Search results for: college student learning experience

Authors: Elucir Gir, Laelson Rochelle Milanês Sousa, Pedro Henrique Tertuliano Leoni, Carla Aparecida Arena Ventura, Ana Cristina de Oliveira e Silva, Renata Karina Reis

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Context and significance: Resilience is a protective agent for the physical and mental well-being of nursing professionals. Team members are constantly subjected to high levels of work stress that can negatively impact care performance and users of health services. Stress levels have been exacerbated with the COVID-19 pandemic. Objective: The aim of this study was to analyze the resilience of nursing professionals in Brazil during the COVID-19 pandemic. Method: Cross-sectional study with a quantitative approach carried out with professionals from nursing teams from all regions of Brazil. Data collection took place in the first year of the pandemic between October and December 2020. Data were obtained through an online questionnaire posted on social networks. The information collected included the sociodemographic characterization of the nursing professionals and the Brief Resilient Coping Scale was applied. Student's t-test for independent samples and analysis of variance (ANOVA) were used to compare resilience scores with sociodemographic variables. Results: 8,792 nursing professionals participated in the study, 5,767 (65.6%) were nurses, 7,437 (84.6%) were female and 2,643 (30.1%) were from the Northeast region of Brazil, 5,124 (58.8% ) had low levels of resilience. The results showed a statistically significant difference between the resilience score and the variables: professional category (p<0.001); sex (p = 0.003); age range (p<0.001); region of Brazil (p<0.001); marital status (p=0.029) and providing assistance in a field hospital (p<0.001). Conclusion: Participants in this study had, in general, low levels of resilience. There is an urgent need for actions aimed at promoting the psychological health of nursing professionals inserted in pandemic contexts. Descriptors: Psychological Resilience; Nursing professionals; COVID-19; SARSCoV-2.

Keywords: psychological resilience, nursing professionals, COVID-19, SARS-CoV-2

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Authors: A. Bagherzadhalimi, E. Di Maria

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The feedbacks obtained regarding the sense of presence from pilot users operating a Mobile Robotic presence (MRP) system to visit a simulated museum are reported in this paper. The aim is to investigate how much the perception of system’s usefulness and ease of use is affected by operators’ sense of social telepresence (presence) in the remote location. Therefore, scenarios of visiting a museum are simulated and the user operators are supposed to perform some regular tasks inside the remote environment including interaction with local users, navigation and visiting the artworks. Participants were divided into two groups, those who had previous experience of operation and interaction with a MRP system and those who never had experience. Based on the results, both groups provided different feedbacks. Moreover, there was a significant association between user’s sense of presence and their perception of system usefulness and ease of use.

Keywords: mobile robotic telepresence, museum, social telepresence, usability test

Procedia PDF Downloads 384

Authors: D Kokt, TF Dreyer

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The main objective of the study was to design a mentoring programme aimed at developing the soft skills of mentors. The mentors are all employed by a multinational corporation. The company had a mentoring plan in place that it did not yield the required results, especially related to the development of soft skills. This prompted the researchers to conduct an extensive literature review followed by a mixed methods approach to ascertain the best practices in developing the soft skills of mentors. The outcomes of the study led to the development of a structured mentoring programme using 25 modules to be completed by mentors. The design incorporated a blended modular approach using both face-to-face teaching and teaching supported by Information Communication Technology (ICT). Blended learning was ideal as the ICT component helped to minimise instructor-mentor physical contact as part of the health measures during the Covid-19 pandemic. The blended learning approach also allowed instructors and mentors an online or offline mode, so that mentors could have more time for creative and cooperative exercises. A range of delivery methodologies were spread out across the different modules to ensure mentor engagement and accelerate mentor development. This included: concept development through in-person instructor-led training sessions, concept development through virtual instructor-led training sessions, simulations and case studies, e-learning, role plays and interactive learning using mentoring toolkits; and experiential learning through application. The mentor development journey included formal modular competency assessments. All modules contained post-competency assessment consisting of 10 questions (comprising of a combination of explanatory questions and multiple-choice questions) to ensure understanding and deal with identified competency gaps. The minimum pass mark for all modular competency assessments was 80%. Mentors were allowed retake the assessment if they scored less than 80%, until they demonstrated understanding at the required level.

Keywords: mentor, mentee, soft skills, mentor development, blended learning, modular approach

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Authors: A. Degale Desta, Cheng Jian

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Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

Procedia PDF Downloads 139

Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

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Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

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Authors: Manik Sharma, Ganapathy Krishnamurthi

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The cost of some drugs and medical treatments has risen in recent years, that many patients are having to go without. A classification project could make researchers more efficient. One of the more surprising reasons behind the cost is how long it takes to bring new treatments to market. Despite improvements in technology and science, research and development continues to lag. In fact, finding new treatment takes, on average, more than 10 years and costs hundreds of millions of dollars. If successful, we could dramatically improve the industry's ability to model cellular images according to their relevant biology. In turn, greatly decreasing the cost of treatments and ensure these treatments get to patients faster. This work aims at solving a part of this problem by creating a cellular image classification model which can decipher the genetic perturbations in cell (occurring naturally or artificially). Another interesting question addressed is what makes the deep-learning model decide in a particular fashion, which can further help in demystifying the mechanism of action of certain perturbations and paves a way towards the explainability of the deep-learning model.

