Search results for: student performance prediction

Authors: Sheila Santharamohana, Susan Bennett

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

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

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Authors: Karl Baker-Green, Ian Woolsey

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Historically, a variety of methods have been used to access the student voice within higher education, including module evaluations and informal classroom feedback. However, currently, the views articulated in student-staff-committee meetings bear the most weight and can therefore have the most significant impact on departmental policy. Arguably, these forums are exclusionary as several students, including those who experience severe anxiety, might feel unable to participate in this face-to-face (large) group activities. Similarly, students who declare a disability, but are not in possession of a learning contract, are more likely to withdraw from their studies than those whose additional needs have been formally recognised. It is also worth noting that whilst the number of disabled students in Higher Education has increased in recent years, the percentage of those who have been issued a learning contract has decreased. These issues foreground the need to explore the educational experiences of students with or without a learning contract in order to identify their respective aspirations and needs and therefore help shape education policy. This is in keeping with the ‘Nothing about us without us’, agenda, which recognises that disabled individuals are best placed to understand their own requirements and the most effective strategies to meet these.

Keywords: education, student voice, student experience, student retention

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Authors: A. A. Altalbe, N. W Bergmann

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There has been significant recent interest in on-line learning, as well as considerable work on developing technologies for virtual laboratories for engineering students. After reviewing the state-of-the-art of virtual laboratories, this paper steps back from the technology issues to look in more detail at the pedagogical issues surrounding virtual laboratories, and examines the role of gathering student feedback in the development of such laboratories. The main contribution of the paper is a set of student surveys before and after a prototype deployment of a simulation laboratory tool, and the resulting analysis which leads to some tentative guidelines for the design of virtual engineering laboratories.

Keywords: engineering education, elearning, electrical engineering, virtual laboratories

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Authors: Ian Marc Gealon Cabugsa, Eleanor Pastrano Corcino, Gina Lapaza Montalan

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This study focused on the effect of Online Web-Learning (OWL) in the performance of the freshmen Civil Engineering Students of Ateneo de Davao University in their Chem 12 subject. The grades of the students that were required to use OWL were compared to students without OWL. The result of the study suggests promising result for the use of OWL in increasing the performance rate of students taking up Chem 12. Furthermore, there was a positive correlation between the final grade and OWL grade of the students that had OWL. While the majority of the students find OWL to be helpful in supporting their chemistry knowledge needs, most of them still prefer to learn using the traditional face-to-face instruction.

Keywords: chemistry education, enhanced performance, engineering chemistry, online web-based learning

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Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami

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Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.

Keywords: ANFIS, MGT, prediction modeling, rail track degradation

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Authors: Vijay Kr. Yadav, Nilam Rathi

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Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set.

Keywords: back-propagation, diabetes mellitus, fuzzy inference system, neuro-fuzzy

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Authors: Amiel Satvedi, Richard Pyne

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Injuries in professional sports occur on a regular basis. Some may be minor, while others can cause huge impact on a player's career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player's number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression model.

Keywords: injury predictor, soccer injury prevention, machine learning in soccer, big data in soccer

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Authors: Ravinder Bahl, Jamini Sharma

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The paper demonstrates analysis of the cervical cancer based on a probabilistic model. It involves technique for classification and prediction by recognizing typical and diagnostically most important test features relating to cervical cancer. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases. The combination of the conventional statistical and machine learning tools is applied for the analysis. Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Keywords: cervical cancer, recurrence, no recurrence, probabilistic, classification, prediction, machine learning

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: D. Hassell, D. De Focatiis

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This paper presents work on the application of wiki based coursework for a fourth-year engineering module delivered as part of both a MEng and MSc programme in Chemical Engineering. The module was taught with an equivalent structure simultaneously on two separate campuses, one in the United Kingdom (UK) and one in Malaysia, and the subsequent results were compared. Student feedback was sought via questionnaires, with 45 respondents from the UK and 49 from Malaysia. Results include discussion on; perceived difficulty; student enjoyment and experiences; differences between MEng and MSc students; differences between cohorts on different campuses. The response of students to the use of wiki-based coursework was found to vary based on their experiences and background, with UK students being generally more positive on its application than those in Malaysia.

