Search results for: machine learning techniques

Authors: Salina Budin, Shaira Ismail

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Students learn effectively in a learning environment with a suitable teaching approach that matches their learning preferences. The main objective of the study is to examine the learning preferences amongst the students in the Science and Technology (S&T), and Social Science (SS) fields of study at the Universiti Teknologi Mara (UiTM), Pulau Pinang. The measurement instrument is based on the Dunn and Dunn Learning Styles which measure five elements of learning styles; environmental, sociological, emotional, physiological and psychological. Questionnaires are distributed amongst undergraduates in the Faculty of Mechanical Engineering and Faculty of Business Management. The respondents comprise of 131 diploma students of the Faculty of Mechanical Engineering and 111 degree students of the Faculty of Business Management. The results indicate that, both S&T and SS students share a similar learning preferences on the environmental aspect, emotional preferences, motivational level, learning responsibility, persistent level in learning and learning structure. Most of the S&T students are concluded as analytical learners and the majority of SS students are global learners. Both S&T and SS students are concluded as visual learners, preferred to be in an active mobility in a relaxing and enjoying mode with some light of refreshments during the learning process and exhibited reflective characteristics in learning. Obviously, the S&T students are considered as left brain dominant, whereas the SS students are right brain dominant. The findings highlighted that both categories of students exhibited similar learning preferences except on psychological preferences.

Keywords: learning preferences, Dunn and Dunn learning style, teaching approach, science and technology, social science

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Authors: Navab Karimi, Tohid Alizadeh

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An intelligent vision-based system was designed to measure the quality and purity of raisins. A machine vision setup was utilized to capture the images of bulk raisins in ranges of 5-50% mixed pure-impure berries. The textural features of bulk raisins were extracted using Grey-level Histograms, Co-occurrence Matrix, and Local Binary Pattern (a total of 108 features). Genetic Algorithm and neural network regression were used for selecting and ranking the best features (21 features). As a result, the GLCM features set was found to have the highest accuracy (92.4%) among the other sets. Followingly, multiple feature combinations of the previous stage were fed into the second regression (linear regression) to increase accuracy, wherein a combination of 16 features was found to be the optimum. Finally, a Support Vector Machine (SVM) classifier was used to differentiate the mixtures, producing the best efficiency and accuracy of 96.2% and 97.35%, respectively.

Keywords: sun-dried organic raisin, genetic algorithm, feature extraction, ann regression, linear regression, support vector machine, south azerbaijan.

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Authors: Faiza Belbachir, Thibault Schienhinski

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The massive development of online social networks allows users to post and share their opinions on various topics. With this huge volume of opinion, it is interesting to extract and interpret these information for different domains, e.g., product and service benchmarking, politic, system of recommendation. This is why opinion detection is one of the most important research tasks. It consists on differentiating between opinion data and factual data. The difficulty of this task is to determine an approach which returns opinionated document. Generally, there are two approaches used for opinion detection i.e. Lexical based approaches and Machine Learning based approaches. In Lexical based approaches, a dictionary of sentimental words is used, words are associated with weights. The opinion score of document is derived by the occurrence of words from this dictionary. In Machine learning approaches, usually a classifier is trained using a set of annotated document containing sentiment, and features such as n-grams of words, part-of-speech tags, and logical forms. Majority of these works are based on documents text to determine opinion score but dont take into account if these texts are really correct. Thus, it is interesting to exploit other information to improve opinion detection. In our work, we will develop a new way to consider the opinion score. We introduce the notion of trust score. We determine opinionated documents but also if these opinions are really trustable information in relation with topics. For that we use lexical SentiWordNet to calculate opinion and trust scores, we compute different features about users like (numbers of their comments, numbers of their useful comments, Average useful review). After that, we combine opinion score and trust score to obtain a final score. We applied our method to detect trust opinions in TRIPADVISOR collection. Our experimental results report that the combination between opinion score and trust score improves opinion detection.

