Search results for: learning assessment

Authors: Ali Y Al-Attraqchi, P. Rajeev, M. Javad Hashemi, Riadh Al-Mahaidi

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This paper presents a probabilistic incremental dynamic analysis (IDA) of a full reinforced concrete building subjected to column loss scenario for the assessment of progressive collapse. The IDA is chosen to explicitly account for uncertainties in loads and system capacity. Fragility curves are developed to predict the probability of progressive collapse given the loss of one or more columns. At a broader scale, it will also provide critical information needed to support the development of a new generation of design codes that attempt to explicitly quantify structural robustness.

Keywords: fire, nonlinear incremental dynamic analysis, progressive collapse, structural engineering

Procedia PDF Downloads 269

Authors: Suzanne El Takach

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The research study explored pre-service teachers’ attitudes towards 6 courses taught in 3rd till 6th semesters at the Faculty of Education, Lebanese University, during the academic year 2015-2016. They assessed science teaching courses that are essential for teacher preparation for Science at the primary and elementary level. These courses were: Action Research I and II in Teaching Science, New trends in Teaching Science, Teaching Science I and II for the elementary level and Teaching Science for Early Childhood Education. Qualitative and Quantitative Data were gathered from a) a survey questionnaire consisting of 23 closed-ended items; some were of Likert scale type, that aimed at collecting students’ opinions on courses, in terms of teaching, assessment and class interaction (N=102 respondents) and b) a second questionnaire of 10 questions was disseminated on a sample of 39 students in their last semester in science and Mathematics, in order to know more about students’ skills gained, suggestions for new courses and improvement. Students were satisfied with science teaching courses and they have admitted that they gained a good pedagogical content knowledge, such as, lesson planning, students’ misconceptions, and use of various teaching and assessment strategies.

Keywords: assessment in higher education, LMD program, pre-service teachers’ attitudes, pre-PCK skills

Procedia PDF Downloads 148

Authors: Edemealem Desalegn Kingawa, Kafte Tasew Timkete, Mekashaw Girmaw Abebe, Terefe Feyisa, Abiyot Bitew Mihretie, Senait Teklemarkos Haile

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Currently, hate speech is a growing challenge for society, individuals, policymakers, and researchers, as social media platforms make it easy to anonymously create and grow online friends and followers and provide an online forum for debate about specific issues of community life, culture, politics, and others. Despite this, research on identifying and detecting hate speech is not satisfactory performance, and this is why future research on this issue is constantly called for. This paper provides a systematic review of the literature in this field, with a focus on approaches like word embedding techniques, machine learning, deep learning technologies, hate speech terminology, and other state-of-the-art technologies with challenges. In this paper, we have made a systematic review of the last six years of literature from Research Gate and Google Scholar. Furthermore, limitations, along with algorithm selection and use challenges, data collection, and cleaning challenges, and future research directions, are discussed in detail.

Keywords: Amharic hate speech, deep learning approach, hate speech detection review, Afaan Oromo hate speech detection

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Authors: Ahmed Hariri

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E-Learning has become a necessity in the advanced education. It is easier for the student and teacher communication also it speed up the process with less time and less effort. With the progress and the enormous development of distance education must keep up with this age of making a website that allows students and teachers to take all the advantages of advanced education. In this regards, we developed University Short courses web application which is specially designed for Faculty of computing and information technology, Rabigh, Kingdom of Saudi Arabia. After an elaborate review of the current state-of-the-art methods of teaching and learning, we found that instructors deliver extra short courses and workshop to students to enhance the knowledge of students. Moreover, this process is completely manual. The prevailing methods of teaching and learning consume a lot of time; therefore in this context, University Short courses web application will help to make process easy and user friendly. The site allows for students can view and register short courses online conducted by instructor also they can see courses starting dates, finishing date and locations. It also allows the instructor to put things on his courses on the site and see the students enrolled in the study material. Finally, student can print the certificate after finished the course online. ASP.NET, SQLSERVER, JavaScript SQL SERVER Database will use to develop the University Short Courses web application.

