Search results for: supervised machine learning algorithm

Authors: Guessabi Fatiha

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Nowadays, there is no sphere of human life that does not use Information and Communication Technologies (ICTs) in practice. This type of development grew widely in the last years of the 20th century and impacted many fields such as education, health, financing, job markets, communication, governments, industrial productivity, etc. Recently, in higher education, the use of ICTs has been essential and significant during the Covid19 pandemic. Thanks to technology, although the universities in Algeria were locked down during the period of covid19, learning was easily continued, and students were collaborating, communicating, socializing, and learning at a distance. Therefore, ICT tools are required in translation courses to enhance and improve translation teaching. This research explores the use of ICT in teaching and learning translation. The research comes along with a theoretical framework; the literature review is produced to highlight some essential ICT concepts and translation teaching. In order to achieve the study objective, a questionnaire is distributed to the third-year English LMD students at Tahri Mohamed University, and an interview is addressed to the translation teacher. The results and discussion obtained from this investigation confirmed the hypothesis and revealed that the use of ICT is essential in translation courses and it improves translation teaching. Hence, by using ICT in the classroom, the students become more active, and the teachers of translation become knowledge facilitators and leaders.

Keywords: COVID19, ICT, learning, students, teaching, TMU, translation

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Authors: Amir Hossein Hejazi, Nima Amjady

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In recent years, due to environmental issues traditional energy sources had been replaced by renewable ones. Wind energy as the fastest growing renewable energy shares a considerable percent of energy in power electricity markets. With this fast growth of wind energy worldwide, owners and operators of wind farms, transmission system operators, and energy traders need reliable and secure forecasts of wind energy production. In this paper, a new forecasting strategy is proposed for short-term wind power prediction based on Echo State Networks (ESN). The forecast engine utilizes state-of-the-art training process including dynamical reservoir with high capability to learn complex dynamics of wind power or wind vector signals. The study becomes more interesting by incorporating prediction of wind direction into forecast strategy. The Big Bang-Big Crunch (BB-BC) evolutionary optimization algorithm is adopted for adjusting free parameters of ESN-based forecaster. The proposed method is tested by real-world hourly data to show the efficiency of the forecasting engine for prediction of both wind vector and wind power output of aggregated wind power production.

Keywords: wind power forecasting, echo state network, big bang-big crunch, evolutionary optimization algorithm

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Authors: Ghodhbani Salah, Jemili Farah

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With the growth of cyber attacks, information safety has become an important issue all over the world. Many firms rely on security technologies such as intrusion detection systems (IDSs) to manage information technology security risks. IDSs are considered to be the last line of defense to secure a network and play a very important role in detecting large number of attacks. However the main problem with today’s most popular commercial IDSs is generating high volume of alerts and huge number of false positives. This drawback has become the main motivation for many research papers in IDS area. Hence, in this paper we present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by an IDS and increase detection accuracy. Our data mining technique is unsupervised clustering method based on hybrid ANT algorithm. This algorithm discovers clusters of intruders’ behavior without prior knowledge of a possible number of classes, then we apply K-means algorithm to improve the convergence of the ANT clustering. Experimental results on real dataset show that our proposed approach is efficient with high detection rate and low false alarm rate.

Keywords: intrusion detection system, alarm filtering, ANT class, ant clustering, intruders’ behaviors, false alarms

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Azzeddine Atibi, Khadija El Kababi, Salim Ahmed, Mohamed Radid

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The principal objective of this study is to undertake a comprehensive comparative analysis of distance learning and blended learning modalities, with a particular emphasis on evaluating their effectiveness during the confinement period mandated by the COVID-19 pandemic. This investigation is rooted in the firsthand experiences of educators at the high school and secondary levels within both private and public educational institutions. To acquire the requisite data, we meticulously designed and distributed a survey to these educators, soliciting detailed narratives of their professional experiences throughout this challenging period. The survey aims to elucidate the specific difficulties encountered by teachers, as well as to highlight the innovative pedagogical strategies they devised in response to these challenges. By synthesizing the insights garnered from this survey, our goal is to foster an exchange of experiences among educators and to generate informed recommendations that will inform future educational reforms. Ultimately, this study aspires to contribute to the ongoing discourse on optimizing educational practices in the face of unprecedented disruptions.

