Search results for: Teaching and Learning.
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2240

Search results for: Teaching and Learning.

1100 End-to-End Spanish-English Sequence Learning Translation Model

Authors: Vidhu Mitha Goutham, Ruma Mukherjee

Abstract:

The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.

Keywords: Attention, encoder-decoder, Fairseq, Seq2Seq, Spanish, translation.

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1099 Collaborative Team Work in Higher Education: A Case Study

Authors: Swapna Bhargavi Gantasala

Abstract:

If teamwork is the key to organizational learning, productivity and growth, then, why do some teams succeed in achieving these, while others falter at different stages? Building teams in higher education institutions has been a challenge and an open-ended constructivist approach was considered on an experimental basis for this study to address this challenge. For this research, teams of students from the MBA program were chosen to study the effect of teamwork in learning, the motivation levels among student team members, and the effect of collaboration in achieving team goals. The teams were built on shared vision and goals, cohesion was ensured, positive induction in the form of faculty mentoring was provided for each participating team and the results have been presented with conclusions and suggestions.

Keywords: Collaboration, Leadership, Motivation, Reinforcement Teamwork.

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1098 ECG-Based Heartbeat Classification Using Convolutional Neural Networks

Authors: Jacqueline R. T. Alipo-on, Francesca I. F. Escobar, Myles J. T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

Abstract:

Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases which are considered as one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis on the ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heart beat types. The dataset used in this work is the synthetic MIT-Beth Israel Hospital (MIT-BIH) Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.

Keywords: Heartbeat classification, convolutional neural network, electrocardiogram signals, ECG signals, generative adversarial networks, long short-term memory, LSTM, ResNet-50.

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1097 Learning to Order Terms: Supervised Interestingness Measures in Terminology Extraction

Authors: Jérôme Azé, Mathieu Roche, Yves Kodratoff, Michèle Sebag

Abstract:

Term Extraction, a key data preparation step in Text Mining, extracts the terms, i.e. relevant collocation of words, attached to specific concepts (e.g. genetic-algorithms and decisiontrees are terms associated to the concept “Machine Learning" ). In this paper, the task of extracting interesting collocations is achieved through a supervised learning algorithm, exploiting a few collocations manually labelled as interesting/not interesting. From these examples, the ROGER algorithm learns a numerical function, inducing some ranking on the collocations. This ranking is optimized using genetic algorithms, maximizing the trade-off between the false positive and true positive rates (Area Under the ROC curve). This approach uses a particular representation for the word collocations, namely the vector of values corresponding to the standard statistical interestingness measures attached to this collocation. As this representation is general (over corpora and natural languages), generality tests were performed by experimenting the ranking function learned from an English corpus in Biology, onto a French corpus of Curriculum Vitae, and vice versa, showing a good robustness of the approaches compared to the state-of-the-art Support Vector Machine (SVM).

Keywords: Text-mining, Terminology Extraction, Evolutionary algorithm, ROC Curve.

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1096 Developing Islamic Module Project for Preschool Teachers Using Modified Delphi Technique

Authors: Mazeni Ismail, Nurul Aliah, Hasmadi Hassan

Abstract:

The purpose of this study is to gather the consensus of experts regarding the use of moral guidance amongst preschool teachers vis-a-vis the Islamic Project module (I-Project Module). This I-Project Module seeks to provide pertinent data on the assimilation of noble values in subject-matter teaching. To obtain consensus for the various components of the module, the Modified Delphi technique was used to develop the module. 12 subject experts from various educational fields of Islamic education, early childhood education, counselling and language fully participated in the development of this module. The Modified Delphi technique was administered in two mean cycles. The standard deviation value derived from questionnaires completed by the participating panel of experts provided the value of expert consensus reached. This was subsequently analyzed using SPSS version 22. Findings revealed that the panel of experts reached a discernible degree of agreement on five topics outlined in the module, viz; content (mean value 3.36), teaching strategy (mean value 3.28), programme duration (mean value 3.0), staff involved and attention-grabbing strategy of target group participating in the value program (mean value 3.5), and strategy to attract attention of target group to utilize i-project (mean value 3.0). With regard to the strategy to attract the attention of the target group, the experts proposed for creative activities to be added in order to enhance teachers’ creativity.

