Search results for: student performance prediction

Authors: Sasitharan Ambicapathy, J. Vignesh, P. Sivaraj, Godfrey Derek Sams, K. Sabarinath, V. R. Sanal Kumar

Abstract:

The external flow simulation of a flying car at take off phase is a daunting task owing to the fact that the prediction of the transient unsteady flow features during its deployment phase is very complex. In this paper 3D numerical simulations of external flow of Ferrari F430 proposed flying car with different NACA 9618 rectangular wings have been carried. Additionally, the aerodynamics characteristics have been generated for optimizing its geometry for achieving the minimum take off velocity with better overall performance in both road and air. The three-dimensional standard k-omega turbulence model has been used for capturing the intrinsic flow physics during the take off phase. In the numerical study, a fully implicit finite volume scheme of the compressible, Reynolds-Averaged, Navier-Stokes equations is employed. Through the detailed parametric analytical studies we have conjectured that Ferrari F430 flying car facilitated with high wings having three different deployment histories during the take off phase is the best choice for accomplishing its better performance for the commercial applications.

Keywords: aerodynamics of flying car, air taxi, negative lift, roadable airplane

Procedia PDF Downloads 416

Authors: Walid Moudani

Abstract:

The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the coronary heart disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: multi-classifier decisions tree, features reduction, dynamic programming, rough sets

Procedia PDF Downloads 405

Authors: Adam Jenkins

Abstract:

The Maritime RobotX Challenge provides a platform for postgraduate students conducting research in autonomous robotic systems to participate in an international competition. Although targeted to postgraduate students, the problem domain lends itself to a wide range of different levels of student expertise. In 2022, undergraduate Information Technology students from the University of South Australia undertook the challenge, utilizing a System of the Systems approach to the project's architecture. Each student group produced an independent solution to an identified task, which was then implemented on a Single Board Computer (SBC). A Central Control System then engaged each solution when appropriate, allowing the encapsulated SBC systems to manage each task as it was encountered. This approach facilitated collaboration among the multiple independent student teams over an 18-month period, and the fundamental system-agnostic architecture allowed for both the variance in student solutions and the limitations caused by the global electronics shortage. By adopting this approach, Information Technology teams were able to work independently yet produce an effective solution, leveraging their expertise to develop and construct an autonomous catamaran capable of meeting the competition's demanding requirements while producing a high level of engagement. The System of Systems approach is recommended to other universities interested in competing at this level and engaging students in a real-world problem.

Keywords: case study, robotics, education, programming, system of systems, multi-disciplinary collaboration

Procedia PDF Downloads 72

Authors: Praveen Chandramenon, Andrew Gascoyne, Fideline Tchuenbou-Magaia

Abstract:

Aquaponics offers a simple conclusive solution to the food and environmental crisis of the world. This approach combines the idea of Aquaculture (growing fish) to Hydroponics (growing vegetables and plants in a soilless method). Smart Aquaponics explores the use of smart technology including artificial intelligence and IoT, to assist farmers with better decision making and online monitoring and control of the system. Identification of different growth stages of Swiss Chard plants and predicting its harvest time is found to be important in Aquaponic yield management. This paper brings out the comparative analysis of a standard Aquaponics with a Vermiponics (Aquaponics with worms), which was grown in the controlled environment, by implementing IoT and deep learning-based growth stage segregation and harvest time prediction of Swiss Chards before and after applying an optimal freshwater replenishment. Data collection, Growth stage classification and Harvest Time prediction has been performed with and without water replenishment. The paper discusses the experimental design, IoT and sensor communication with architecture, data collection process, image segmentation, various regression and classification models and error estimation used in the project. The paper concludes with the results comparison, including best models that performs growth stage segregation and harvest time prediction of the Aquaponic and Vermiponic testbed with and without freshwater replenishment.