Keywords: distance learning, blended learning, covid 19, secondary/ high school, teachingperformance, evaluation

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Authors: Hamid Sharif, Nazish Saleem Abbas, Muhammad Haris Jamil

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An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal. Because of the complexity of the optimization algorithms, most adaptive filters are digital filters. Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications, including adaptive noise cancellation and echo cancellation. Acoustic echo cancellation is a common occurrence in today’s telecommunication systems. The signal interference caused by acoustic echo is distracting to both users and causes a reduction in the quality of the communication. In this paper, we review different techniques of adaptive filtering to reduce this unwanted echo. In this paper, we see the behavior of techniques and algorithms of adaptive filtering like Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Variable Step-Size Least Mean Square (VSLMS), Variable Step-Size Normalized Least Mean Square (VSNLMS), New Varying Step Size LMS Algorithm (NVSSLMS) and Recursive Least Square (RLS) algorithms to reduce this unwanted echo, to increase communication quality.

Keywords: adaptive acoustic, echo cancellation, LMS algorithm, adaptive filter, normalized least mean square (NLMS), variable step-size least mean square (VSLMS)

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Authors: Toshinori Nawata

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In this paper a nonlinear feedback control called augmented automatic choosing control (AACC) for a class of nonlinear systems with constrained input is presented. When designing the control, a constant term which arises from linearization of a given nonlinear system is treated as a coefficient of a stable zero dynamics. Parameters of the control are suboptimally selected by maximizing the stable region in the sense of Lyapunov with the aid of a genetic algorithm. This approach is applied to a field excitation control problem of power system to demonstrate the splendidness of the AACC. Simulation results show that the new controller can improve performance remarkably well.

Keywords: augmented automatic choosing control, nonlinear control, genetic algorithm, zero dynamics

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Authors: Jussi Horelli, Salla Niittymäki

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In this acceleratingly developing world, it will be crucial for our students to not only to adapt to continuous change, but to be the driving force of it. This raises the question of how can the educational processes motivate and encourage the students to learn the perhaps most important skill there for their further work career: the ability to learn and absorb more by themselves. In engineering education, the learning contents and methods have traditionally been very substance oriented and teacher-centered. In Häme University of Applied Sciences (HAMK), the pedagogical model has been completely renewed during the past few years. Terms like phenomenon or skills-based learning and collaborative teaching are things which have not very often been related to engineering education, but are now the foundation of HAMK’s pedagogical model in all disciplines, even in engineering studies. In this paper, a new flexible way of executing engineering studies will be introduced. The paper will summarize three years’ experiences and observations of a process where traditional teacher-centric mechanical engineering teaching was converted into a model where teachers work collaboratively in teams supporting the students’ learning processes.

Keywords: team teaching, collaborative learning, engineering education, new pedagogy

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Authors: Saratu Yusuf Ilu, Mumtaz B. Mustafa, Siti Salwah Salim, Mehdi Malekzadeh

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Children today use computer based application in various activities especially for learning and education. Many of these tools and application such as the Computer Aided Pronunciation Training (CAPT) system enable children to explore and experience them with little supervision from the adults. In order for these tools and application to have maximum effect on the children’s learning and education, it must be attractive to the children to use them. This could be achieved with the proper user interface (UI) design. As children grow, so do their ability, taste and preferences. They interact differently with these applications as they grow older. This study reviews several articles on how age factor influences the UI design. The review focuses on age related abilities such as cognitive, literacy, concentration and feedback requirement. We have also evaluated few of existing CAPT systems and determine the influence of age-based factors on the interface design.