Keywords: Islamic project, modified Delphi technique, project approach, teacher moral guidance.

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1095 Training Undergraduate Engineering Students in Robotics and Automation through Model-Based Design Training: A Case Study at Assumption University of Thailand

Authors: Sajed A. Habib

Abstract:

Problem-based learning (PBL) is a student-centered pedagogy that originated in the medical field and has also been used extensively in other knowledge disciplines with recognized advantages and limitations. PBL has been used in various undergraduate engineering programs with mixed outcomes. The current fourth industrial revolution (digital era or Industry 4.0) has made it essential for many science and engineering students to receive effective training in advanced courses such as industrial automation and robotics. This paper presents a case study at Assumption University of Thailand, where a PBL-like approach was used to teach some aspects of automation and robotics to selected groups of undergraduate engineering students. These students were given some basic level training in automation prior to participating in a subsequent training session in order to solve technical problems with increased complexity. The participating students’ evaluation of the training sessions in terms of learning effectiveness, skills enhancement, and incremental knowledge following the problem-solving session was captured through a follow-up survey consisting of 14 questions and a 5-point scoring system. From the most recent training event, an overall 70% of the respondents indicated that their skill levels were enhanced to a much greater level than they had had before the training, whereas 60.4% of the respondents from the same event indicated that their incremental knowledge following the session was much greater than what they had prior to the training. The instructor-facilitator involved in the training events suggested that this method of learning was more suitable for senior/advanced level students than those at the freshmen level as certain skills to effectively participate in such problem-solving sessions are acquired over a period of time, and not instantly.

Keywords: Automation, industry 4.0, model-based design training, problem-based learning.

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1094 Ensemble Approach for Predicting Student's Academic Performance

Authors: L. A. Muhammad, M. S. Argungu

Abstract:

Educational data mining (EDM) has recorded substantial considerations. Techniques of data mining in one way or the other have been proposed to dig out out-of-sight knowledge in educational data. The result of the study got assists academic institutions in further enhancing their process of learning and methods of passing knowledge to students. Consequently, the performance of students boasts and the educational products are by no doubt enhanced. This study adopted a student performance prediction model premised on techniques of data mining with Students' Essential Features (SEF). SEF are linked to the learner's interactivity with the e-learning management system. The performance of the student's predictive model is assessed by a set of classifiers, viz. Bayes Network, Logistic Regression, and Reduce Error Pruning Tree (REP). Consequently, ensemble methods of Bagging, Boosting, and Random Forest (RF) are applied to improve the performance of these single classifiers. The study reveals that the result shows a robust affinity between learners' behaviors and their academic attainment. Result from the study shows that the REP Tree and its ensemble record the highest accuracy of 83.33% using SEF. Hence, in terms of the Receiver Operating Curve (ROC), boosting method of REP Tree records 0.903, which is the best. This result further demonstrates the dependability of the proposed model.

Keywords: Ensemble, bagging, Random Forest, boosting, data mining, classifiers, machine learning.

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1093 Software Model for a Computer Based Training for an HVDC Control Desk Simulator

Authors: José R. G. Braga, Joice B. Mendes, Guilherme H. Caponetto, Alexandre C. B. Ramos

Abstract:

With major technological advances and to reduce the cost of training apprentices for real-time critical systems, it was necessary the development of Intelligent Tutoring Systems for training apprentices in these systems. These systems, in general, have interactive features so that the learning is actually more efficient, making the learner more familiar with the mechanism in question. In the home stage of learning, tests are performed to obtain the student's income, a measure on their use. The aim of this paper is to present a framework to model an Intelligent Tutoring Systems using the UML language. The various steps of the analysis are considered the diagrams required to build a general model, whose purpose is to present the different perspectives of its development.

Keywords: Computer based training, Hypermedia, Software modeling.