Keywords: aquaponics, deep learning, internet of things, vermiponics

Procedia PDF Downloads 61

Authors: Sara Bilal, Abdi Omar Shuriye, Raihan Othman

Abstract:

Numerical Methods is a course that can be conducted using workshops and group discussion. This study has been implemented on undergraduate students of level two at the Faculty of Engineering, International Islamic University Malaysia. The Numerical Method course has been delivered to two Sections 1 and 2 with 44 and 22 students in each section, respectively. Systematic steps have been followed to apply the student centered learning approach in teaching Numerical Method course. Initially, the instructor has chosen the topic which was Euler’s Method to solve Ordinary Differential Equations (ODE) to be learned. The students were then divided into groups with five members in each group. Initial instructions have been given to the group members to prepare their subtopics before meeting members from other groups to discuss the subtopics in an expert group inside the classroom. For the time assigned for the classroom discussion, the setting of the classroom was rearranged to accommodate the student centered learning approach. Teacher strength was by monitoring the process of learning inside and outside the class. The students have been assessed during the migrating to the expert groups, recording of a video explanation outside the classroom and during the final examination. Euler’s Method to solve the ODE was set as part of Question 3(b) in the final exam. It is observed that none of the students from both sections obtained a zero grade in Q3(b), compared to Q3(a) and Q3(c). Also, for Section 1(44 students), 29 students obtained the full mark of 7/7, while only 10 obtained 7/7 for Q3(a) and no students obtained 6/6 for Q3(c). Finally, we can recommend that the Numerical Method course be moved toward more student-centered Learning classrooms where the students will be engaged in group discussion rather than having a teacher one man show.

Keywords: teacher centered learning, student centered learning, mathematic, numerical methods

Procedia PDF Downloads 359

Authors: Vaidehi Iyer, Konstantin Borozdin

Abstract:

Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.

Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning

Procedia PDF Downloads 224

Authors: Hossein Havaeji, Tony Wong, Thien-My Dao

Abstract:

1. Research Problems and Research Objectives: Blockchain Technology-enabled Supply Chain System (BT-enabled SCS) is the system using BT to drive SCS transparency, security, durability, and process integrity as SCS data is not always visible, available, or trusted. The costs of operating BT in the SCS are a common problem in several organizations. The costs must be estimated as they can impact existing cost control strategies. To account for system and deployment costs, it is necessary to overcome the following hurdle. The problem is that the costs of developing and running a BT in SCS are not yet clear in most cases. Many industries aiming to use BT have special attention to the importance of BT installation cost which has a direct impact on the total costs of SCS. Predicting BT installation cost in SCS may help managers decide whether BT is to be an economic advantage. The purpose of the research is to identify some main BT installation cost components in SCS needed for deeper cost analysis. We then identify and categorize the main groups of cost components in more detail to utilize them in the prediction process. The second objective is to determine the suitable Supervised Learning technique in order to predict the costs of developing and running BT in SCS in a particular case study. The last aim is to investigate how the running BT cost can be involved in the total cost of SCS. 2. Work Performed: Applied successfully in various fields, Supervised Learning is a method to set the data frame, treat the data, and train/practice the method sort. It is a learning model directed to make predictions of an outcome measurement based on a set of unforeseen input data. The following steps must be conducted to search for the objectives of our subject. The first step is to make a literature review to identify the different cost components of BT installation in SCS. Based on the literature review, we should choose some Supervised Learning methods which are suitable for BT installation cost prediction in SCS. According to the literature review, some Supervised Learning algorithms which provide us with a powerful tool to classify BT installation components and predict BT installation cost are the Support Vector Regression (SVR) algorithm, Back Propagation (BP) neural network, and Artificial Neural Network (ANN). Choosing a case study to feed data into the models comes into the third step. Finally, we will propose the best predictive performance to find the minimum BT installation costs in SCS. 3. Expected Results and Conclusion: This study tends to propose a cost prediction of BT installation in SCS with the help of Supervised Learning algorithms. At first attempt, we will select a case study in the field of BT-enabled SCS, and then use some Supervised Learning algorithms to predict BT installation cost in SCS. We continue to find the best predictive performance for developing and running BT in SCS. Finally, the paper will be presented at the conference.