Keywords: cellular images, genetic perturbations, deep-learning, explainability

Procedia PDF Downloads 90

Authors: Shih-Chieh Liao, Jen-Ying Ma

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Dual protagonists game always bring a unique experience compared to the other games. This research wants to discuss whether the dual protagonists have the moving ability and special ability or not; it will affect the preference of the players. This research will focus on the single-player dual protagonists game. After the observation, we found that when players control the dual protagonists, the moving ability and special ability are a great point defining the preference of players. When players control the character, which is lack of moving ability, they often feel impatient with the inconvenient mechanism and then reduce the will to play with the character or even the game. Furthermore, the special ability is also important in the situation that there is another character to compare with. When the character is too powerful, players tend not to use the weaker one. In addition, gender is a big deal in the games. It surprisingly controls the will of play occasionally. In view of these, this research makes a single-player dual protagonists game and the dual protagonists are limited to male and female. The experiment content detected with Electrodermal Activity (EDA) includes seven different situations. (1) male and females both have the moving ability and special ability. (2) male and female both have a special ability, but female does not have the moving ability. (3) male and females both have a special ability, but the male does not have the moving ability. (4) male and female both have the moving ability, but the male does not have special ability (5) male and female both have the moving ability, but female does not have a special ability (6) male-only has the moving ability and female-only has a special ability (7) male-only has a special ability and female only has the moving ability. The experiment will evaluate the emotional changes of the subjects in those situations. The result sorted by the significance of player preference is (6)>(4)>(1)>(2)>(5)>(3)>(7). The result demonstrates that players prefer females with special abilities or males with moving abilities. The game developer could design the ability of dual protagonists based on this research. Therefore, players may have a better experience.

Keywords: biofeedback, dual protagonists, emotional responses, psychology, user experience

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Authors: Revoti Prasad Bora, Nikita Katyal, Saurabh Yadav

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Pre-processing plays an important role in various image processing applications. Most of the time due to the similar nature of images, a particular pre-processing or a set of pre-processing steps are sufficient to produce the desired results. However, in the education domain, there is a wide variety of images in various aspects like images with line-based diagrams, chemical formulas, mathematical equations, etc. Hence a single pre-processing or a set of pre-processing steps may not yield good results. Therefore, a Deep Learning based approach for dynamically selecting a relevant pre-processing technique for each image is proposed. The proposed method works as a classifier to detect hidden patterns in the images and predicts the relevant pre-processing technique needed for the image. This approach experimented for an image similarity matching problem but it can be adapted to other use cases too. Experimental results showed significant improvement in average similarity ranking with the proposed method as opposed to static pre-processing techniques.

Keywords: deep-learning, classification, pre-processing, computer vision, image processing, educational data mining

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Authors: Rebecca Laycock

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Many Urban Agriculture (UA) initiatives, including community/allotment gardens, Community Supported Agriculture, and community/social farms, depend on volunteers. However, initiatives supported or run by volunteers are often faced with a high turnover of labour as a result of the involvement of episodic volunteers (a term describing ad hoc, one-time, and seasonal volunteers), leading to challenges with maintaining project continuity and retaining skills/knowledge within the initiative. This is a notable challenge given that food growing is a knowledge intensive activity where the fruits of labour appear months or sometimes years after investment. Participatory Action Research (PAR) is increasingly advocated for in the field of UA as a solution-oriented approach to research, providing concrete results in addition to advancing theory. PAR is a cyclical methodological approach involving researchers and stakeholders collaboratively 'identifying' and 'theorising' an issue, 'planning' an action to address said issue, 'taking action', and 'reflecting' on the process. Through iterative cycles and prolonged engagement, the theory is developed and actions become better tailored to the issue. The demand for PAR in UA research means that understanding how to use PAR with episodic volunteers is of critical importance. The aim of this paper is to explore (1) the challenges of doing PAR in UA initiatives with episodic volunteers, and (2) how PAR can be harnessed to advance sustainable development of UA through theoretically-informed action. A 2.5 year qualitative PAR study on three English case study student-led food growing initiatives took place between 2014 and 2016. University UA initiatives were chosen as exemplars because most of their volunteers were episodic. Data were collected through 13 interviews, 6 workshops, and a research diary. The results were thematically analysed through eclectic coding using Computer-Assisted Qualitative Data Analysis Software (NVivo). It was found that the challenges of doing PAR with transient participants were (1) a superficial understanding of issues by volunteers because of short term engagement, resulting in difficulties ‘identifying’/‘theorising’ issues to research; (2) difficulties implementing ‘actions’ given those involved in the ‘planning’ phase often left by the ‘action’ phase; (3) a lack of capacity of participants to engage in research given the ongoing challenge of maintaining participation; and (4) that the introduction of the researcher acted as an ‘intervention’. The involvement of a long-term stakeholder (the researcher) changed the group dynamics, prompted critical reflections that had not previously taken place, and improved continuity. This posed challenges for providing a genuine understanding the episodic volunteering PAR initiatives, and also challenged the notion of what constitutes an ‘intervention’ or ‘action’ in PAR. It is recommended that researchers working with episodic volunteers using PAR should (1) adopt a first-person approach by inquiring into the researcher’s own experience to enable depth in theoretical analysis to manage the potentially superficial understandings by short-term participants; and (2) establish safety mechanisms to address the potential for the research to impose artificial project continuity and knowledge retention that will end when the research does. Through these means, we can more effectively use PAR to conduct solution-oriented research about UA.

