Search results for: learning methods

Authors: Kanlaya Ratanasuphakarn, Suttipong Boonphadung

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The research on the development of ability in reading comprehension based on metacognitive strategies aimed to (1) improve the students’development of ability in reading comprehension based on metacognitive strategies, (2) evaluate the students’ satisfaction on using metacognitive strategies in learning as a tool developing the ability in reading comprehension. Forty-eight of Mattayom 3 students who have enrolled in the subject of research for learning development of semester 2 in 2013 were purposively selected as the research cohort. The research tools were lesson plans for reading comprehension, pre-posttest and satisfaction questionnaire that were approved as content validity and reliability (IOC=.66-1.00,0.967). The research found that the development of ability in reading comprehension of the research samples before using metacognitive strategies in learning activities was in the normal high level. Additionally, the research discovered that the students’ satisfaction of the research cohort after applying model in learning activities appeared to be high level of satisfaction on using metacognitive strategies in learning as a tool for the development of ability in reading comprehension.

Keywords: development of ability, metacognitive strategies, satisfaction, reading comprehension

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Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

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The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: foot recognition, deep learning, knee rehabilitation, convolutional neural network

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Authors: Naser Alajmi, Ali Alobaidly, Mubarak Alhajri, Salem Salamah, Muhammad Alsubaie

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Iterative Learning Control (ILC) known to be a controlling tool to overcome periodic disturbances for repetitive systems. This technique is required to let the error signal tends to zero as the number of operation increases. The learning process that lies within this context is strongly dependent on the initial input which if selected properly tends to let the learning process be more effective compared to the case where a system starts from blind. ILC uses previous recorded execution data to update the following execution/trial input such that a reference trajectory is followed to a high accuracy. Error convergence in ILC is generally highly dependent on the input applied to a plant for trial $1$, thus a good choice of initial starting input signal would make learning faster and as a consequence the error tends to zero faster as well. In the work presented within, an upper limit based on the Singular Values Principle (SV) is derived for the initial input signal applied at trial $1$ such that the system follow the reference in less number of trials without responding aggressively or exceeding the working envelope where a system is required to move within in a robot arm, for example. Simulation results presented illustrate the theory introduced within this paper.

Keywords: initial input, iterative learning control, maximum input, singular values

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Authors: Payal Maheshwari, Maheaswari Brindavan

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The present study aimed at studying the level of perceived self-efficacy of children with learning disability and their mother’s perception about the efficacy of the child and the relationship between the two. The study further aimed at finding out the relationship between the level of perceived self-efficacy of children with learning disability and their academic achievement and their mother’s perception about the Efficacy of the child and child’s Academic Achievement. The sample comprised of 80 respondents (40 children with learning disability and their mothers). Children with learning disability as their primary condition, belonging to middle or upper middle class, living with both the parents, residing in Mumbai and their mothers were selected. Purposive or judgmental and snowball sampling technique was used to select the sample for the present study. Proformas in the form of questionnaires were used to obtain the background information of the children with learning disability and their mother’s. A self-constructed Mother’s Perceived Efficacy of their Child Assessment Scale was used to measure mothers perceived level of efficacy of their child with learning disability. Self-constructed Child’s Perceived Self-Efficacy Assessment Scale was used to measure the level of child’s perceived self-efficacy. Academic scores of the child were collected from the child’s parents or teachers and were converted into percentage. The data were analyzed quantitatively using frequencies, mean and standard deviation. Correlations were computed to ascertain the relationships between the different variables. The findings revealed that majority of the mother’s perceived efficacy about their child with learning disability was above average as well as majority of the children with learning disability also perceived themselves as having above average level of self-efficacy. Further in the domains of self-regulated learning and emotional self-efficacy majority of the mothers perceived their child as having average or below average efficacy, 50% of the children also perceived their self-efficacy in the two domains at average or below average level. A significant (r=.322, p < .05) weak correlation (Spearman’s rho) was found between mother’s perceived efficacy about their child, and child’s perceived self-efficacy and a significant (r=.377, p < .01) weak correlation (Pearson Correlation) was also found between mother’s perceived efficacy about their child and child’s academic achievement. Significant weak positive correlation was found between child’s perceived self-efficacy and academic achievement (r=.332, p < .05). Based on the findings, the study discussed the need for intervention program for children in non-academic skills like self-regulation and emotional competence.