Keywords: blockchain technology, blockchain technology-enabled supply chain system, installation cost, supervised learning

Procedia PDF Downloads 114

Authors: Purevdolgor Luvsantseren, Ajnai Luvsan-Ish, Oyuntsetseg Sandag, Javzmaa Tsend, Akhit Tileubai, Baasandorj Chilhaasuren, Jargalbat Puntsagdash, Galbadrakh Chuluunbaatar

Abstract:

One of the indicators of the quality of university services is student satisfaction. Aim: We aimed to study the level of satisfaction of students in the first year of premedical courses in the course of Medical Physics using the cluster method. Materials and Methods: In the framework of this goal, a questionnaire was collected from a total of 324 students who studied the medical physics course of the 1st course of the premedical course at the Mongolian National University of Medical Sciences. When determining the level of satisfaction, the answers were obtained on five levels of satisfaction: "excellent", "good", "medium", "bad" and "very bad". A total of 39 questionnaires were collected from students: 8 for course evaluation, 19 for teacher evaluation, and 12 for student evaluation. From the research, a database with 39 fields and 324 records was created. Results: In this database, cluster analysis was performed in MATLAB and R programs using the k-means method of data mining. Calculated the Hopkins statistic in the created database, the values are 0.88, 0.87, and 0.97. This shows that cluster analysis methods can be used. The course evaluation sub-fund is divided into three clusters. Among them, cluster I has 150 objects with a "good" rating of 46.2%, cluster II has 119 objects with a "medium" rating of 36.7%, and Cluster III has 54 objects with a "good" rating of 16.6%. The teacher evaluation sub-base into three clusters, there are 179 objects with a "good" rating of 55.2% in cluster II, 108 objects with an "average" rating of 33.3% in cluster III, and 36 objects with an "excellent" rating in cluster I of 11.1%. The sub-base of student evaluations is divided into two clusters: cluster II has 215 objects with an "excellent" rating of 66.3%, and cluster I has 108 objects with an "excellent" rating of 33.3%. Evaluating the resulting clusters with the Silhouette coefficient, 0.32 for the course evaluation cluster, 0.31 for the teacher evaluation cluster, and 0.30 for student evaluation show statistical significance. Conclusion: Finally, to conclude, cluster analysis in the model of the medical physics lesson “good” - 46.2%, “middle” - 36.7%, “bad” - 16.6%; 55.2% - “good”, 33.3% - “middle”, 11.1% - “bad” in the teacher evaluation model; 66.3% - “good” and 33.3% of “bad” in the student evaluation model.

Keywords: questionnaire, data mining, k-means method, silhouette coefficient

Procedia PDF Downloads 40

Authors: Jens Friedrich, Matthias A. Gebele, Armin Lechler, Alexander Verl

Abstract:

Chatter vibrations and process instabilities are the most important factors limiting the productivity of the milling process. Chatter can leads to damage of the tool, the part or the machine tool. Therefore, the estimation and prediction of the process stability is very important. The process stability depends on the spindle speed, the depth of cut and the width of cut. In milling, the process conditions are defined in the NC-program. While the spindle speed is directly coded in the NC-program, the depth and width of cut are unknown. This paper presents a new simulation based approach for the prediction of the depth and width of cut of a milling process. The prediction is based on a material removal simulation with an analytically represented tool shape and a multi-dexel approach for the work piece. The new calculation method allows the direct estimation of the depth and width of cut, which are the influencing parameters of the process stability, instead of the removed volume as existing approaches do. The knowledge can be used to predict the stability of new, unknown parts. Moreover with an additional vibration sensor, the stability lobe diagram of a milling process can be estimated and improved based on the estimated depth and width of cut.

Keywords: dexel, process stability, material removal, milling

Procedia PDF Downloads 515

Authors: Diqin Qi, Jiaming Li, Siman Li

Abstract:

Based on the use of the three-point smoothing method for selectively processing original data columns, this paper establishes a group of grey GM(1,1) models to predict the concentration ranges of four major air pollutants in Shanghai from 2023 to 2024. The results indicate that PM₁₀, SO₂, and NO₂ maintain the national Grade I standards, while the concentration of PM₂.₅ has decreased but still remains within the national Grade II standards. Combining the forecast results, recommendations are provided for the Shanghai municipal government's efforts in air pollution prevention and control.