Keywords: community garden, continuity, first-person research, higher education, knowledge retention, project management, transience, university

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Authors: Mario Gianni, Manuel A. Ruiz Garcia, Fiora Pirri

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In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV.

Keywords: Dirichlet processes, gaussian mixture models, learning motion patterns, tracked robots for urban search and rescue

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Authors: Willy Mbenza, Sho Kenjiro

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Increasing efforts are directed towards a better understanding and foreknowledge of extreme precipitation likelihood, given the adverse effects associated with their occurrence. This knowledge plays a crucial role in long-term planning and the formulation of effective emergency response. However, predicting extreme events reliably presents a challenge to conventional empirical/statistics due to the involvement of numerous variables spanning different time and space scales. In the recent time, Machine Learning has emerged as a promising tool for predicting the dynamics of extreme precipitation. ML techniques enables the consideration of both local and regional physical variables that have a strong influence on the likelihood of extreme precipitation. These variables encompasses factors such as air temperature, soil moisture, specific humidity, aerosol concentration, among others. In this study, we develop an ML model that incorporates both local and regional variables while establishing a robust relationship between physical variables and precipitation during the downscaling process. Furthermore, the model provides valuable information on the frequency and duration of a given intensity of precipitation.

Keywords: machine learning (ML), predictions, rainfall events, regional variables

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Authors: Houda Zouar, Hanane Sarnou

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In the 21st, century the concept of learners' autonomy is crucial. The concept of self-regulated learning has come forward as a result of enabling learners to direct their learning with autonomy towards academic goals achievement. Academic self-regulation is defined as the process by which learners systematically plan, monitor and asses their learning to achieve their academic established goals. In the field of English as a foreign language, teachers emphasise the role of learners’ autonomy to foster the process of English language learning. Consequently, academic self-regulation is considered as a vehicle to enhance autonomy among English language learners. However, not all learners can be equally self-regulators if not well assisted, mainly those novice pupils of basic education. For this matter, understanding the role of teachers in fostering academic self- regulation must be among the preliminary objectives in searching and developing this area. The present research work targets the role of the Algerian middle school teachers in enhancing academic self-regulation and teaching pupils how to learn, besides their role as models in the trajectory of teaching their pupils to become self-regulators. Despite the considerable endeavours in the field of educational setting on Self-Regulated Learning, the literature of the Algerian context indicates confined endeavours to undertake and divulge this notion. To go deeper into this study, a mixed method approach was employed to confirm our hypothesis. For data collection, teachers were observed and addressed by a questionnaire on their role in enhancing academic self- regulation among their pupils. The result of the research indicates that the attempts of middle school Algerian teachers are implicit and limited. This study emphasises the need to prepare English language teachers with the necessary skills to promote autonomous and self-regulator English learners.

Keywords: Algeria, English as a foreign language, middle school, self-regulation, Teachers' role

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Authors: Shaheeb Abdul Azeez, Siong-Choy Chong

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Background: Talent retention is increasingly becoming a major challenge for many industries due to the high turnover rate. Public higher learning institutions in the Maldives have a similar situation with the turnover of their employees'. This paper is to identify whether Human Resource Management (HRM) practices have any impact on employee retention in public higher learning institutions in the Maldives. Purpose: This paper aims to identify the influence of HRM practices on employee retention in public higher learning institutions in the Maldives. A total of 15 variables used in this study; 11 HRM practices as independent variables (leadership, rewards, salary, employee participation, compensation, training and development, career development, recognition, appraisal system and supervisor support); job satisfaction and motivation as mediating variables; demographic profile as moderating variable and employee retention as dependent variable. Design/Methodology/Approach: A structured self-administered questionnaire was used for data collection. A total of 300 respondents were selected as the study sample, representing the academic and administrative from public higher learning institutions using a stratified random sampling method. AMOS was used to test the hypotheses constructed. Findings: The results suggest that there is no direct effect between the independent variable and dependent variable. Also, the study concludes that no moderate effects of demographic profile between independent and dependent variables. However, the mediating effects of job satisfaction and motivation in the relationship between HRM practices and employee retention were significant. Salary had a significant influence on job satisfaction, whilst both compensation and recognition have significant influence on motivation. Job satisfaction and motivation were also found to significantly influence employee retention. Research Limitations: The study consists of many variables more time consuming for the respondents to answer the questionnaire. The study is focussed only on public higher learning institutions in the Maldives due to no participation from the private sector higher learning institutions. Therefore, the researcher is unable to identify the actual situation of the higher learning industry in the Maldives. Originality/Value: To our best knowledge, no study has been conducted using the same framework throughout the world. This study is the initial study conducted in the Maldives in this study area and can be used as a baseline for future researches. But there are few types of research conducted on the same subject throughout the world. Some of them concluded with positive findings while others with negative findings. Also, they have used 4 to 7 HRM practices as their study framework.

