Search results for: student performance prediction

Authors: G. Singer, M. Golan

Abstract:

Providing students with learning disabilities (LD) with extra time to grant them equal access to the exam is a necessary but insufficient condition to compensate for their LD; there should also be a clear indication that the additional time was actually used. For example, if students with LD use more time than students without LD and yet receive lower grades, this may indicate that a different accommodation is required. If they achieve higher grades but use the same amount of time, then the effectiveness of the accommodation has not been demonstrated. The main goal of this study is to evaluate the effect of including parameters related to LD and extended exam time, along with other commonly-used characteristics (e.g., student background and ability measures such as high-school grades), on the ability of ordinal decision-tree algorithms to predict exam performance. We use naturally-occurring data collected from hundreds of undergraduate engineering students. The sub-goals are i) to examine the improvement in prediction accuracy when the indicator of exam performance includes 'actual time used' in addition to the conventional indicator (exam grade) employed in most research; ii) to explore the effectiveness of extended exam time on exam performance for different courses and for LD students with different profiles (i.e., sets of characteristics). This is achieved by using the patterns (i.e., subgroups) generated by the algorithms to identify pairs of subgroups that differ in just one characteristic (e.g., course or type of LD) but have different outcomes in terms of exam performance (grade and time used). Since grade and time used to exhibit an ordering form, we propose a method based on ordinal decision-trees, which applies a weighted information-gain ratio (WIGR) measure for selecting the classifying attributes. Unlike other known ordinal algorithms, our method does not assume monotonicity in the data. The proposed WIGR is an extension of an information-theoretic measure, in the sense that it adjusts to the case of an ordinal target and takes into account the error severity between two different target classes. Specifically, we use ordinal C4.5, random-forest, and AdaBoost algorithms, as well as an ensemble technique composed of ordinal and non-ordinal classifiers. Firstly, we find that the inclusion of LD and extended exam-time parameters improves prediction of exam performance (compared to specifications of the algorithms that do not include these variables). Secondly, when the indicator of exam performance includes 'actual time used' together with grade (as opposed to grade only), the prediction accuracy improves. Thirdly, our subgroup analyses show clear differences in the effect of extended exam time on exam performance among different courses and different student profiles. From a methodological perspective, we find that the ordinal decision-tree based algorithms outperform their conventional, non-ordinal counterparts. Further, we demonstrate that the ensemble-based approach leverages the strengths of each type of classifier (ordinal and non-ordinal) and yields better performance than each classifier individually.

Keywords: actual exam time usage, ensemble learning, learning disabilities, ordinal classification, time extension

Procedia PDF Downloads 67

Authors: Suriya

Abstract:

Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

Procedia PDF Downloads 11

Authors: Chau-Kuang Chen, John Hughes, Jr., A. Dexter Samuels

Abstract:

The prediction models for the United States Medical Licensure Examination (USMLE) Steps 1 and 2 performances were constructed by the Monte Carlo simulation modeling approach via linear regression. The purpose of this study was to build robust simulation models to accurately identify the most important predictors and yield the valid range estimations of the Steps 1 and 2 scores. The application of simulation modeling approach was deemed an effective way in predicting student performances on licensure examinations. Also, sensitivity analysis (a/k/a what-if analysis) in the simulation models was used to predict the magnitudes of Steps 1 and 2 affected by changes in the National Board of Medical Examiners (NBME) Basic Science Subject Board scores. In addition, the study results indicated that the Medical College Admission Test (MCAT) Verbal Reasoning score and Step 1 score were significant predictors of the Step 2 performance. Hence, institutions could screen qualified student applicants for interviews and document the effectiveness of basic science education program based on the simulation results.

Keywords: prediction model, sensitivity analysis, simulation method, USMLE

Procedia PDF Downloads 308

Authors: Charles Lemckert

Abstract:

E-Learning and E-Teaching can mean many things to different people. For some, the implication is that all material must be delivered in an E way, while for others it only forms part of the learning/teaching process, and (unfortunately) for some it is considered too much work. However, just look around and you will see all generations learning using E devices. In this study we used different forms of teaching, including E, to look at how students responded to set activities and how they performed academically. The particular context was set around a postgraduate university course where students were either present at a face-to-face intensive workshop (on water treatment plant design) or where they were not. For the latter, students needed to make sole use of E media. It is relevant to note that even though some were at the face-to-face class, they were still exposed to E material as the lecturer did use PC projections. Additionally, some also accessed the associate E material (pdf slides and video recordings) to assist their required activities. Analysis of the student performance, in their set assignment, showed that the actual form of delivery did not affect the student performance. This is because, in the end, all the students had access to the recorded/presented E material. The study also showed (somewhat expectedly) that when the material they required for the assignment was clear, the student performance did drop. Therefore, it is possible to enhance future delivery of courses through careful reflection and appropriate support. In the end, we must remember innovation is not just restricted to E.