Keywords: Tripadvisor, opinion detection, SentiWordNet, trust score

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Authors: Bhavani Veasuvalingam

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Case-based learning (CBL) is a popular instructional method aimed to bridge theory to clinical practice. This study aims to explore CBL mixed modality curriculum in influencing students’ learning styles and strategies that support learning. An explanatory sequential mixed method study was employed with initial phase, 44-itemed Felderman’s Index of Learning Style (ILS) questionnaire employed across year one medical students (n=142) using convenience sampling to describe the preferred learning styles. The qualitative phase utilised three focus group discussions (FGD) to explore in depth on the multimodal learning style exhibited by the students. Most students preferred combination of learning stylesthat is reflective, sensing, visual and sequential i.e.: RSVISeq style (24.64%) from the ILS analysis. The frequency of learning preference from processing to understanding were well balanced, with sequential-global domain (66.2%); sensing-intuitive (59.86%), active- reflective (57%), and visual-verbal (51.41%). The qualitative data reported three major themes, namely Theme 1: CBL mixed modalities navigates learners’ learning style; Theme 2: Multimodal learners active learning strategies supports learning. Theme 3: CBL modalities facilitating theory into clinical knowledge. Both quantitative and qualitative study strongly reports the multimodal learning style of the year one medical students. Medical students utilise multimodal learning styles to attain the clinical knowledge when learning with CBL mixed modalities. Educators’ awareness of the multimodal learning style is crucial in delivering the CBL mixed modalities effectively, considering strategic pedagogical support students to engage and learn CBL in bridging the theoretical knowledge into clinical practice.

Keywords: case-based learning, learnign style, medical students, learning

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Authors: Nico Rosamilia

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The study evaluates the importance of non-financial disclosure practices for regulators, investors, businesses, and markets. It aims to create a sector-specific set of indicators for environmental, social, and governance (ESG) performances alternative to the ratings of the agencies. The existing literature extensively studies the implementation of ESG rating systems. Conversely, this study has a twofold outcome. Firstly, it should generalize incentive systems and governance policies for ESG and sustainable principles. Therefore, it should contribute to the EU Sustainable Finance Disclosure Regulation. Secondly, it concerns the market and the investors by highlighting successful sustainable investing. Indeed, the study contemplates the effect of ESG adoption practices on corporate value. The research explores the asset pricing angle in order to shed light on the fragmented argument on the finance of ESG. Investors may be misguided about the positive or negative effects of ESG on performances. The paper proposes a different method to evaluate ESG performances. By comparing the results of a traditional econometric approach (Lasso) with a machine learning algorithm (Random Forest), the study establishes a set of indicators for ESG performance. Therefore, the research also empirically contributes to the theoretical strands of literature regarding model selection and variable importance in a finance framework. The algorithms will spit out sector-specific indicators. This set of indicators defines an alternative to the compounded scores of ESG rating agencies and avoids the possible offsetting effect of scores. With this approach, the paper defines a sector-specific set of indicators to standardize ESG disclosure. Additionally, it tries to shed light on the absence of a clear understanding of the direction of the ESG effect on corporate value (the problem of endogeneity).

Keywords: ESG ratings, non-financial information, value of firms, sustainable finance

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Authors: Abdool Qaiyum Mohabuth, Syed Munir Ahmad

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Assessment enables students to focus on their learning, assessment. It engages them to work hard and motivates them in devoting time to their studies. Student learning is directly influenced by the type of assessment involved in the programme. Summative Assessment aims at providing measurement of student understanding. In fact, it is argued that summative assessment is used for reporting and reviewing, besides providing an overall judgement of achievement. While summative assessment is a well defined process for learning that takes place in the classroom environment, its application within the practice environment is still being researched. This paper discusses findings from a mixed-method study for exploring the effectiveness of summative assessment in practice learning. A survey questionnaire was designed for exploring the perceptions of mentors and students about summative assessment in practice learning. The questionnaire was administered to the University of Mauritius students and mentors who supervised students for their Work-Based Learning (WBL) practice at the respective placement settings. Some students, having undertaken their WBL practice, were interviewed, for capturing their views and experiences about the application of summative assessment in practice learning. Semi-structured interviews were also conducted with three experienced mentors who have assessed students on practice learning. The findings reveal that though learning in the workplace is entirely different from learning at the University, most students had positive experiences about their summative assessments in practice learning. They felt comfortable and confident to be assessed by their mentors in their placement settings and wished that the effort and time that they devoted to their learning be recognised and valued. Mentors on their side confirmed that the summative assessment is valid and reliable, enabling them to better monitor and coach students to achieve the expected learning outcomes.