Keywords: human resource management practices, employee retention, motivation, job satisfaction

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Authors: Mary Deyo, Zmara Harrison

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This study reports the outcomes of an 8-week experiential learning program for 6 adults with Intellectual and Developmental Disabilities (IDD) at a day habilitation program. The intervention foci for this program include executive function, language learning in the domains of expressive, receptive, and pragmatic language, and quality of life. The interprofessional collaboration aimed at supporting adults with IDD to reach person-centered, functional goals across skill domains is critical. This study is a significant addition to the speech-language pathology literature in that it examines a therapy method that potentially meets this need while targeting domains within the speech-language pathology scope of practice. Communication therapy was provided during highly valued and meaningful hands-on learning experiences, referred to as the Garden Club, which incorporated all aspects of planting and caring for a garden as well as related journaling, sensory, cooking, art, and technology-based activities. Direct care staff and an undergraduate research assistant were trained by SLP to be impactful language guides during their interactions with participants in the Garden Club. SLP also provided direct therapy and modeling during Garden Club. Research methods used in this study included a mixed methods analysis of a literature review, a quasi-experimental implementation of communication therapy in the context of experiential learning activities, Quality of Life participant surveys, quantitative pre- post- data collection and linear mixed model analysis, qualitative data collection with qualitative content analysis and coding for themes. Outcomes indicated overall positive changes in expressive vocabulary, following multi-step directions, sequencing, problem-solving, planning, skills for building and maintaining meaningful social relationships, and participant perception of the Garden Project’s impact on their own quality of life. Implementation of this project also highlighted supports and barriers that must be taken into consideration when planning similar projects. Overall findings support the use of experiential learning projects in day habilitation programs for adults with IDD, as well as additional research to deepen understanding of best practices, supports, and barriers for implementation of experiential learning with this population. This research provides an important contribution to research in the fields of speech-language pathology and other professions serving adults with IDD by describing an interprofessional experiential learning program with positive outcomes for executive function, language learning, and quality of life.

Keywords: experiential learning, adults, intellectual and developmental disabilities, expressive language, receptive language, pragmatic language, executive function, communication therapy, day habilitation, interprofessionalism, quality of life

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Authors: Chandrakala Varatharajoo, Adelina Binti Asmawi, Nabeel Abdallah Mohammad Abedalaziz

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This study investigated the impact of inflectional and derivational morphemic analysis awareness on ESL secondary school students’ vocabulary learning strategy. The quasi-experimental study was conducted with 106 low proficiency secondary school students in two experimental groups (inflectional and derivational) and one control group. The students’ vocabulary acquisition was assessed through two measures: Morphemic Analysis Test and Vocabulary- Morphemic Test in the pretest and posttest before and after an intervention programme. Results of ANCOVA revealed that both the experimental groups achieved a significant score in Morphemic Analysis Test and Vocabulary-Morphemic Test. However, the inflectional group obtained a fairly higher score than the derivational group. Thus, the results indicated that ESL low proficiency secondary school students performed better on inflectional morphemic awareness as compared to derivatives. The results also showed that the awareness of inflectional morphology contributed more on the vocabulary acquisition. Importantly, learning inflectional morphology can help ESL low proficiency secondary school students to develop both morphemic awareness and vocabulary gain. Theoretically, these findings show that not all morphemes are equally useful to students for their language development. Practically, these findings indicate that morphological instruction should at least be included in remediation and instructional efforts with struggling learners across all grade levels, allowing them to focus on meaning within the word before they attempt the text in large for better comprehension. Also, by methodologically, by conducting individualized intervention and assessment this study provided fresh empirical evidence to support the existing literature on morphemic analysis awareness and vocabulary learning strategy. Thus, a major pedagogical implication of the study is that morphemic analysis awareness strategy is a definite boon for ESL secondary school students in learning English vocabulary.

Keywords: ESL, instruction, morphemic analysis, vocabulary

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Authors: Patricio Alberto Cullen

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Teaching Science in high school has been decreasing its quality for several years, and it is an obvious theme of discussion over more than 30 years. As a teacher of Secondary Education and a Professor of Technological University was necessary to work with some projects that attempt to articulate the different methodologies and concepts between both levels. Teaching Physics in Engineering Career is running between two waters. Disciplinary content and inconsistent training students got in high school. In the heady times facing humanity, teaching Science has become a race against time, and this is where it is worth stopping. Professor of Physics has outdated teaching tools against the relentless growth of knowledge in the Academic World. So we have raised from a pedagogical point of view the following question: Laboratory practices must continue to focus on traditional physics or should develop alternatives between old practices and new physics methodologies. Faced with this paradox, we stopped to try to answer from our experience, and our teaching and learning practice. These are one of the greatest difficulties presented in the Engineering work. The physics team will try to find new methodologies that are appealing to the population of students in the 21st century. Currently, the methodology used is question students about their personal interests. Once discovered mentioned interests, will be held some lines of action to facilitate achieving the goals.