Keywords: atmospheric pollutant prediction, Grey GM(1, 1), model group, three-point smoothing method

Procedia PDF Downloads 32

Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

Abstract:

Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: instance selection, data reduction, MapReduce, kNN

Procedia PDF Downloads 249

Authors: Huseyin Demir

Abstract:

Foreign language learners are able to make mistakes in their pronunciation and writing when they encounter with alphabetical indications that are not available in their own language. The fifth-grade students who learn Turkish language at Demirel College in Georgia constitute the concrete example. ‘F’, ‘y’, ‘ö’, ‘ü’ letters in the Turkish alphabet are the most common mistakes they make. After a careful comparative linguistic study, it was found out that the mistakes caused by the fact that these signs were not available in Georgian. These problems have been tried to be solved through comparative language teaching method by using the pronunciation possibilities in other languages, which are spoken or known by students. First of all, other languages known by students are identified, the similar pronunciation difficulties in Turkish are also found in those languages in order to minimize the pronunciation problem in Turkish, pronunciation possibilities are that are available in those language are utilized. In this context, visual animations are made for pronunciation of English words such as year (yr), earn (örn), fair (fêir) and made student familiar with pronunciation with these words through repetition. With this study, it is observed that student’s motivation has been increased and with these indications, student’s mistakes are minimized.

Keywords: pronunciation, Demirel college, motivations, Turkish as a foreign language

Procedia PDF Downloads 245

Authors: Lina S. Calucag

Abstract:

Integration of research and teaching in higher education can provide valuable ways of enhancing the student learning experience, but establishing such integrative links can be complex and problematic, given different practices and levels of understanding. This study contributes to the pedagogical literature in drawing on findings from students’ survey exploring perceptions of research-informed teaching to examine how links between research and teaching can be suitably strengthened. The study employed a descriptive research design limited to the undergraduate students taking thesis/capstone courses in the tertiary levels private or public colleges and universities across the globe as respondents of the study. The findings noted that the students’ responses from different disciplines: engineering, science, education, business-related, and computer on the nexus between research and teaching is remarkable in fostering student expectations of research-informed teaching approaches. Students’ expectations on research-led, research-oriented, research-based, and research-tutored are enablers in linking research and teaching. It is recommended that experimental studies should be conducted using the four different research-informed teaching approaches in the classroom, namely: research-led, research-oriented, research-based, and research-tutored.

Keywords: research-led, research-informed teaching, research-oriented teaching, research-tutored, research-based

Procedia PDF Downloads 151

Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

Abstract:

The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

Procedia PDF Downloads 148

Authors: Aidan J. Bowes, Reaz Hasan

Abstract:

The automotive industry is continually aiming to develop the aerodynamics of car body design. This may be for a variety of beneficial reasons such as to increase speed or fuel efficiency by reducing drag. However recently there has been a greater amount of focus on wind noise produced while driving. Designers in this industry seek a combination of both simplicity of approach and overall effectiveness. This combined with the growing availability of commercial CFD (Computational Fluid Dynamics) packages is likely to lead to an increase in the use of RANS (Reynolds Averaged Navier-Stokes) based CFD methods. This is due to these methods often being simpler than other CFD methods, having a lower demand on time and computing power. In this investigation the effectiveness of turbulent flow and acoustic noise prediction using RANS based methods has been assessed for different wing mirror geometries. Three different RANS based models were used, standard k-ε, realizable k-ε and k-ω SST. The merits and limitations of these methods are then discussed, by comparing with both experimental and numerical results found in literature. In general, flow prediction is fairly comparable to more complex LES (Large Eddy Simulation) based methods; in particular for the k-ω SST model. However acoustic noise prediction still leaves opportunities for more improvement using RANS based methods.

