Search results for: real-world learning experiences

Authors: Nihal Esam Abuzinadah, Areej Abbas Malibari, Arwa Abdulaziz Allinjawi, Paul Krause

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Deaf and hearing-impaired students face many obstacles throughout their education, especially with learning applied sciences such as computer programming. In addition, there is no clear signs in the Arabic Sign Language that can be used to identify programming logic terminologies such as while, for, case, switch etc. However, hearing disabilities should not be a barrier for studying purpose nowadays, especially with the rapid growth in educational technology. In this paper, we develop an Avatar based system to teach computer programming to deaf and hearing-impaired students using Arabic Signed language with new signs vocabulary that is been developed for computer programming education. The system is tested on a number of high school students and results showed the importance of visualization in increasing the comprehension or understanding of concepts for deaf students through the avatar.

Keywords: hearing-impaired students, isolation, self-esteem, learning difficulties

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Authors: Praveen S. Muthukumarana, Achala C. Aponso

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A large percentage of global crop harvest is lost due to crop diseases. Timely identification and treatment of crop diseases is difficult in many developing nations due to insufficient trained professionals in the field of agriculture. Many crop diseases can be accurately diagnosed by visual symptoms. In the past decade, deep learning has been successfully utilized in domains such as healthcare but adoption in agriculture for plant disease detection is rare. The literature shows that models trained with popular datasets such as PlantVillage does not generalize well on real world images. This paper attempts to find out how to make plant disease identification models that generalize well with real world images.

Keywords: agriculture, convolutional neural network, deep learning, plant disease classification, plant disease detection, plant disease diagnosis

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Authors: Karina Zaccari, Ernesto Cordeiro Marujo

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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research

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Authors: Joan Casas-Roma

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Technology has become one of the main channels through which people engage in most of their everyday activities; from working to learning, or even when socializing, technology often acts as both an enabler and a mediator of such activities. Moreover, the affordances and interactions created by those technological tools determine the way in which the users interact with one another, as well as how they relate to the relevant environment, thus favoring certain kinds of actions and behaviors while discouraging others. In this regard, virtue ethics theories place a strong focus on a person's daily practice (understood as their decisions, actions, and behaviors) as the means to develop and enhance their habits and ethical competences --such as their awareness and sensitivity towards certain ethically-desirable principles. Under this understanding of ethics, this set of technologically-enabled affordances and interactions can be seen as the possibility space where the daily practice of their users takes place in a wide plethora of contexts and situations. At this point, the following question pops into mind: could these affordances and interactions be shaped in a way that would promote behaviors and habits basedonethically-desirable principles into their users? In the field of game design, the MDA framework (which stands for Mechanics, Dynamics, Aesthetics) explores how the interactions enabled within the possibility space of a game can lead to creating certain experiences and provoking specific reactions to the players. In this sense, these interactions can be shaped in ways thatcreate experiences to raise the players' awareness and sensitivity towards certain topics or principles. This research brings together the notions of technological affordances, the notions of practice and practical wisdom from virtue ethics, and the MDA framework from game design in order to explore how the possibility space created by technological interactions can be shaped in ways that enable and promote actions and behaviors supporting certain ethically-desirable principles. When shaped accordingly, interactions supporting certain ethically-desirable principlescould allow their users to carry out the kind of practice that, according to virtue ethics theories, provides the grounds to develop and enhance their awareness, sensitivity, and ethical reasoning capabilities. Moreover, and because ethical practice can happen collaterally in almost every context, decision, and action, this additional layer could potentially be applied in a wide variety of technological tools, contexts, and functionalities. This work explores the theoretical background, as well as the initial considerations and steps that would be needed in order to harness the potential ethically-desirable benefits that technology can bring, once it is understood as the space where most of their users' daily practice takes place.

