Search results for: traditional learning approach

Authors: John White

Abstract:

This study investigated the effects of practice schedules on learning and performance in golf putting, specifically focusing on the impact of increasing contextual interference (CI). University students (n=7) were randomly assigned to blocked, random, or increasing practice schedules. During acquisition, participants performed 135 putting trials using different weighted golf balls. The blocked group followed a specific sequence of ball weights, while the random group practiced with the balls in a random order. The increasing group started with a blocked schedule, transitioned to a serial schedule, and concluded with a random schedule. Retention and transfer tests were conducted 24 hours later. The results indicated that high levels of CI (random practice) were more beneficial for learning than low levels of CI (blocked practice). The increasing practice schedule, incorporating blocked, serial, and random practice, demonstrated advantages over traditional blocked and random schedules. Additionally, EEG was used to explore the neurophysiological effects of the increasing practice schedule.

Keywords: skill acquisition, motor control, learning, contextual interference

Procedia PDF Downloads 96

Authors: Vinai K. Singh

Abstract:

In Neural Network-based Learning techniques, there are several models of Convolutional Networks. Whenever the methods are deployed with large datasets, only then can their applicability and appropriateness be determined. Clinical and pathological pictures of lobular carcinoma are thought to exhibit a large number of random formations and textures. Working with such pictures is a difficult problem in machine learning. Focusing on wet laboratories and following the outcomes, numerous studies have been published with fresh commentaries in the investigation. In this research, we provide a framework that can operate effectively on raw photos of various resolutions while easing the issues caused by the existence of patterns and texturing. The suggested approach produces very good findings that may be used to make decisions in the diagnosis of cancer.

Keywords: lobular carcinoma, convolutional neural networks (CNN), deep learning, histopathological imagery scans

Procedia PDF Downloads 137

Authors: Sudhir Kumar Singh, Debashish Chakravarty

Abstract:

Slope stability analysis is an important aspect in the field of geotechnical engineering. It is also important from safety, and economic point of view as any slope failure leads to loss of valuable lives and damage to property worth millions. This paper aims at mitigating the risk of slope failure by studying the effect of different joint sets on the stability of mine pit slopes under the influence of various external factors, namely degree of saturation, rainfall intensity, and seismic coefficients. Supervised machine learning approach has been utilized for making accurate and reliable predictions regarding the stability of slopes based on the value of Factor of Safety. Numerous cases have been studied for analyzing the stability of slopes using the popular Finite Element Method, and the data thus obtained has been used as training data for the supervised machine learning models. The input data has been trained on different supervised machine learning models, namely Random Forest, Decision Tree, Support vector Machine, and XGBoost. Distinct test data that is not present in training data has been used for measuring the performance and accuracy of different models. Although all models have performed well on the test dataset but Random Forest stands out from others due to its high accuracy of greater than 95%, thus helping us by providing a valuable tool at our disposition which is neither computationally expensive nor time consuming and in good accordance with the numerical analysis result.

Keywords: finite element method, geotechnical engineering, machine learning, slope stability

Procedia PDF Downloads 102

Authors: Siti Nurjanah, Supahar

Abstract:

E-learning has become an increasingly popular mode of instruction, particularly in the field of physics education, where it offers opportunities for interactive and engaging learning experiences. This research aims to analyze the trends of research that investigated e-learning in physics education. Data was extracted from Scopus's database using the keywords "physics" and "e-learning". Of the 380 articles obtained based on the search criteria, a trend analysis of the research was carried out with the help of RStudio using the biblioshiny package and VosViewer software. Analysis showed that publications on this topic have increased significantly from 2014 to 2021. The publication was dominated by researchers from the United States. The main journal that publishes articles on this topic is Proceedings Frontiers in Education Conference fie. The most widely cited articles generally focus on the effectiveness of Moodle for physics learning. Overall, this research provides an in-depth understanding of the trends and key findings of research related to e-learning in physics.