Keywords: practice learning, judgement, summative assessment, knowledge, skills, workplace

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Authors: Kevin Fernagut, Olivier Flauzac, Erick M. G. Robledo, Florent Nolot

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The adoption of modern lightweight virtualization often comes with new threats and network vulnerabilities. This paper seeks to assess this with a different approach studying the behavior of a testbed built with tools such as Kernel-Based Virtual Machine (KVM), Linux Containers (LXC) and Docker, by performing stress tests within a platform where students experiment simultaneously with cyber-attacks, and thus observe the impact on the campus network and also find the best solution for cyber-security learning. Interesting outcomes can be found in the literature comparing these technologies. It is, however, difficult to find results of the effects on the global network where experiments are carried out. Our work shows that other physical hosts and the faculty network were impacted while performing these trials. The problems found are discussed, as well as security solutions and the adoption of new network policies.

Keywords: containerization, containers, cybersecurity, cyberattacks, isolation, performance, virtualization, virtual machines

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Authors: Siwar Fadhel, Imen Bahri, Man Zhang

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The switched reluctance machine (SRM) has undeniable qualities in terms of low cost and mechanical robustness. However, its highly nonlinear character and its uncertain parameters justify the development of complicated controls. In this paper, authors present the design of a robust H-infinity current controller for an 8/6 SRM with taking into account the nonlinearity of the SRM and with rejection of disturbances. The electromagnetic torque is indirectly regulated through the current controller. To show the performances of this control, a robustness analysis is performed by comparing the H-infinity and PI controller simulation results. This comparison demonstrates better performances for the presented controller. The effectiveness and robustness of the presented controller are also demonstrated by experimental tests.

Keywords: current regulation, experimentation, robust H-infinity control, switched reluctance machine

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

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Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: computer vision, deep learning, object detection, semiconductor

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Authors: Ayliana Dharmawan, Heng Yong Sheng, Zhang Xiaojin, Tan Thai Lian

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To facilitate discharge planning, doctors are presently required to assign an Estimated Discharge Date (EDD) for each patient admitted to the hospital. This assignment of the EDD is largely based on the doctor’s judgment. This can be difficult for cases which are complex or relatively new to the doctor. It is hypothesized that a data-driven approach would be able to facilitate the doctors to make accurate estimations of the discharge date. Making use of routinely collected data on inpatient discharges between January 2013 and May 2016, a predictive model was developed using machine learning techniques to predict the Length of Stay (and hence the EDD) of inpatients, at the point of admission. The predictive performance of the model was compared to that of the clinicians using accuracy measures. Overall, the best performing model was found to be able to predict EDD with an accuracy improvement in Average Squared Error (ASE) by -38% as compared to the first EDD determined by the present method. It was found that important predictors of the EDD include the provisional diagnosis code, patient’s age, attending doctor at admission, medical specialty at admission, accommodation type, and the mean length of stay of the patient in the past year. The predictive model can be used as a tool to accurately predict the EDD.

Keywords: inpatient, estimated discharge date, EDD, prediction, data-driven

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Authors: Amin Nezarat, Naeime Seifadini

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Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

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Authors: Islam Elsayed Hussein

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The aim of this paper is to discover the impact of students’ attitude towards social media and the skills required to adopt social media as a university e-learning (2.0) platform. In addition, it measures the effect of social media adoption on interactive learning effectiveness. The population of this study was students at Faculty of tourism and Hotels, Fayoum University. A questionnaire was used as a research instrument to collect data from respondents, which had been selected randomly. Data had been analyzed using quantitative data analysis method. Findings showed that the students have a positive attitude towards adopting social networking in the learning process and they have also good skills for effective use of social networking tools. In addition, adopting social media is effectively affecting the interactive learning environment.

Keywords: attitude, skills, e-learning 2.0, interactive learning, Egypt

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Authors: Frederic Jumelle, Kelvin So, Didan Deng

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In this paper, we are introducing a model of artificial general intelligence (AGI), the functional neural network (FNN), for modeling human decision-making processes. The FNN is composed of multiple artificial mirror neurons (AMN) racing in the network. Each AMN has a similar structure programmed independently by the users and composed of an intention wheel, a motor core, and a sensory core racing at a specific velocity. The mathematics of the node’s formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education, and medicine, including the opportunity to design an intelligent learning agent with application in AGI. We believe that FNN has a promising potential to transform the way we can compute decision-making and lead to a new generation of AI chips for seamless human-machine interactions (HMI).