Keywords: children, age-based interaction, learning application, age-based capability

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Authors: Shannon Melideo

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Post-secondary schools that provide specialized instruction for college students with special needs have been in existence for some time in the United States of America. Whether students experience learning disabilities, visual impairments, physical limitations, Autism Spectrum Disorders or any other issue that impacts their learning are able to attend universities that intentionally cater to their needs. While this selection of post-secondary education may be preferred by some students, other have sought a different experience. Over the last ten years, the number of students with Autism Spectrum Disorders (ASD) attending traditional universities in the United States of America has increased significantly. Students with ASD tend to select smaller, private institutions that appear to offer more personal attention and services. This paper will examine how traditional American universities are preparing for this relatively new group of students in their college classrooms. This paper will provide a brief historical timeline of access to university instruction for students with Autism Spectrum Disorders, and how and if students with ASD are received in colleges around the globe, and best research supported practices for success.

Keywords: autism spectrum disorders, access to learning, university instruction, accommodations

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Authors: Thelma Mort

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Currently, in South African schools, under the fast-paced and content-heavy CAPS curriculum, the notion of play is being lost in the foundation phase. Even in Grade R, aimed at improving the quality of education, there is a focus on mathematical literacy, language, and life skills (DoE, 2001). This is largely due to the dichotomizing of play and learning. And although the play is meant to be the primary means of achieving these skills, it somehow loses its playfulness in the face of early academic pressure. Student teachers similarly have not been trained to use play in the early years of schooling. This action research study shares findings from the “Learn to Play” intervention in teacher training at one university in which student teachers were given substantial training in types of play, the ways they could use and promote play, and the changing roles of teachers in play-based learning. Using observation focus group interviews, reflections, student teacher engagement in learning communities, and Theories of Change, the study measures the changes made by the intervention in student teachers’ approaches and attitudes to play in the classroom. Key findings were that the student teachers learned new skills, had better relationships with pupils, and became more confident in their foundation phase settings.

Keywords: action research, foundation phase, South Africa, student teacher training

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Authors: Rose Shayeghi, Pejman Hosseiniun, Ghasem Ghorbanirostam

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The present study was conducted to investigate the Iranian EFL learners’ attitudes toward the use of computer technology in language classes as a method of improving English learning. To this end, 120 male and female Iranian learners participated in the study. Instrumentation included a 20-item questionnaire. The analysis of the data revealed that the majority of learners had a positive attitude towards the application of CALL in language classes. Moreover, independent samples t-tests indicated that male participants had a significantly more positive attitude compared with that of the female participants. Finally, the results obtained through ANOVA revealed that the youngest age group had a significantly more positive attitude toward the use of technology in language classes compared to the other age groups.

Keywords: EFL learners, Iranian learners, CALL, language learning

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Authors: Mamta M. Barapatre, V. N. Sahare

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Since from long time two wheeled vehicle self-balancing has always been a back-breaking task for both human and robots. Leaning a bicycle driving is long time process and goes through building knowledge base for parameter decision making while balancing robots. In order to create this machine learning phase with embedded system the proposed system is designed. The system proposed aims to construct a bicycle automaton, power-driven by an electric motor, which could balance by itself and move along a specific path. This path could be wavy with bumps and varying widths. The key aim was to construct a cycle which self-balances itself by controlling its handle. In order to take a turn, the mass was transferred to the center. In order to maintain the stability, the bicycle bot automatically turned the handle and a turn. Some problems were faced by the team which were Speed, Steering mechanism through mass- distribution (leaning), Center of mass location and gyroscopic effect of its wheel. The idea proposed have potential applications in automation of transportation system and is most efficient.