Keywords: e-learning, short courses, ASP.NET, SQL SERVER

Procedia PDF Downloads 137

Authors: Jinan Fiaidhi, Sabah Mohammed

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Diagnosing cataract severity is an important factor in deciding to undertake surgery. It is usually conducted by an ophthalmologist or through taking a variety of fundus photography that needs to be examined by the ophthalmologist. This paper carries out an investigation using a Siamese neural net that can be trained with small anchor samples to score cataract severity. The model used in this paper is based on a triplet loss function that takes the ophthalmologist best experience in rating positive and negative anchors to a specific cataract scaling system. This approach that takes the heuristics of the ophthalmologist is generally called the thick data approach, which is a kind of machine learning approach that learn from a few shots. Clinical Relevance: The lens of the eye is mostly made up of water and proteins. A cataract occurs when these proteins at the eye lens start to clump together and block lights causing impair vision. This research aims at employing thick data machine learning techniques to rate the severity of the cataract using Siamese neural network.

Keywords: thick data analytics, siamese neural network, triplet-loss model, few shot learning

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Authors: Md. Mahamud Hassan, Shantanu Biswas Linkon, Nur Mohammad Khan

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Over the last three decades, the primary education system in Bangladesh has experienced significant improvement, but it has failed to cope with different social and cultural aspects, which present many challenges for children, families, and the public school system. Neglecting our own contextual learning environment, it is a matter of sorrow that much attention has been paid to the more physical outcome-focused model, which is nothing but mere infrastructural development, and less subtle to the environment that suits the child's psychology and improves their social, emotional, physical, and moral competency. In South Asia, the symbol of education was never the little red house of colonial architecture but “A Guru sitting under a tree", whereas a responsive and inclusive design approach could help to create more innovative learning environments. Such an approach incorporates how the built, natural, and cultural environment shapes the learner; in turn, learners shape the learning. This research will be conducted to, i) identify the major issues and drawbacks of government policy for primary education development programs; ii) explore and evaluate the morphology of the conventional model of school, and iii) propose an alternative model in a collaborative design process with the stakeholders for maximizing the relationship between the physical learning environments and learners by treating “the built environment” as “the third teacher.” Based on observation, this research will try to find out to what extent built, and natural environments can be utilized as a teaching tool for a more optimal learning environment. It should also be evident that there is a significant gap in the state policy, predetermined educational specifications, and implementation process in response to stakeholders’ involvement. The outcome of this research will contribute to a people-place sensitive design approach through a more thoughtful and responsive architectural process.

Keywords: built environment, conventional planning, indigenous learning space, responsive design

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Authors: M. Hadavi, Z. Hashemi

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Learning strategies play an important role in the development of language skills. Vocabulary learning strategies as the backbone of these strategies have become a major part of English language teaching. This study is a comparative analysis of Vocabulary Learning Strategies (VLS) use and preference among freshmen and senior EFL medical sciences students with different fields of study. 449 students (236 freshman and 213 seniors) participated in the study. 64.6% were female and 35.4% were male. The instrument utilized in this research was a questionnaire consisting of 41 items related to the students’ approach to vocabulary learning. The items were classified under eight sections as dictionary strategies, guessing strategies, study preferences, memory strategies, autonomy, note- taking strategies, selective attention, and social strategies. The participants were asked to answer each item with a 5-point Likert-style frequency scale as follows:1) I never or almost never do this, 2) I don’t usually do this, 3) I sometimes do this, 4) I usually do this, and 5)I always or almost always do this. The results indicated that freshmen students and particularly surgical technology students used more strategies compared to the seniors. Overall guessing and dictionary strategies were the most frequently used strategies among all the learners (p=0/000). The mean and standard deviation of using VLS in the students who had no previous history of participating in the private English language classes was less than the students who had attended these type of classes (p=0/000). Female students tended to use social and study preference strategies whereas male students used mostly guessing and dictionary strategies. It can be concluded that the senior students under instruction from the university have learned to rely on themselves and choose the autonomous strategies more, while freshmen students use more strategies that are related to the study preferences.

Keywords: vocabulary leaning strategies, medical sciences, students, linguistics

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Authors: M. F. Wu, F. T. Cheng

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Leather industry is the most important traditional industry to provide the leather products in the world for thousand years. The fierce global competitive environment and common awareness of global carbon reduction make livestock supply quantities falling, salt and wet blue leather material reduces and the price skyrockets significantly. Exchange rate fluctuation led sales revenue decreasing which due to the differences of export exchanges and compresses the overall profitability of leather industry. This paper applies activity-based benefit assessment approach to build up fitness fur input mixed model, fur is Wet Blue, which concerned with four key factors: the output rate of wet blue, unit cost of wet blue, yield rate and grade level of Wet Blue to achieve the low cost strategy under given unit price of leather product condition of the company. The research findings indicate that applying this model may improve the input cost structure, decrease numbers of leather product inventories and to raise the competitive advantages of the enterprise in the future.