Keywords: postgraduate, engineering, assignment, perforamance

Procedia PDF Downloads 302

Authors: B. O. Diyaolu

Abstract:

The web created by sport in academics has made it difficult for it to be separated from adolescent educational development. The enthusiasm expressed towards sport by students in higher institutions is quite enormous. Primarily, academic performance should be the pride of all students but whether sports affect the academic performance of student-athletes remain an unknown fact. This study investigated the influence of sports participation on academic performance among Afe Babalola University student-athletes. Ex post facto research design was used. Two groups of students were used for the study; Student-athlete (SA) and Regular Students (RS). Purposive sampling technique was used to select 224 student-athletes, only those that are regular in the university sports team training were considered and their records (i.e. name, department, level, matriculation number, and phone number) were collected through the assistance of their coaches. For the regular students, purposive sampling technique was used to select 224 participants, only those that have no interest in sports were considered and their records were retrieved from the college registration officer. The first and second semester examination results of the two groups were compared in 10 general study courses without their knowledge, using descriptive statistics of frequency counts, mean, and standard deviation. Out of the 10 compared courses, 7 courses result showed no significant difference between students-athlete and regular students while student-athletes perform better in 3 practically oriented courses. Sports role in academics is quite significant. Exposure to sports can help build the confidence that athletes need especially when it comes to practical courses. Student-athletes can perform better in academics if the environment is friendly and not intimidating. Lecturers and coaches need to work together in order to build a well cultured and intelligent graduate.

Keywords: academic performance, regular students, sports participation, student-athlete, university sports team

Procedia PDF Downloads 129

Authors: Bin Li, Mei Liu

Abstract:

Arrival time information is a crucial component of advanced public transport system (APTS). The advertisement of arrival time at stops can help reduce the waiting time and anxiety of passengers, and improve the quality of service. In this research, an experiment was conducted to compare the performance on prediction accuracy and precision between the link-based and the path-based historical travel time based model with the automatic vehicle location (AVL) data collected from an actual bus route. The research results show that the path-based model is superior to the link-based model, and achieves the best improvement on peak hours.

Keywords: bus transit, arrival time prediction, link-based, path-based

Procedia PDF Downloads 333

Authors: Dimitrios Vlachopoulos, George Tsokkas

Abstract:

Although it is not intended to identify distance education students as a homogeneous group, recent research has demonstrated that there are some demographic and personality common traits among most of them that provide the basis for the description of a typical distance learning student. The purpose of this paper is to describe these common traits and to facilitate their learning journey within a distance education program. The described research is an initiative of the Distance Education Unit at the European University Cyprus (Laureate International Universities) in the context of its action for the improvement of the students’ performance.

Keywords: distance education students, successful student performance, European University Cyprus, common traits

Procedia PDF Downloads 460

Authors: Giovanni Gonzalez Araujo, Michael Kyrilov, Angelo Kyrilov

Abstract:

STEAMplug is a web-based innovative educational platform which makes teaching easier and learning more effective. It requires no setup, eliminating the barriers to entry, allowing students to focus on their learning throughreal-world development environments. The student-centric tools enable easy collaboration between peers and teachers. Analyzing user interactions with the system enables us to predict student performance and identify at-risk students, allowing early instructor intervention.