Keywords: neural computing, human machine interation, artificial general intelligence, decision processing

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: Arun Sanjel, Greg Speegle

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Every second, massive amounts of data are generated, and Data Intensive Scalable Computing (DISC) frameworks have evolved into effective tools for analyzing such massive amounts of data. Since the underlying architecture of these distributed computing platforms is often new to users, building a DISC application can often be time-consuming and prone to errors. The automated conversion of a sequential program to a DISC program will consequently significantly improve productivity. However, synthesizing a user’s intended program from an input specification is complex, with several important applications, such as distributed program synthesizing and code refactoring. Existing works such as Tyro and Casper rely entirely on deductive synthesis techniques or similar program synthesis approaches. Our approach is to develop a data-driven synthesis technique to identify sequential components and translate them to equivalent distributed operations. We emphasize using reinforcement learning and unit testing as feedback mechanisms to achieve our objectives.

Keywords: program synthesis, distributed computing, reinforcement learning, unit testing, DISC

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Authors: Deepika Christopher, Garima Anand

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To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

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Authors: N. Paparisteidi, D. Rodamitou

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This article focuses on the skills development and path planning of students studying computer science in EPITECH: french private institute of Higher Education. The researchers examine students’ points of view and experience in a blended learning model based on a skills development curriculum. The study is based on the collection of four main categories of data: semi-participant observation, distribution of questionnaires, interviews, and analysis of internal school databases. The findings seem to indicate that a skills-based program on active learning enables students to develop their learning strategies as well as their personal skills and to actively engage in the creation of their career path and contribute to providing additional information to curricula planners and decision-makers about learning design in higher education.

Keywords: active learning, blended learning, higher education, skills development

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Authors: C. Brecher, M. Fey, F. Du Bois-Reymond, S. Neus

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Additive manufacturing has emerged into a fast-growing section within the manufacturing technologies. Established machine tool manufacturers, such as DMG MORI, recently presented machine tools combining milling and laser welding. By this, machine tools can realize a higher degree of flexibility and a shorter production time. Still there are challenges that have to be accounted for in terms of maintaining the necessary machining accuracy - especially due to thermal effects arising through the use of high power laser processing units. To study the thermal behavior of laser-integrated machine tools, it is essential to analyze and simulate the thermal behavior of machine components, individual and assembled. This information will help to design a geometrically stable machine tool under the influence of high power laser processes. This paper presents an approach to decrease the loss of machining precision due to thermal impacts. Real effects of laser machining processes are considered and thus enable an optimized design of the machine tool, respective its components, in the early design phase. Core element of this approach is a matched FEM model considering all relevant variables arising, e.g. laser power, angle of laser beam, reflective coefficients and heat transfer coefficient. Hence, a systematic approach to obtain this matched FEM model is essential. Indicating the thermal behavior of structural components as well as predicting the laser beam path, to determine the relevant beam intensity on the structural components, there are the two constituent aspects of the method. To match the model both aspects of the method have to be combined and verified empirically. In this context, an essential machine component of a five axis machine tool, the turn-swivel table, serves as the demonstration object for the verification process. Therefore, a turn-swivel table test bench as well as an experimental set-up to measure the beam propagation were developed and are described in the paper. In addition to the empirical investigation, a simulative approach of the described types of experimental examination is presented. Concluding, it is shown that the method and a good understanding of the two core aspects, the thermo-elastic machine behavior and the laser beam path, as well as their combination helps designers to minimize the loss of precision in the early stages of the design phase.

Keywords: additive manufacturing, laser beam machining, machine tool, thermal effects

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Authors: Jacqueline Żammit

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Online learning has become more popular as a substitute for traditional classroom instruction because of the COVID-19 epidemic. The study aimed to investigate how adult Maltese language learners evaluated the benefits and drawbacks of online instruction. 35 adult participants provided data through semi-structured interviews with open-ended questions. NVivo software was used to analyze the interview data using the thematic analysis method in order to find themes and group the data based on common responses. The advantages of online learning that the participants mentioned included accessing subject content even without live learning sessions, balancing learning with household duties, and lessening vulnerability to problems like fatigue, time-wasting traffic, school preparation, and parking space constraints. Conversely, inadequate Internet access, inadequate IT expertise, a shortage of personal computers, and domestic distractions adversely affected virtual learning. Lack of an Internet connection, IT expertise, a personal computer, or a phone with Internet access caused inequality in access to online learning sessions. Participants thought online learning was a way to resume academic activity, albeit with drawbacks. In order to address the challenges posed by online learning, several solutions are proposed in the research's conclusion.