Keywords: ethics, design methodology, human-computer interaction, philosophy of technology

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Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

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This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

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Authors: Theresa Veach, Erin Crisp

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It has been shown that traditional students flourish as a function of several factors including level of academic challenge, student/faculty interactions, active/collaborative learning, enriching educational experiences, and supportive campus environment. With the increase in demand for remote or online courses, factors that result in academic flourishing in the virtual classroom have become more crucial to understand than ever before. This study seeks to give insight into those factors that impact student learning, overall student wellbeing, and flourishing among college students enrolled in an online program. 4160 unique students participated in the completion of End of Course Survey (EOC) before final grades were released. Quantitative results from the survey are used by program directors as a measure of student satisfaction with both the curriculum and the faculty. In addition, students also submitted narrative comments in an open comment field. No prompts were given for the comment field on the survey. The purpose of this analysis was to report on the qualitative data available with the goal of gaining insight into what matters to students. Survey results from July 1st, 2016 to December 1st, 2016 were compiled into spreadsheet data sets. The analysis approach used involved both key word and phrase searches and reading results to identify patterns in responses and to tally the frequency of those patterns. In total, just over 25,000 comments were included in the analysis. Preliminary results indicate that it is the professor-student relationship, frequency of feedback and overall engagement of both instructors and students that are indicators of flourishing in college programs offered in an online format. This qualitative study supports the notion that college students flourish with regard to 1) education, 2) overall student well-being and 3) program satisfaction when overall engagement of both the instructor and the student is high. Ways to increase engagement in the online college environment were also explored. These include 1) increasing student participation by providing more project-based assignments, 2) interacting with students in meaningful ways that are both high in frequency and in personal content, and 3) allowing students to apply newly acquired knowledge in ways that are meaningful to current life circumstances and future goals.

Keywords: college, engagement, flourishing, online

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Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

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Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

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Authors: Abdelmenen Abobghala, Mahmud Ahmed, Mohamed Alwaheshi, Anwar Fanan, Meftah Mehdawi, Ahmed Abuhatira

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This research aims to predict academic performance and identify weak points in students to aid teachers in understanding their learning needs. Both quantitative and qualitative methods are used to identify difficult test items and the factors causing difficulties. The study uses interventions like focus group discussions, interviews, and action plans developed by the students themselves. The research questions explore the predictability of final grades based on mock exams and assignments, the student's response to action plans, and the impact on learning performance. Ethical considerations are followed, respecting student privacy and maintaining anonymity. The research aims to enhance student engagement, motivation, and responsibility for learning.

Keywords: prediction, academic performance, weak points, understanding, learning, quantitative methods, qualitative methods, formative assessments, feedback, emotional responses, intervention, focus group discussion, interview, action plan, student engagement, motivation, responsibility, ethical considerations

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Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: Asma Ameur, Dhafer Malouche

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Hotel competitiveness research is an essential phase of the marketing strategy for any hotel. Certainly, knowing the hotels' competitors helps the hotelier to grasp its position in the market and the citizen to make the right choice in picking a hotel. Thus, competitiveness is an important indicator that can be influenced by various factors. In fact, the issue of competitiveness, this ability to cope with competition, remains a difficult and complex concept to define and to exploit. Therefore, the purpose of this article is to make an exploratory study to calculate a competitiveness indicator for hotels. Further on, this paper makes it possible to determine the criteria of direct or indirect effect on the image and the perception of a hotel. The actual research is used to look into the right model for hotel ‘competitiveness. For this reason, we exploit different theoretical contributions in the field of machine learning. Thus, we use some statistical techniques such as the Principal Component Analysis (PCA) to reduce the dimensions, as well as other techniques of statistical modeling. This paper presents a survey covering of the techniques and methods in hotel competitiveness research. Furthermore, this study allows us to deduct the significant variables that influence the determination of hotel’s competitors. Lastly, the discussed experiences in this article found that the hotel competitors are influenced by several factors with different rates.

Keywords: competitiveness, e-reputation, hotels' competitors, online hotel’ review, principal component analysis, statistical modeling

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Authors: Erfaneh Razavipour Naghani, Masoud Kianpour

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Human dignity is a subjective concept indicating an inner feeling of worth which depends on one’s perceptions and life experiences. Yet the notion is also very much under the influence of societal and cultural factors. Scholars have identified human dignity as a context-based concept that lies at the intersection of culture, gender, religion, and individual characteristics. Migration may constitute an individual or collective strategy for people seeking to situations that bolster rather than undermine their human dignity. Through the use of a phenomenological method, this study will explore how Iranian students in Canada perceive human dignity through such values and characteristics as honor, respect, self-determination, self-worth, autonomy, freedom, love, and equality in Canada as compared to their perceptions of the same in Iran. In-depth interviewing will be used to collect data from Iranian students who have lived in Canada for at least two years. The aim is to discover which essential themes constitute participants’ understanding of human dignity and how this understanding compares to their pre-Canadian experience in Iran. We will use criterion sampling as our sampling method. This study will clarify how being exposed to a different culture can affect perceptions of human dignity among university students.