Keywords: learning disability, perceived self efficacy, academic achievement, mothers, children

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Authors: Suriya

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

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

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Authors: Khan Zeenat, S. B. Dandegaonkar

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Dyslexia is a neurological disorder which affects the reading and writing ability of children. A sample of 72 dyslexic children (36 from English medium and 36 from Marathi medium schools) of class V from English and Marathi medium schools were selected. The Experimental method was used to study the effect of Remedial Teaching Programme on the Learning outcome of Dyslexic students. The findings showed that there is a Positive effect of remedial teaching programme on the Learning outcome of English and Marathi medium students.

Keywords: remedial teaching, Dyslexic students, learning outcome, neurological

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Authors: Asra Yousuf, Ajaykumar Tannirkulum

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Inferring rural-urban migration trends can help design effective policies that promote better urban planning and rural development. In this paper, we describe how machine learning algorithms can be applied to predict internal migration decisions of people. We consider data collected from household surveys in Tamil Nadu to train our model. To measure the performance of the model, we use data on past migration from National Sample Survey Organisation of India. The factors for training the model include socioeconomic characteristic of each individual like age, gender, place of residence, outstanding loans, strength of the household, etc. and his past migration history. We perform a comparative analysis of the performance of a number of machine learning algorithm to determine their prediction accuracy. Our results show that machine learning algorithms provide a stronger prediction accuracy as compared to statistical models. Our goal through this research is to propose the use of data science techniques in understanding human decisions and behaviour in developing countries.

Keywords: development, migration, internal migration, machine learning, prediction

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Authors: Peng Liu

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Deep Q networks (DQNs) represent a significant advancement in reinforcement learning, utilizing neural networks to approximate the optimal Q-value for guiding sequential decision processes. This paper presents a comprehensive introduction to reinforcement learning principles, delves into the mechanics of DQNs, and explores its application in portfolio optimization. By evaluating the performance of DQNs against traditional benchmark portfolios, we demonstrate its potential to enhance investment strategies. Our results underscore the advantages of DQNs in dynamically adjusting asset allocations, offering a robust portfolio management framework.

Keywords: deep reinforcement learning, deep Q networks, portfolio optimization, multi-period optimization

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Authors: Paulo Sérgio Ribeiro de Araújo Bogas

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Although several studies have assumed (at least implicitly) that learners' approaches to learning develop into deeper approaches to higher education, there appears to be no clear theoretical basis for this assumption and no empirical evidence. 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 result 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 student's responses 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 the 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 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, mixed methods, reflective learning, marketing

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Authors: Olukemi E. Adetuyi-Olu-Francis

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Learning is natural for living. One stops learning when life ends. Hence, there is no negotiating life-long learning. An individual has the innate ability to learn as many languages as he/she desires as long as life exists. French language education to every Nigerian teacher-trainee is a necessity. Nigeria’s geographical location requires that the French language should be upheld for economic and cultural co-operations between Nigeria and the francophone countries sharing borders with her. The French language will enhance the leadership roles of the teacher-trainees and their ability to function across borders. The 21st century learning tools are basically digital, and many apps are complementing the actual classroom interactions. This study examined the communicative competence in the French language to equip Nigerian teacher-trainees in the new-normal society using mobile apps as a lifelong learning tool. Three research questions and hypotheses guided the study, and the researcher adopted a pre-test, a post-test experimental design, using a sample size of 87 teacher-trainees in South-south geopolitical zone of Nigeria. Results showed that the use of mobile apps is effective for learning the French language. One of the recommendations is that the use of mobile apps should be encouraged for all Nigerian youths to learn the French language for enhancing leadership roles in the world of work and for international interactions for socio-economic co-operations with Nigerian neighboring countries.