Keywords: bibliometric analysis, physics education, biblioshiny, E-learning

Procedia PDF Downloads 44

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

Abstract:

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

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

Procedia PDF Downloads 95

Authors: Kika Ioannou Kazamia

Abstract:

This paper examines the action research cycle of the second phase of longitudinal research on sustainable interior design practices, between two groups of stakeholders, designers and clients. During this phase of the action research, the second step - the change stage - of Lewin’s change management model has been utilized to change values, approaches, and attitudes toward sustainable design practices among the participants. Affective domain learning theory is utilized to attach new values. Learning with the use of information technology, collaborative learning, and problem-based learning are the learning methods implemented toward the acquisition of the objectives. Learning methods, and aims, require the design of interventions with participants' involvement in activities that would lead to the acknowledgment of the benefits of sustainable practices. Interventions are steered to measure participants’ decisions for the worth and relevance of ideas, and experiences; accept or commit to a particular stance or action. The data collection methods used in this action research are observers’ reports, participants' questionnaires, and interviews. The data analyses use both quantitative and qualitative methods. The main beneficial aspect of the quantitative method was to provide the means to separate many factors that obscured the main qualitative findings. The qualitative method allowed data to be categorized, to adapt the deductive approach, and then examine for commonalities that could reflect relevant categories or themes. The results from the data indicate that during the second phase, designers and clients' participants altered their behaviours.

Keywords: design, change, sustainability, learning, practices

Procedia PDF Downloads 80

Authors: Lewis Mehl-Madrona, Barbara Mainguy

Abstract:

We describe the type of counseling done by traditional cultural healers in North America. We follow an autoethnographic course development through the first author’s integration of mainstream training and Native-American heritage and study with traditional medicine people. We assemble traditional healing elders from North America and discuss with them their practices and their philosophies of healing. We draw parallels for their approaches in some European-based philosophies and religion, including the work of Heidegger, Levin, Fox, Kierkegaard, and others. An example of the treatment process with a depressed client is provided and similarities and differences with conventional psychotherapies are described.

Keywords: indigenous approaches to counseling, indigenous bodywork, indigenous healing, North American indigenous people

Procedia PDF Downloads 274

Authors: Natarajan Hema, Tamilarasan Karunakaran

Abstract:

Technology is remodeling the process of teaching and learning. An inflection point is faced where technological interventions are rewiring learning process in formal classrooms. Employment depends on dynamic learning capability. Transforming the functionalities of teaching-learning-assessment through innovation is needed to modify the roles of teacher to enabler and learner to the dynamic learner. This makeover is vital for English language teaching where English is acquired as a skill, exercised as ability and get stabilized as a competence. This reshaping could be achieved through providing autonomy to participants of learning. This paper explores parameters and components aiding such a transformation. The differentiated responsibilities and other critical learning support systems are projected as viable options. New age teaching practices are studied for feasibilities to aid transformation and being put forth an inter-operable teaching-learning system for a learner-centric ELT classrooms. LOTUS model developed by the authors is also studied for its inclusiveness to promote skill acquisition.

Keywords: ELT methodology, communicative competence, skill acquisition , new age teaching

Procedia PDF Downloads 358

Authors: Lucy W. Mburu, Richard Karanja, Simon N. Mwendia

Abstract:

Over recent years, learners have experienced a constant need to access on demand knowledge that is fully aligned with the paradigm of cloud computing. As motivated by the global sustainable development goal to ensure inclusive and equitable learning opportunities, this research has developed a framework hinged on the knowledge-as-a-service architecture that utilizes knowledge from ambient learning systems. Through statistical analysis and decision tree modeling, the study discovers influential variables for ambient learning among university students. The main aim is to generate a platform for disseminating and exploiting the available knowledge to aid the learning process and, thus, to improve educational support on the ambient learning system. The research further explores how collaborative effort can be used to form a knowledge network that allows access to heterogeneous sources of knowledge, which benefits knowledge consumers, such as the developers of ambient learning systems.

Keywords: actionable knowledge, ambient learning, cloud computing, decision trees, knowledge as a service

Procedia PDF Downloads 161

Authors: Pia Kastl

Abstract:

By combining face-to-face sessions with digital selflearning units, the learning process can be enhanced and learning success improved. Potentials of blended learning are the flexibility and possibility to get in touch with lecturers and fellow students face-toface. It also offers the opportunity to individualize and self-regulate the learning process. Aim of this article is to analyse how different learning environments affect students’ learning behavior and how digital tools can be used effectively. The analysis also considers the extent to which the field of study affects the students’ preferences. Semi-structured interviews were conducted with students from different disciplines at two German universities (N= 60). The questions addressed satisfaction and perception of online, faceto-face and blended learning courses. In addition, suggestions for improving learning experience and the use of digital tools in the different learning environments were surveyed. The results show that being present on campus has a positive impact on learning success and online teaching facilitates flexible learning. Blended learning can combine the respective benefits, although one challenge is to keep the time investment within reasonable limits. The use of digital tools differs depending on the subject. Medical students are willing to use digital tools to improve their learning success and voluntarily invest more time. Students of the humanities and social sciences, on the other hand, are reluctant to invest additional time. They do not see extra study material as an additional benefit their learning success. This study illustrates how these heterogenous demands on learning environments can be met. In addition, potential for improvement will be identified in order to foster both learning process and learning success. Learning environments can be meaningfully enriched with digital elements to address student diversity in higher education.