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1092 Effects of an Educative Model in Socially Responsible Behavior and Other Psychological Variables

Authors: Gracia V. Navarro, Maria V. Gonzalez, Carlos G. Reed

Abstract:

The eudaimonic perspective in philosophy and psychology suggests that a good life is closely related to developing oneself in order to contribute to the well-being and happiness of other people and of the world as a whole. Educational psychology can help to achieve this through the design and validation of educative models. Since 2004, the University of Concepcion and other Chilean universities apply an educative model to train socially responsible professionals, people that in the exercise of their profession contribute to generate equity for the development and assess the impacts of their decisions, opting for those that serve the common good. The main aim is to identify if a relationship exists between achieved learning, attitudes toward social responsibility, self-attribution of socially responsible behavior, value type, professional behavior observed and, participation in a specific model to train socially responsible (SR) professionals. The Achieved Learning and Attitudes Toward Social Responsibility Questionnaire, interview with employers and Values Questionnaire and Self-attribution of SR Behavior Questionnaire is applied to 394 students and graduates, divided into experimental and control groups (trained and not trained under the educative model), in order to identify the professional behavior of the graduates. The results show that students and graduates perceive cognitive, affective and behavioral learning, with significant differences in attitudes toward social responsibility and self-attribution of SR behavior, between experimental and control. There are also differences in employers' perceptions about the professional practice of those who were trained under the model and those who were not. It is concluded that the educative model has an impact on the learning of social responsibility and educates for a full life. It is also concluded that it is necessary to identify mediating variables of the model effect.

Keywords: Educative model, good life, professional social responsibility (SR), values.

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1091 A Hybrid Machine Learning System for Stock Market Forecasting

Authors: Rohit Choudhry, Kumkum Garg

Abstract:

In this paper, we propose a hybrid machine learning system based on Genetic Algorithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators. The results show that the hybrid GA-SVM system outperforms the stand alone SVM system.

Keywords: Genetic Algorithms, Support Vector Machines, Stock Market Forecasting.

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1090 Usability and Affordances: Examinations of Object-Naming and Object-Task Performance in Haptic Interfaces

Authors: Mia Sorensen

Abstract:

The introduction of haptic elements in a graphic user interfaces are becoming more widespread. Since haptics are being introduced rapidly into computational tools, investigating how these models affect Human-Computer Interaction would help define how to integrate and model new modes of interaction. The interest of this paper is to discuss and investigate the issues surrounding Haptic and Graphic User Interface designs (GUI) as separate systems, as well as understand how these work in tandem. The development of these systems is explored from a psychological perspective, based on how usability is addressed through learning and affordances, defined by J.J. Gibson. Haptic design can be a powerful tool, aiding in intuitive learning. The problems discussed within the text is how can haptic interfaces be integrated within a GUI without the sense of frivolity. Juxtaposing haptics and Graphic user interfaces has issues of motivation; GUI tends to have a performatory process, while Haptic Interfaces use affordances to learn tool use. In a deeper view, it is noted that two modes of perception, foveal and ambient, dictate perception. These two modes were once thought to work in tandem, however it has been discovered that these processes work independently from each other. Foveal modes interpret orientation is space which provide for posture, locomotion, and motor skills with variations of the sensory information, which instructs perceptions of object-task performance. It is contended, here, that object-task performance is a key element in the use of Haptic Interfaces because exploratory learning uses affordances in order to use an object, without meditating an experience cognitively. It is a direct experience that, through iteration, can lead to skill-sets. It is also indicated that object-task performance will not work as efficiently without the use of exploratory or kinesthetic learning practices. Therefore, object-task performance is not as congruently explored in GUI than it is practiced in Haptic interfaces.

Keywords: Affordances, Graphic User Interface, HapticInterfaces, Tool-Use, Object-Naming, Object-Task Performance

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1089 Virtual Laboratory for Learning Biology – A Preliminary Investigation

Authors: Murniza Muhamad, Halimah Badioze Zaman, Azlina Ahmad

Abstract:

This study aims to conduct a preliminary investigation to determine the topic to be focused in developing Virtual Laboratory For Biology (VLab-Bio). Samples involved in answering the questionnaire are form five students (equivalent to A-Level) and biology teachers. Time and economical resources for the setting up and construction of scientific laboratories can be solved with the adaptation of virtual laboratories as an educational tool. Thus, it is hoped that the proposed virtual laboratory will help students to learn the abstract concepts in biology. Findings show that the difficult topic chosen is Cell Division and the learning objective to be focused in developing the virtual lab is “Describe the application of knowledge on mitosis in cloning".