Keywords: gyroscope-flywheel, accelerometer, servomotor-controller, self stability concept

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Authors: G. Singer, M. Golan

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

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

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Authors: R. R. Ramsheeja, R. Sreeraj

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For view the internal structures of the human body such as liver, brain, kidney etc have a wide range of different modalities for medical images are provided nowadays. Computer Tomography is one of the most significant medical image modalities. In this paper use CT liver images for study the use of automatic computer aided techniques to calculate the volume of the liver tumor. Segmentation method is used for the detection of tumor from the CT scan is proposed. Gaussian filter is used for denoising the liver image and Adaptive Thresholding algorithm is used for segmentation. Multiple Region Of Interest(ROI) based method that may help to characteristic the feature different. It provides a significant impact on classification performance. Due to the characteristic of liver tumor lesion, inherent difficulties appear selective. For a better performance, a novel proposed system is introduced. Multiple ROI based feature selection and classification are performed. In order to obtain of relevant features for Support Vector Machine(SVM) classifier is important for better generalization performance. The proposed system helps to improve the better classification performance, reason in which we can see a significant reduction of features is used. The diagnosis of liver cancer from the computer tomography images is very difficult in nature. Early detection of liver tumor is very helpful to save the human life.

Keywords: computed tomography (CT), multiple region of interest(ROI), feature values, segmentation, SVM classification

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Authors: Jasbir Singh Gill, Baljit Singh

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Scheduling and mapping the application task graph on multiprocessor parallel systems is considered as the most crucial and critical NP-complete problem. Many genetic algorithms have been proposed to solve such problems. In this paper, two genetic approach based algorithms have been designed and developed with or without task duplication. The proposed algorithms work on two fitness functions. The first fitness i.e. task fitness is used to minimize the total finish time of the schedule (schedule length) while the second fitness function i.e. process fitness is concerned with allocating the tasks to the available highly efficient processor from the list of available processors (load balance). Proposed genetic-based algorithms have been experimentally implemented and evaluated with other state-of-art popular and widely used algorithms.

Keywords: parallel computing, task scheduling, task duplication, genetic algorithm

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Authors: Refilwe Moeletsi

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Dimension stone quarrying around Brits and Belfast areas started in the early 1930s and has been growing rapidly since then. Environmental impacts associated with these quarries have not been documented, and hence this study aims at detecting any change in the environment that might have been caused by these activities. Landsat images that were used to assess land use/land cover changes in Brits quarries from 1998 - 2015. A supervised classification using maximum likelihood classifier was applied to classify each image into different land use/land cover types. Classification accuracy was assessed using Google Earth™ as a source of reference data. Post-classification change detection method was used to determine changes. The results revealed significant increase in granite quarries and corresponding decrease in vegetation cover within the study region.

Keywords: remote sensing, GIS, change detection, granite quarries

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Authors: Hooman Torabifard

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In the last few years, with the progress of technology and computers and artificial intelligence entry into all kinds of scientific and industrial fields, the lifestyles of human life have changed and in general, the way of humans live on earth has many changes and development. Until now, some of the changes has occurred in the context of digital images and image processing and still continues. However, besides all the benefits, there have been disadvantages. One of these disadvantages is the multiplicity of images with high volume and data; the focus of this paper is on improving and developing a method for summarizing and enhancing the productivity of these images. The general method used for this purpose in this paper consists of a set of methods based on data obtained from image processing and using the PSO (Particle swarm optimization) algorithm. In the remainder of this paper, the method used is elaborated in detail.

Keywords: image summarization, particle swarm optimization, image threshold, image processing

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Authors: Jennifer Buz, Alvin Spivey