Keywords: engineering education, student differences, student learning, web based coursework

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Authors: Chhavi Rana, Sanjay K Jain

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All sides of every education debate agree that quality learning happens when knowledgeable, caring teachers use sound pedagogy. Many deliberations of pedagogy make the fault of considering it as principally being about teaching. There has been lot of research about how to build a positive climate for learning, improve student curiosity, and enhance classroom association. However, these things can only be facilitated when teachers are equipped with better teaching techniques that use sound and accurate pedagogy. Pedagogy is the science and art of education. Its aims range from the full development of the human being to skills acquisition. In India, a project named Mission 10 x has been started by an esteemed IT Corporation Wipro as a faculty development programme (FDP) that particularly focus on elements that facilitated teachers in developing curriculum and new pedagogies that can lead to improvement in student engagement. This paper presents a study of these FDPs and examines (1) the parameters that help teachers in building new pedagogies (2) the extent to which appropriate usage of pedagogy is improved after the conduct of Mission 10 x FDPs, and (3) whether institutions differ in terms of their ability to convert usage of improved pedagogy into academic performance via these FDPs. The sample consisted of 2,236 students at 6 four-year engineering colleges and universities that completed several FDPs during 2012-2014. Many measures of usage of better pedagogy were linked positively with such FDPs, although some of the relationships were weak in strength. The results suggest that the usage of pedagogy were more benefited after conducting these FDPs and application of novel approaches in conducting classes.

Keywords: student engagement, critical thinking; achievement, student learning, pedagogy

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Authors: Matthew David Marmet

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Knowing your audience is important regardless of what profession you are in. Whether this audience is comprised of customers or students, having an idea of who these people are, where they come from, and some of the challenges they may have faced allows us to build better relationships with them. This paper will recap a field study experience that has been dubbed a "reverse field trip" to a local high school. Here, going back in time produced not only a great deal of nostalgia, but also served as a reminder of who prospective university students are before they arrive. This information is invaluable as it can help inform classroom (and other) strategies that may help them succeed, and persist through the college years, which will no doubt present them with undeniable changes and challenges. Interviews with school staff and observations of student behavior, both inside and outside the classroom, yielded several lessons learned (i.e., issues to address). These include considerations of regimen, three separate yet related levels of context, and expectation-setting. Each issue will be presented in detail, along with pedagogical strategies to help address them. These strategies have both student-level and institutional benefits as they have the potential not only to increase student engagement, but also improve retention rates.

Keywords: pedagogy, Student engagement, student retention, teaching strategy

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Authors: Feng Guolin, Gong Zhiqiang

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In order to study the spatial structure and dynamical mechanism of extreme precipitation in East Asia, a corresponding climate network is constructed by employing the method of event synchronization. It is found that the area of East Asian summer extreme precipitation can be separated into two regions: one with high area weighted connectivity receiving heavy precipitation mostly during the active phase of the East Asian Summer Monsoon (EASM), and another one with low area weighted connectivity receiving heavy precipitation during both the active and the retreat phase of the EASM. Besides，a way for the prediction of extreme precipitation is also developed by constructing a directed climate networks. The simulation accuracy in East Asia is 58% with a 0-day lead, and the prediction accuracy is 21% and average 12% with a 1-day and an n-day (2≤n≤10) lead, respectively. Compare to the normal EASM year, the prediction accuracy is lower in a weak year and higher in a strong year, which is relevant to the differences in correlations and extreme precipitation rates in different EASM situations. Recognizing and identifying these effects is good for understanding and predicting extreme precipitation in East Asia.