Keywords: plagiarism detection, identifying at-Risk Students, education technology, e-learning system, collaborative development, learning and teaching with technology

Procedia PDF Downloads 122

Authors: Nooraisah Katmon, Hartini Jaafar, Hazianti Abdul Halim, Jessnor Elmy Mat Jizat

Abstract:

Our study empirically examines the effect of student activities on Facebook and religion on academic performance. We extend prior research in this area in a number of ways. First, given the paucity of the research in this area particularly from the Asian context, we provide the evidence from developing country like Malaysia. Second, our sample drawn from Sultan Idris Education University in Malaysia, where graduates from these universities are unique since they are expected to be able to work in both education and industry environment, and presumed to play significant roles in shaping the development of future student’s intellectual at the Malaysian secondary school and Malaysian economy in general. Third, we control for religiosity aspect when examining the association between Facebook and academic performance, something that has been predominantly neglected by the prior studies. Fourth, unlike prior studies that circulating around the Christian sphere in measuring religiosity, we provide evidence from the Islamic perspective where the act of worships and practices are much more comprehensive rather than the Christian counterparts. Fifth, we examine whether Facebook activities and religiosity are complementary or substitutive each other in improving student’s academic performance. Our sample comprise of 60 undergraduates. Our result exhibit that students with high number of friends on facebook and frequent engagement on facebook activities, such as sharing links, send message, posting photo, tagging video as well as spending long hours on facebook generally are associated with lower academic performance. Our results also reported that student’s engagement in religious activities promotes better academic performance. When we examine the potential interaction effect between facebook and religiosity, our result revealed that religiosity is effective in reducing student’s interest on facebook, hence lead to better academic achievement. In other words, religious student will be less interested in joining activities on facebook and make them more perform than their counterparts. Our findings from this study should be able to assist the university management in shaping university policies and curriculum to regulate and manage student’s activities in order to enhance overall student’s quality. Moreover, the findings from this study are also of use to the policy maker such as Malaysian Communication and Multimedia Commissions to regulate the policy on the student’s access and activities on facebook.

Keywords: facebook, religiosity, academic performance, effect of student activities

Procedia PDF Downloads 283

Authors: Feras Hanandeh, Majdi Shannag

Abstract:

This research studies the different factors that could affect the Faculty of Information Technology in Hashemite University students’ accumulative average. The research paper verifies the student information, background, their academic records, and how this information will affect the student to get high grades. The student information used in the study is extracted from the student’s academic records. The data mining tools and techniques are used to decide which attribute(s) will affect the student’s accumulative average. The results show that the most important factor which affects the students’ accumulative average is the student Acceptance Type. And we built a decision tree model and rules to determine how the student can get high grades in their courses. The overall accuracy of the model is 44% which is accepted rate.

Keywords: data mining, classification, extracting rules, decision tree

Procedia PDF Downloads 382

Authors: Sonia Arradaza-Pajaron, Maria Aida Manila

Abstract:

Teaching is considered the noblest yet believed to be one of the most complicated and challenging professions. Along this view, every teacher-producing institution should look into producing quality pre-service graduates who are efficacious enough with the right attitude and to deal with the task accorded to them. This study investigated the association between self-efficacy and attitude of graduating pre-service teachers with their actual student teaching performance. Survey questionnaires on self-efficacy and attitude toward practice teaching were fielded to the 90 actual respondents while their practice teaching grade was extracted to serve as the other main variable. Data were analyzed and treated statistically utilizing weighted mean and Pearson r to determine the relationship of variables of the study. Findings revealed that attitude of respondents of the three curricular programs was favorable, and they are self-efficacious. Their practice teaching performance was interpreted as very good. Results further showed a significant positive relationship between their self-efficacy and practice teaching performance. It showed that their rating was a manifestation of self- efficacious group. Although they exude positive attitude towards practice teaching, yet no significant relationship was seen with their attitude and performance. Moreover, data manifested that most of them can pay attention during their conduct of lessons in the class, as well as, listen attentively to their cooperating teachers during post conferences. They can perform student teaching tasks better even when there were other interesting things to do. Most of all, they can regulate or suppress not so pleasant thoughts or feelings and take things lightly even in most challenging situations. As gleaned from the results, it can be concluded that there was an association between self-efficacy and practice teaching performance of the respondents.

Keywords: academic achievement, attitude, self-efficacy, student teaching performance

Procedia PDF Downloads 288

Authors: Kanika Sood, Sijie Shang

Abstract:

A fundamental goal of education includes preparing students to become a part of the global workforce by making beneficial contributions to society. In this paper, we analyze student performance for multiple courses that involve different teaching pedagogies: a cooperative learning technique and an inquiry-based learning strategy. Student performance includes student engagement, grades, and attendance records. We perform this study in the Computer Science department for online and in-person courses for 450 students. We will perform correlation analysis to study the relationship between student scores and other parameters such as gender, mode of learning. We use natural language processing and machine learning to analyze student feedback data and performance data. We assess the learning outcomes of two teaching pedagogies for undergraduate and graduate courses to showcase the impact of pedagogical adoption and learning outcome as determinants of academic achievement. Early findings suggest that when using the specified pedagogies, students become experts on their topics and illustrate enhanced engagement with peers.