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The most common greenhouse gas emitted through human activities, carbon dioxide (CO2), is naturally consumed by plants during photosynthesis. This process is actively being monetized by companies wishing to offset their carbon dioxide emissions. For example, companies are now able to purchase protections for vegetated land due-to-be clear cut or purchase barren land for reforestation. Therefore, by actively preventing the destruction/decay of plant matter or by introducing more plant matter (reforestation), a company can theoretically offset some of their emissions. One of the biggest issues in the carbon credit market is validating and verifying carbon offsets. There is a need for a system that can accurately and frequently ensure that the areas sold for carbon credits have the vegetation mass (and therefore for carbon offset capability) they claim. Traditional techniques for measuring vegetation mass and determining health are costly and require many person-hours. Orbital Sidekick offers an alternative approach that accurately quantifies carbon mass and assesses vegetation health through satellite hyperspectral imagery, a technique which enables us to remotely identify material composition (including plant species) and condition (e.g., health and growth stage). How much carbon a plant is capable of storing ultimately is tied to many factors, including material density (primarily species-dependent), plant size, and health (trees that are actively decaying are not effectively storing carbon). All of these factors are capable of being observed through satellite hyperspectral imagery. This abstract focuses on speciation. To build a species classification model, we matched pixels in our remote sensing imagery to plants on the ground for which we know the species. To accomplish this, we collaborated with the researchers at the Teakettle Experimental Forest. Our remote sensing data comes from our airborne “Kato” sensor, which flew over the study area and acquired hyperspectral imagery (400-2500 nm, 472 bands) at ~0.5 m/pixel resolution. Coverage of the entire teakettle experimental forest required capturing dozens of individual hyperspectral images. In order to combine these images into a mosaic, we accounted for potential variations of atmospheric conditions throughout the data collection. To do this, we ran an open source atmospheric correction routine called ISOFIT1 (Imaging Spectrometer Optiman FITting), which converted all of our remote sensing data from radiance to reflectance. A database of reflectance spectra for each of the tree species within the study area was acquired using the Teakettle stem map and the geo-referenced hyperspectral images. We found that a wide variety of machine learning classifiers were able to identify the species within our images with high (>95%) accuracy. For the most robust quantification of carbon mass and the best assessment of the health of a vegetated area, speciation is critical. Through the use of high resolution hyperspectral data, ground-truth databases, and complex analytical techniques, we are able to determine the species present within a pixel to a high degree of accuracy. These species identifications will feed directly into our carbon mass model.

Keywords: hyperspectral, satellite, carbon, imagery, python, machine learning, speciation

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Authors: Sibawu Witness Siyepu

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This study explores the effects of instructional design using blended learning in the learning of radian measures among Engineering students. Blended learning is an education programme that combines online digital media with traditional classroom methods. It requires the physical presence of both lecturer and student in a mathematics computer laboratory. Blended learning provides element of class control over time, place, path or pace. The focus was on the use of Khan Academy to supplement traditional classroom interactions. Khan Academy is a non-profit educational organisation created by educator Salman Khan with a goal of creating an accessible place for students to learn through watching videos in a computer assisted computer. The researcher who is an also lecturer in mathematics support programme collected data through instructing students to watch Khan Academy videos on radian measures, and by supplying students with traditional classroom activities. Classroom activities entails radian measure activities extracted from the Internet. Students were given an opportunity to engage in class discussions, social interactions and collaborations. These activities necessitated students to write formative assessments tests. The purpose of formative assessments tests was to find out about the students’ understanding of radian measures, including errors and misconceptions they displayed in their calculations. Identification of errors and misconceptions serve as pointers of students’ weaknesses and strengths in their learning of radian measures. At the end of data collection, semi-structure interviews were administered to a purposefully sampled group to explore their perceptions and feedback regarding the use of blended learning approach in teaching and learning of radian measures. The study employed Algebraic Insight Framework to analyse data collected. Algebraic Insight Framework is a subset of symbol sense which allows a student to correctly enter expressions into a computer assisted systems efficiently. This study offers students opportunities to enter topics and subtopics on radian measures into a computer through the lens of Khan Academy. Khan academy demonstrates procedures followed to reach solutions of mathematical problems. The researcher performed the task of explaining mathematical concepts and facilitated the process of reinvention of rules and formulae in the learning of radian measures. Lastly, activities that reinforce students’ understanding of radian were distributed. Results showed that this study enthused the students in their learning of radian measures. Learning through videos prompted the students to ask questions which brought about clarity and sense making to the classroom discussions. Data revealed that sense making through reinvention of rules and formulae assisted the students in enhancing their learning of radian measures. This study recommends the use of Khan Academy in blended learning to be introduced as a socialisation programme to all first year students. This will prepare students that are computer illiterate to become conversant with the use of Khan Academy as a powerful tool in the learning of mathematics. Khan Academy is a key technological tool that is pivotal for the development of students’ autonomy in the learning of mathematics and that promotes collaboration with lecturers and peers.