Keywords: communicative competence, french language, life long learning, mobile apps, new normal society, teacher trainees

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Authors: Nune Ayvazyan

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Since the proclamation of monolingual foreign-language learning methods (the Berlitz Method in the early 20ᵗʰ century and the like), the dilemma has been to allow or not to allow learners’ mother tongue in the foreign-language learning process. The reason for not allowing learners’ mother tongue is reported to create a situation of immersion where students will only use the target language. It could be argued that this artificial monolingual situation is defective, mainly because there are very few real monolingual situations in the society. This is mainly due to the fact that societies are nowadays increasingly multilingual as plurilingual speakers are the norm rather than an exception. More recently, the use of learners’ mother tongue and translation has been put under the spotlight as valid foreign-language teaching tools. The logic dictates that if learners were permitted to use their mother tongue in the foreign-language learning process, that would not only be natural, but also would give them additional means of participation in class, which could eventually lead to learning. For example, when learners’ metalinguistic skills are poor in the target language, a question they might have could be asked in their mother tongue. Otherwise, that question might be left unasked. Attempts at empirically testing the role of translation as a didactic tool in foreign-language teaching are still very scant. In order to fill this void, this study looks into the interaction patterns between students in two kinds of English-learning classes: one with translation and the other in English only (immersion). The experiment was carried out with 61 students enrolled in a second-year university subject in English grammar in Spain. All the students underwent the two treatments, classes with translation and in English only, in order to see how they interacted under the different conditions. The analysis centered on four categories of interaction: teacher talk, teacher-initiated student interaction, student-initiated student-to-teacher interaction, and student-to-student interaction. Also, pre-experiment and post-experiment questionnaires and individual interviews gathered information about the students’ attitudes to translation. The findings show that translation elicited more student-initiated interaction than did the English-only classes, while the difference in teacher-initiated interactional turns was not statistically significant. Also, student-initiated participation was higher in comprehension-based activities (into L1) as opposed to production-based activities (into L2). As evidenced by the questionnaires, the students’ attitudes to translation were initially positive and mainly did not vary as a result of the experiment.

Keywords: foreign language, learning, mother tongue, translation

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Authors: Natascha C. D'Amico, Enzo Grossi, Giovanni Valbusa, Ala Malasevschi, Gianpiero Cardone, Sergio Papa

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Purpose: To characterize, through a radiomic approach, the nature of areas classified PI-RADS (Prostate Imaging Reporting and Data System) 3/5, recognized in multiparametric prostate magnetic resonance with T2-weighted (T2w), diffusion and perfusion sequences with paramagnetic contrast. Methods and Materials: 24 cases undergoing multiparametric prostate MR and biopsy were admitted to this pilot study. Clinical outcome of the PI-RADS 3/5 was found through biopsy, finding 8 malignant tumours. The analysed images were acquired with a Philips achieva 1.5T machine with a CE- T2-weighted sequence in the axial plane. Semi-automatic tumour segmentation was carried out on MR images using 3DSlicer image analysis software. 45 shape-based, intensity-based and texture-based features were extracted and represented the input for preprocessing. An evolutionary algorithm (a TWIST system based on KNN algorithm) was used to subdivide the dataset into training and testing set and select features yielding the maximal amount of information. After this pre-processing 20 input variables were selected and different machine learning systems were used to develop a predictive model based on a training testing crossover procedure. Results: The best machine learning system (three-layers feed-forward neural network) obtained a global accuracy of 90% ( 80 % sensitivity and 100% specificity ) with a ROC of 0.82. Conclusion: Machine learning systems coupled with radiomics show a promising potential in distinguishing benign from malign tumours in PI-RADS 3/5 areas.

Keywords: machine learning, MR prostate, PI-Rads 3, radiomics

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Authors: Manthekeleng Linake, Fesi Liziwe

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The ongoing argument over the use of English as a learning and teaching language in schools was examined in this study. The nature of the language proficiency gap is particularly relevant in light of the present emphasis on learning and educational quality in contemporary debates, as well as the education sustainable development goal. As a result, an interpretivist paradigm, a qualitative technique, and a case study-based research design were used in the work. Two school principals, two teachers, two members of the School Governing Body (SGB), and four learners were chosen using purposive sampling from two schools in the Amathole West Education District. The researchers were able to acquire in-depth information on the disputes surrounding the use of English as a language of learning and teaching by using semi-structured interview questions and focus groups. Despite knowing that they do not have the potential to do well in English, teachers found that despite appreciating the value of mother tongue and cultural identity, they prefer to use English as the language of teaching in schools. The findings, on the other hand, revealed that proponents of mother-language-based education argue that learning one's mother tongue is a human right.