Keywords: biology education, computer simulation, virtual laboratory, virtual reality

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1088 Second Language Development with an Intercultural Approach: A Pilot Program Applied to Higher Education Students from a Escuela Normal in Atequiza, Mexico

Authors: Frida C. Jaime Franco, C. Paulina Navarro Núñez, R. Jacob Sánchez Nájera

Abstract:

The importance of developing multi-language abilities in our global society is noteworthy. However, the necessity, interest, and consciousness of the significance that the development of another language represents, apart from the mother tongue, is not always the same in all contexts as it is in multicultural communities, especially in rural higher education institutions immersed in small communities. Leading opportunities for digital interaction among learners from Mexico and abroad partners represents scaffolding towards, not only language skills development but also intercultural communicative competences (ICC). This study leads us to consider what should be the best approach to work while applying a program of ICC integrated into the practice of EFL. While analyzing the roots of the language, it is possible to obtain the main objective of learning another language, to communicate with a functional purpose, as well as attaching social practices to the learning process, giving a result of functionality and significance to the target language. Hence, the collateral impact that collaborative learning leads to, aims to contribute to a better global understanding as well as a means of self and other cultural awareness through intercultural communication. While communicating through the target language by online collaboration among students in platforms of long-distance communication, language is used as a tool of interaction to broaden students’ perspectives reaching a substantial improvement with the help of their differences. This process should consider the application of the target language in the inquiry of sociocultural information, expecting the learners to integrate communicative skills to handle cultural differentiation at the same time they apply the knowledge of their target language in a real scenario of communication, despite being through virtual resources.

Keywords: Collaborative learning, English as a Foreign language, intercultural communication, intercultural communicative competences, virtual partnership.

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1087 The Motivating and Limiting Factors of Learners’ Engagement in an Online Discussion Forum

Authors: K. Durairaj, I. N. Umar

Abstract:

Lately, asynchronous discussion forum is integrated in higher educational institutions as it may increase learning process, learners’ understanding, achievement and knowledge construction. The asynchronous discussion forum is used to complement the traditional, face-to-face learning session in hybrid learning courses. However, studies have proven that students’ engagement in online forums is still unconvincing. Thus, the aim of this study is to investigate the motivating factors and obstacles that affect the learners’ engagement in asynchronous discussion forum. This study is carried out in one of the public higher educational institutions in Malaysia with 18 postgraduate students as samples. The authors have developed a 40-items questionnaire based on literature review. The results indicate several factors that have encouraged or limited students’ engagement in asynchronous discussion forum: (a) the practices or behaviors of peers, or instructors, (b) the needs for the discussions, (c) the learners’ personalities, (d) constraints in continuing the discussion forum, (e) lack of ideas, (f) the level of thoughts, (g) the level of knowledge construction, (h) technical problems, (i) time constraints and (j) misunderstanding. This study suggests some recommendations to increase the students’ engagement in online forums. Finally, based upon the findings, some implications are proposed for further research.

Keywords: Asynchronous Discussion Forum, Engagement, Factors, Motivating, Limiting.

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1086 Awareness and Attitudes of Primary Grade Teachers (1-4thGrade) towards Inclusive Education

Authors: P. Maheshwari, M. Shapurkar

Abstract:

The present research aimed at studying the awareness and attitudes of teachers towards inclusive education. The sample consisted of 60 teachers, teaching in the primary section (1st – 4th) of regular schools affiliated to the SSC board in Mumbai. Sample was selected by Multi-stage cluster sampling technique. A semi-structured self-constructed interview schedule and a self-constructed attitude scale was used to study the awareness of teachers about disability and Inclusive education, and their attitudes towards inclusive education respectively. Themes were extracted from the interview data and quantitative data was analyzed using SPSS package. Results revealed that teachers had some amount of awareness but an inadequate amount of information on disabilities and inclusive education. Disability to most (37) teachers meant “an inability to do something”. The difference between disability and handicap was stated by most as former being cognitive while handicap being physical in nature. With regard to Inclusive education, a large number (46) stated that they were unaware of the term and did not know what it meant. Majority (52) of them perceived maximum challenges for themselves in an inclusive set up, and emphasized on the role of teacher training courses in the area of providing knowledge (49) and training in teaching methodology (53). Although, 83.3% of teachers held a moderately positive attitude towards inclusive education, a large percentage (61.6%) of participants felt that being in inclusive set up would be very challenging for both children with special needs and without special needs. Though, most (49) of the teachers stated that children with special needs should be educated in regular classroom but they further clarified that only those should be in a regular classroom who have physical impairments of mild or moderate degree.

Keywords: Attitudes, awareness, inclusive education, teachers.

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1085 Reflective Thinking and Experiential Learning: A Quasi-Experimental Quanti-Quali Response to Greater Diversification of Activities and Greater Integration of Student Profiles

Authors: P. Bogas

Abstract:

As a scientific contribution to this discussion, a pedagogical intervention of a quasi-experimental nature was developed, with a mixed methodology, evaluating the intervention within a single curricular unit of Marketing, using cases based on real challenges of brands, business simulation and customer projects. Primary and secondary experiences were incorporated in the intervention: the primary experiences are the experiential activities themselves; the secondary experiences resulted from the primary experience, such as reflection and discussion in work teams. A diversified learning relationship was encouraged through the various connections between the different members of the learning community. The present study concludes that in the same context, the students' response can be described as: students who reinforce the initial deep approach, students who maintain the initial deep approach level and others who change from an emphasis on the deep approach to one closer to superficial. This typology did not always confirm studies reported in the literature, namely, whether the initial level of deep processing would influence the superficial and the opposite. The result of this investigation points to the inclusion of pedagogical and didactic activities that integrate different motivations and initial strategies, leading to a possible adoption of deep approaches to learning, since it revealed statistically significant differences in the difference in the scores of the deep/superficial approach and the experiential level. In the case of real challenges, the categories of “attribution of meaning and meaning of studied” and the possibility of “contact with an aspirational context” for their future professional stand out. In this category, the dimensions of autonomy that will be required of them were also revealed when comparing the classroom context of real cases and the future professional context and the impact they may have on the world. Regarding to the simulated practice, two categories of response stand out: on the one hand, the motivation associated with the possibility of measuring the results of the decisions taken, an awareness of oneself and, on the other hand, the additional effort that this practice required for some of the students.

Keywords: Experiential learning, higher education, marketing, mixed methods, reflective thinking.

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1084 A Convolutional Deep Neural Network Approach for Skin Cancer Detection Using Skin Lesion Images

Authors: Firas Gerges, Frank Y. Shih

Abstract:

Malignant Melanoma, known simply as Melanoma, is a type of skin cancer that appears as a mole on the skin. It is critical to detect this cancer at an early stage because it can spread across the body and may lead to the patient death. When detected early, Melanoma is curable. In this paper we propose a deep learning model (Convolutional Neural Networks) in order to automatically classify skin lesion images as Malignant or Benign. Images underwent certain pre-processing steps to diminish the effect of the normal skin region on the model. The result of the proposed model showed a significant improvement over previous work, achieving an accuracy of 97%.

Keywords: Deep learning, skin cancer, image processing, melanoma.

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1083 Towards Developing a Self-Explanatory Scheduling System Based on a Hybrid Approach

Authors: Jian Zheng, Yoshiyasu Takahashi, Yuichi Kobayashi, Tatsuhiro Sato

Abstract:

In the study, we present a conceptual framework for developing a scheduling system that can generate self-explanatory and easy-understanding schedules. To this end, a user interface is conceived to help planners record factors that are considered crucial in scheduling, as well as internal and external sources relating to such factors. A hybrid approach combining machine learning and constraint programming is developed to generate schedules and the corresponding factors, and accordingly display them on the user interface. Effects of the proposed system on scheduling are discussed, and it is expected that scheduling efficiency and system understandability will be improved, compared with previous scheduling systems.