Keywords: algebraic insight framework, blended learning, Khan Academy, radian measures

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Authors: A. K. M. Kamrul Islam, Abdelhamid Bouchachia, Suang Cang, Hongnian Yu

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Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models.

Keywords: fuzzy time series (fts), particle swarm optimization, clustering algorithm, hybrid forecasting model

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Authors: S. Studente, S. Ellis, S. F. Garivaldis

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We report upon a study introducing a chatbot to develop learning communities at a London University, with a largely international student base. The focus of the chatbot was twofold; to ease the transition for students into their first year of university study, and to increase study engagement. Four learning communities were created using the chatbot; level 3 foundation, level 4 undergraduate, level 6 undergraduate and level 7 post-graduate. Students and programme leaders were provided with access to the chat bot via mobile app prior to their study induction and throughout the autumn term of 2019. At the end of the term, data were collected via questionnaires and focus groups with students and teaching staff to allow for identification of benefits and challenges. Findings indicated a positive correlation between study engagement and engagement with peers. Students reported that the chatbot enabled them to obtain support and connect to their programme leader. Both staff and students also made recommendation on how engagement could be further enhanced using the bot in terms of; clearly specified purpose, integration with existing university systems, leading by example and connectivity. Extending upon these recommendations, a second pilot study is planned for September 2020, for which the focus will be upon improving attendance rates, student satisfaction and module pass rates.

Keywords: chatbot, e-learning, learning communities, student engagement

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Authors: Maria Visitacion N. Gumabay, Byron Joseph A. Hallar, Annjeannette Alain D. Galang

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This study was intended to provide a more efficient and convenient way for mobile users to enhance their knowledge about ASEAN countries. The researchers evaluated the utility of the developed crossword puzzle application and assessed the general usability of its user interface for its intended purpose and audience of users. The descriptive qualitative research method for the research design and the Mobile-D methodology was employed for the development of the software application output. With a generally favorable reception from its users, the researchers concluded that the iSEA Mobile Based Learning Application can be considered ready for general deployment and use. It was also concluded that additional studies can also be done to make a more complete assessment of the knowledge gained by its users before and after using the application.

Keywords: mobile learning, eLearning, crossword, ASEAN, iSEA

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Authors: Romisaa Ali

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This paper presents a comparison between twin-delayed Deep Deterministic Policy Gradient (TD3) and Soft Actor-Critic (SAC) reinforcement learning algorithms in the context of training robust navigation policies for Jackal robots. By leveraging an open-source framework and custom motion control environments, the study evaluates the performance, robustness, and transferability of the trained policies across a range of scenarios. The primary focus of the experiments is to assess the training process, the adaptability of the algorithms, and the robot’s ability to navigate in previously unseen environments. Moreover, the paper examines the influence of varying environmental complexities on the learning process and the generalization capabilities of the resulting policies. The results of this study aim to inform and guide the development of more efficient and practical reinforcement learning-based navigation policies for Jackal robots in real-world scenarios.

Keywords: Jackal robot environments, reinforcement learning, TD3, SAC, robust navigation, transferability, custom environment

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Authors: Ajish Sreedharan

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With the fast evolution of digital data exchange, security information becomes much important in data storage and transmission. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access. As encryption process is applied to the whole image in AES ,it is difficult to improve the efficiency. In this paper, wavelet decomposition is used to concentrate the main information of image to the low frequency part. Then, AES encryption is applied to the low frequency part. The high frequency parts are XORed with the encrypted low frequency part and a wavelet reconstruction is applied. Theoretical analysis and experimental results show that the proposed algorithm has high efficiency, and satisfied security suits for image data transmission.