Keywords: activity-based benefit assessment approach, input mixed, output rate, wet blue

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Authors: N. Mesatywa

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The aim of the article is to emphasize and motivate for the role of integrating cultures in institutions of learning. The article has used a literature review methodology. Findings indicate that cultures espouse immense social capital that can: facilitate and strengthen moral education that will help learners in mitigating moral decadence and HIV/AIDS; embrace and strengthen the tenets of peace and tranquility among learners from different backgrounds; can form education against xenophobia; can facilitate the process of cultural paradigm shift that will slow down cultural attrition and decadence; can bring back cultural strength, cultural revival, cultural reawakening and cultural emancipation, etc. The article recommends governments to finance cultural activities in institutions of learning; to allow cultural practitioners to be part and parcel of cultural education; and challenge people to pride in the social capital of their indigenous cultures.

Keywords: cultures, cultural practitioners, integration, traditional healers

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Authors: Pinn Tsin Isabel Yee

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With the advent of internet technologies, students are increasingly turning toward social media and cross-platform messaging apps such as WhatsApp, Line, and WeChat to support their teaching and learning processes. Although each messaging app has varying features, WhatsApp remains one of the most popular cross-platform apps that allow for fast, simple, secure messaging and free calls anytime, anywhere. With a plethora of advantages, students could easily assimilate WhatsApp as a supportive technology in their learning process. There could be peer to peer learning, and a teacher will be able to share knowledge digitally via the creation of WhatsApp groups. Content analysis techniques were utilized to analyze data collected by closed-ended question forms. Studies demonstrated that 98.8% of college students (n=80) from the Monash University foundation year agreed that the employment of WhatsApp groups was helpful as a learning tool. Approximately 71.3% disagreed that notifications and alerts from the WhatsApp group were disruptions in their studies. Students commented that they could silence the notifications and hence, it would not disturb their flow of thoughts. In fact, an overwhelming majority of students (95.0%) found it enjoyable to participate in WhatsApp groups for educational purposes. It was a common perception that some students felt pressured to post a reply in such groups, but data analysis showed that 72.5% of students did not feel pressured to comment or reply. It was good that 93.8% of students felt satisfactory if their posts were not responded to speedily, but was eventually attended to. Generally, 97.5% of students found it useful if their teachers provided their handphone numbers to be added to a WhatsApp group. If a teacher posts an explanation or a mathematical working in the group, all students would be able to view the post together, as opposed to individual students asking their teacher a similar question. On whether students preferred using Facebook as a learning tool, there was a 50-50 divide in the replies from the respondents as 51.3% of students liked WhatsApp, while 48.8% preferred Facebook as a supportive technology in teaching and learning. Taken altogether, the utilization of WhatsApp groups as a supportive technology in teaching and learning should be implemented in all classes to continuously engage our generation Y students in the ever-changing digital landscape.-

Keywords: education, learning, messaging app, technology, WhatsApp groups

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Authors: Renukswamy Chikkamath, Vishvapalsinhji Ramsinh Parmar, Christoph Hewel, Markus Endres

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Given a patent document, identifying distinct semantic annotations is an interesting research aspect. Text annotation helps the patent practitioners such as examiners and patent attorneys to quickly identify the key arguments of any invention, successively providing a timely marking of a patent text. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This semantic annotation process is laborious and time-consuming. To alleviate such a problem, we proposed a dataset to train machine learning algorithms to automate the highlighting process. The contributions of this work are: i) we developed a multi-class dataset of size 150k samples by traversing USPTO patents over a decade, ii) articulated statistics and distributions of data using imperative exploratory data analysis, iii) baseline Machine Learning models are developed to utilize the dataset to address patent paragraph highlighting task, and iv) future path to extend this work using Deep Learning and domain-specific pre-trained language models to develop a tool to highlight is provided. This work assists patent practitioners in highlighting semantic information automatically and aids in creating a sustainable and efficient patent analysis using the aptitude of machine learning.