Keywords: English first additional language learners, social justice, human capabilities, language proficiency

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Authors: Khaled M. Alhawiti

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Natural Language Processing (NLP) is an effective approach for bringing improvement in educational setting. This involves initiating the process of learning through the natural acquisition in the educational systems. It is based on following effective approaches for providing the solution for various problems and issues in education. Natural Language Processing provides solution in a variety of different fields associated with the social and cultural context of language learning. It is based on involving various tools and techniques such as grammar, syntax, and structure of text. It is effective approach for teachers, students, authors, and educators for providing assistance for writing, analysis, and assessment procedure. Natural Language Processing is widely integrated in the large number of educational contexts such as research, science, linguistics, e-learning, evaluations system, and various other educational settings such as schools, higher education system, and universities. Natural Language Processing is based on applying scientific approach in the educational settings. In the educational settings, NLP is an effective approach to ensure that students can learn easily in the same way as they acquired language in the natural settings.

Keywords: natural language processing, education, application, e-learning, scientific studies, educational system

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Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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Authors: Namubiru Zula, Aganyira Kelle, Van der Linden Josje, Openjuru George Laadah

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Learning to know is a key principle to lifelong learning, with self-direction as the cornerstone. This study sought to initiate self-direction for lifelong learning through social constructivism among fishers; with the major goal of creating a community of fishers who continuously learn from each other for sustainable fishing. Government of Uganda has instituted several mechanisms like co-management with Beach Management Unit (BMU) System against illegal fishing. However, illegal fishing persists, there is reduced fish stocks with several outcry on how fishers are handled. Some studies have indicated that it’s the poor orientation of BMU leaders and fishers which are top down. This initial engagement of fishers was conducted through a meeting and use of stake holder’s analysis tool to discuss the relevance of the study; harnessing fishers’ knowledge for sustainable fisheries on Lake Victoria, its objectives, the key stake holders to enable them fish sustainably. It revealed initial attempt to learn from each other and learning to know among fishers, with some elements of self-direction. However, fishers attempt to learning and self-direction are affected by prior brutal enforcement experiences. This meeting led to fishers gain some sense of hope towards enforcement brutality. The key stakeholders highlighted include MAAIF, FAO, UNBS, NaFIRRI, LVFO, BMU, UFPEA, Fishers m employers, Fisheries Protection Unit, GIZ, and any Non-Government organization but declined the Association of Fisheries and Lake Users in Uganda.

Keywords: self direction, lifelong learning, social constructivism, sustainable fishing

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Authors: Fadoua Ouamani, Narjès Bellamine Ben Saoud, Henda Hajjami Ben Ghézala

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In the last few years and over the last decades, there was a growing interest in the development of Computer Supported Collaborative Learning (CSCL) environments. However, the existing systems ignore the variety of learners and their socio-cultural differences, especially in the case of distant and networked learning. In fact, within such collaborative learning environments, learners from different socio-cultural backgrounds may interact together. These learners evolve within various cultures and social contexts and acquire different socio-cultural values and behaviors. Thus, they should be assisted while communicating and collaborating especially in an intercultural group. Besides, the communication and collaboration tools provided to each learner must depend on and be adapted to her/his socio-cultural profile. The main goal of this paper is to present the proposed socio-cultural adaptation approach based on and guided by ontologies to adapt CSCL environments to the socio-cultural profiles of its users (learners or others).

Keywords: CSCL, socio-cultural profile, adaptation, ontology

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Authors: C. W. Kan

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Heat setting is a commonly used technique in textile industry for treating synthetic fibers. In this study, we examined the effect of heat-setting process on the dyeing properties of polyester fabric. The heat setting conditions were varied, and these conditions would affect the dyeing results. The aim of this study is to illustrate the proper application method of heat setting process to polyester fabric, and the results could provide guidance note to the students in learning this topic. Acknowledgment: Authors would like to thank the financial support from the Hong Kong Polytechnic University for this work.