Keywords: adult learners, online education, e-learning, challenges of online learning, benefits ofonline learning

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Authors: Pinar Ozdemir Ayber, Zeina Hojeij

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Blended Learning (BL) is a rapidly growing teaching and learning approach, which brings together the best of both face-to-face and online learning to expand learning opportunities for students. However, there is limited research on the practices, opportunities and quality of instruction in Blended Classrooms, and on the role of the teaching faculty as well as the learners in these types of classes. This paper will highlight the researchers’ experiences and reflections on blending their classes. It will focus on the importance of designing effective lesson plans that emphasize learner engagement and motivation in alignment with course learning outcomes. In addition, it will identify the changing roles of the teacher and the learners and suggest appropriate variations to the traditional classroom setting taking into consideration the benefits and the challenges of the Blended Classroom. It is hoped that this paper would provide sufficient input for participants to reflect on ways they can blend their own lessons to promote ubiquitous learning and student autonomy. Practical tips and ideas will be shared with the participants on various strategies and technologies that were used in the researchers’ classes.

Keywords: blended learning, learner autonomy, learner engagement, learner motivation, mobile learning tools

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Authors: Yuri Bizzoni, Marianne Reboul

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Our program compares French and Italian translations of Homer’s Odyssey, from the XVIth to the XXth century. We focus on the third point, showing how distributional semantics systems can be used both to improve alignment between different French translations as well as between the Greek text and a French translation. Although we focus on French examples, the techniques we display are completely language independent.

Keywords: classical receptions, computational linguistics, distributional semantics, Homeric poems, machine translation, translation studies, text alignment

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Authors: Montserrrat Dopico Gonzalez, Ramon Lopez Facal

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The development of a critical, open and plural citizenship constitutes one of the main challenges of the school institution in the present multicultural societies. Didactics in Social Sciences has conducted important contributions to the development of active methodologies to promote the development of the intercultural competencies of the student body. Research in intercultural education has demonstrated the efficiency of the cooperative learning techniques to improve the intercultural relations in the classroom. Our study proposes to check the effect that, concerning the development of intercultural competencies of the student body, the emotional education can have in the context of the use of active methodologies such as the learning by projects and the cooperative learning. To that purpose, a programme of intervention based on activities focussed on controversial issues related to cultural diversity has been implemented in several secondary schools. Through a methodology which combines intercultural competence scales with interviews and also with the analysis of the school body’s productions, the persistence of stereotypes against immigration and the efficacy of the introduction of emotional education elements in the development of intercultural competencies have both been observed.

Keywords: active methodologies, didactics in social sciences, intercultural competences, intercultural education

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: Michelle Siao-Cing Guo

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Technology is widely adopted to assist instruction and learning across disciplines. Traditional teaching method fails to capture the attention of the generation of digital native and does not accommodate diverse needs of today’s learners. The innovation in technology allows new pedagogical approaches. One approach that converts the traditional learning classroom to a more flexible learning time and space is known as the flipped classroom. This new pedagogy extends and enhances learning and accommodates different learning styles. The flipped classroom employs technology to offer course materials online 24 hours/day and to promote active class learning. However, will Taiwanese students who are used to more traditional instructional methods embrace the flipped classroom using E-learning? Will the flipped approach have an effect on Taiwanese students’ English mastery and learning autonomy? The researcher compares a flipped classroom model using E-learning and the traditional-lecture model. A pre- and post-test and a questionnaire were utilized to examine the effect of the flipped classroom on Taiwanese college students. The test results showed that the flipped approach had a positive effect on learners’ English proficiency level, topical knowledge, and willingness to participate in class. The questionnaire also demonstrates the acceptance of the new teaching model.

Keywords: flipped classroom , E-learning, innovative teaching, technology

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Authors: Khaleel Bader Bataineh

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Past studies have maintained that the Arab learners' beliefs about language learning hold vital effects on their performance. Thus, this study was carried out to investigate the language learning beliefs of Irbid National University students. It aimed at identifying the language learning beliefs according to gender. This study is a descriptive design that employed survey questionnaire of Language Learning Beliefs Inventory (BALLI). The data were elicited from 83 English major students during the class sessions. The data were analyzed using an SPSS program in which frequency analysis and t-test were performed to examine the students’ responses. Thus, the major findings of this research indicated that there is a variation in responses with regards to the subjects’ beliefs about English learning. Also, the findings show significant differences in four questionnaire items according to gender. It is hoped that the findings provide valuable insights to educators about the learners’ beliefs which assist them to develop the teaching and learning English language process in Jordan universities.