Keywords: Constraint programming, Factors considered in scheduling, machine learning, scheduling system.

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1082 An Active Solar Energy System to Supply Heating Demands of the Teaching Staff Dormitory of Islamic Azad University Ramhormoz Branch

Authors: M. Talebzadegan, S. Bina, I. Riazi

Abstract:

The purpose of this paper is to present an active solar energy system to supply heating demands of the teaching staff dormitory of the Islamic Azad University of Ramhormoz. The design takes into account the solar radiations and climate data of Ramhormoz town and is based on the daily warm water consumption for health demands of 450 residents of the dormitory, which is equal to 27000 lit of 50-C° water, and building heating requirements with an area of 3500 m² well-protected by heatproof materials. First, heating demands of the building were calculated, then a hybrid system made up of solar and fossil energies was developed and finally, the design was economically evaluated. Since there is only roof space for using 110 flat solar water heaters, the calculations were made to hybridize solar water heating system with heat pumping system in which solar energy contributes 67% of the heat generated. According to calculations, the net present value “N.P.V.” of revenue stream exceeds “N.P.V.” of cash paid off in this project over three years, which makes economically quite promising. The return of investment and payback period of the project is 4 years. Also, the internal rate of return (IRR) of the project was 25%, which exceeds bank rate of interest in Iran and emphasizes the desirability of the project.

Keywords: Solar energy, heat demand, renewable, pollution.

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1081 Using Wiki for Enhancing the Knowledge Transfer to Newcomers: An Experience Report

Authors: H. O. Barbosa, A. C. R. da Silva, C. M. de Almeida, E. M. dos Santos, F. O. de Sousa, F. B. da S. Souza, F. B. da S. Souza, F. de O. Lima, L. H. Albuquerque, R. F. do Valle Cunha

Abstract:

Software development is intrinsic human-based knowledge-intensive. Due to globalization, software development has become a complex challenge and we usually face barriers related to knowledge management, team building, costly testing processes, especially in distributed settings. In this paper, we present the use of experimental studies to improve our knowledge management process using the Wiki system. According to the results, it was possible to identify learning preferences from our software projects leader team, organize and improve the learning experience of our Wiki, and facilitate collaboration by newcomers to improve Wiki with new contents available in the Wiki.

Keywords: Mobile products, knowledge management process, Wiki system, Global Software Development.

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1080 Data Analysis Techniques for Predictive Maintenance on Fleet of Heavy-Duty Vehicles

Authors: Antonis Sideris, Elias Chlis Kalogeropoulos, Konstantia Moirogiorgou

Abstract:

The present study proposes a methodology for the efficient daily management of fleet vehicles and construction machinery. The application covers the area of remote monitoring of heavy-duty vehicles operation parameters, where specific sensor data are stored and examined in order to provide information about the vehicle’s health. The vehicle diagnostics allow the user to inspect whether maintenance tasks need to be performed before a fault occurs. A properly designed machine learning model is proposed for the detection of two different types of faults through classification. Cross validation is used and the accuracy of the trained model is checked with the confusion matrix.

Keywords: Fault detection, feature selection, machine learning, predictive maintenance.

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1079 Learners’ Violent Behaviour and Drug Abuse as Major Causes of Tobephobia in Schools

Authors: Prakash Singh

Abstract:

Many schools throughout the world are facing constant pressure to cope with the violence and drug abuse of learners who show little or no respect for acceptable and desirable social norms. These delinquent learners tend to harbour feelings of being beyond reproach because they strongly believe that it is well within their rights to engage in violent and destructive behaviour. Knives, guns, and other weapons appear to be more readily used by them on the school premises than before. It is known that learners smoke, drink alcohol, and use drugs during school hours, hence, their ability to concentrate, work, and learn, is affected. They become violent and display disruptive behaviour in their classrooms as well as on the school premises, and this atrocious behaviour makes it possible for drug dealers and gangsters to gain access onto the school premises. The primary purpose of this exploratory quantitative study was therefore to establish how tobephobia (TBP), caused by school violence and drug abuse, affects teaching and learning in schools. The findings of this study affirmed that poor discipline resulted in producing poor quality education. Most of the teachers in this study agreed that educating learners who consumed alcohol and other drugs on the school premises resulted in them suffering from TBP. These learners are frequently abusive and disrespectful, and resort to violence to seek attention. As a result, teachers feel extremely demotivated and suffer from high levels of anxiety and stress. The word TBP will surely be regarded as a blessing by many teachers throughout the world because finally, there is a word that will make people sit up and listen to their problems that cause real fear and anxiety in schools.