Keywords: learning materials, heat setting, polyester, dyeing

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Authors: Soheila Sadeghi

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In construction projects, predicting project performance metrics accurately is essential for effective management and successful delivery. However, conventional methods often depend on fixed baseline plans, disregarding the evolving nature of project progress and external influences. To address this issue, we introduce a distinct approach based on machine learning to forecast key performance indicators, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category within an urban road reconstruction project. Our proposed model leverages time series forecasting techniques, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance by analyzing historical data and project progress. Additionally, the model incorporates external factors, including weather patterns and resource availability, as features to improve forecast accuracy. By harnessing the predictive capabilities of machine learning, our performance forecasting model enables project managers to proactively identify potential deviations from the baseline plan and take timely corrective measures. To validate the effectiveness of the proposed approach, we conduct a case study on an urban road reconstruction project, comparing the model's predictions with actual project performance data. The outcomes of this research contribute to the advancement of project management practices in the construction industry by providing a data-driven solution for enhancing project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, schedule variance, earned value management

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Authors: Christina Largent, Tazley Hobbs

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Objective: A literature review was performed to examine the existing research on COVID-19 lockdown as it relates to ADHD child/adolescent individuals, media use, and impact on educational performance/learning. It was surmised that with the COVID-19 shut-down and transition to remote learning, a less structured learning environment, increased screen time, in addition to potential difficulty accessing school resources would impair ADHD individuals’ performance and learning. A resulting increase in the number of youths diagnosed and treated for ADHD would be expected. As of yet, there has been little to no published data on the incidence of ADHD as it relates to COVID-19 outside of reports from several nonprofit agencies such as CHADD (Children and Adults with Attention-Deficit/Hyperactivity Disorder ), who reported an increased number of calls to their helpline, The New York based Child Mind Institute, who reported an increased number of appointments to discuss medications, and research released from Athenahealth showing an increase in the number of patients receiving new diagnosis of ADHD and new prescriptions for ADHD medications. Methods: A literature search for articles published between 2020 and 2021 from Pubmed, Google Scholar, PsychInfo, was performed. Search phrases and keywords included “covid, adhd, child, impact, remote learning, media, screen”. Results: Studies primarily utilized parental reports, with very few from the perspective of the ADHD individuals themselves. Most findings thus far show that with the COVID-19 quarantine and transition to online learning, ADHD individuals’ experienced decreased ability to keep focused or adhere to the daily routine, as well as increased inattention-related problems, such as careless mistakes or lack of completion in homework, which in turn translated into overall more difficulty with remote learning. To add further injury, one study showed (just on evaluation of two different sites within the US) that school based services for these individuals decreased with the shift to online-learning. Increased screen time, television, social media, and gaming were noted amongst ADHD individuals. One study further differentiated the degree of digital media, identifying individuals with “problematic “ or “non-problematic” use. ADHD children with problematic digital media use suffered from more severe core symptoms of ADHD, negative emotions, executive function deficits, damage to family environment, pressure from life events, and a lower motivation to learn. Conclusions and Future Considerations: Studies found not only was online learning difficult for ADHD individuals but it, in addition to greater use of digital media, was associated with worsening ADHD symptoms impairing schoolwork, in addition to secondary findings of worsening mood and behavior. Currently, data on the number of new ADHD cases, in addition to data on the prescription and usage of stimulants during COVID-19, has not been well documented or studied; this would be well-warranted out of concern for over diagnosing or over-prescribing our youth. It would also be well-worth studying how reversible or long-lasting these negative impacts may be.

Keywords: COVID-19, remote learning, media use, ADHD, child, adolescent

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Authors: Mst Shapna Akter, Hossain Shahriar

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Neural network approaches are machine learning methods used in many domains, such as healthcare and cyber security. Neural networks are mostly known for dealing with image datasets. While training with the images, several fundamental mathematical operations are carried out in the Neural Network. The operation includes a number of algebraic and mathematical functions, including derivative, convolution, and matrix inversion and transposition. Such operations require higher processing power than is typically needed for computer usage. Central Processing Unit (CPU) is not appropriate for a large image size of the dataset as it is built with serial processing. While Graphics Processing Unit (GPU) has parallel processing capabilities and, therefore, has higher speed. This paper uses advanced Neural Network techniques such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models to compare CPU and GPU resources. A system for classifying autism disease using face images of an autistic and non-autistic child was used to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It has been observed that GPU runs faster than the CPU in all tests performed. Moreover, the performance of the Neural Network models in terms of accuracy increases on GPU compared to CPU.