Keywords: machine learning, patents, patent sentiment analysis, patent information retrieval

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Authors: Christopher Celozzi, Sarah Kochav

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Our research investigates the numerous challenges faced by Chinese ESL university students enrolled in the medical and related healthcare professional fields. The over-arching research question is how gender influences classroom participation and learning. The second research question addressed is 'what instructional strategies may be utilized to promote student participation and language acquisition?'. Participants’ language ability has been assessed and evaluated in order to facilitate the establishment of a statistical baseline for the subsequent intervention. This research delves deeper into each individual’s personal and academic circumstances, in an effort to reveal any held intrinsic gender beliefs and social identities that may influence learning. Also considered is the impact on learning for a homogenized student population within a uniform, highly structured learning environment. Specially, what is the influence of China’s ‘one-child policy’ on individual learning habits? The impact of their millennial identity and reliance on social media is also examined. A qualitative methodology with a case study approach is employed, with interviews conducted among the participants. Student response to the intervention and selected remediation strategies are documented, analyzed and discussed. The findings of the study may serve to inform educator instructional practice, while advancing the student learner in their pursuit of English competency in highly competitive professions.

Keywords: Chinese students, gender, English, language acquisition

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Authors: Natalia Kartushina

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Foreign (second) language (L2) learning is highly promoted in modern society. Students are encouraged to study abroad (SA) to achieve the most effective learning outcomes. However, L2 learning has side effects for native language (L1) production, as L1 sounds might show a drift from the L1 norms towards those of the L2, and this, even after a short period of L2 learning. L1 assimilatory drift has been attributed to a strong perceptual association between similar L1 and L2 sounds in the mind of L2 leaners; thus, a change in the production of an L2 target leads to the change in the production of the related L1 sound. However, nowadays, it is quite common that speakers acquire two languages from birth, as, for example, it is the case for many bilingual communities (e.g., Basque and Spanish in the Basque Country). Yet, it remains to be established how FL learning affects native production in individuals who have two native languages, i.e., in simultaneous or very early bilinguals. Does FL learning (here a third language, L3) affect bilinguals’ both languages or only one? What factors determine which of the bilinguals’ languages is more susceptible to change? The current study examines the effects of L3 (English) learning on the production of vowels in the two native languages of simultaneous Spanish-Basque bilingual adolescents enrolled into the Erasmus SA English program. Ten bilingual speakers read five Spanish and Basque consonant-vowel-consonant-vowel words two months before their SA and the next day after their arrival back to Spain. Each word contained the target vowel in the stressed syllable and was repeated five times. Acoustic analyses measuring vowel openness (F1) and backness (F2) were performed. Two possible outcomes were considered. First, we predicted that L3 learning would affect the production of only one language and this would be the language that would be used the most in contact with English during the SA period. This prediction stems from the results of recent studies showing that early bilinguals have separate phonological systems for each of their languages; and that late FL learner (as it is the case of our participants), who tend to use their L1 in language-mixing contexts, have more L2-accented L1 speech. The second possibility stated that L3 learning would affect both of the bilinguals’ languages in line with the studies showing that bilinguals’ L1 and L2 phonologies interact and constantly co-influence each other. The results revealed that speakers who used both languages equally often (balanced users) showed an F1 drift in both languages toward the F1 of the English vowel space. Unbalanced speakers, however, showed a drift only in the less used language. The results are discussed in light of recent studies suggesting that the amount of language use is a strong predictor of the authenticity in speech production with less language use leading to more foreign-accented speech and, eventually, to language attrition.

Keywords: language-contact, multilingualism, phonetic drift, bilinguals' production

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Authors: Maciej Zaręba, Paweł Dawid

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The changing picture of modern education requires innovative methodologies to meet the demands of new generations of students. Serious games, which use gamification principles to enrich the learning process, have become key tools for increasing student engagement and developing basic skills. This article explores the use of serious games in Education 4.0, focusing on their implementation in teaching manufacturing management and engineering principles at the high school and university levels. By simulating complex, real-world challenges and their solutions, serious games provide immersive and interactive learning environments that appeal to Generation Y and Z. These simulations enable students to participate in decision-making in realistic contexts, effectively bridging the gap between theoretical teaching and practical application. Based on the principles of Education 4.0 - which emphasizes personalized, experiential and technology-based learning - serious games foster cognitive engagement, critical thinking, problem-solving solving and collaborative competencies. Additionally, this study assesses the transformative potential of serious games in reshaping traditional educational practices, equipping students with the flexible skills necessary to thrive in an increasingly connected and dynamic global landscape.

