year, 9 (23.1%) second-year and 2 (5.1%) fourth-yearstudents. Thelargestshare of the last practicaltrainigwas in nursing, with 27 (69.2%) respondents. Mainlythementorswerenursesfor 32 (82,1%) of students.Satisfactionwiththementoring (4.4 ± 0.83) and wardnursemanager`sleaderhiostyle (4.4 ± 0.7), ratedthehighest and therole of thenurseteacherwasratedthelowest (3,7 ± 0.83.In Spearmann'scorrelationanalysis, therewas a statisticallystrongcorrelationbetween a positiveattitudetowardsthesupervisor'ssupervision and receivingfeedbackfromthesupervisor (r =0.755; p <0.001), studentsatisfactionwithsupervision (r = 0.742; p <0.001), supervisionbased on cooperation (r = 0.77) and instructionbased on theprinciple of equalitythatpromotedlearning (r = 0.755; p <0.001). Conclusions: Theresults of theresearchshowedhighsatisfactionwiththesupervisionand therole of wardmanager. Stillbettercooperationisneededbetweenpracticalplacement and nursingschooltoenhancethestudents`satisfactionwithsupervision.

Keywords: CLES+T, clinical environment, nurse teacher, statisfaction, pediatric ward, mentorship

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Authors: Mario Chiasson, Monique Boudreau

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Problem statement: Innovation in education has become a central topic of discussion at various levels, including schools and scholarly literature, driven by the global technological advancements of Industry 4.0. This study aims to contribute to the ongoing dialogue by examining the role of innovation in transforming school culture through the reimagination of traditional structures. The study argues that such a transformation necessitates an understanding and experience of systems leadership. This paper presents the case of the Francophone South School District, where a transformative initiative created an innovative learning environment by engaging students, teachers, and community members collaboratively through eco-communities. Traditional barriers and structures in education were dismantled to facilitate this process. The research component of this paper focuses on the Intr’Appreneur project, a unique initiative launched by the district team in the New Brunswick, Canada to support a system-wide transformation towards progressive and innovative organizational models. Methods This study is part of a larger research project that focuses on the transformation of educational systems in six pilot schools involved in the Intr’Appreneur project. Due to COVID-19 restrictions, the project was downscaled to three schools, and virtual qualitative interviews were conducted with volunteer teachers and administrators. Data was collected from students, teachers, and principals regarding their perceptions of the new learning environment and experiences. The analysis process involved developing categories, establishing codes for emerging themes, and validating the findings. The study emphasizes the importance of system leadership in achieving successful transformation. Results: The findings demonstrate that school principals played a vital role in enabling system-wide change by fostering a dynamic, collaborative, and inclusive culture, coordinating and mobilizing community members, and serving as educational role models who facilitated active and personalized pedagogy among the teaching staff. These qualities align with the characteristics of Leadership 4.0 and are crucial for successful school system transformations. Conclusion: This paper emphasizes the importance of systems leadership in driving educational transformations that extend beyond pedagogical and technological advancements. The research underscores the potential impact of such a leadership approach on teaching, learning, and leading processes in Education 4.0.

Keywords: leadership, system transformation, innovation, innovative learning environment, Education 4.0, system leadership

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Authors: Han-Jeong Hwang, Jae-Hyun Jo, Fatemeh Alimardani

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An accurate diagnosis of psychiatric diseases is a challenging issue, in particular when distinct symptoms for different diseases are overlapped, such as delusions appeared in bipolar mood disorder (BMD) and schizophrenia (SCH). In the present study, we propose a useful way to discriminate BMD and SCH using electroencephalography (EEG). A total of thirty BMD and SCH patients (15 vs. 15) took part in our experiment. EEG signals were measured with nineteen electrodes attached on the scalp using the international 10-20 system, while they were exposed to a visual stimulus flickering at 16 Hz for 95 s. The flickering visual stimulus induces a certain brain signal, known as steady-state visual evoked potential (SSVEP), which is differently observed in patients with BMD and SCH, respectively, in terms of SSVEP amplitude because they process the same visual information in own unique way. For classifying BDM and SCH patients, machine learning technique was employed in which leave-one-out-cross validation was performed. The SSVEPs induced at the fundamental (16 Hz) and second harmonic (32 Hz) stimulation frequencies were extracted using fast Fourier transformation (FFT), and they were used as features. The most discriminative feature was selected using the Fisher score, and support vector machine (SVM) was used as a classifier. From the analysis, we could obtain a classification accuracy of 83.33 %, showing the feasibility of discriminating patients with BMD and SCH using EEG. We expect that our approach can be utilized for psychiatrists to more accurately diagnose the psychiatric disorders, BMD and SCH.