Keywords: bag-of-words, cooperative learning, education, inquiry-based learning, in-person learning, natural language processing, online learning, sentiment analysis, teaching pedagogy

Procedia PDF Downloads 43

Authors: B. Sir, M. Podhoranyi, S. Kuchar, T. Kocyan

Abstract:

Rainfall-runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15–May 18 2014). The prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice.

Keywords: flood, HEC-HMS, prediction, rainfall, runoff

Procedia PDF Downloads 362

Authors: Jelena Zivkovic, Haidar Mohammed, Elaf Saeed

Abstract:

Student affairs services play a critical role in contributing to the wholistic development and success of students as they progress through their academic careers. The study encompasses a multifaceted examination of student affairs service offerings amongst 10 private and 3 public Baghdad universities. Student affairs administrators (13) were surveyed along with over 300 students to determine university-sponsored services and student satisfaction and awareness. The student affairs service research findings varied significantly between private and public institutions and those that followed a national and international curriculum. Universities need to persist to adapt to changing demographics and technological advancements to enhance students' personal and academic successes, and student affairs services are key to preparing graduates to thrive in a diverse global world.

Keywords: student affairs, student success, higher education, Iraq, universities, Baghdad

Procedia PDF Downloads 62

Authors: Riznaldi Akbar

Abstract:

In this study, we compared the performance of the Artificial Neural Network (ANN) model with back-propagation algorithm in correctly predicting in-sample and out-of-sample external debt crisis in Indonesia. We found that exchange rate, foreign reserves, and exports are the major determinants to experiencing external debt crisis. The ANN in-sample performance provides relatively superior results. The ANN model is able to classify correctly crisis of 89.12 per cent with reasonably low false alarms of 7.01 per cent. In out-of-sample, the prediction performance fairly deteriorates compared to their in-sample performances. It could be explained as the ANN model tends to over-fit the data in the in-sample, but it could not fit the out-of-sample very well. The 10-fold cross-validation has been used to improve the out-of-sample prediction accuracy. The results also offer policy implications. The out-of-sample performance could be very sensitive to the size of the samples, as it could yield a higher total misclassification error and lower prediction accuracy. The ANN model could be used to identify past crisis episodes with some accuracy, but predicting crisis outside the estimation sample is much more challenging because of the presence of uncertainty.

Keywords: debt crisis, external debt, artificial neural network, ANN

Procedia PDF Downloads 416

Authors: S. M. Gupta, Vanita Aggarwal, Som Nath Sachdeva

Abstract:

The work reported through this paper is an experimental work conducted on High Performance Concrete (HPC) with super plasticizer with the aim to develop some models suitable for prediction of compressive strength of HPC mixes. In this study, the effect of varying proportions of fly ash (0% to 50% at 10% increment) on compressive strength of high performance concrete has been evaluated. The mix designs studied were M30, M40 and M50 to compare the effect of fly ash addition on the properties of these concrete mixes. In all eighteen concrete mixes have been designed, three as conventional concretes for three grades under discussion and fifteen as HPC with fly ash with varying percentages of fly ash. The concrete mix designing has been done in accordance with Indian standard recommended guidelines i.e. IS: 10262. All the concrete mixes have been studied in terms of compressive strength at 7 days, 28 days, 90 days and 365 days. All the materials used have been kept same throughout the study to get a perfect comparison of values of results. The models for compressive strength prediction have been developed using Linear Regression method (LR), Artificial Neural Network (ANN) and Leave One Out Validation (LOOV) methods.