Keywords: serious games, education 4.0, gamification, eLearning

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Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu

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This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.

Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence

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Authors: Hussaini Garba Mohammed

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Background/Significance: The lack of data gathering is affecting NGOs world-wide in general to have good data information about educational and health related issues among communities in any country and around the world. For example, HIV/AIDS smoking (Tuberculosis diseases) and COVID-19 virus carriers is becoming a serious public health problem, especially among old men and women. But there is no full details data survey assessment from communities, villages, and rural area in some countries to show the percentage of victims and patients, especial with this world COVID-19 virus among the people. These data are essential to inform programming targets, strategies, and priorities in getting good information about data gathering in any society.

Keywords: reliable information, data assessment, data mining, data communication

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Authors: Giuseppe Liverano

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Today’s school iscalledupon to take the lead role in supporting students towards the formation of conscious identity and a sense of responsible citizenship, through the development of key competencies for lifelong learning A rolethatrequiresit to be ready for change and to respond to the ever new needs of students, by adopting new pedagogical and didactic models and new didactic devices. Information and Communication Technologies, in this sense, reveal themselves to be usefulresourcesthatpermit to focus attention on the learning of eachindividualstudentunderstoodas a dynamic and relational process of constructing shared and participatedmeanings. The use of collaborative writing apps represents a democratic and shared knowledge way of constructionthroughICTs. It promotes the learning of reading-writing, literacy, and the development of transversal competencies in an inclusive perspective peer-to-peer comparison and reflectionthatstimulates the transfer of thought into speech and writing, the transformation of knowledge through a trialogicalapproach to learning generates enthusiasm and strengthensmotivationItrepresents a “different” way of expressing the training needs which come from several disciplinary fields of subjects with different cultures. The contribution aims to reflect on the formative value of collaborative writing through apps and analyse some proposals on line at school during the time of pandemic in order to highlight their critical aspects and pedagogical perspectives.

Keywords: collaborative writing, formative value, online, apps, pandemic

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Authors: Rachel S. E. Lim, Winnie L. C. Tan

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Visual literacy is about making meaning through the interaction of images, words, and sounds. Graphic communication students typically develop visual literacy through critique and production of studio-based projects for their portfolios. However, the abrupt switch to online learning during the COVID-19 pandemic has made it necessary to consider new strategies of visualization and planning to scaffold teaching and learning. This study, therefore, investigated how MIRO, a cloud-based visual collaboration platform, could be used to develop the visual literacy confidence of 30 diploma in graphic communication students attending a graphic design course at a Singapore arts institution. Due to COVID-19, the course was taught fully online throughout a 16-week semester. Guided by Kolb’s Experiential Learning Cycle, the two lecturers developed students’ engagement with visual literacy concepts through different activities that facilitated concrete experiences, reflective observation, abstract conceptualization, and active experimentation. Throughout the semester, students create, collaborate, and centralize communication in MIRO with infinite canvas, smart frameworks, a robust set of widgets (i.e., sticky notes, freeform pen, shapes, arrows, smart drawing, emoticons, etc.), and powerful platform capabilities that enable asynchronous and synchronous feedback and interaction. Students then drew upon these multimodal experiences to brainstorm, research, and develop their motion design project. A survey was used to examine students’ perceptions of engagement (E), confidence (C), learning strategies (LS). Using multiple regression, it¬ was found that the use of MIRO helped students develop confidence (C) with visual literacy, which predicted performance score (PS) that was measured against their application of visual literacy to the creation of their motion design project. While students’ learning strategies (LS) with MIRO did not directly predict confidence (C) or performance score (PS), it fostered positive perceptions of engagement (E) which in turn predicted confidence (C). Content analysis of students’ open-ended survey responses about their learning strategies (LS) showed that MIRO provides organization and structure in documenting learning progress, in tandem with establishing standards and expectations as a preparatory ground for generating feedback. With the clarity and sequence of the mentioned conditions set in place, these prerequisites then lead to the next level of personal action for self-reflection, self-directed learning, and time management. The study results show that the affordances of MIRO can develop visual literacy and make up for the potential pitfalls of student isolation, communication, and engagement during online learning. The context of how MIRO could be used by lecturers to orientate students for learning in visual literacy and studio-based projects for future development are discussed.