Keywords: bipolar mood disorder, electroencephalography, schizophrenia, machine learning

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Authors: Fernando Augusto Ullmann Tobe, Moacyr Amaral Domingues, Figueiredo, Stephany Rie Yamamoto Gushiken

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The purpose of this paper is to present the process of elaborating the layout of an assembly line of a welding industry using the principles of lean manufacturing as the main driver. The objective of this paper is relevant since the current layout of the assembly line causes non-productive times for operators, being related to the lean waste of unnecessary movements. The methodology used for the project development was Project-based Learning (PBL), which is an active way of learning focused on real problems. The process of selecting the methodology for layout planning was developed considering three criteria to evaluate the most relevant one for this paper's goal. As a result of this evaluation, Systematic Layout Planning was selected, and three steps were added to it – Value Stream Mapping for the current situation and after layout changed and the definition of lean tools and layout type. This inclusion was to consider lean manufacturing in the layout redesign of the industry. The layout change resulted in an increase in the value-adding time of operations carried out in the sector, reduction in movement times between previous and final assemblies, and in cost savings regarding the man-hour value of the employees, which can be invested in productive hours instead of movement times.

Keywords: assembly line, layout, lean manufacturing, systematic layout planning

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Authors: Soni Ariawan

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The discourse of language and culture relation is an interesting issue to be researched. The debate is not about what comes first, language or culture, but it strongly argues that learning foreign language also means learning the culture of the language. The more interesting issue found once constructing an EFL textbook dealing with proportional representation among source culture, target culture and international culture. This study investigates cultural content representation in EFL textbook grade 10 year 2017 in Indonesia. Cortazzi and Jin’s theoretical framework is employed to analyse the reading texts, conversations, and images. The finding shows that national character as the main agenda of Indonesian government is revealed in this textbook since the textbook more frequently highlights the source culture (Indonesian culture) compared to target and international culture. This is aligned with the aim of Indonesian government to strengthen the national identity and promoting local culture awareness through education. To conclude, the study is expected to be significant in providing the idea for government to consider cultural balances representation in constructing textbook. Furthermore, teachers and students should be aware of cultural content revealed in the EFL textbook and be able to enhance intercultural communication not only in the classroom but also in a wider society.

Keywords: EFL textbook, intercultural communication, local culture, target culture, international culture

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Authors: Mehar Fatima

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This study intends to make analysis of Emotional Intelligence (EI) in the process of pedagogy and look into its repercussions in different educational institutions including school, college, and university in the capital state of India, Delhi in 2015. Field of education is a complex area with challenging issues in a modern society. Education is the breeding ground for nurturing human souls, and personalities. Since antiquity, man has been in search of truth, wisdom, contentment, peace. His efforts have brought him to acquire these through hardship, evidently through the process of teaching and learning. Computer aids and artificial intelligence have made life easy but complex. Efficient pedagogy involves direct human intervention despite the flux of technological advancements. Time and again, pedagogical practices demand sincere human efforts to understand and improve upon life’s many pragmatic concerns. Apart from the intense academic scientific approaches, EI in academia plays a vital role in the growth of education, positively achieving national progression; ‘pedagogy of pragmatic purpose.’ Use of literature is found to be one of the valuable pragmatic tools of Emotional Intelligence. This research examines the way literature provides useful influence in building better practices in teaching-learning process. The present project also scrutinizes various pieces of world literature and translation, incorporating efforts of intellectuals in promoting comprehensive amity. The importance of EI in educational arena with its pragmatic uses was established by the study of interviews, and questionnaire collected from teachers and students. In summary the analysis of obtained empirical data makes it possible to accomplish that the use Emotional Intelligence in academic scenario yields multisided positive pragmatic outcomes; positive attitude, constructive aptitude, value-added learning, enthusiastic participation, creative thinking, lower apprehension, diminished fear, leading to individual as well as collective advancement, progress, and growth of pedagogical agents.