Keywords: high school and university, level, students, physics, teaching physics

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Authors: Elisabeth Desiana Mayasari, Laurensia Aptik Evanjeli, Brigitta Erlita Tri Anggadewi

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The condition of the COVID-19 pandemic demands adaptation in various aspects of human life, including in the field of education. Teachers are expected to do distance learning or Learning From Home (LFH). The teacher said that he experienced stress, anxiety, feeling depressed, and afraid based on the interview. Learning adaptations and pandemic situations can impact the mental health of teachers, so the purpose of this study is to determine the mental health of teachers while teaching online. This research was conducted with a quantitative approach using a survey method. The subjects in this study were 330 elementary school teachers under the auspices of a foundation in Yogyakarta. Teachers' mental health was measured using the Indonesian version of The Mental Health Inventory (MHI-38), which has a reliability of 0.888. The results showed that the teachers generally had a good mental health condition marked by a lower negative aspect score than the positive aspect. In addition, the overall mental health aspect shows that some teachers have better mental health when compared to the average score, as well as higher positive aspect scores in all sub-aspects.

Keywords: mental health, teacher, COVID-19 pandemic, MHI-38

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Authors: C. Manjula, Lilly Florence

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Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: decision tree, genetic algorithm, machine learning, software defect prediction

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Authors: Daniela Velichová

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Plenty of ideas on how to adopt active learning methods in education are available nowadays. Mathematics is a subject where the active involvement of students is required in particular in order to achieve desirable results regarding sustainable knowledge and deep understanding. The present article is based on the outcomes of an Erasmus+ project DrIVE-MATH, that was aimed at developing a novel and integrated framework to teach maths classes in engineering courses at the university level. It is fundamental for students from the early years of their academic life to have agile minds. They must be prepared to adapt to their future working environments, where enterprises’ views are always evolving, where all collaborate in teams, and relations between peers are thought for the well-being of the whole - workers and company profit. This reality imposes new requirements on higher education in terms of adaptation of different pedagogical methods, such as project-based and active-learning methods used within the course curricula. Active learning methodologies are regarded as an effective way to prepare students to meet the challenges posed by enterprises and to help them in building critical thinking, analytic reasoning, and insight to the solved complex problems from different perspectives. Fostering learning-by-doing activities in the pedagogical process can help students to achieve learning independence, as they could acquire deeper conceptual understanding by experimenting with the abstract concept in a more interesting, useful, and meaningful way. Clear information about learning outcomes and goals might help students to take more responsibility for their learning results. Active learning methods implemented by the project team members in their teaching practice, eduScrum and Jigsaw in particular, proved to provide better scientific and soft skills support to students than classical teaching methods. EduScrum method enables teachers to generate a working environment that stimulates students' working habits and self-initiative as they become aware of their responsibilities within the team, their own acquired knowledge, and their abilities to solve problems independently, though in collaboration with other team members. This method enhances collaborative learning, as students are working in teams towards a common goal - knowledge acquisition, while they are interacting with each other and evaluated individually. Teams consisting of 4-5 students work together on a list of problems - sprint; each member is responsible for solving one of them, while the group leader – a master, is responsible for the whole team. A similar principle is behind the Jigsaw technique, where the classroom activity makes students dependent on each other to succeed. Students are divided into groups, and assignments are split into pieces, which need to be assembled by the whole group to complete the (Jigsaw) puzzle. In this paper, analysis of students’ perceptions concerning the achievement of deeper conceptual understanding in mathematics and the development of soft skills, such as self-motivation, critical thinking, flexibility, leadership, responsibility, teamwork, negotiation, and conflict management, is presented. Some new challenges are discussed as brought by introducing active learning methods in the basic mathematics courses. A few examples of sprints developed and used in teaching basic maths courses at technical universities are presented in addition.

Keywords: active learning methods, collaborative learning, conceptual understanding, eduScrum, Jigsaw, soft skills

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Authors: Danilo Pesce, Emilio Paolucci, Mariolina Affatato

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Looking at the future and at the post-digital era, innovations commonly tend to dismiss the old and replace it with the new. The aim of this research is to study the role that digital innovation can play alongside the information chain within the traditional sectors and the subsequent value creation opportunities that actors and stakeholders can exploit. By drawing on a wide body of literature on innovation and strategic management and by conducting a case study on the cultural heritage industry, namely Google Arts & Culture, this study shows that technology augments complements, and amplifies the way people experience their cultural interests and experience. Furthermore, the study shows a process of democratization of art since museums can exploit new digital and virtual ways to distribute art globally. Moreover, new needs arose from the 2020 pandemic that hit and forced the world to a state of cultural fasting and caused a radical transformation of the paradigm online vs. onsite. Finally, the study highlights the capabilities that are emerging at different stages of the value chain, owing to the technological innovation available in the market. In essence, this research underlines the role of Google in allowing museums to reach users worldwide, thus unlocking new mechanisms of value creation in the cultural heritage industry. Likewise, this study points out how Google provides value to users by means of increasing the provision of artworks, improving the audience engagement and virtual experience, and providing new ways to access the online contents. The paper ends with a discussion of managerial and policy-making implications.