Keywords: Canada, human dignity, Iran, migration, university students

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Authors: Ratima Damkham, Natasha Dejdumrong, Priyakorn Pusawiro

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This paper explored the solution in game design to help game designers in the educational game designing using digital educational game model canvas (DEGMC) and digital educational game form (DEGF) based on Outcome-based Education program. DEGMC and DEGF can help designers develop an overview of the game while designing and planning their own game. The way to clearly assess players’ ability from learning outcomes and support their game learning design is by using the tools. Designers can balance educational content and entertainment in designing a game by using the strategies of the Business Model Canvas and design the gameplay and players’ ability assessment from learning outcomes they need by referring to the Constructive Alignment. Furthermore, they can use their design plan in this research to write their Game Design Document (GDD). The success of the research was evaluated by four experts’ perspectives in the education and computer field. From the experiments, the canvas and form helped the game designers model their game according to the learning outcomes and analysis of their own game elements. This method can be a path to research an educational game design in the future.

Keywords: constructive alignment, constructivist theory, educational game, outcome-based education

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Authors: Mariyan Tomov

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Bulgarian working class immigrants are increasingly concerned with UK’s recent immigration policies in the context of Brexit. The new ID system would exclude many people currently working in Britain and would break the usual immigrant travel patterns. Post-Brexit Britain would aim to repeal seasonal immigrants. Measures for keeping long-term and life-long immigrants have been implemented and migrants that aim to remain in Britain and establish a household there would be more privileged than temporary or seasonal workers. The results of such regulating mechanisms come at the expense of migrants’ longings for a ‘normal’ existence, especially for those coming from Central and Eastern Europe. Based on in-depth interviews with Bulgarian working class immigrants, the study found out that their major concerns following the decision of the UK to leave the EU are related with the freedom to travel, reside and work in the UK. Furthermore, many of the interviewed women are concerned that they could lose some of the EU's fundamental rights, such as maternity and protection of pregnant women from unlawful dismissal. The soar of commodity prices and university fees and the limited access to public services, healthcare and social benefits in the UK, are also subject to discussion in the paper. The most serious problem, according to the interview, is that the attitude towards Bulgarians and other immigrants in the UK is deteriorating. Both traditional and social media in the UK often portray the migrants negatively by claiming that they take British job positions while simultaneously abuse the welfare system. As a result, the Bulgarian migrants often face social exclusion, which might have negative influence on their health and welfare. In this sense, some of the interviewed stress on the fact that the most important changes after Brexit must take place in British society itself. The aim of the proposed study is to provide a better understanding of the Bulgarian migrants’ economic, health and sociocultural experience in the context of Brexit. Methodologically, the proposed paper leans on: 1. Analysing ethnographic materials dedicated to the pre- and post-migratory experiences of Bulgarian working class migrants, using SPSS. 2. Semi-structured interviews are conducted with more than 50 Bulgarian working class migrants [N > 50] in the UK, between 18 and 65 years. The communication with the interviewees was possible via Viber/Skype or face-to-face interaction. 3. The analysis is guided by theoretical frameworks. The paper has been developed within the framework of the research projects of the National Scientific Fund of Bulgaria: DCOST 01/25-20.02.2017 supporting COST Action CA16111 ‘International Ethnic and Immigrant Minorities Survey Data Network’.

Keywords: Bulgarian migrants in UK, economic experiences, sociocultural experiences, Brexit

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Authors: Kristen Emory, Jerry Flores