Keywords: autism disease, neural network, CPU, GPU, transfer learning

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Authors: F. Uriel, M. M. Fernandez Liporace

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In the last decade, academic achievement in higher education has become a topic of agenda in Argentina, regarding the high figures of adjustment problems, academic failure and dropout, and the low graduation rates in the context of massive classes and traditional teaching methods. Psychological variables, such as perceived social support, academic motivation and learning styles and strategies have much to offer since their measurement by tests allows a proper diagnose of their influence on academic achievement. Framed in a major research, several studies analysed multiple samples, totalizing 5135 students attending Argentinean public universities. The first goal was aimed at the identification of statistically significant differences in psychological variables -perceived social support, learning styles, learning strategies, and academic motivation- by age, gender, and degree of academic advance (freshmen versus sophomores). Thus, an inferential group differences study for each psychological dependent variable was developed by means of student’s T tests, given the features of data distribution. The second goal, aimed at examining associations between the four psychological variables on the one hand, and academic achievement on the other, was responded by correlational studies, calculating Pearson’s coefficients, employing grades as the quantitative indicator of academic achievement. The positive and significant results that were obtained led to the formulation of different predictive models of academic achievement which had to be tested in terms of adjustment and predictive power. These models took the four psychological variables above mentioned as predictors, using regression equations, examining predictors individually, in groups of two, and together, analysing indirect effects as well, and adding the degree of academic advance and gender, which had shown their importance within the first goal’s findings. The most relevant results were: first, gender showed no influence on any dependent variable. Second, only good achievers perceived high social support from teachers, and male students were prone to perceive less social support. Third, freshmen exhibited a pragmatic learning style, preferring unstructured environments, the use of examples and simultaneous-visual processing in learning, whereas sophomores manifest an assimilative learning style, choosing sequential and analytic processing modes. Despite these features, freshmen have to deal with abstract contents and sophomores, with practical learning situations due to study programs in force. Fifth, no differences in academic motivation were found between freshmen and sophomores. However, the latter employ a higher number of more efficient learning strategies. Sixth, freshmen low achievers lack intrinsic motivation. Seventh, models testing showed that social support, learning styles and academic motivation influence learning strategies, which affect academic achievement in freshmen, particularly males; only learning styles influence achievement in sophomores of both genders with direct effects. These findings led to conclude that educational psychologists, education specialists, teachers, and universities must plan urgent and major changes. These must be applied in renewed and better study programs, syllabi and classes, as well as tutoring and training systems. Such developments should be targeted to the support and empowerment of students in their academic pathways, and therefore to the upgrade of learning quality, especially in the case of freshmen, male freshmen, and low achievers.

Keywords: academic achievement, academic motivation, coping, learning strategies, learning styles, perceived social support

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Authors: Inkeri Jaaskelainen

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The aim of this study is to shed light on what is it like to study in apprenticeship training using intermediate (or even lower level) Finnish. The aim is to find out and describe these students' experiences and feelings while acquiring a profession in Finnish as it is important to understand how immigrant background adult learners learn and how their needs could be better taken into account. Many students choose apprenticeships and start vocational training while their language skills in Finnish are still very weak. At work, students should be able to simultaneously learn Finnish and do vocational studies in a noisy, demanding, and stressful environment. Learning and understanding new things is very challenging under these circumstances, and sometimes students get exhausted and experience a lot of stress - which makes learning even more difficult. Students are different from each other, and so are their ways to learn. Both duties at work and school assignments require reasonably good general language skills, and, especially at work, language skills are also a safety issue. The empirical target of this study is a group of students with an immigrant background who studied in various fields with intensive L2 support in 2016–2018 and who by now have completed a vocational upper secondary qualification. The interview material for this narrative study was collected from those who completed apprenticeship training in 2019–2020. The data collection methods used are a structured thematic interview, a questionnaire, and observational data. Interviewees with an immigrant background have an inconsistent cultural and educational background - some have completed an academic degree in their country of origin while others have learned to read and write only in Finland. The analysis of the material utilizes thematic analysis, which is used to examine learning and related experiences. Learning a language at work is very different from traditional classroom teaching. With evolving language skills, at an intermediate level at best, rushing and stressing makes it even more difficult to understand and increases the fear of failure. Constant noise, rapidly changing situations, and uncertainty undermine the learning and well-being of apprentices. According to preliminary results, apprenticeship training is well suited to the needs of an adult immigrant student. In apprenticeship training, students need a lot of support for learning and understanding a new communication and working culture. Stress can result in, e.g., fatigue, frustration, and difficulties in remembering and understanding. Apprenticeship training can be seen as a good path to working life. However, L2 support is a very important part of apprenticeship training, and it indeed helps students to believe that one day they will graduate and even get employed in their new country.