Keywords: big data, digital platforms, digital transformation, digitization, Google Arts and Culture, stakeholders’ interests

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Authors: Maryam M. Bashir, Luai A. Ahmed, Meera R. Alshamsi, Sara Almahrooqi, Taif Alyammahi, Shooq A. Alshehhi, Waad I. Alhammadi, Fatima H. Alhammadi, Hind A. Alhosani, Rami H. Al-Rifai, Fatma Al-Maskari

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Obesity in the Middle East and North Africa (MENA) region has exponentially increased over the past five decades due to rapid urbanization and unhealthy lifestyle changes. It has been well established that overweight and obesity increase the risk of non-communicable diseases (NCDs) and are the leading cause of mortality and economic burden locally, and globally. In the United Arab Emirates (UAE), there is a growing epidemic of obesity and other chronic diseases like type 2 diabetes mellitus and cardiovascular diseases. Prevalence of overweight and obesity in UAE range up to 70% depending on the group being studied. Hence, there is a need to explore their patterns in the country for more targeted and responsive interventions. Our study aimed to explore the patterns of overweight and obesity and some self-reported chronic diseases among university students in Abu Dhabi, the capital city of UAE. A validated online self-administered questionnaire was used to collect data from UAE University (UAEU) students, 18years and above, from August to September 2021. Students’ characteristics were summarized using appropriate descriptive statistics. Overweight, obesity and self-reported chronic diseases were described and compared between male and female students using chi-square and t tests. Other associated factors were also explored in relation to overweight and obesity. All analyses were conducted using STATA statistical software version 16.1 (StataCorp LLC, College Station, TX, USA). 902 students participated in the study. 79.8% were females and mean age was 21.90 ± 5.19 years. Majority of the respondents were undergraduate students (80.71%). The prevalence of self-reported chronic diseases was 22.95%. Obesity (BMI≥30kg/m2), Diabetes Mellitus, and Asthma/Allergies were the commonest diseases (12.48%, 4.21% & 3.22%, respectively). Approximately 5% of the students reported more than one chronic disease. Out of the 833 participating students who had complete weight and height data, prevalence of overweight and obesity was 34.81% (22.33% and 12.48%, respectively). More than half of the male students (54.36%) were overweight or obese. This is significantly higher than in female students (30.56%, p=0.001). Overweight/obesity when compared to normal weight is associated with increasing mean age [23.40 vs 21.01, respectively (p=0.001)]. In addition to gender and age, being married [57.63% vs 31.05% (p=0.001)], being a postgraduate student [51.59% vs 30.92% (p=0.001)] and having two or more chronic diseases [65.85% vs 33.21% (p=0.001)] were also significantly associated with overweight/obesity. Our study showed that almost a quarter of the participating university students reported at least one chronic disease. Obesity was the commonest and more than 1 in 3 students were either overweight or obese. This shows the need for intensive health promotion and screening programs on obesity and other chronic diseases to meet the health needs of these students. This study is also a basis for further research, especially qualitative, to explore the relevant risk factors and risk groups for more targeted interventions.

Keywords: chronic disease, obesity, overweight, students, United Arab Emirates

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Authors: Yingqi Cui, Changran Huang, Raymond Lee

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In this paper, we proposed an AI Tutor using ontology and natural language process techniques to generate a computer science domain knowledge graph and answer users’ questions based on the knowledge graph. We define eight types of relation to extract relationships between entities according to the computer science domain text. The AI tutor is separated into two agents: learning agent and Question-Answer (QA) agent and developed on JADE (a multi-agent system) platform. The learning agent is responsible for reading text to extract information and generate a corresponding knowledge graph by defined patterns. The QA agent can understand the users’ questions and answer humans’ questions based on the knowledge graph generated by the learning agent.

Keywords: artificial intelligence, natural Language processing, knowledge graph, intelligent agents, QA system

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Authors: M. C. Venter, G. Hitge, S. C. Krygsman, J. Thiart

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For decades, South Africa has been planning transportation systems from a supply, rather than a demand side, perspective. In terms of parking, this relates to requiring the minimum parking provision that is enforced by city officials. Newer insight is starting to indicate that South Africa needs to re-think this philosophy in light of a new policy environment that desires a different outcome. Urban policies have shifted from reliance on the private car for access, to employing a wide range of alternative modes. Car dominated travel is influenced by various parameters, of which the availability and location of parking plays a significant role. The question is therefore, what is the right strategy to achieve the desired transport outcomes for SA. The focus of this paper is used to assess this issue with regard to parking provision, and specifically at a tertiary institution. A parking audit was conducted at the Stellenbosch campus of Stellenbosch University, monitoring occupancy at all 60 parking areas, every hour during business hours over a five-day period. The data from this survey was compared with the prescribed number of parking bays according to the Stellenbosch Municipality zoning scheme (requiring a minimum of 0.4 bays per student). The analysis shows that by providing 0.09 bays per student, the maximum total daily occupation of all the parking areas did not exceed an 80% occupation rate. It is concluded that the prevailing parking standards are not supportive of the new urban and transport policy environment, but that it is extremely conservative from a practical demand point of view.