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Purpose: We explore the unique and underrepresented experiences of Indigenous women living in Toronto, Canada, during the first year of the COVID-19 pandemic. The purpose of this study is to better document the impacts of COVID-19 on the mental health and well-being of Indigenous women in Toronto, Canada, in order to better understand unmet needs, as well as lay the groundwork for more targeted research and potential interventions based on these needs. Background: It has been fairly well documented that the COVID-19 pandemic has increased mental health concerns among various populations globally. There have also been numerous studies indicating increases in substance use and abuse in response to the stress of the pandemic. There is also evidence that the COVID-19 pandemic has disproportionately impacted a variety of historically marginalized populations in Canada, the US, and globally, including Indigenous populations. While these studies provide some insight into how the COVID-19 pandemic is impacting the global population, much less is known about the lived experiences of Indigenous populations during the time of COVID-19. Better understanding these experiences will allow public health professionals, governments, and non-governmental organizations better combat health inequities related to the pandemic. Methods: In-depth qualitative semi-structured virtual (due to COVID-19) interviews with 13 Indigenous women were conducted during the first year of the COVID-19 pandemic (2020). Interviews were recorded, transcribed, and analyzed by team members using Dedoose qualitative analysis software. Findings: COVID-19 negatively affected Indigenous females identifying participants’ mental health and corresponding reported increases in substance use. In addition to the daily stress of the unpredictability of life in the time of the COVID-19 pandemic, participants cited job loss, economic concerns, homeschooling, and lack of access to medical resources as primary factors in increasing their stress and decreasing mental health and wellbeing. In response to these stressors, a majority of participants cited coping mechanisms such as increased substance use to help deal with the uncertainty. In particular, alcohol and tobacco emerged as coping mechanisms to help participants cope with stress related to the pandemic (as well as its social and economic toll on respondents' lives). We will present qualitative data to be presented, including participant direct quotes, explaining their experiences with COVID-19, mental health, and increased substance use, as well as analysis and synthesis with the existing scientific evidence base. Conclusion: This research is among the good studies to our knowledge that scientifically explore the impact of COVID-19 on mental health and well-being and corresponding increases in reported substance use.

Keywords: mental health, covid-19, indigenous, inequity, anxiety, depression, stress

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Authors: Alyona Cerfontyne, Levita D'Souza, Lefteris Patlamazoglou

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Unlike adoption and donor-assisted reproduction, misattributed paternity occurring within the context of spontaneous conception and outside of formally recognised practices of having a child remains largely an understudied phenomenon. In adulthood, to discover misattributed paternity, i.e., that the man you call your father is not related to you genetically, can have profound implications for everyone affected. Until the advent of direct-to-consumer DNA testing 20 years ago, such discoveries were relatively rare. Despite the growing number of individuals uncovering their biogenetic paternity through genetic testing, there is very limited research on misattributed paternity from the perspective of adult children affected by it. No research exists on how to support these individuals through counselling post-discovery. Framed as insider action research, this study aimed to explore the perceived psychosocial consequences of misattributed paternity discoveries and coping strategies used by individuals who discover their misattributed paternity status in adulthood. In total, 12 individuals with misattributed paternity participated in semi-structured interviews in July-August 2022. The collected data was analysed using reflexive thematic analysis. The study’s results indicate that discovering misattributed paternity in adulthood can be likened to a watershed moment forever changing the trajectory of one’s life. Psychological experiences consistent with trauma, as well as grief and loss, re-evaluation of close family relationships, reestablishment of one’s identity, as well as experiencing a profound need to belong are the key themes emerging from the analysis of psychosocial experiences. Post-discovery, individuals with misattributed paternity employ a wide range of emotional and problem-focused coping strategies, amongst which seeking connection with those who understand, searching for information on the new biogenetic family and finding new meanings to life are most prominent. The study contributes both to the academic and practical knowledge of experiences of misattributed paternity and highlights the importance of further research on the topic.

Keywords: discovery of misattributed paternity, misattributed paternity, paternal discrepancy, psychosocial consequences, coping

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Authors: Dianah Kitiabi

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This study examines the experiences of graduate international students in Study Abroad (SA) in order to understand the evolution of their second language (L2) skills during the period of their sojourn abroad. The study documents students’ perspectives through analysis of interview data situated within the context of their overall SA experience. Based on a phenomenological approach, the study focuses on a sample of nine graduate students with at least one year of SA experience. Gass & Mackey’s (2007) interaction approach and Vygotsky’s (1962) sociocultural theory help frame the study within the discourse of second language acquisition (SLA) in SA, such as to highlight the effects of SA on L2 skills of advanced-level learners. The findings of the study are first presented as individual case vignettes where students’ interpretations of their personal experiences are described in entirety, followed by an analysis across the cases that highlight emergent themes. The results of this study show that the linguistic outcomes of international students studying abroad are highly individualized. Although students reported to have improved some of their L2 skills, they also reported a lack of improvement in other L2 skills, most of which differed by case. What emerges is that besides contextual factors, students’ pre-program exposure to L2, interactions with NSs, frequency of L2 use in context, and personal beliefs contribute to their linguistic gains in SA.