Keywords: synchronization, climate network, prediction, rainfall

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Authors: Lennon Furtado, Sandro Oliveira

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The importance of an effective measurement program lies in the ability to control and predict what can be measured. Thus, the measurement program has the capacity to provide bases in decision-making to support the interests of an organization. Therefore, it is only possible to apply for an effective measurement program with a team of software engineers well trained in the measurement area. However, the literature indicates that are few computer science courses that have in their program the teaching of the software measurement process. And even these, generally present only basic theoretical concepts of said process and little or no measurement in practice, which results in the student's lack of motivation to learn the measurement process. In this context, according to some experts in software process improvements, one of the most used approaches to maintaining the motivation and commitment to software process improvements program is the use of the gamification. Therefore, this paper aims to present a proposal of teaching the measurement process by gamification. Which seeks to improve student motivation and performance in the assimilation of tasks related to software measurement, by incorporating elements of games into the practice of measurement process, making it more attractive for learning. And as a way of validating the proposal will be made a comparison between two distinct groups of 20 students of Software Quality class, a control group, and an experiment group. The control group will be the students that will not make use of the gamification proposal to learn software measurement process, while the experiment group, will be the students that will make use of the gamification proposal to learn software measurement process. Thus, this paper will analyze the objective and subjective results of each group. And as objective result will be analyzed the student grade reached at the end of the course, and as subjective results will be analyzed a post-course questionnaire with the opinion of each student about the teaching method. Finally, this paper aims to prove or refute the following hypothesis: If the gamification proposal to teach software measurement process does appropriate motivate the student, in order to attribute the necessary competence to the practical application of the measurement process.

Keywords: education, gamification, software measurement process, software engineering

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Authors: Maria Hopkins

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Research on student engagement is founded on a desire to improve the quality of online instruction in both course design and delivery. A high level of student engagement is associated with a wide range of educational practices including purposeful student-faculty contact, peer to peer contact, active and collaborative learning, and positive factors such as student satisfaction, persistence, achievement, and learning. By encouraging student engagement, institutions of higher education can have a positive impact on student success that leads to retention and degree completion. The current research presents the results of an online student engagement survey which support faculty teaching practices to maximize the learning experience for online students. The ‘Indicators of Engaged Learning Online’ provide a framework that measures level of student engagement. Social constructivism and collaborative learning form the theoretical basis of the framework. Social constructivist pedagogy acknowledges the social nature of knowledge and its creation in the minds of individual learners. Some important themes that flow from social constructivism involve the importance of collaboration among instructors and students, active learning vs passive consumption of information, a learning environment that is learner and learning centered, which promotes multiple perspectives, and the use of social tools in the online environment to construct knowledge. The results of the survey indicated themes that emphasized the importance of: Interaction among peers and faculty (collaboration); Timely feedback on assignment/assessments; Faculty participation and visibility; Relevance and real-world application (in terms of assignments, activities, and assessments); and Motivation/interest (the need for faculty to motivate students especially those that may not have an interest in the coursework per se). The qualitative aspect of this student engagement study revealed what instructors did well that made students feel engaged in the course, but also what instructors did not do well, which could inform recommendations to faculty when expectations for teaching a course are reviewed. Furthermore, this research provides evidence for the connection between higher student engagement and persistence and retention in online programs, which supports our rationale for encouraging student engagement, especially in the online environment because attrition rates are higher than in the face-to-face environment.

Keywords: instructional design, learning effectiveness, online learning, student engagement

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Authors: Lyndsee Baumann-Birkbeck, Sohil A. Khan, Shailendra Anoopkumar-Dukie, Gary D. Grant