Keywords: high performance concrete, fly ash, concrete mixes, compressive strength, strength prediction models, linear regression, ANN

Procedia PDF Downloads 413

Authors: Hafsat Mustapha Hanga

Abstract:

The purpose of the this study was to investigate the effect of broken home on students' academic performance. Therefore, it focused on academic performance and Parental care of the student from and intact home from a cognitive motivational perceptive. The broken and intact home and also to find out if they differ in parental care this is done by using 376 subjects out of the population of 21,378. The sample was obtained using stratified random sampling techniques as the population contained sub-groups the study design was ex-post facto. The data was collected using 3 kind of instruments. To test the first and second hypotheses. Junior secondary school placement examination result was obtained to test the academic performance of the boys fron broken home and boys from and boys from intact home and then girl from broken home and girls from intact home.T-Test was used in the analysis of first and second hypotheses. For the third hypotheses two different kind of questionnaires were developed, the first was used to identify student that are from broken home while the second was for testing parental care between the subject. Chi-square was used to analyze the third hypotheses. Alkh the three 3 hypotheses were tested and rejected and were all in favor of student from intact home. The study found that there was a significant difference in the academic performance of the boys from brokeb and boys from intact home. When boys from intact home better then those boys from broken home. It also reveals that a student from a intact from intact home receives good parental care, love and concern than those from broken home.on the strength of these findings the need to establish an institution which will help those parent who have parenting problems was stressed and also the need to foster. Home school partnership was also stressed and advocate.

Keywords: broken homes, academic performance, parental care, foster

Procedia PDF Downloads 426

Authors: Shital Suresh Borse, Vijayalaxmi Kadroli

Abstract:

E-commerce industries nowadays implement the latest AI, ML Techniques to improve their own performance and prediction accuracy. This helps to gain a huge profit from the online market. Ant Colony Optimization, Genetic algorithm, Particle Swarm Optimization, Neural Network & GWO help many e-commerce industries for up-gradation of their predictive performance. These algorithms are providing optimum results in various applications, such as stock price prediction, prediction of drug-target interaction & user ratings of similar products in e-commerce sites, etc. In this study, customer reviews will play an important role in prediction analysis. People showing much interest in buying a lot of services& products suggested by other customers. This ultimately increases net profit. In this work, a convolution neural network (CNN) is proposed which further is useful to optimize the prediction accuracy of an e-commerce website. This method shows that CNN is used to optimize hyperparameters of GWO algorithm using an appropriate coding scheme. Accurate model results are verified by comparing them to PSO results whose hyperparameters have been optimized by CNN in Amazon's customer review dataset. Here, experimental outcome proves that this proposed system using the GWO algorithm achieves superior execution in terms of accuracy, precision, recovery, etc. in prediction analysis compared to the existing systems.

Keywords: prediction analysis, e-commerce, machine learning, grey wolf optimization, particle swarm optimization, CNN

Procedia PDF Downloads 82

Authors: J. Balaraju, M. Govinda Raj, C. S. N. Murthy

Abstract:

Every industry is constantly looking for enhancement of its day to day production and productivity. This can be possible only by maintaining the men and machinery at its adequate level. Prediction of performance characteristics plays an important role in performance evaluation of the equipment. Analytical and statistical approaches will take a bit more time to solve complex problems such as performance estimations as compared with software-based approaches. Keeping this in view the present study deals with an Artificial Neural Network (ANN) modelling of a Load-Haul-Dump (LHD) machine to predict the performance characteristics such as reliability, availability and preventive maintenance (PM). A feed-forward-back-propagation ANN technique has been used to model the Levenberg-Marquardt (LM) training algorithm. The performance characteristics were computed using Isograph Reliability Workbench 13.0 software. These computed values were validated using predicted output responses of ANN models. Further, recommendations are given to the industry based on the performed analysis for improvement of equipment performance.

Keywords: load-haul-dump, LHD, artificial neural network, ANN, performance, reliability, availability, preventive maintenance

Procedia PDF Downloads 111

Authors: Seongjun Kim, Sanghoon Shim, Jinwooung Kim, Jaehwan Jung, Sung-Ah Kim

Abstract:

As the development of information and communication technology, the convergence of machine learning of the ICT area and design is attempted. In this way, it is possible to grasp the correlation between various design elements, which was difficult to grasp, and to reflect this in the design result. In architecture, there is an attempt to predict the performance, which is difficult to grasp in the past, by finding the correlation among multiple factors mainly through machine learning. In architectural design area, some attempts to predict the performance affected by various factors have been tried. With machine learning, it is possible to quickly predict performance. The aim of this study is to propose a model that predicts performance according to the block arrangement of apartment housing through machine learning and the design alternative which satisfies the performance such as the daylight hours in the most similar form to the alternative proposed by the designer. Through this study, a designer can proceed with the design considering various design alternatives and accurate performances quickly from the early design stage.