Keywords: apprenticeship training, vocational basic degree, Finnish learning, wee-being

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Authors: Zixuan Xu, Yichun Lou, Yang Song, Zihuai Lin

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MediaPipe is an open-source machine learning computer vision framework that can be ported into a multi-platform environment, which makes it easier to use it to recognize the human activity. Based on this framework, many human recognition systems have been created, but the fundamental issue is the recognition of human behavior and posture. In this paper, two methods are proposed to recognize human gestures based on MediaPipe, the first one uses the Adaptive Boosting algorithm to recognize a series of fitness gestures, and the second one uses the Fast Dynamic Time Warping algorithm to recognize 413 continuous fitness actions. These two methods are also applicable to any human posture movement recognition.

Keywords: computer vision, MediaPipe, adaptive boosting, fast dynamic time warping

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Authors: Wera Supa, Panita Wannapiroon

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This paper is a report on the designing of the social collaborative learning model based on proactive involvement to Promote the global merit principle in cultivating youths’ morality. The research procedures into two phases, the first phase is to design the social collaborative learning model based on proactive involvement to promote the global merit principle in cultivating youths’ morality, and the second is to evaluate the social collaborative learning model based on proactive involvement. The sample group in this study consists of 15 experts who are dominant in proactive participation, moral merit principle and youths’ morality cultivation from executive level, lecturers and the professionals in information and communication technology expertise selected using the purposive sampling method. Data analyzed by arithmetic mean and standard deviation. This study has explored that there are four significant factors in promoting the hands-on collaboration of global merit scheme in order to implant virtues to adolescences which are: 1) information and communication Technology Usage; 2) proactive involvement; 3) morality cultivation policy, and 4) global merit principle. The experts agree that the social collaborative learning model based on proactive involvement is highly appropriate.

Keywords: social collaborative learning, proactive involvement, global merit principle, morality

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Authors: Craig Mertens

Abstract:

Teaching art history or any other academic subject to EFL students can be done successfully. A course called Western Images was created to teach Japanese students art history while only using English in the classroom. An approach known as Content and Language Integrated Learning (CLIL) was used as a basis for this course. This paper’s purpose is to state the reasons why learning about art history is important, go through the process of creating content for the course, and suggest multiple tasks to help students practice the critical thinking skills used in analyzing and drawing conclusions of works of art from western culture. As a guide for this paper, Brown’s (1995) six elements of a language curriculum will be used. These stages include needs analysis, goals and objectives, assessment, materials, teaching method and tasks, and evaluation of the course. The goal here is to inspire debate and discussion regarding CLIL and its pros and cons, and to question current curriculum in university language courses.

Keywords: art history, EFL, content and language integration learning, critical thinking

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Authors: Mohammad Reza Taherkhani‎

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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make a connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: underwater sensor networks, clustering, learning automata, energy consumption

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Authors: Stephen Grant Atkins

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Radical use of project-based learning (PBL) in a small New Zealand business school provides an opportunity to longitudinally examine its effects over a decade of pre-Covid data. Prior to this business school’s implementation of PBL, starting in 2012, the business pedagogy literature presented just one example of PBL replacing an entire core-set of courses. In that instance, a British business school merged four of its ‘degree Year 3’ accounting courses into one PBL semester. As radical as that would have seemed, to students aged 20-to-22, the PBL experiment conducted in a New Zealand business school was notably more extreme: 41 nationally-approved Learning Outcomes (L.O.s), these deriving from 8 separate core courses, were aggregated into one grand set of L.O.s, and then treated as a ‘full-year’/‘full-time’ single course. The 8 courses in question were all components of this business school’s compulsory ‘degree Year 1’ curriculum. Thus, the students involved were notably younger (…ages 17-to-19…), and no ‘part-time’ enrolments were allowed. Of interest are this PBL experiment’s effects on subsequent performance outcomes in ‘degree Years 2 & 3’ (….which continued to operate in their traditional ways). Of special interest is the quality of ‘group project’ outcomes. This is because traditionally, ‘degree Year 1’ course assessments are only minimally based on group work. This PBL experiment altered that practice radically, such that PBL ‘degree Year 1’ alumni entered their remaining two years of business coursework with far more ‘project group’ experience. Timeline-wise, thus of interest here, firstly, is ‘degree Year 2’ performance outcomes data from years 2010-2012 + 2016-2018, and likewise ‘degree Year 3’ data for years 2011-2013 + 2017-2019. Those years provide a pre-&-post comparative baseline for performance outcomes in students never exposed to this school’s radical PBL experiment. That baseline is then compared to PBL alumni outcomes (2013-2016….including’Student Evaluation of Course Quality’ outcomes…) to clarify ‘radical PBL’ effects.