Keywords: discrete wavelet transforms, AES, dynamic SBox

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Authors: Scott McCutcheon, Karen Schreder

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Higher education is facing a new reality. Student connection with coursework, instructor, and peers competes with online gaming, screen time, and instant gratification. Pedagogical methods that align student connection and critical thinking in a content-rich environment are important in supporting student learning, a sense of community, and emotional health. This mixed methods study focuses on exploring how the use of educational escape rooms (EERs) can support student learning and learning retention while fostering engagement with each other, the instructor, and the coursework. EERs are content-specific, cooperative, team-based learning activities designed to be completed within a short segment of a typical class. Data for the study was collected over three semesters and includes results from the implementation of EERs in science-based and liberal studies courses taught by different instructors. Twenty-seven students were surveyed regarding their learning experiences with this pedagogy, and interviews with four student volunteers were conducted to add depth to the survey data. A key finding from this research indicates that students felt more connected to each other and the course content after participating in the escape room activity. Additional findings point to increased engagement and comprehension of the class material. Data indicates that the use of an EER pedagogy supports student engagement, well-being, subject comprehension, and student-student and student-instructor connection.

Keywords: gamification, innovative pedagogy, student engagement, student emotional well being

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Authors: Norzaliza Md. Nor, Sh-Hussain Salleh, Mahyar Hamedi, Hadrina Hussain, Wahab Abdul Rahman

Abstract:

Motivational factor is mainly the students’ desire to involve in learning process. However, it also depends on the goal towards their involvement or non-involvement in academic activity. Even though, the students’ motivation might be in the same level, but the basis of their motivation may differ. In this study, it focuses on the intrinsic motivational factor which student enjoy learning or feeling of accomplishment the activity or study for its own sake. The intrinsic motivational factor of students in learning mathematics and science has found as difficult to be achieved because it depends on students’ interest. In the Program for International Student Assessment (PISA) for mathematics and science, Malaysia is ranked as third lowest. The main problem in Malaysian educational system, students tend to have extrinsic motivation which they have to score in exam in order to achieve a good result and enrolled as university students. The use of electroencephalogram (EEG) signals has found to be scarce especially to identify the students’ intrinsic motivational factor in learning science and mathematics. In this research study, we are identifying the correlation between precursor emotion and its dynamic emotion to verify the intrinsic motivational factor of students in learning mathematics and science. The 2-D Affective Space Model (ASM) was used in this research in order to identify the relationship of precursor emotion and its dynamic emotion based on the four basic emotions, happy, calm, fear and sad. These four basic emotions are required to be used as reference stimuli. Then, in order to capture the brain waves, EEG device was used, while Mel Frequency Cepstral Coefficient (MFCC) was adopted to be used for extracting the features before it will be feed to Multilayer Perceptron (MLP) to classify the valence and arousal axes for the ASM. The results show that the precursor emotion had an influence the dynamic emotions and it identifies that most students have no interest in mathematics and science according to the negative emotion (sad and fear) appear in the EEG signals. We hope that these results can help us further relate the behavior and intrinsic motivational factor of students towards learning of mathematics and science.

Keywords: EEG, MLP, MFCC, intrinsic motivational factor

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Authors: Gülay Dalgıç, Gildis Tachir

Abstract:

Architectural design studios are ambiencein where architecture design is realized as a palpable product in architectural education. In the design studios that the architect candidate will use in the design processthe information, the methods of approaching the design problem, the solution proposals, etc., are set uptogetherwith the studio coordinators. The architectural design process, on the other hand, is complex and uncertain.Candidate architects work in a process that starts with abstre and ill-defined problems. This process starts with the generation of alternative solutions with the help of representation tools, continues with the selection of the appropriate/satisfactory solution from these alternatives, and then ends with the creation of an acceptable design/result product. In the studio ambience, many designs and thought relationships are evaluated, the most important step is the early design phase. In the early design phase, the first steps of converting the information are taken, and converted information is used in the constitution of the first design decisions. This phase, which positively affects the progress of the design process and constitution of the final product, is complex and fuzzy than the other phases of the design process. In this context, the aim of the study is to investigate the effects of face-to-face learning model and e-learning model on the early design phase. In the study, the early design phase was defined by literature research. The data of the defined early design phase criteria were obtained with the feedback graphics created for the architect candidates who performed e-learning in the first year of architectural education and continued their education with the face-to-face learning model. The findings of the data were analyzed with the common graphics program. It is thought that this research will contribute to the establishment of a contemporary architectural design education model by reflecting the evaluation of the data and results on architectural education.