Keywords: apartment housing, machine learning, multi-objective optimization, performance prediction

Procedia PDF Downloads 442

Authors: Mohammad Ghavami, Reza S. Dilmaghani

Abstract:

This paper presents an adaptive framework for modelling financial markets using equity risk premiums, risk free rates and volatilities. The recorded economic factors are initially used to train four adaptive filters for a certain limited period of time in the past. Once the systems are trained, the adjusted coefficients are used for modelling and prediction of an important financial market index. Two different approaches based on least mean squares (LMS) and recursive least squares (RLS) algorithms are investigated. Performance analysis of each method in terms of the mean squared error (MSE) is presented and the results are discussed. Computer simulations carried out using recorded data show MSEs of 4% and 3.4% for the next month prediction using LMS and RLS adaptive algorithms, respectively. In terms of twelve months prediction, RLS method shows a better tendency estimation compared to the LMS algorithm.

Keywords: adaptive methods, LSE, MSE, prediction of financial Markets

Procedia PDF Downloads 300

Authors: Zhou Yang, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

Abstract:

Traffic prediction is essential in a multitude of ways in modern urban life. The researchers of earlier work in this domain carry out the investigation chiefly with two major focuses: (1) the accurate forecast of future values in multiple time series and (2) knowledge extraction from spatial-temporal correlations. However, two key considerations for traffic prediction are often missed: the completeness of raw data and the full context of the prediction timestamp. Concentrating on the two drawbacks of earlier work, we devise an approach that can address these issues in a two-phase framework. First, we utilize the raw trajectories to a greater extent through building a VLA table and data compression. We obtain the intra-trajectory features with graph-based encoding and the intertrajectory ones with a grid-based model and the technique of back projection that restore their surrounding high-resolution spatial-temporal environment. To the best of our knowledge, we are the first to study direct feature extraction from raw trajectories for traffic prediction and attempt the use of raw data with the least degree of reduction. In the prediction phase, we provide a broader context for the prediction timestamp by taking into account the information that are around it in the training dataset. Extensive experiments on several well-known datasets have verified the effectiveness of our solution that combines the strength of raw trajectory data and prediction context. In terms of performance, our approach surpasses several state-of-the-art methods for traffic prediction.

Keywords: traffic prediction, raw data utilization, context building, data reduction

Procedia PDF Downloads 93

Authors: Hadi Molaei, Yasavoli Davoud, Keshavarz, Mozhde Poordana

Abstract:

The present study aimed was to prediction the academic performance based on academic motivation, self-efficacy and Resiliency in the students. The present study was descriptive and correlational. Population of the study consisted of all students in Arak schools in year 1393-94. For this purpose, the number of 304 schools students in Arak was selected using multi-stage cluster sampling. They all questionnaires, self-efficacy, Resiliency and academic motivation Questionnaire completed. Data were analyzed using Pearson correlation and multiple regressions. Pearson correlation showed academic motivation, self-efficacy, and Resiliency with academic performance had a positive and significant relationship. In addition, multiple regression analysis showed that the academic motivation, self-efficacy and Resiliency were predicted academic performance. Based on the findings could be conclude that in order to increase the academic performance and further progress of students must provide the ground to strengthen academic motivation, self-efficacy and Resiliency act on them.

Keywords: academic motivation, self-efficacy, resiliency, academic performance

Procedia PDF Downloads 447

Authors: Indhumathi Gopal, Ashley Johnson

Abstract:

Social media use for communication among college student-athletes is growing. There is little research on student-athletes use of Blogs, one of the online communication tool outlets. Twenty-seven student-athletes, aged 18-24 years completed a student perception questionnaire which assessed demographics, the effect of blogging on college student-athletes self-concept, the correlation of age, GPA and blogging as well as the training students received in the use of social media. Descriptive statistics and Pearson correlations were analyzed examined. Results indicated a significant correlation between use of Blogs and student age (p < .01) and student GPA earned (p < .01). With respect to self-concept, results suggest that blogging could be a useful tool for communication but can present challenges, could affect student self-esteem either, positively or negatively. The training student-athletes received in the use of social media was not adequate. College athletes’ can more easily divulge information about their personal lives and opinions on social media and challenge the athletic programs and their own future. The findings of the study suggest implications for student-athletes to be better prepared for the current media climate.