Keywords: project-based learning, longitudinal mixed-methods, students criticism, effects-on-learning

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Authors: Alper Tolga Kumtepe, Erdem Erdogdu, M. Recep Okur, Eda Kaypak, Ozlem Kaya, Serap Ugur, Deniz Dincer, Hakan Yildirim

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Interaction is one of the most important components of open and distance learning. According to Moore, who proposed one of the keystones on interaction types, there are three basic types of interaction: learner-teacher, learner-content, and learner-learner. From these interaction types, learner-content interaction, without doubt, can be identified as the most fundamental one on which all education is based. Efficacy, efficiency, and attraction of open and distance learning systems can be achieved by the practice of effective learner-content interaction. With the development of new technologies, interactive e-learning materials have been commonly used as a resource in open and distance learning, along with the printed books. The intellectual engagement of the learners with the content that is course materials may also affect their satisfaction for the open and distance learning practices in general. Learner satisfaction holds an important place in open and distance learning since it will eventually contribute to the achievement of learning outcomes. Using the learner-content interaction activities in course materials, Anadolu University, by its Open Education system, tries to involve learners in deep and meaningful learning practices. Especially, during the e-learning material design and production processes, identifying appropriate learner-content interaction activities within the context of learning outcomes holds a big importance. Considering the lack of studies adopting this approach, as well as its being a study on the use of e-learning materials in Open Education system, this research holds a big value in open and distance learning literature. In this respect, the present study aimed to investigate a) which learner-content interaction activities included in interactive courses are the most effective in learners’ achievement of verbal information learning outcomes and b) to what extent distance learners are satisfied with these learner-content interaction activities. For this study, the quasi-experimental research design was adopted. The 120 participants of the study were from Anadolu University Open Education Faculty students living in Eskişehir. The students were divided into 6 groups randomly. While 5 of these groups received different learner-content interaction activities as a part of the experiment, the other group served as the control group. The data were collected mainly through two instruments: pre-test and post-test. In addition to those tests, learners’ perceived learning was assessed with an item at the end of the program. The data collected from pre-test and post-test were analyzed by ANOVA, and in the light of the findings of this approximately 24-month study, suggestions for the further design of e-learning materials within the context of learner-content interaction activities will be provided at the conference. The current study is planned to be an antecedent for the following studies that will examine the effects of activities on other learning domains.

Keywords: interaction, distance education, interactivity, online courses

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Authors: Rahma Adinda Sartika

Abstract:

The use of technology in learning mathematics has received a positive response from both students and teachers, so many researchers have conducted research on this theme. Based on the findings carried out in this study, 807 documents relevant to this theme have been published in Scopus from 1995-2024. After going through the stages of identification, screening, eligibility, and including, the documents that meet the criteria are 227 documents. These documents are then analyzed using the bibliometric method so that it can be seen that the most published documents in the Scopus database occurred in 2020, with 38 documents, and the lowest was from 1996 to 2000 and 2004 to 2007, namely, no documents published. The highest number of citations is in documents published in 2018, with a total of 349 citations, so the h-index is higher than the others. The country that published the most documents relevant to this theme is Indonesia with a total of 91 documents. The second largest is the United States, with a total of 28 published documents, and the third largest is China, with a total of 15 documents. Indonesia and the United States have the most working relationships between countries compared to other countries. The focus of research related to this theme is 1) mathematics learning, 2) learning systems, 3) engineering education, 4) technology and 5) mathematical concepts.

Keywords: technology, bibliometric, mathematics learning, mathematical concepts

Procedia PDF Downloads 26