Keywords: emotional intelligence, human efforts, pedagogy, pragmatic concerns

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Authors: Masayuki Mori

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This study investigates the relationship between teachers’ perceptions of the usability of classroom space and various elements, including both physical and non-physical, of classroom environments. With the introduction of the GIGA School funding program in Japan in 2019, understanding its impact on learning in classroom space is crucial. The program enabled local educational authorities (LEA) to make it possible to provide one PC/tablet for each student of both elementary and junior high schools. Moreover, at the same time, the program also supported LEA to purchase other electronic devices for educational purposes such as electronic whiteboards, large displays, and real image projectors. A nationwide survey was conducted using random sampling methodology among 100 junior high schools to collect data on classroom space. Of those, 60 schools responded to the survey. The survey covered approximately fifty items, including classroom space size, class size, and educational electronic devices owned. After the data compilation, statistical analysis was used to identify correlations between the variables and to explore the extent to which classroom environment elements influenced teachers’ perceptions. Furthermore, decision tree analysis was applied to visualize the causal relationships between the variables. The findings indicate a significant negative correlation between class size and teachers’ evaluation of usability. In addition to the class size, the way students stored their belongings also influenced teachers’ perceptions. As for the placement of educational electronic devices, the installation of a projector produced a small negative correlation with teachers’ perceptions. The study suggests that while the GIGA School funding program is not significantly influential, traditional educational conditions such as class size have a greater impact on teachers’ perceptions of the usability of classroom space. These results highlight the need for awareness and strategies to integrate various elements in designing the learning environment of the classroom for teachers and students to improve their learning experience.

Keywords: classroom space, GIGA School, questionnaire survey, teachers’ perceptions

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Authors: Michael Smalenberger

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Intelligent tutoring systems (ITS) are computer-based platforms which can incorporate artificial intelligence to provide step-by-step guidance as students practice problem-solving skills. ITS can replicate the benefits of one-on-one tutoring, foster transactivity in collaborative environments, and lead to substantial learning gains when used to supplement the instruction of a teacher or when used as the sole method of instruction. Developments improving the ease of ITS creation have recently increased their proliferation, leading many K-12 schools and institutions of higher education in the United States to regularly use ITS within classrooms. We investigated how students perceive their experience using an ITS. In this study, 111 undergraduate students used an ITS in a college-level introductory statistics course and were subsequently asked for feedback on their experience. Results show that their perceptions were generally favorable of the ITS, and most would seek to use an ITS both for STEM and non-STEM courses in the future. Along with detailed transaction-level data, this feedback also provides insights on the design of user-friendly interfaces, guidance on accessibility for students with impairments, the sequencing of exercises, students’ expectation of achievement, and comparisons to other tutoring experiences. We discuss how these findings are important for the creation, implementation, and evaluation of ITS as a mode and method of teaching and learning.

Keywords: college statistics course, intelligent tutoring systems, in vivo study, student perceptions of tutoring

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Authors: Joy Cao, Min Zhou

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Purpose: Acute Type A aortic dissection is a well-known cause of extremely high mortality rate. A highly accurate and cost-effective non-invasive predictor is critically needed so that the patient can be treated at earlier stage. Although various CFD approaches have been tried to establish some prediction frameworks, they are sensitive to uncertainty in both image segmentation and boundary conditions. Tedious pre-processing and demanding calibration procedures requirement further compound the issue, thus hampering their clinical applicability. Using the latest physics informed deep learning methods to establish an accurate and cost-effective predictor framework are amongst the main goals for a better Type A aortic dissection treatment. Methods: Via training a novel physics-informed deep residual network, with non-invasive 4D MRI displacement vectors as inputs, the trained model can cost-effectively calculate all these biomarkers: aortic blood pressure, WSS, and OSI, which are used to predict potential type A aortic dissection to avoid the high mortality events down the road. Results: The proposed deep learning method has been successfully trained and tested with both synthetic 3D aneurysm dataset and a clinical dataset in the aortic dissection context using Google colab environment. In both cases, the model has generated aortic blood pressure, WSS, and OSI results matching the expected patient’s health status. Conclusion: The proposed novel physics-informed deep residual network shows great potential to create a cost-effective, non-invasive predictor framework. Additional physics-based de-noising algorithm will be added to make the model more robust to clinical data noises. Further studies will be conducted in collaboration with big institutions such as Cleveland Clinic with more clinical samples to further improve the model’s clinical applicability.