Keywords: context, interaction, second language acquisition, study abroad

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Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Josef Steinberger, Vanni Zavarella, Hristo Tanev

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Natural disasters cause tens of thousands of victims and massive material damages. We refer to all those events caused by natural disasters, such as damage on people, infrastructure, vehicles, services and resource supply, as micro events. This paper addresses the problem of micro - event detection in online media sources. We present a natural language grammar learning algorithm and apply it to online news. The algorithm in question is based on distributional clustering and detection of word collocations. We also explore the extraction of micro-events from social media and describe a Twitter mining robot, who uses combinations of keywords to detect tweets which talk about effects of disasters.

Keywords: online news, natural language processing, machine learning, event extraction, crisis computing, disaster effects, Twitter

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Authors: Sara Corvino

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The aim of this study is to explore the potential of inclusive learning and assessment strategies to foster students' engagement with historical debates surrounding the field of graphic design. The goal is to respond to the diversity of L4 Graphic Design students, at Nottingham Trent University, in a way that instead of 'lowering standards' can benefit everyone. This research tests, measures, and evaluates the impact of a specific intervention, an assessment task, to develop students' critical visual analysis skills and stimulate a deeper engagement with the subject matter. Within the action research approach, this work has followed a case study research method to understand students' views and perceptions of a specific project. The primary methods of data collection have been: anonymous electronic questionnaire and a paper-based anonymous critical incident questionnaire. NTU College of Business Law and Social Sciences Research Ethics Committee granted the Ethical approval for this research in November 2019. Other methods used to evaluate the impact of this assessment task have been Evasys's report and students' performance. In line with the constructivist paradigm, this study embraces an interpretative and contextualized analysis of the collected data within the triangulation analytical framework. The evaluation of both qualitative and quantitative data demonstrates that active learning strategies and the disruption of thinking patterns can foster greater students' engagement and can lead to meaningful learning.

Keywords: active learning, assessment for learning, graphic design, higher education, student engagement

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Authors: P. Halder, A. Zaman

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It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

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Authors: Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty

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A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs, and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine-learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient but not the magnitude. A neural network with two hidden layers were then used to learn the coefficient magnitudes along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.

Keywords: quantum energy calculations, atomic orbitals, electron-repulsion integrals, ensemble machine learning, random forests, neural networks, feature extraction

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Authors: Lily Ye

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The contemporary fashion industry has witnessed the global rise of Chinese fashion designers. China plays more and more important role in this sector globally. One of the key debates in contemporary time is the conception of Chinese fashion. A close look at previous discussions on Chinese fashion reveals that most of them are explored through the lens of cultural knowledge and assumptions, using the dichotomous models of East and West. The results of these studies generate an essentialist and orientalist notion of Chinoiserie and Chinese fashion, which sees individual designers from China as undifferential collective members marked by a unique and fixed set of cultural scripts. This study challenges this essentialist conceptualization and brings fresh insights to the discussion of Chinese fashion identity against the backdrop of globalisation. Different from a culturalist approach to researching Chinese fashion, this paper presents an alternative position to address the research agenda through the mobilisation of Giddens’ (1991) theory of reflexive identity formation, privileging individuals’ agency and reflexivity. This approach to the discussion of identity formation not only challenges the traditional view seeing identity as the distinctive and essential characteristics belonging to any given individual or shared by all members of a particular social category or group but highlights fashion designers’ strategic agency and their role as fashion activist. This study draws evidence from a textual analysis of published stories of a group of established Chinese designers such as Guo Pei, Huishan Zhang, Masha Ma, Uma Wang, and Ma Ke. In line with Giddens’ concept of 'reflexive project of the self', this study uses a narrative methodology. Narratives are verbal accounts or stories relating to experiences of Chinese fashion designers. This approach offers the fashion designers a chance to 'speak' for themselves and show the depths and complexities of their experiences. It also emphasises the nuances of identity formation in fashion designers, whose experiences cannot be captured in neat typologies. Thematic analysis (Braun and Clarke, 2006) is adopted to identify and investigate common themes across the whole dataset. At the centre of the analysis is individuals’ self-articulation of their perceptions, experiences and themselves in relation to culture, fashion and identity. The finding indicates that identity is constructed around anchors such as agency, cultural hybridity, reflexivity and sustainability rather than traditional collective categories such as culture and ethnicity. Thus, the old East-West dichotomy is broken down, and essentialised social categories are challenged by the multiplicity and fragmentation of self and cultural hybridity created within designers’ 'small narratives'.