Keywords: parking provision, parking requirements, travel behaviour, travel demand management

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Authors: Catriona Hilliard

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Food identity is synonymous with many national tourism destinations and perceptions in tourist source markets – stereotypes could include snails in France; beer in Britain and Germany; paella in Spain - and is an accepted element of national identity that can be incorporated into tourism experiences. Irish transatlantic food connections are culturally strong with diaspora subsequent generations in the US displaying an online interest in traditional Irish food, even with a twist. Back ‘home’, the value of the local indigenous experience was a specific element of the way The Gathering 2013 was promoted to the Irish diaspora, developing community interest and input to tourism. Over the past 20 years, Ireland has realized the value of its food industry to tourism. This has included the establishment of food development programmes for the hospitality industry; food festivals as a possible element of the tourist experience; and a programmes of food ambassadors to market Irish produce and to encourage service providers to understand; utilize and incorporate this into their offerings. Irish produce is being now actively marketed as part of the proposed tourism experience, to particular segment markets including transatlantic visitors. In addition, individual providers are becoming aware of the value of the market, and how to gain from it. Also, networks of food providers have developed collaborative structures of promoting their experiences to audiences, displaying a cluster approach of tourism development towards that sector. A power point presentation will look at how Irish produce contributes to tourism marketing and promotion of Ireland to America; how that may have assisted sustainable development of communities here; and hopes to elicit some discussion relating to longer term identification of Irish food, as part of tourism, for the potential benefit of the ‘locale’.

Keywords: Irish, USA, food, tourism

Procedia PDF Downloads 371

Authors: Akshat Kumar, Vidushi

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This paper identifies 27 KOIs, 26 of which are currently classified as candidates and one as false positives that have a high probability of being confirmed. For this purpose, 11 machine learning algorithms were implemented on the cumulative kepler dataset sourced from the NASA exoplanet archive; it was observed that the best-performing model was HistGradientBoosting and XGBoost with a test accuracy of 93.5%, and the lowest-performing model was Gaussian NB with a test accuracy of 54%, to test model performance F1, cross-validation score and RUC curve was calculated. Based on the learned models, the significant characteristics for confirm exoplanets were identified, putting emphasis on the object’s transit and stellar properties; these characteristics were namely koi_count, koi_prad, koi_period, koi_dor, koi_ror, and koi_smass, which were later considered to filter out the potential KOIs. The paper also calculates the Earth similarity index based on the planetary radius and equilibrium temperature for each KOI identified to aid in their classification.

Keywords: Kepler objects of interest, exoplanets, space exploration, machine learning, earth similarity index, transit photometry

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Authors: John White

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This study investigated the effects of practice schedules on learning and performance in golf putting, specifically focusing on the impact of increasing contextual interference (CI). University students (n=7) were randomly assigned to blocked, random, or increasing practice schedules. During acquisition, participants performed 135 putting trials using different weighted golf balls. The blocked group followed a specific sequence of ball weights, while the random group practiced with the balls in a random order. The increasing group started with a blocked schedule, transitioned to a serial schedule, and concluded with a random schedule. Retention and transfer tests were conducted 24 hours later. The results indicated that high levels of CI (random practice) were more beneficial for learning than low levels of CI (blocked practice). The increasing practice schedule, incorporating blocked, serial, and random practice, demonstrated advantages over traditional blocked and random schedules. Additionally, EEG was used to explore the neurophysiological effects of the increasing practice schedule.

Keywords: skill acquisition, motor control, learning, contextual interference

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Authors: Angsumalin Puntho

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Disruptive technology has had a great influence on our everyday lives and the existence of an organization. Geologists in the public sector need to keep up with digital technology and be able to work and collaborate in a more effective manner. The result from SWOT and 7S McKinsey analyses suggest that there are inadequate IT personnel, no individual digital literacy development plan, and a misunderstanding of management policies. The Office of Civil Service Commission develops digital literacy skills that civil servants and government officers should possess in order to work effectively; it consists of nine dimensions, including computer skills, internet skills, cyber security awareness, word processing, spreadsheets, presentation programs, online collaboration, graphics editors and cyber security practices; and six steps of digital literacy development including self-assessment, individual development plan, self-learning, certified test, learning reflection, and practices. Geologists can use digital literacy as a learning tool to develop themselves for better career opportunities.