Keywords: type-a aortic dissection, deep residual networks, blood flow modeling, data-driven modeling, non-invasive diagnostics, deep learning, artificial intelligence.

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Authors: Mina Adel Shokry Fahim, Jūratė Sužiedelytė Visockienė

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With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realisation often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.

Keywords: air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter

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Authors: Madhavi-Priya Singh, Liam Lowe, Farouk Arnaout, Ludmilla Pillay, Giordan Perez, Luke Mischker, Steve Costa

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Introduction: An Emergency Department consultant identified the failure of medical students to complete the task of clerking a patient in its entirety. As six medical students on our first clinical placement, we recognised our own failure and endeavored to examine why this failure was consistent among all medical students that had been given this task, despite our best motivations as adult learners. Aim: Our aim is to understand and investigate the elements which impeded our ability to learn and perform as medical students in the clinical environment, with reference to the prescribed task. We also aim to generate a discussion around the delivery of medical education with potential solutions to these barriers. Methods: Six medical students gathered together to have a comprehensive reflective discussion to identify possible factors leading to the failure of the task. First, we thoroughly analysed the delivery of the instructions with reference to the literature to identify potential flaws. We then examined personal, social, ethical, and cultural factors which may have impacted our ability to complete the task in its entirety. Results: Through collation of our shared experiences, with support from discussion in the field of medical education and ethics, we identified two major areas that impacted our ability to complete the set task. First, we experienced an ethical conflict where we believed the inconvenience and potential harm inflicted on patients did not justify the positive impact the patient interaction would have on our medical learning. Second, we identified a lack of confidence stemming from multiple factors, including the conflict between preclinical and clinical learning, perceptions of perfectionism in the culture of medicine, and the influence of upward social comparison. Discussion: After discussions, we found that the various factors we identified exacerbated the fears and doubts we already had about our own abilities and that of the medical education system. This doubt led us to avoid completing certain aspects of the tasks that were prescribed and further reinforced our vulnerability and perceived incompetence. Exploration of philosophical theories identified the importance of the role of doubt in education. We propose the need for further discussion around incorporating both pedagogic and andragogic teaching styles in clinical medical education and the acceptance of doubt as a driver of our learning. Conclusion: Doubt will continue to permeate our thoughts and actions no matter what. The moral or psychological distress that arises from this is the key motivating factor for our avoidance of tasks. If we accept this doubt and education embraces this doubt, it will no longer linger in the shadows as a negative and restrictive emotion but fuel a brighter dialogue and positive learning experience, ultimately assisting us in achieving our full potential.

Keywords: ethics, medical student, doubt, medical education, faith

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Authors: Junjie Zhong, Kai Hong, Lei Wang

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Passage Retrieval (PR) plays an important role in many Natural Language Processing (NLP) tasks. Traditional efficient retrieval models relying on exact term-matching, such as TF-IDF or BM25, have nowadays been exceeded by pre-trained language models which match by semantics. Though they gain effectiveness, deep language models often require large memory as well as time cost. To tackle the trade-off between efficiency and effectiveness in PR, this paper proposes Graph Passage Retriever (GraphPR), a graph-based model inspired by the development of graph learning techniques. Different from existing works, GraphPR is end-to-end and integrates both term-matching information and semantics. GraphPR constructs a passage-level graph from BM25 retrieval results and trains a GCN-like model on the graph with graph-based objectives. Passages were regarded as nodes in the constructed graph and were embedded in dense vectors. PR can then be implemented using embeddings and a fast vector-similarity search. Experiments on a variety of real-world retrieval datasets show that the proposed model outperforms related models in several evaluation metrics (e.g., mean reciprocal rank, accuracy, F1-scores) while maintaining a relatively low query latency and memory usage.

Keywords: efficiency, effectiveness, graph learning, language model, passage retrieval, term-matching model

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