Keywords: acoustics, aerodynamics, RANS models, turbulent flow

Procedia PDF Downloads 441

Authors: Ajai Singh

Abstract:

Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.

Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation

Procedia PDF Downloads 357

Authors: Parthiban Srinivasan

Abstract:

With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

Procedia PDF Downloads 354

Authors: Nishank Raisinghani

Abstract:

Efficiently predicting drug response remains a challenge in the realm of drug discovery. To address this issue, we propose four model architectures that combine graphical representation with varying positions of multiheaded self-attention mechanisms. By leveraging two types of multi-omics data, transcriptomics and genomics, we create a comprehensive representation of target cells and enable drug response prediction in precision medicine. A majority of our architectures utilize multiple transformer models, one with a graph attention mechanism and the other with a multiheaded self-attention mechanism, to generate latent representations of both drug and omics data, respectively. Our model architectures apply an attention mechanism to both drug and multiomics data, with the goal of procuring more comprehensive latent representations. The latent representations are then concatenated and input into a fully connected network to predict the IC-50 score, a measure of cell drug response. We experiment with all four of these architectures and extract results from all of them. Our study greatly contributes to the future of drug discovery and precision medicine by looking to optimize the time and accuracy of drug response prediction.

Keywords: drug discovery, transformers, graph neural networks, multiomics

Procedia PDF Downloads 142

Authors: Hanwei Cheng, Chengbei Xu, Yijie Wang

Abstract:

More and more college students are accustomed to surfing the Internet everyday. As community members, college students have ability to express opinions and participate in social affairs, they not only accept information passively, but also voice their concerns on the Internet. We interpret the online discourses featured with anonymization, so it helps us more effectively and conveniently understand the behaviors and thoughts of college students, and educators can thus grasp the scales and directions in guiding online language. We analyzed online comments in both content and form aspects in one student forum (named Dandan, the BNU’s campus forum), and through methods of literature review and interview, we found that in term of content, college students pay attention to practical information online, emphasize on personal development and pursue hot issues; in term of form, college students' online language displays cross-border quality sometimes under the general feature of normative, and they often explore a certain topic in the form of question or discussion, and they like to show feelings in ironic and stream-of-consciousness ways. It is argued that college students intend to establish a community to facilitate personal development and meet emotional needs through the student forum, and by making comments at the forum they are also able to get involved in public affairs. We should pay attention to problems of college students' online discourse, such as boundary issues (like informal advertisement and information authenticity), emotional issues and the spread of gossip. Some possible solutions to solving online discourse problems can be applied, like we can improve access systems of student forum, clarify principles of Internet langue use, change oversimplified management approaches and use some other tactics, in order to form a mechanism of student self-regulation, also deepen the trust and cooperation between school administrators and students.

Keywords: online language, youth discourse, content and form, implication and strategy

Procedia PDF Downloads 133

Authors: Donna H. Gabor

Abstract:

This study aimed to develop and validate an electronic module in physics for first-year college students of Iloilo and find out if there would be a significant difference in the performance of students before and after using the electronic module. The e-module was composed of one topic with two sub-lessons in linear motion (kinematics). The participants of the study were classified into three groups: the subject matter experts who are physics instructors who suggested the content, physical appearance, and limitations of the e-module; the IT experts who are active both in teaching and developing computer programs; and 28 students divided into two groups, 15 in the pilot group and 13 in the final test group. A researcher created 30 items checklist form (difficulty of a sample problem, comprehension, application, and definition of terms) was prepared and validated by the experts in subject matter for gathering data. To test the difference in student performance in physics, the researcher prepared an achievement test containing 25 items, multiple choices. The findings revealed that there was an increase in the performance of students in the pretest and post-test. T-test results revealed that there was a significant difference in the test scores of the students before and after using the module which can be used as a future reference for linear motion as an additional teaching tool in physics.