Keywords: disruptive technology, digital technology, digital literacy, computer skills

Procedia PDF Downloads 92

Authors: Ngoc-Hieu Vu

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A study was conducted to estimate the record keeping, genetic selection, educational experience, and farm management effect on monthly milk yield per farm, average milk yield per cow, monthly milk revenue per farm, and monthly milk revenue per cow of dairy farms in the Southern region of Vietnam. The dataset contained 5448 monthly record collected from January 2013 to May 2015. Results showed that longer experience increased (P < 0.001) monthly milk yields and revenues. Better educated farmers produced more monthly milk per farm and monthly milk per cow and revenues (P < 0.001) than lower educated farmers. Farm that kept records on individual animals had higher (P < 0.001) for monthly milk yields and revenues than farms that did not. Farms that used hired people produced the highest (p < 0.05) monthly milk yield per farm, milk yield per cow and revenues, followed by farms that used both hire and family members, and lowest values were for farms that used family members only. Farms that used crosses Holstein in herd were higher performance (p < 0.001) for all traits than farms that used purebred Holstein and other breeds. Farms that used genetic information and phenotypes when selecting sires were higher (p < 0.05) for all traits than farms that used only phenotypes and personal option. Farms that received help from Vet, organization staff, or government officials had higher monthly milk yield and revenues than those that decided by owner. These findings suggest that dairy farmers should be training in systematic, must be considered and continuous support to improve farm milk production and revenues, to increase the likelihood of adoption on a sustainable way.

Keywords: dairy farming, education, milk yield, Southern Vietnam

Procedia PDF Downloads 307

Authors: Tung-Kuang Wu, Hsing-Pei Hsieh, Ying-Ru Meng

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Special Education in Taiwan has been facing difficulties including shortage of teachers and lack in resources. Some students need to receive special education are thus not identified or admitted. Fortunately, information technologies can be applied to relieve some of the difficulties. For example, on-line multimedia courseware can be used to assist the learning of special education students and take pretty much workload from special education teachers. However, there may exist cognitive variations between students in special or regular educations, which suggests the design of online courseware requires different considerations. This study aims to investigate the difference in focus of attention (FOA) between special and regular education students of primary school in viewing the computer screen. The study is essential as it helps courseware developers in determining where to put learning elements that matter the most on the right position of screen. It may also assist special education specialists to better understand the subtle differences among various subtypes of learning disabilities. This study involves 76 special education students (among them, 39 are students with mental retardation, MR, and 37 are students with learning disabilities, LDs) and 42 regular education students. The participants were asked to view a computer screen showing a picture partitioned into 3 × 3 areas with each area filled with text or icon. The subjects were then instructed to mark on the prior given paper sheets, which are also partitioned into 3 × 3 grids, the areas corresponding to the pictures on the computer screen that they first set their eyes on. The data are then collected and analyzed. Major findings are listed: 1. In both text and icon scenario, significant differences exist in the first preferred FOA between special and regular education students. The first FOA for the former is mainly on area 1 (upper left area, 53.8% / 51.3% for MR / LDs students in text scenario; and 53.8% / 56.8% for MR / LDs students in icons scenario), while the latter on area 5 (middle area, 50.0% and 57.1% in text and icons scenarios). 2. The second most preferred area in text scenario for students with MR and LDs are area 2 (upper-middle, 20.5%) and 5 (middle area, 24.3%). In icons scenario, the results are similar, but lesser in percentage. 3. Students with LDs that show similar preference (either in text or icons scenarios) in FOA to regular education students tend to be of some specific sub-type of learning disabilities. For instance, students with LDs that chose area 5 (middle area, either in text or icon scenario) as their FOA are mostly ones that have reading or writing disability. Also, three (out of 13) subjects in this category, after going through the rediagnosis process, were excluded from being learning disabilities. In summary, the findings suggest when designing multimedia courseware for students with MR and LDs, the essential learning elements should be placed on area 1, 2 and 5. In addition, FOV preference may also potentially be used as an indicator for diagnosing students with LDs.

Keywords: focus of attention, learning disabilities, mental retardation, on-line multimedia courseware, special education

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Authors: Nastja Utrosa, Matevz Juvancic

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Hospital buildings are complex public environments characterized by intricate functional arrangements and architectural layouts. Effective wayfinding is essential for patients, visitors, students, and staff. However, spatial orientation planning is often overlooked until after construction. While these environments meet functional needs, they frequently neglect the psychological aspects of user experience. This study investigates wayfinding within complex urban healthcare environments, focusing on the influences of spatial design, spatial cognition, and user experience. The inherent complexity of these environments, with extensive spatial dimensions and dispersed buildings, exacerbates the problem. Gradual expansions and additions contribute to disorientation and navigational difficulties for users. Effective route guidance in urban healthcare settings has become increasingly crucial. However, research on the environmental elements that influence wayfinding in such environments remains limited. To address this gap, we conducted a study at the University Medical Centre Ljubljana (UMCL), Slovenia's largest university hospital. Using a questionnaire, we assessed how individuals' perceptions and use of outdoor hospital spaces with a diverse sample (n=179). We evaluated the area’s usability by analyzing visit frequency, stops, modes of arrival, and parking patterns and examined the visitors' age distribution. Additionally, we investigated spatial aids and the use of color as an orientation element at three specific locations within the medical center. Our study explored the impact of color on entrance selection and the effectiveness of warm versus cool colors for wayfinding. Our findings highlight the significance of graphic adjustments in shaping perceptions of hospital outdoor spaces. Most participants preferred visually organized entrances, underscoring the importance of effective visual communication. Implementing these adaptations can substantially enhance the user experience, reducing stress and increasing satisfaction in hospital environments.

Keywords: hospital layout design, healthcare facilities, wayfinding, navigational aids, spatial orientation, color, signage

Procedia PDF Downloads 12