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Technology-enhanced education tools are being rapidly integrated into health programs globally. These tools provide an interactive platform for students and can be used to deliver topics in various modes including games and simulations. Simulations are of particular interest to healthcare education, where they are employed to enhance clinical knowledge and help to bridge the gap between theory and practice. Simulations will often assess competencies for practical tasks, yet limited research examines the effects of simulation on student perceptions of their learning. The aim of this study was to determine the effects of an interactive virtual patient simulation for pharmacology education and clinical practice on student knowledge, skills and confidence. Ethics approval for the study was obtained from Griffith University Research Ethics Committee (PHM/11/14/HREC). The simulation was intended to replicate the pharmacy environment and patient interaction. The content was designed to enhance knowledge of proton-pump inhibitor pharmacology, role in therapeutics and safe supply to patients. The tool was deployed into a third-year clinical pharmacology and therapeutics course. A number of core practice areas were examined including the competency domains of questioning, counselling, referral and product provision. Baseline measures of student self-reported knowledge, skills and confidence were taken prior to the simulation using a specifically designed questionnaire. A more extensive questionnaire was deployed following the virtual patient simulation, which also included measures of student engagement with the activity. A quiz assessing student factual and conceptual knowledge of proton-pump inhibitor pharmacology and related counselling information was also included in both questionnaires. Sixty-one students (response rate >95%) from two cohorts (2014 and 2015) participated in the study. Chi-square analyses were performed and data analysed using Fishers exact test. Results demonstrate that student knowledge, skills and confidence within the competency domains of questioning, counselling, referral and product provision, show improvement following the implementation of the virtual patient simulation. Statistically significant (p<0.05) improvement occurred in ten of the possible twelve self-reported measurement areas. Greatest magnitude of improvement occurred in the area of counselling (student confidence p<0.0001). Student confidence in all domains (questioning, counselling, referral and product provision) showed a marked increase. Student performance in the quiz also improved, demonstrating a 10% improvement overall for pharmacology knowledge and clinical practice following the simulation. Overall, 85% of students reported the simulation to be engaging and 93% of students felt the virtual patient simulation enhanced learning. The data suggests that the interactive virtual patient simulation developed for clinical pharmacology and therapeutics education enhanced students knowledge, skill and confidence, with respect to the competency domains of questioning, counselling, referral and product provision. These self-reported measures appear to translate to learning outcomes, as demonstrated by the improved student performance in the quiz assessment item. Future research of education using virtual simulation should seek to incorporate modern quantitative measures of student learning and engagement, such as eye tracking.

Keywords: clinical simulation, education, pharmacology, simulation, virtual learning

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

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In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

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Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: Chen Chan Wei

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The purpose of this study was to investigate the impact of swim 1500m, 10000m run, VO2 max, and body fat on Olympic distance triathlon competition performance. The subjects were thirteen college triathletes with endurance training, with an average age, height and weight of 20.61±1.04 years (mean ± SD), 171.76±8.54 cm and 65.32±8.14 kg respectively. All subjects were required to take the tests of swim 1500m, run 10000m, VO2 max, body fat, and participate in the Olympic distance triathlon competition. First, the swim 1500m test was taken in the standardized 50m pool, with a depth of 2m, and the 10000m run test on the standardized 400m track. After three days, VO2 max was tested with the MetaMax 3B and body fat was measured with the DEXA machine. After two weeks, all 13 subjects joined the Olympic distance triathlon competition at the 2016 New Taipei City Asian Cup. The relationships between swim 1500m, 10000m run, VO2 max, body fat test, and Olympic distance triathlon competition performance were evaluated using Pearson's product-moment correlation. The results show that 10000m run and body fat had a significant positive correlation with Olympic distance triathlon performance (r=.830, .768), but VO2 max has a significant negative correlation with Olympic distance triathlon performance (r=-.735). In conclusion, for improved non-draft Olympic distance triathlon performance, triathletes should focus on running than swimming training and can be measure VO2 max to prediction triathlon performance. Also, managing body fat can improve Olympic distance triathlon performance. In addition, swimming performance was not significantly correlated to Olympic distance triathlon performance, possibly because the 2016 New Taipei City Asian Cup age group was not a drafting competition. The swimming race is the shortest component of Olympic distance triathlons. Therefore, in a non-draft competition, swimming ability is not significantly correlated with overall performance.

Keywords: triathletes, olympic, non-drafting, correlation

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Authors: Amal A. Abdel-Sattar

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The curriculum of architecture department in Prince Sultan University includes ‘Building Surveying’ course which is usually a part of civil engineering courses. As a fundamental requirement of the course, it requires a strong background in mathematics and physics, which are not usually preferred subjects to the architecture students and many of them are not giving the required and necessary attention to these courses during their preparation year before commencing their architectural study. This paper introduces the concept and the methodology of the student-centered learning approach in the course of building surveying for architects. One of the major outcomes is the improvement in the students’ involvement in the course and how this will cover and strength their analytical weak points and improve their mathematical skills. The study is conducted through three semesters with a total number of 99 students. The effectiveness of the student-centered learning approach is studied using the student survey at the end of each semester and teacher observations. This survey showed great acceptance of the students for these methods. Also, the teachers observed a great improvement in the students’ mathematical abilities and how keener they became in attending the classes which were clearly reflected on the low absence record.