Keywords: electronic module, kinematics, linear motion, physics

Procedia PDF Downloads 127

Authors: Sulmaz Mozaffari, Zahra Mozaffari, Saman Mozaffari

Abstract:

Training is a counting and orderly process which purpose is to grow all as peals of the students to get the human knowledge and have the social norms. Also to help them grow their talents. Social science as in educational and training science at the sometime is very important for schools and universities. Unfortunately the method which is mostly used for teaching and training at present is student- teacher method and because of its ease the other methods are ignored. This research is to consider the most efficient methods in social science and analyse them. The Results show that the best methods in which the students are present during the teaching procedure.

Keywords: social science, methodology, student base methodology, technology

Procedia PDF Downloads 429

Authors: Thanawit Tongmai, Chatchawan Saewor

Abstract:

Learning management is very important for students’ development. To promote students’ potential, the teacher should design appropriate learning activity that brings their students potential out. Problem based learning has been using worldwide and it has presented numerous of success. This research aims to study third year students’ attitude and their achievement in scientific research course. To find the results, mix method was used to design research conduction. The researcher used PBL and reflection activity in the class. The students had to choose a topic, reviewed information, designed experimental, wrote academic report and presented their research by themselves. The researcher was only a facilitator. Reflection activity was used to progressing and consulting their research. The data was collected along with research conduction by questionnaire and test, including attitude, opinion and their achievement. The result of this study showed that 74.71% from all of students (n = 87) benefited from PBL and reflection activity, while 25.19% were just satisfied. 100% of students had a positive reflection toward PBL activity and they believed that PBL was the best pedagogy method for scientific research course. The achievements of these students were higher than the previous study (P < 0.05). The student’s learning achievement, A, B+ and B, was 48.28, 28.74 and 22.98% respectively. Therefore, it can conclude that PBL activity is appropriate for scientific research course and it can also promote student’s achievement.

Keywords: reflection, attitude, learning, achievement, PBL

Procedia PDF Downloads 278

Authors: Amira Abdelrasoul, Huu Doan, Ali Lohi

Abstract:

An efficient and well-planned ultrafiltration process is becoming a necessity for monetary returns in the industrial settings. The aim of the present study was to develop a mathematical model for an accurate prediction of ultrafiltration membrane fouling of latex effluent applied to homogeneous and heterogeneous membranes with uniform and non-uniform pore sizes, respectively. The models were also developed for an accurate prediction of power consumption that can handle the large-scale purposes. The model incorporated the fouling attachments as well as chemical and physical factors in membrane fouling for accurate prediction and scale-up application. Both Polycarbonate and Polysulfone flat membranes, with pore sizes of 0.05 µm and a molecular weight cut-off of 60,000, respectively, were used under a constant feed flow rate and a cross-flow mode in ultrafiltration of the simulated paint effluent. Furthermore, hydrophilic ultrafilic and hydrophobic PVDF membranes with MWCO of 100,000 were used to test the reliability of the models. Monodisperse particles of 50 nm and 100 nm in diameter, and a latex effluent with a wide range of particle size distributions were utilized to validate the models. The aggregation and the sphericity of the particles indicated a significant effect on membrane fouling.

Keywords: membrane fouling, mathematical modeling, power consumption, attachments, ultrafiltration

Procedia PDF Downloads 466

Authors: Radin Siti Aishah Radin A Rahman, Norasmah Othman, Zaidatol Akmaliah Lope Pihie, Hariyaty Ab. Wahid

Abstract:

The recent instability in economy was found to be influencing the situation in Malaysia whether directly or indirectly. Taking that into consideration, the government needs to find the best approach to balance its citizen’s socio-economic strata level urgently. Through education platform is among the efforts planned and acted upon for the purpose of balancing the effects of the influence, through the exposure of social entrepreneurial activity towards youth especially those in higher institution level. Armed with knowledge and skills that they gained, with the support by entrepreneurial culture and environment while in campus; indirectly, the students will lean more on making social entrepreneurship as a career option when they graduate. Following the issues of marketability and workability of current graduates that are becoming dire, research involving how far the willingness of student to create social innovation that contribute to the society without focusing solely on personal gain is relevant enough to be conducted. With that, this research is conducted with the purpose of identifying the level of entrepreneurial intention and social entrepreneurship among higher institution students in Malaysia. Stratified random sampling involves 355 undergraduate students from five public universities had been made as research respondents and data were collected through surveys. The data was then analyzed descriptively using min score and standard deviation. The study found that the entrepreneurial intention of higher education students are on moderate level, however it is the contrary for social entrepreneurship activities, where it was shown on a high level. This means that while the students only have moderate level of willingness to be a social entrepreneur, they are very committed to created social innovation through the social entrepreneurship activities conducted. The implication from this study can be contributed towards the higher institution authorities in prediction the tendency of student in becoming social entrepreneurs. Thus, the opportunities and facilities for realizing the courses related to social entrepreneurship must be created expansively so that the vision of creating as many social entrepreneurs as possible can be achieved.

Keywords: entrepreneurial intention, higher education institutions (HEIs), social entrepreneurship, social entrepreneurial activity, gender

Procedia PDF Downloads 257

Authors: Dewi Juliah Ratnaningsih, Imas Sukaesih Sitanggang

Abstract:

Classification is one of data mining techniques that aims to discover a model from training data that distinguishes records into the appropriate category or class. Data mining classification methods can be applied in education, for example, to determine the classification of non-active students in Indonesia Open University. This paper presents a comparison of three methods of classification: Naïve Bayes, Bagging, and C.45. The criteria used to evaluate the performance of three methods of classification are stratified cross-validation, confusion matrix, the value of the area under the ROC Curve (AUC), Recall, Precision, and F-measure. The data used for this paper are from the non-active Indonesia Open University students in registration period of 2004.1 to 2012.2. Target analysis requires that non-active students were divided into 3 groups: C1, C2, and C3. Data analyzed are as many as 4173 students. Results of the study show: (1) Bagging method gave a high degree of classification accuracy than Naïve Bayes and C.45, (2) the Bagging classification accuracy rate is 82.99 %, while the Naïve Bayes and C.45 are 80.04 % and 82.74 % respectively, (3) the result of Bagging classification tree method has a large number of nodes, so it is quite difficult in decision making, (4) classification of non-active Indonesia Open University student characteristics uses algorithms C.45, (5) based on the algorithm C.45, there are 5 interesting rules which can describe the characteristics of non-active Indonesia Open University students.

Keywords: comparative analysis, data mining, clasiffication, Bagging, Naïve Bayes, C.45, non-active students, Indonesia Open University

Procedia PDF Downloads 308

Authors: Jareeluk Ratanaphan

Abstract:

The purposed of this research were; 1. To survey the teacher’s needs on knowledge about special education management for special needs student 2. Development of distance training packages for teacher on special education management for special needs student 3. to study the effects of using the packages on trainee’s achievement 4. to study the effects of using the packages on trainee’s opinion on the distance training packages. The design of the experiment was research and development. The research sample for survey were 86 teachers, and 22 teachers for study the effects of using the packages on achievement and opinion. The research instrument comprised: 1) training packages on special education management for special needs student 2) achievement test 3) questionnaire. Mean, percentage, standard deviation, t-test and content analysis were used for data analysis. The findings of the research were as follows: 1. The teacher’s needs on knowledge about teaching for a learner with learning disability, mental retardation, autism, physical and health impairment and research in special education. 2. The package composed of special education management for special needs student document and manual of distance training packages. The document consisted by the name of packages, the explanation for the educator, content’s structure, concept, objectives, content and activities. Manual of distance training packages consisted by the explanation about a document, objectives, explanation about using the package, training schedule, and evaluation. The efficiency of packages was established at 79.50/81.35. 3. The results of using the packages were the posttest average scores of trainee’s achievement were higher than the pretest. 4. The trainee’s opinion on the package was at the highest level.

Keywords: distance training package, teacher, learner with special needs

Procedia PDF Downloads 481

Authors: Jaisridhar P.