Keywords: college student-athletes, self-concept, use of social media training, social networking

Procedia PDF Downloads 561

Authors: Abderrazek Ben Maatoug, Ibrahim Fatnassi, Wassim Ben Ayed

Abstract:

In this paper we investigate the issue of market risk quantification for Middle East and North Africa (MENA) Islamic market equity. We use Value-at-Risk (VaR) as a measure of potential risk in Islamic stock market, for long and short position, based on Riskmetrics model and the conditional parametric ARCH class model volatility with normal, student and skewed student distribution. The sample consist of daily data for the 2006-2014 of 11 Islamic stock markets indices. We conduct Kupiec and Engle and Manganelli tests to evaluate the performance for each model. The main finding of our empirical results show that (i) the superior performance of VaR models based on the Student and skewed Student distribution, for the significance level of α=1% , for all Islamic stock market indices, and for both long and short trading positions (ii) Risk Metrics model, and VaR model based on conditional volatility with normal distribution provides the best accurate VaR estimations for both long and short trading positions for a significance level of α=5%.

Keywords: value-at-risk, risk management, islamic finance, GARCH models

Procedia PDF Downloads 555

Authors: Hyun-Woo Cho

Abstract:

Detection of incipient abnormal events in production processes is important to improve safety and reliability of manufacturing operations and reduce losses caused by failures. The construction of calibration models for predicting faulty conditions is quite essential in making decisions on when to perform preventive maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of process measurement data. The calibration model is used to predict faulty conditions from historical reference data. This approach utilizes variable selection techniques, and the predictive performance of several prediction methods are evaluated using real data. The results shows that the calibration model based on supervised probabilistic model yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.

Keywords: calibration model, monitoring, quality improvement, feature selection

Procedia PDF Downloads 329

Authors: Mark Deschaine, David Whale

Abstract:

Utilization of online instruction continues to increase at universities, placing more emphasis on the exploration of issues related to adult graduate student engagement. This reflective case study reviews non-traditional student engagement in online courses. The goals of the study are to enhance student focus, attention and interaction. Findings suggest that interactivity seemed to be a key in keeping students involved and achieving, with specific activities routinely favored by students. It is recommended that time spent engaging students is worthwhile and results in greater course satisfaction and academic effort.

Keywords: online learning, student achievement, student engagement, technology

Procedia PDF Downloads 313

Authors: Mohammadreza Mohebbi, Masoumeh Sanagou

Abstract:

The ability to predict clinical outcomes is of great importance to physicians and clinicians. A number of different methods have been used in an effort to accurately predict these outcomes. These methods include the development of scoring systems based on multivariate statistical modelling, and models involving the use of classification and regression trees. The process usually consists of two consecutive phases, namely model development and external validation. The model development phase consists of building a multivariate model and evaluating its predictive performance by examining calibration and discrimination, and internal validation. External validation tests the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. A motivate example focuses on prediction modeling using a sample of patients undergone coronary artery bypass graft (CABG) has been used for illustrative purpose and a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study has been proposed.

Keywords: clinical prediction models, clinical decision rule, prognosis, external validation, model calibration, biostatistics

Procedia PDF Downloads 264

Authors: Muhammet Baldan, Emel Timuçin

Abstract:

Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, molecular descriptors, machine learning, random forest

Procedia PDF Downloads 16

Authors: Robert Cui

Abstract:

COVID-19 has drastically changed the way students interact and engage with their environments. Students, in particular, have been forced to change from in-person to online learning. How can we ensure that students continue to remain motivated even as their mode of education transitions to online learning? In this study conducted on high school students from a small private school (n = 50), we investigate the factors that predict student motivation during online learning. Using the framework of self-determination theory, we examine the three facets of student motivation during online learning: engagement, autonomy, and competence. We find that students' perception of their peers' engagement with the curriculum, feelings of parental academic expectations, perceptions of favoritism by the teacher, and perceived clarity of instruction given by the teacher all predict student engagement in online learning. Student autonomy is predicted by the amount of parental control a student feels, the clarity of instruction given by the teacher, and also the amount to which a student is perceiving their peers to be paying attention. Finally, competence is predicted by favoritism a student perceives from a teacher and also the amount of which a student is perceiving their peers to be paying attention. Based on these findings, we provide insights on how three important stakeholders –parents, teachers, and peers can enhance students' motivation during online learning.

Keywords: academic performance, motivation, online learning, parental influence, teacher, peers

Procedia PDF Downloads 114