Keywords: Chinoiserie, fashion identity, fashion activism, narrative inquiry

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Authors: Fu-Chi Chuang, Yu-Liang, Chang, Wen-Der Wang

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Our environment has been contaminated by many artificial chemicals, such as plastics, pesticides. Many of them have hormone-like activity and are classified as 'environmental hormone (also named endocrine disruptors)'. These chemicals interfere with or mimic hormones have adverse effects that persist into adulthood. Environmental education is an important way to teach students to become engaged in real-world issues that transcend classroom walls. Elementary education is the first stage to perform environmental education and it is an important component to help students develop adequate environmental knowledge, attitudes, and behavior. However, elementary teachers' knowledge plays a critical role in this mission. Therefore, we use a questionnaire to survey the knowledge of environmental hormone of elementary school teachers and their learning motivation of the environmental hormone-regarding knowledge. We collected 218 questionnaires from Taiwanese elementary teachers and the results indicate around 73% of elementary teachers do not have enough knowledge about environmental hormones. Our results also reveal the in-service elementary teachers’ learning motivation of environmental hormones knowledge is positively enhanced once they realized their insufficient cognitive ability of environmental hormones. We believe our study will provide the powerful reference for Ministry of Education to set up the policy of environmental education to enrich all citizens sufficient knowledge of the effects of the environmental hormone on organisms, and further to enhance our correct environmental behaviors.

Keywords: elementary teacher, environmental hormones, learning motivation, questionnaire

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Authors: Sachin Nagargoje

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Complete labeled data is often difficult to obtain in a practical scenario. Even if one manages to obtain the data, the quality of the data is always in question. In shopping vertical, offers are the input data, which is given by advertiser with or without a good quality of information. In this paper, an author investigated the possibility of using a very simple Semi-supervised learning approach to increase the recall of unhealthy offers (has badly written Offer Title or partial product details) in shopping vertical domain. The author found that the semisupervised learning method had improved the recall in the Smart Phone category by 30% on A=B testing on 10% traffic and increased the YoY (Year over Year) number of impressions per month by 33% at production. This also made a significant increase in Revenue, but that cannot be publicly disclosed.

Keywords: semi-supervised learning, clustering, recall, coverage

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Authors: Eunice H. Li

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This e-poster explores some of the issues that are linked to Open Educational Resources (OER). It describes how OER is explained by experts in the field and relates its value in attaining and using knowledge. ‘Open', 'open pedagogy', self-direction, freedom, and autonomy are the main issues identified for the discussion. All of these issues make essential contributions to OER in one way or another. Nevertheless, there are seemingly areas of contentions with regard to applying these concepts in teaching and learning practices. For this e-Poster, it is the teaching-learning aspects of OER that it is primarily concerned with. The basis for the discussion comes from a 2013 critique of OER presented by Jeremy Knox of the University of Edinburgh, tutor of the MSc in Digital Education Programme. This discussion is also supported by the analysis of other research work and papers in this area. The general view on OER is that it is a useful tool for the advancement of learner-centred models of education, but in whatever context, pedagogy cannot be diminished and overlooked. It should take into consideration how to deal with the issues identified above in order to allow learners to gain full benefit from OER.

Keywords: open, pedagogy, e-learning technologies, autonomy, knowledge

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Authors: Eric Agrie Ambele

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The norms and models for learning pronunciation in relation to the teaching and learning of English pronunciation are key issues nowadays in English Language Teaching in ESL contexts. This paper discusses these issues based on a study on the attitudes of some Francophone university students in Cameroon towards three English accents spoken in Cameroon: Cameroon Francophone English (CamFE), Cameroon English (CamE), and Hyperlectal Cameroon English (near standard British English). With the desire to know more about the treatment that these English accents receive among these students, an aspect that had hitherto received little attention in the literature, a language attitude questionnaire, and the matched-guise technique was used to investigate this phenomenon. Two methods of data analysis were employed: (1) the percentage count procedure, and (2) the semantic differential scale. The findings reveal that the participants’ attitudes towards the selected accents vary in degree. Though Hyperlectal CamE emerged first, CamE second and CamFE third, no accent, on average, received a negative evaluation. It can be deduced from this findings that, first, CamE is gaining more and more recognition and can stand as an autonomous accent; second, that the participants all rated Hyperlectal CamE higher than CamE implies that they would be less motivated in a context where CamE is the learning model. By implication, in the teaching of English pronunciation to francophone learners learning English in Cameroon, Hyperlectal Cameroon English should be the model.