Keywords: architecture, building surveying, student-centered learning, teaching and learning

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Authors: Sanja Bracun, Nikolina Kasunic

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After graduation, the student population of applied sciences will become the population of employees on IT experts’ positions or "just" business users of certain IT technologies for which the level of awareness of existing cybersecurity risks is extremely important. This research results define the current cybersecurity awareness level of students at Zagreb University of Applied Sciences (TVZ), what can be useful not only for teaching staff to form a curriculum related to cybersecurity more accurately but also to employers to know what to expect from their future employees regarding cybersecurity awareness level.

Keywords: student population cybersecurity awareness, cybersecurity awareness, cybersecurity, applied sciences students

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Authors: Tauha Hussain Ali, Shabir Hussain Khahro, Nafees Ahmed Memon

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Accurate prediction of project time for planning and bid preparation stage should contain realistic dates. Constructors use their experience to estimate the project duration for the new projects, which is based on intuitions. It has been a constant concern to both researchers and constructors to analyze the accurate prediction of project duration for bid preparation stage. In Pakistan, such study for time cost relationship has been lacked to predict duration performance for the construction projects. This study is an attempt to explore the time cost relationship that would conclude with a mathematical model to predict the time for the drainage rehabilitation projects in the province of Sindh, Pakistan. The data has been collected from National Engineering Services (NESPAK), Pakistan and regression analysis has been carried out for the analysis of results. Significant relationship has been found between time and cost of the construction projects in Sindh and the generated mathematical model can be used by the constructors to predict the project duration for the upcoming projects of same nature. This study also provides the professionals with a requisite knowledge to make decisions regarding project duration, which is significantly important to win the projects at the bid stage.

Keywords: BTC Model, project time, relationship of time cost, regression

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Authors: Elham Delzendeh, Song Wu, Mustafa Al-Adhami, Rima Alaaeddine

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Over the last 15 years, prediction of energy consumption has become a common practice and necessity at different stages of the building’s lifecycle, particularly, at the design and post-occupancy stages for planning and maintenance purposes. This is due to the ever-growing response of governments to address sustainability and reduction of CO₂ emission in the building sector. However, there is a level of uncertainty in the estimation of energy consumption in buildings. The accuracy of energy consumption predictions is directly related to the precision of the initial inputs used in the energy assessment process. In this study, multiple cases of large non-residential buildings at design, construction, and post-occupancy stages are investigated. The energy consumption process and inputs, and the actual and predicted energy consumption of the cases are analysed. The findings of this study have pointed out and evidenced various parameters that cause uncertainty in the prediction of energy consumption in buildings such as modelling, location data, and occupant behaviour. In addition, unavailability and insufficiency of energy-consumption-related inputs at different stages of the building’s lifecycle are classified and categorized. Understanding the roots of uncertainty in building energy analysis will help energy modellers and energy simulation software developers reach more accurate energy consumption predictions in buildings.

Keywords: building lifecycle, efficiency, energy analysis, energy performance, uncertainty

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Authors: S. Channgam, A. Sae-Tang, T. Termsaithong

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In this research, the occurrences of large size events in various system sizes of the Bak-Tang-Wiesenfeld sandpile model are considered. The system sizes (square lattice) of model considered here are 25×25, 50×50, 75×75 and 100×100. The cross-correlation between the ratio of sites containing 3 grain time series and the large size event time series for these 4 system sizes are also analyzed. Moreover, a prediction method of the large-size event for the 50×50 system size is also introduced. Lastly, it can be shown that this prediction method provides a slightly higher efficiency than random predictions.