Keywords: Aims and objectives of quality education, Debilitating effects of tobephobia, Fear of failure associated with education, learners’ violent behaviour and drug abuse.

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1078 Learning an Overcomplete Dictionary using a Cauchy Mixture Model for Sparse Decay

Authors: E. S. Gower, M. O. J. Hawksford

Abstract:

An algorithm for learning an overcomplete dictionary using a Cauchy mixture model for sparse decomposition of an underdetermined mixing system is introduced. The mixture density function is derived from a ratio sample of the observed mixture signals where 1) there are at least two but not necessarily more mixture signals observed, 2) the source signals are statistically independent and 3) the sources are sparse. The basis vectors of the dictionary are learned via the optimization of the location parameters of the Cauchy mixture components, which is shown to be more accurate and robust than the conventional data mining methods usually employed for this task. Using a well known sparse decomposition algorithm, we extract three speech signals from two mixtures based on the estimated dictionary. Further tests with additive Gaussian noise are used to demonstrate the proposed algorithm-s robustness to outliers.

Keywords: expectation-maximization, Pitman estimator, sparsedecomposition

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1077 Effective Online Staff Training: Is This Possible?

Authors: C. Rogerson, E. Scott

Abstract:

The purpose of this paper is to consider the introduction of online courses to replace the current classroom-based staff training. The current training is practical, and must be completed before access to the financial computer system is authorized. The long term objective is to measure the efficacy, effectiveness and efficiency of the training, and to establish whether a transfer of knowledge back to the workplace has occurred. This paper begins with an overview explaining the importance of staff training in an evolving, competitive business environment and defines the problem facing this particular organization. A summary of the literature review is followed by a brief discussion of the research methodology and objective. The implementation of the alpha version of the online course is then described. This paper may be of interest to those seeking insights into, or new theory regarding, practical interventions of online learning in the real world.

Keywords: Computer-based courses, e-learning, online training, workplace training.

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1076 Modeling Engagement with Multimodal Multisensor Data: The Continuous Performance Test as an Objective Tool to Track Flow

Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

Abstract:

Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: Affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, Signal Detection Theory, student engagement.

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1075 Discovering the Dimension of Abstractness: Structure-Based Model that Learns New Categories and Categorizes on Different Levels of Abstraction

Authors: Georgi I. Petkov, Ivan I. Vankov, Yolina A. Petrova

Abstract:

A structure-based model of category learning and categorization at different levels of abstraction is presented. The model compares different structures and expresses their similarity implicitly in the forms of mappings. Based on this similarity, the model can categorize different targets either as members of categories that it already has or creates new categories. The model is novel using two threshold parameters to evaluate the structural correspondence. If the similarity between two structures exceeds the higher threshold, a new sub-ordinate category is created. Vice versa, if the similarity does not exceed the higher threshold but does the lower one, the model creates a new category on higher level of abstraction.

Keywords: Analogy-making, categorization, learning of categories, abstraction, hierarchical structure.

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1074 A Case Study to Observe How Students’ Perception of the Possibility of Success Impacts Their Performance in Summative Exams

Authors: Rochelle Elva

Abstract:

Faculty in Higher Education today are faced with the challenge of convincing their students of the importance of the mastery of skills through learning. This is because most students often have a single motivation -to get high grades. If it appears that this goal will not be met, they lose their motivation and their academic efforts wane. This is true even for students in the competitive fields of STEM, including Computer Science majors. As educators, we have to understand our students and leverage what motivates them, to achieve our learning outcomes. This paper presents a case study that utilizes cognitive psychology’s Expectancy-Value Theory and Motivation Theory, to investigate the effect of sustained expectancy for success on students’ learning outcomes. In our case study, we explore how students’ motivation and persistence in their academic efforts are impacted by providing them with an unexpected path to success, which continues to the end of the semester. The approach was tested in an undergraduate computer science course with n = 56. The results of the study indicate that when presented with the real possibility of success, despite existing low grades, both low and high-scoring students persisted in their efforts to improve their performance. Their final grades were on average one place higher on the +/-letter grade scale, with some students scoring as high as three places above their predicted grade.