Abstract:

The present study aimed to investigate preferred teaching styles of young faculties in agricultural education among 23 constituent colleges of Tamil Nadu Agricultural University (TNAU) using Staffordshire Evaluation of Teaching Styles (SETS). An onlinesurvey was conducted among 156 young faculties of 2014 Batch working in different constituent colleges of TNAU and 73 faculties respondent to the survey. The results showed that 62.53 percent preferred “The one-off teacher” stylefollowed by62.26 percent preferring “The student centered, sensitive teacher” style.“The all-round flexible and adaptable teaching style” was preferred by 61.64 percent. The Official Curriculum Teacher” with 61.23 per cent preferring this style.58.97 per cent preferred “The Big Conference Teacher” followed by 58.08 percent of the faculties preferring “The Straight Fact no Non-sense Teacher” type of teaching style. From the results, it wasconcluded that blended teaching approach can balance a teacher’s personal strengths and interest with student’s needs, and curricular requirements enables a teacher to tailor their teaching according to the student’s needs and as per subject matter.

Keywords: teaching styles, assistant professors, agriculture, tamil nadu

Procedia PDF Downloads 112

Authors: Vera Wilden, Benno Hoffmeister, Georgios Balaskas, Lukas Rauber, Burkhard Walter

Abstract:

Light-weight timber frame elements represent an efficient structural solution for wooden multistory buildings. The wall elements of such buildings – which act as shear diaphragms- provide lateral stiffness and resistance to wind and seismic loads. The tendency towards multi-story structures leads to challenges regarding the prediction of stiffness, strength and ductility of the buildings. Lightweight timber frame elements are built up of several structural parts (sheeting, fasteners, frame, support and anchorages); each of them contributing to the dynamic response of the structure. This contribution describes the experimental and numerical investigation and development of enhanced lightweight timber frame buildings. These developments comprise high-performance timber frame walls with the variable arrangements of sheathing planes and dissipative anchors at the base of the timber buildings, which reduce damages to the timber structure and can be exchanged after significant earthquakes. In order to prove the performance of the developed elements in the context of a real building a full-scale two-story building core was designed and erected in the laboratory and tested experimentally for its seismic performance. The results of the tests and a comparison of the test results to the predicted behavior are presented. Observation during the test also reveals some aspects of the design and details which need to consider in the application of the timber walls in the context of the complete building.

Keywords: dissipative anchoring, full scale test, push-over-test, wood shear walls

Procedia PDF Downloads 236

Authors: Abdullah Abu-Tineh, Youmen Chaaban

Abstract:

This study examines the development of a professional development (PD) model for teacher growth and learning that is embedded into the school context. The School based Support Program (SBSP), designed for the Qatari context, targets the practices, knowledge and skills of both school leadership and teachers in an attempt to improve student learning outcomes. Key aspects of the model include the development of learning communities among teachers, strong leadership that supports school improvement activities, and the use of research-based PD to improve teacher practices and student achievement. This paper further presents findings from an evaluation of this PD program. Based on an adaptation of Guskey’s evaluation of PD models, 100 teachers at the participating schools were selected for classroom observations and 40 took part in in-depth interviews to examine changed classroom practices. The impact of the PD program on student learning was also examined. Teachers’ practices and their students’ achievement in English, Arabic, mathematics and science were measured at the beginning and at the end of the intervention.

Keywords: initiative, professional development, school based support Program (SBSP), school reform

Procedia PDF Downloads 488

Authors: Mohammad Naseh Nasrollahi Shahri

Abstract:

The article draws on Michael Bakhtin’s theory of language to examine peer interactions. It represents an analysis of other-repetition in student interactions. Several recent studies have explored various aspects of repetition in multiple contexts. However, other-repetition in peer interactions has not received enough attention. Building on previous studies, this study examines patterns of other-repetition or appropriation in the context of discussion activities performed by EFL learners. The analysis highlights the meaningfulness of other-repetition in a way that distinguishes them from rote-repetition. It is suggested that instances of repetition constitute third spaces between the self and other which provide ideal settings for language learning and demonstrate student agency and engagement.

Keywords: repetition, agency, Bakhtin, dialogue

Procedia PDF Downloads 628