Keywords: Bak-Tang-Wiesenfeld sandpile model, cross-correlation, avalanches, prediction method

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Authors: E. Akpinar, A. Erol, M.F. Cakir

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Precast reinforced concrete (RC) structures are excellent alternatives for construction world all over the globe, thanks to their rapid erection phase, ease mounting process, better quality and reasonable prices. Such structures are rather popular for industrial buildings. For the sake of economic importance of such industrial buildings as well as significance of safety, like every other type of structures, performance assessment and structural risk analysis are important. Fragility curves are powerful tools for damage projection and assessment for any sort of building as well as precast structures. In this study, a comparative review of current knowledge on fragility analysis of industrial precast RC structures were presented and findings in previous studies were compiled. Effects of different structural variables, parameters and building geometries as well as soil conditions on fragility analysis of precast structures are reviewed. It was aimed to briefly present the information in the literature about the procedure of damage probability prediction including fragility curves for such industrial facilities. It is found that determination of the aforementioned structural parameters as well as selecting analysis procedure are critically important for damage prediction of industrial precast RC structures using fragility curves.

Keywords: damage prediction, fragility curve, industrial buildings, precast reinforced concrete structures

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Authors: Yalçın Bozkurt

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Prediction models were developed for accurate prediction of bodyweight (BW) by using Digital Images of beef cattle body dimensions by Artificial Neural Networks (ANN). For this purpose, the animal data were collected at a private slaughter house and the digital images and the weights of each live animal were taken just before they were slaughtered and the body dimensions such as digital wither height (DJWH), digital body length (DJBL), digital body depth (DJBD), digital hip width (DJHW), digital hip height (DJHH) and digital pin bone length (DJPL) were determined from the images, using the data with 1069 observations for each traits. Then, prediction models were developed by ANN. Digital body measurements were analysed by ANN for body prediction and R2 values of DJBL, DJWH, DJHW, DJBD, DJHH and DJPL were approximately 94.32, 91.31, 80.70, 83.61, 89.45 and 70.56 % respectively. It can be concluded that in management situations where BW cannot be measured it can be predicted accurately by measuring DJBL and DJWH alone or both DJBD and even DJHH and different models may be needed to predict BW in different feeding and environmental conditions and breeds

Keywords: artificial neural networks, bodyweight, cattle, digital body measurements

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Authors: Jianhua Wang, Ying Zhou, Xi Zhang

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To establish the influential mechanism of learning engagement on interpreter’s performance, the present study submitted a questionnaire to a sample of 927 English major students with 804 valid ones and used the structural equation model as the basis for empirical analysis and statistical inference on the sample data. In order to explore the mechanism for interpreting learning engagement on student interpreters’ performance, a path model of interpreting processes with three variables of ‘input-environment-output’ was constructed. The results showed that the effect of each ‘environment’ variable on interpreting ability was different from and greater than the ‘input’ variable, and learning engagement was the greatest influencing factor. At the same time, peer interaction on interpreting performance has significant influence. Results suggest that it is crucial to provide effective guidance for optimizing learning engagement and interpreting teaching research by both improving the environmental support and building the platform of peer interaction, beginning with learning engagement.

Keywords: learning engagement, interpreting performance, interpreter training, English major students

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Authors: Yuan Liu, Hongyun Liu

Abstract:

Teacher-student relationship plays an important role on facilitating students’ learning behavior, school engagement, and academic outcomes. It is believed that good relationship will enhance the human agency—the intrinsic motivation—mainly through the strengthening of autonomic support, feeling of relatedness, and the individual’s competence to increase the academic outcomes. This is in line with self-determination theory (SDT), which generally views that the intrinsic motivation imbedded with human basic needs is one of the most important factors that would lead to better school engagement, academic outcomes, and well-being. Based on SDT, the present study explored the relation of among teacher-student relationship (teacher’s encouragement, respect), students’ motivation (extrinsic and intrinsic), and achievement outcomes. The study was based on a large scale academic assessment and questionnaire survey conducted by the Center for Assessment and Improvement of Basic Education Quality in Mainland China (2013) on Grade 8 students. The results indicated that intrinsic motivation mediated the relation between teacher-student relationship and academic achievement outcomes.

Keywords: teacher-student relationship, intrinsic motivation, academic achievement, mediation

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Authors: Marjan Golmaryami, Marzieh Behzadi

Abstract:

Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

Procedia PDF Downloads 525