Keywords: design education, graphic communication, online learning, visual literacy

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Authors: Afsha Shaikh, Mohammed Azizur Rahman, Ibrahim Hassan, Mayur Pal

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Student engagement in learning is instantly affected by the sources of learning methods available for them, such as videos showing the applications of the concept or showing a practical demonstration. Specific to the engineering discipline, there exist enormous challenging concepts that can be simplified when they are connected to real-world scenarios. For this study, the concept of heat exchangers was used as it is a part of multidisciplinary engineering fields. To make the learning experience enjoyable and impactful, 3-D printed heat exchanger models were created for students to use while working on in-class activities and assignments. Students were encouraged to use the 3-D printed heat exchanger models to enhance their understanding of theoretical concepts associated with its applications. To assess the effectiveness of the method, feedback was received by students pursuing undergraduate engineering via an anonymous electronic survey. To make the feedback more realistic, unbiased, and genuine, students spent nearly two to three weeks using the models in their in-class assignments. The impact of these tools on their learning was assessed through their performance in their ungraded assignments as well as their interactive discussions with peers. ‘Having to apply the theory learned in class whilst discussing with peers on a class assignment creates a relaxed and stress-free learning environment in classrooms’; this feedback was received by more than half the students who took the survey and found 3-D models of heat exchanger very easy to use. Amongst many ways to enhance learning and make students more engaged through interactive models, this study sheds light on the importance of physical tools that help create a lasting mental representation in the minds of students. Moreover, in this technologically enhanced era, the concept of augmented reality was considered in this research. E-drawings application was recommended to enhance the vision of engineering students so they can see multiple views of the detailed 3-D models and cut through its different sides and angles to visualize it properly. E-drawings could be the next tool to implement in classrooms to enhance students’ understanding of engineering concepts.

Keywords: student engagement, life-long-learning, visual demonstration, 3-D printed models, engineering education

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Authors: J. Jayasuriya

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In this research paper, a procedure for installing and assessment of a solar PV plant to generate optimal solar energy SPAV hostel at Vijayawada city was analyzed. The hostel was experiencing power disruption and had a need for an unceasing energy source. The solar panel is one of the best solutions to obtain uninterrupted clean renewable energy for an institutional building as it neither makes din nor pollutes the atmosphere. The electricity usage per month was initially measured to discriminate the energy change. The solar array was installed with its financial and environmental assessment considering recent market prices. All the aspects related to a solar PV plant were considered for the feasibility and efficiency of PV plant near this site i.e., the orientation of the site, the size and shape of the terrace, the sun path were considered while installing panels. Various precautions were taken to intercept the factors which cause interference in energy generation, with respect to temperature, overshadowing, the wiring of panels, pollution etc. The solar panels were frequently installed, monitored and maintained properly to procure optimal energy output. Result obtained with the assessment of the proposed plant and deflation in the electric bill will show the maximal energy that can be generated in a month on that particular site.

Keywords: solar efficiency, building sustainability, PV panel, solar energy

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Authors: Valentina Colonnello, Paolo Maria Russo

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Living kidney donation requires psychological evaluation and ongoing follow-up. A crucial aspect of this evaluation is assessing the social functioning of donors after donation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a review of quantitative and qualitative studies on the psychological assessment of living kidney donors' social functioning. The majority of quantitative studies examining the long-term social health post-donation have primarily utilized the Short Form Health Survey (SF) and the World Health Organization Quality of Life-BREF (WHOQoL-BREF) questionnaires. These studies have indicated that donors' social functioning and relationships either remained stable post-donation or returned to pre-donation levels. In some instances, donors' social functioning even surpassed that of the general population. Qualitative studies, conducted through interviews and focus groups, have revealed donors' experiences and emotional concerns that are often overlooked in quantitative analyses. Specifically, qualitative analysis has identified two main themes: "connecting to others" and "acknowledgment and social support." Our review highlights that the majority of published quantitative studies on donors have employed measures of social functioning that may not fully capture donors' experiences and needs. It underscores the importance of further investigation in quantitative studies to assess donors' actual social health and psychological needs accurately. Overall, this review provides valuable insights into specific constructs that warrant deeper exploration in quantitative studies concerning the assessment of donors' social health and psychological well-being.