Keywords: Expectancy for success and persistence, motivation and performance, computer science education, motivation and performance in computer science.

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1073 Multi-Factor Optimization Method through Machine Learning in Building Envelope Design: Focusing on Perforated Metal Façade

Authors: Jinwooung Kim, Jae-Hwan Jung, Seong-Jun Kim, Sung-Ah Kim

Abstract:

Because the building envelope has a significant impact on the operation and maintenance stage of the building, designing the facade considering the performance can improve the performance of the building and lower the maintenance cost of the building. In general, however, optimizing two or more performance factors confronts the limits of time and computational tools. The optimization phase typically repeats infinitely until a series of processes that generate alternatives and analyze the generated alternatives achieve the desired performance. In particular, as complex geometry or precision increases, computational resources and time are prohibitive to find the required performance, so an optimization methodology is needed to deal with this. Instead of directly analyzing all the alternatives in the optimization process, applying experimental techniques (heuristic method) learned through experimentation and experience can reduce resource waste. This study proposes and verifies a method to optimize the double envelope of a building composed of a perforated panel using machine learning to the design geometry and quantitative performance. The proposed method is to achieve the required performance with fewer resources by supplementing the existing method which cannot calculate the complex shape of the perforated panel.

Keywords: Building envelope, machine learning, perforated metal, multi-factor optimization, façade.

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1072 Knowledge Transformation Flow (KTF) of Visually Impaired Students: The Virtual Knowledge System as a New Service Innovation

Authors: Chatcai Tangsri, Onjaree Na-Takuatoong

Abstract:

This paper aims to present the key factors that support the decision to use the technology and to present the knowledge transformation flow of visually impaired students after the use of virtual knowledge system as proposed as a new service innovation to universities in Thailand. Correspondents of 27 visually impaired students are involved in this research. Total of 25 students are selected from university that mainly conducts non-classroom teaching environment; while another 2 visually impaired students are selected from classroom teaching environment. All of them are fully involved in the study along 8 weeks duration. All correspondents are classified into 5 small groups in various conditions. The research results revealed that the involvement from knowledge facilitator can push out for the behavioral actual use of the virtual knowledge system although there is no any developed intention to use behaviors. Secondly, the situations that the visually impaired students inadequate of the knowledge sources that usually provided by assistants i.e. peers, audio files etc. In this case, they will use the virtual knowledge system for both knowledge access and knowledge transfer request. With this evidence, the need of knowledge would play a stronger role than all technology acceptance factors. Finally, this paper revealed that the knowledge transfer in normal method that students have a chance to physically meet up is still confirmed as their preference method. In term of other aspects of technology acceptance, it will be discussed together with challenges and recommendations at the end of this paper.

Keywords: Knowledge system, Visually impaired students, Higher education, Knowledge management enable technology, Synchronous/Asynchronous knowledge access, Synchronous/Asynchronous knowledge transfer.

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1071 To Design Holistic Health Service Systems on the Internet

Authors: Åsa Smedberg

Abstract:

There are different kinds of online systems on the Internet for people who need support and develop new knowledge. Online communities and Ask the Expert systems are two such systems. In the health care area, the number of users of these systems has increased at a rapid pace. Interactions with medical trained experts take place online, and people with concerns about similar health problems come together to share experiences and advice. The systems are also used as storages and browsed for health information. Over the years, studies have been conducted of the usage of the different systems. However, in what ways the systems can be used together to enhance learning has not been explored. This paper presents results from a study of online health-communities and an Ask the Expert system for people who suffer from overweight. Differences and similarities in regards to posted issues and replies are discussed, and suggestions for a new holistic design of the two systems are presented.

Keywords: Learning, Ask the Expert, online community, healthcare, holistic, overweight.

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