Keywords: education modeling, architecture education, design education, design process

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Authors: Yagya Raj Pandeya, Joonwhoan Lee

Abstract:

The dynamic behavior of music and video makes it difficult to evaluate musical instrument playing in a video by computer system. Any television or film video clip with music information are rich sources for analyzing musical instruments using modern machine learning technologies. In this research, we integrate the audio and video information sources using convolutional neural network (CNN) and pass network learned features through recurrent neural network (RNN) to preserve the dynamic behaviors of audio and video. We use different pre-trained CNN for music and video feature extraction and then fine tune each model. The music network use 2D convolutional network and video network use 3D convolution (C3D). Finally, we concatenate each music and video feature by preserving the time varying features. The long short term memory (LSTM) network is used for long-term dynamic feature characterization and then use late fusion with generalized mean. The proposed network performs better performance to recognize the musical instrument using audio-video multimodal neural network.

Keywords: multimodal, 3D convolution, music-video feature extraction, generalized mean

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Authors: Alexander Huang

Abstract:

Online learning sessions have become an indispensable complement to in-classroom-learning sessions in the past two years due to the emergence of Covid-19. Due to social distance requirements, many courses and interaction-intensive sessions, ranging from music classes to debate camps, are online. However, online learning imposes a significant challenge for engaging students effectively during the learning sessions. To resolve this problem, Project PWR, a non-profit organization formed by high school students, developed an online kid-teach-kid learning environment to boost students' learning interests and further improve students’ engagement during online learning. Fundamentally, the kid-teach-kid learning model creates an affinity space to form learning groups, where like-minded peers can learn and teach their interests. The role of the teacher can also help a kid identify the instructional task and set the rules and procedures for the activities. The approach also structures initial discussions to reveal a range of ideas, similar experiences, thinking processes, language use, and lower student-to-teacher ratio, which become enriched online learning experiences for upcoming lessons. In such a manner, a kid can practice both the teacher role and the student role to accumulate experiences on how to convey ideas and questions over the online session more efficiently and effectively. In this research work, we conducted two case studies involving a 3D-Design course and a Speech and Debate course taught by high-school kids. Through Project PWR, a kid first needs to design the course syllabus based on a provided template to become a student-teacher. Then, the Project PWR academic committee evaluates the syllabus and offers comments and suggestions for changes. Upon the approval of a syllabus, an experienced and voluntarily adult mentor is assigned to interview the student-teacher and monitor the lectures' progress. Student-teachers construct a comprehensive final evaluation for their students, which they grade at the end of the course. Moreover, each course requires conducting midterm and final evaluations through a set of surveyed replies provided by students to assess the student-teacher’s performance. The uniqueness of Project PWR lies in its established kid-teach-kids affinity space. Our research results showed that Project PWR could create a closed-loop system where a student can help a teacher improve and vice versa, thus improving the overall students’ engagement. As a result, Project PWR’s approach can train teachers and students to become better online learners and give them a solid understanding of what to prepare for and what to expect from future online classes. The kid-teach-kid learning model can significantly improve students' engagement in the online courses through the Project PWR to effectively supplement the traditional teacher-centric model that the Covid-19 pandemic has impacted substantially. Project PWR enables kids to share their interests and bond with one another, making the online learning environment effective and promoting positive and effective personal online one-on-one interactions.

Keywords: kid-teach-kid, affinity space, online learning, engagement, student-teacher

Procedia PDF Downloads 131