Keywords: reported outcomes, personalized medicine, individual differences, emotions, psychological assessment

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Authors: Alain Gholam

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Visual thinking routines are principles based on several theories, approaches, and strategies. Such routines promote thinking skills, call for collaboration and sharing of ideas, and above all, make thinking and learning visible. Visual thinking routines were implemented in the teaching methodology graduate course at the American University in Dubai. The study used mixed methods. It was guided by the following two research questions: 1). To what extent do visual thinking inspire learning in the classroom, and make time for students’ questions, contributions, and thinking? 2). How do visual thinking routines inspire learning in the classroom and make time for students’ questions, contributions, and thinking? Eight student teachers enrolled in the teaching methodology course at the American University in Dubai (Spring 2017) participated in the following study. First, they completed a survey that measured to what degree they believed visual thinking routines inspired learning in the classroom and made time for students’ questions, contributions, and thinking. In order to build on the results from the quantitative phase, the student teachers were next involved in a qualitative data collection phase, where they had to answer the question: How do visual thinking routines inspire learning in the classroom and make time for students’ questions, contributions, and thinking? Results revealed that the implementation of visual thinking routines in the classroom strongly inspire learning in the classroom and make time for students’ questions, contributions, and thinking. In addition, student teachers explained how visual thinking routines allow for organization, variety, thinking, and documentation. As with all original, new, and unique resources, visual thinking routines are not free of challenges. To make the most of this useful and valued resource, educators, need to comprehend, model and spread an awareness of the effective ways of using such routines in the classroom. It is crucial that such routines become part of the curriculum to allow for and document students’ questions, contributions, and thinking.

Keywords: classroom display, student engagement, thinking classroom, visual thinking routines

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Authors: Sadixa Sanjel, Ahmed Sadek, Naseef Mansoor, Zelalem Denekew

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Collegiate ice hockey (NCAA) sports analytics is different from the national level hockey (NHL). We apply and compare multiple machine learning models such as Linear Regression, Random Forest, and Neural Networks to predict the win ratio for a team based on their statistics. Data exploration helps determine which statistics are most useful in increasing the win ratio, which would be beneficial to coaches and team managers. We ran experiments to select the best model and chose Random Forest as the best performing. We conclude with how to bridge the gap between the college and national levels of sports analytics and the use of machine learning to enhance team performance despite not having a lot of metrics or budget for automatic tracking.

Keywords: NCAA, NHL, sports analytics, random forest, regression, neural networks, game predictions

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Authors: Temitayo Olaitan

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There is a growing concern about the quality of Nigerian tertiary education. This paper maintains that quality in tertiary education relates to the development of intellectual independence, which sharpens the minds of the individual and helps transform the society economically, socially and politically. However, the paper underscores underfunding as a critical challenge to the quality of teaching and learning in tertiary education. To this end, this paper emphasizes the role of internally generated revenue (IGR) and other alternative funding strategies (public-private partnership) as inevitable for quality tertiary education. This paper hinges on stakeholders approach as a means of ensuring quality teaching and learning in tertiary education. This paper recommends that school managers should seek professional and more efficient ways of developing their revenue generating systems. It also recommends that institutions should restructure to accommodate an alternative funding strategy such as private/corporate sponsorship to ensure that sustainable initiatives are created. The paper concludes that Nigerian government should come up with a policy on how private sectors should support in improving the quality of tertiary education through active participation in funding and physical facilities development in Nigerian higher institutions of learning.

Keywords: alternative funding, budgetary allocation, quality education, tertiary education

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Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

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In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: agricultural object detection, deep learning, machine vision, YOLO family

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Authors: Muhammad Waqar Ashraf

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Selected heavy metals, namely Cu, Zn, Fe, Mn, Cd, Pb, and As, in seven popular varieties of edible vegetable oils collected from Saudi Arabia, were determined by graphite furnace atomic absorption spectrometry (GF-AAS) using microwave digestion. The accuracy of procedure was confirmed by certified reference materials (NIST 1577b). The concentrations for copper, zinc, iron, manganese, lead and arsenic were observed in the range of 0.035 - 0.286, 0.955 - 3.10, 17.3 - 57.8, 0.178 - 0.586, 0.011 - 0.017 and 0.011 - 0.018 µg/g, respectively. Cadmium was found to be in the range of 2.36 - 6.34 ng/g. The results are compared internationally and with standards laid down by world health agencies. A risk assessment study has been carried out to assess exposure to these metals via consumption of vegetable oils. A comparison has been made with safety intake levels for these heavy metals recommended by Institute of Medicine of the National Academies (IOM), US Environmental Protection Agency (US EPA) and Joint FAO/WHO Expert Committee on Food Additives (JECFA). The results indicated that the dietary intakes of the selected heavy metals from daily consumption of 25 g of edible vegetable oils for a 70 kg individual should pose no significant health risk to local population.