Keywords: foreign language, students’ beliefs, language learning, Arab students

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Authors: Timna Mayer

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This paper explores the vast potential of self-directed learning in violin pedagogy. Based in practice and drawing on concepts from neuropsychology, the author, a violinist and teacher, outlines five learning principles. Self-directed learning is defined as an ongoing process based on problem detection, definition, and resolution. The traditional roles of teacher and student are reimagined within this context. A step-by-step guide to applied self-directed learning suggests a model for both teachers and students that realizes student independence in the classroom, leading to higher-level understanding and more robust performance. While the value of self-directed learning is well-known in general pedagogy, this paper is novel in applying the approach to the study of musical performance, a field which is currently dominated by habit and folklore, rather than informed by science.

Keywords: neuropsychology and musical performance, self-directed learning, strategic problem solving, violin pedagogy

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Authors: Yudi Efendi, Hasanuddin

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Web-based learning program is the complementary of Printed Teaching Material (BMP) that serves and helps students clarify the parts that require additional explanation or illustration. This research attempts to analyze a prototype of web-based learning program. A prototype of web-based learning program which is interactive is completed with exercises and formative tests. Using qualitative descriptive method, the research presents the analysis from the content expert and media expert. Besides, the interviews from tutors of Political and Social Sciences will be presented. The research also analyzes questionnaires from the students of English and literature program in Jakarta. The questionnaire deals with the display of the content, the audio video, the usability, and the navigation. In the long run, it is expected that the program could be recommended to use by the university as an ideal program.

Keywords: web learning, prototype, content expert, media expert

Procedia PDF Downloads 247

Authors: Jui-Pui Hung, Yong-Run Chen, Wei-Cheng Shih, Shen-He Tsui, Kung-Da Wu

Abstract:

This paper presents the effect on the tooling conditions on the machining stabilities of a milling machine tool. The machining stability was evaluated in different feeding direction in the X-Y plane, which was referred as the orientation-dependent machining stability. According to the machining mechanics, the machining stability was determined by the frequency response function of the cutter. Thus, we first conducted the vibration tests on the spindle tool of the milling machine to assess the tool tip frequency response functions along the principal direction of the machine tool. Then, basing on the orientation dependent stability analysis model proposed in this study, we evaluated the variation of the dynamic characteristics of the spindle tool and the corresponding machining stabilities at a specific feeding direction. Current results demonstrate that the stability boundaries and limited axial cutting depth of a specific cutter were affected to vary when it was fixed in the tool holder with different overhang length. The flute of the cutter also affects the stability boundary. When a two flute cutter was used, the critical cutting depth can be increased by 47 % as compared with the four flute cutter. The results presented in study provide valuable references for the selection of the tooling conditions for achieving high milling performance.

Keywords: tooling condition, machining stability, milling machine, chatter

Procedia PDF Downloads 431

Authors: Hameed Olalekan Bolaji

Abstract:

The digital natives are driving the trends of literacy in the use of electronic devices for learning purposes. This has reconfigured the context of learning in the exploration of knowledge in a social learning environment. This study explores the impact of Ubiquitous Collaborative Mobile Learning (UCML) instructional design model in a quantitative designed-based research approach. The UCML model was a synergetic blend of four models that are relevant to the design of instructional content for a social learning environment. The UCML model serves as the treatment and instructions were transmitted via mobile device based on the principle of ‘bring your own device’ (BYOD) to promote social learning. Three research questions and two hypotheses were raised to guide the conduct of this study. A researcher-designed questionnaire was used to collate data and the it was subjected to reliability of Cronbach Alpha which yielded 0.91. Descriptive statistics of mean and standard deviation were used to answer research questions while inferential statistics of independent sample t-test was used to analyze the hypotheses. The findings reveal that the UCML model was adequately evolved and it promotes social learning its design principles through the use of mobile devices.

Keywords: collaboration, mobile device, social learning, ubiquitous

Procedia PDF Downloads 157

Authors: L. A. Alfarani, M. McPherson, N. Morris

Abstract:

The growth of mobile learning depends primarily on the participation of teachers and their belief in the possibilities that this technology has for enhancing learning. The need to integrate technology into education seems clear-cut, however, its acceptance in Saudi higher education remains low. Thus, determining the particular factors that affect faculty acceptance of technology is vital. This paper focuses on TAM which depends on two factors: perceived usefulness and perceived ease of use, this theory are used to predict faculty members’ behavioural intentions towards using mobile learning technology. 279 faculty members in one Saudi university have responded to the online questionnaire. The findings have revealed that there is a statistically significant difference in both usefulness and ease of using m-learning factors.

Keywords: TAM theory, mobile learning technology acceptance, usefulness, ease of use

Procedia PDF Downloads 524