Keywords: teaching pronunciation, English accents, Francophone learners, attitudes

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Authors: Daniel Asiamah Ameyaw

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This research attempts to investigate how lecturer evaluation makes a difference in enhancing higher education teaching and learning processes. The research questions to guide this research work states first as, “What are the perspectives on the difference made by evaluating academic teachers in order to enhance higher education teaching and learning processes?” and second, “What are the implications of the findings for Policy and Practice?” Data for this research was collected mainly through interviewing and partly documents review. Data analysis was conducted under the framework of grounded theory. The findings showed that for individual lecturer level, lecturer evaluation provides a continuous improvement of teaching strategies, and serves as source of data for research on teaching. At the individual student level, it enhances students learning process; serving as source of information for course selection by students; and by making students feel recognised in the educational process. At the institutional level, it noted that lecturer evaluation is useful in personnel and management decision making; it assures stakeholders of quality teaching and learning by setting up standards for lecturers; and it enables institutions to identify skill requirement and needs as a basis for organising workshops. Lecturer evaluation is useful at national level in terms of guaranteeing the competencies of graduates who then provide the needed manpower requirement of the nation. Besides, it mentioned that resource allocation to higher educational institution is based largely on quality of the programmes being run by the institution. The researcher concluded, that the findings have implications for policy and practice, therefore, higher education managers are expected to ensure that policy is implemented as planned by policy-makers so that the objectives can successfully be achieved.

Keywords: academic quality, higher education, lecturer evaluation, teaching and learning processes

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Authors: Azusa Katsumata

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Where there is collaboration between universities and companies in the educational context, companies seek ‘knowledge’ from universities and provide a ‘place of practice’ to them. Several universities have introduced activities aimed at the mutual enlightenment of a diversity of people in career education. However, several programs emphasize on delivering results, and on practicing the prepared materials as planned. Few programs focus on unexpected failures and setbacks. This way of learning is important in career education so that classmates can help each other, overcome difficulties, draw out each other’s strengths, and learn from them. Seijo University in Tokyo offered Tokyo Tourism, a Project-Based Learning course, as a first-year career education course until 2016. In cooperation with a travel agency, students participate in planning actual tourism products for foreigners visiting Japan, undertake tours serving as guides. This paper aims to study the 'learning platform' created by a series of processes such as the fieldwork, planning tours, the presentation, selling the tourism products, and guiding the tourists. We conducted a questionnaire to measure the development of work-related skills in class. From the results of the questionnaire, we can see, in the example of this class, that students demonstrated an increased desire to be pro-active and an improved motivation to learn. Students have not, however, acquired policy or business skills. This is appropriate for first-year careers education, but we need to consider how this can be incorporated into future courses. In the questionnaire filled out by the students after the class, the following results were found. Planning and implementing travel products while learning from each other, and helping the teams has led to improvements in the student workforce. This course is a collaborative project between Japanese universities and the 2020 Tokyo Olympics and Paralympic Games committee.

Keywords: university career education, platform of learning, project-based learning, collaboration between university and company

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Authors: Walid Cherif

Abstract:

Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

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Authors: Hari Sharan Pandit

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Mathematics teaching in Nepal has been centralized and guided by the notion of transfer of knowledge and skills from teachers to students. The overemphasis on an algorithm-centric approach of mathematics teaching and the focus on ‘rote–learning’ as the ultimate way of solving mathematical problems since the early years of schooling have been creating severe problems in school-level mathematics in Nepal. In this context, the author argues that students should learn real-world mathematical problems through various interesting, creative and collaborative, as well as artistic and alternative ways of knowing. The collaboration-incorporated pedagogy is an distinct pedagogical approach that offers a better alternative as an integrated and interdisciplinary approach to learning that encourages students to think more broadly and critically about real-world problems. The paper, as a summarized report of action research designed, developed and implemented by the author, focuses on the needs and usefulness of collaboration-incorporated pedagogy in the Nepali context to make mathematics teaching more meaningful for producing creative and critical citizens. This paper is useful for mathematics teachers, teacher educators and researchers who argue on arts integration in mathematics teaching.

Keywords: algorithm-centric, rote-learning, collaboration - incorporated pedagogy, action research

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