Keywords: vegetable oils, heavy metals, contamination, health risk assessment

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Authors: Jadon Matthiasa, Cindy Donga, Ali Al Jibouria, Hsin Kuo

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The environmental impact of emissions from the Covanta Waste-to-Energy facility in Burnaby, BC, was comprehensively evaluated, focusing on the dispersion of air pollutants and the subsequent assessment of heavy metal contamination in surrounding soils. A Gaussian Plume Model, implemented in MATLAB, was utilized to simulate the dispersion of key pollutants to understand their atmospheric behaviour and potential deposition patterns. The MATLAB code developed for this study enhanced the accuracy of pollutant concentration predictions and provided capabilities for visualizing pollutant dispersion in 3D plots. Furthermore, the code could predict the maximum concentration of pollutants at ground level, eliminating the need to use the Ranchoux model for predictions. Complementing the modelling approach, empirical soil sampling and analysis were conducted to evaluate heavy metal concentrations in the vicinity of the facility. This integrated methodology underscored the importance of computational modelling in air pollution assessment and highlighted the necessity of soil analysis to obtain a holistic understanding of environmental impacts. The findings emphasized the effectiveness of current emissions controls while advocating for ongoing monitoring to safeguard public health and environmental integrity.

Keywords: air emissions, Gaussian Plume Model, MATLAB, soil contamination, air pollution monitoring, waste-to-energy, pollutant dispersion visualization, heavy metal analysis, environmental impact assessment, emission control effectiveness

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Shafaq Rubab

Abstract:

The primary reason for dropping out of school is associated with students’ lack of motivation in class, especially in mathematics. Digital game-based learning is an approach that is being actively explored; there are very few learning games based on proven instructional design models or frameworks due to which the effectiveness of the learning games suffers. The purpose of this research was twofold: first, developing an appropriate instructional design model and second, evaluating the impact of the instructional design model on students’ motivation. This research contributes significantly to the existing literature in terms of student motivation and the impact of instructional design model in digital game-based learning. The sample size for this study consists of two hundred out-of-school students between the age of 6 and 12 years. The research methodology used for this research was a quasi-experimental approach and data was analyzed by using the instructional material motivational survey questionnaire which is adapted from the Keller Arcs model. Control and experimental groups consisting of two hundred students were analyzed by utilizing instructional material motivational survey (IMMS), and comparison of result from both groups showed the difference in the level of motivation of the students. The result of the research showed that the motivational level of student in the experimental group who were taught by the game was higher than the student in control group (taught by conventional methodology). The mean score of the experimental group against all subscales (attention, relevance, confidence, and satisfaction) of IMMS survey was higher; however, no statistical significance was found between the motivational scores of control and experimental group. The positive impact of game-based learning on students’ level of motivation, as measured in this study, strengthens the case for the use of pedagogically sound instructional design models in the design of interactive learning applications. In addition, the present study suggests learning from interactive, immersive applications as an alternative solution for children, especially in Third World countries, who, for various reasons, do not attend school. The mean score of experimental group against all subscales of IMMS survey was higher; however, no statistical significance was found between motivational scores of control and experimental group.

Keywords: digital game-based learning, students’ motivation, and instructional designing, instructional material motivational survey

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Authors: Paola Bordon, Breno Braga

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This paper investigates whether firms use university prestige to statistically discriminate among college graduates. The test is based on the employer learning literature which suggests that if firms use a characteristic for statistical discrimination, this variable should become less important for earnings as a worker gains labor market experience. In this framework, we use a regression discontinuity design to estimate a 19% wage premium for recent graduates of two of the most selective universities in Chile. However, we find that this premium decreases by 3 percentage points per year of labor market experience. These results suggest that employers use college selectivity as a signal of workers' quality when they leave school. However, as workers reveal their productivity throughout their careers, they become rewarded based on their true quality rather than the prestige of their college.

Keywords: employer learning, statistical discrimination, college returns, college selectivity

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