Keywords: blended learning, higher education, diversity, learning styles

Procedia PDF Downloads 70

Authors: Parveen Kumar

Abstract:

The cope the rapid change of technology and to control the ephemeral rate of information generation, librarians along with their professional colleagues need to equip themselves as per the requirement of the electronic information society. E-learning is purely based on computer and communication technologies. The terminologies like computer based learning. It is the delivery of content via all electronic media through internet, internet, Extranets television broadcast, CD-Rom documents, etc. E-learning poses lot of issues in the transformation of literature or knowledge from the conventional medium to ICT based format and web based services.

Keywords: e-learning, digital libraries, online learning, electronic information society

Procedia PDF Downloads 510

Authors: Ramin Shadani

Abstract:

This study introduces adaptive performance as a key organizational performance dimension and explores the relationship between the dimensions of a learning organization and adaptive performance. A survey was therefore conducted using the dimensions of the Learning Organization Questionnaire (DLOQ), followed by factor analysis and structural equation modeling in order to investigate the dynamics between learning organization practices and adaptive performance. Results confirm that adaptive performance is indeed one important dimension of organizational performance. The study also shows that perceived knowledge and adaptive performance mediate the positive relationship between the practices of a learning organization with perceived financial performance. We extend existing DLOQ research by demonstrating that adaptive performance, as a nonfinancial organizational learning outcome, has a significant impact on financial performance. Our study also provides additional validation of the measures of DLOQ's performance. Indeed, organizations need to take a glance at how the activities of learning and development can provide better overall improvement in performance, especially in enhancing adaptive capability. The study has provided requisite empirical support that activities of learning and development within organizations allow much-improved intangible performance outcomes, especially through adaptive performance.

Keywords: adaptive performance, continuous learning, financial performance, leadership style, organizational learning, organizational performance

Procedia PDF Downloads 34

Authors: Wayne DeFehr

Abstract:

This study is rooted in a pedagogical approach that emphasizes student engagement as fundamental to meaningful learning in the classroom. This approach creates a paradigmatic shift, from a teaching practice that reinforces the teacher’s central authority to a practice that disperses that authority among the students in the classroom through networks that they themselves develop. The methodology of this study about creating optimal conditions for learning in the classroom includes providing a conceptual framework within which the students work, as well as providing clearly stated expectations for work standards, content quality, group methodology, and learning outcomes. These learning conditions are nurtured in a variety of ways. First, nearly every class includes a lecture from the professor with key concepts that students need in order to complete their work successfully. Secondly, students build on this scholarly material by forming their own networks, where students face each other and engage with each other in order to collaborate their way to solving a particular problem relating to the course content. Thirdly, students are given short, medium, and long-term goals. Short term goals relate to the week’s topic and involve workshopping particular issues relating to that stage of the course. The medium-term goals involve students submitting term assignments that are evaluated according to a well-defined rubric. And finally, long-term goals are achieved by creating a capstone project, which is celebrated and shared with classmates and interested friends on the final day of the course. The essential conclusions of the study are drawn from courses that focus on video game narrative. Enthusiastic student engagement is created not only with the dynamic energy and expertise of the instructor, but also with the inter-dependence of the students on each other to build knowledge, acquire skills, and achieve successful results.

Keywords: collaboration, education, learning networks, video games

Procedia PDF Downloads 118

Authors: Bruno Pinto Vieira, Marco Antonio Calijorne Soares, Armando Sérgio de Aguiar Filho

Abstract:

Nowadays, a massive amount of information has been produced by different data sources, including mobile devices and transactional systems. In this scenario, concerns arise on how to maintain or establish data quality, which is now treated as a product to be defined, measured, analyzed, and improved to meet consumers' needs, which is the one who uses these data in decision making and companies strategies. Information that reaches low levels of quality can lead to issues that can consume time and money, such as missed business opportunities, inadequate decisions, and bad risk management actions. The step of selecting, identifying, evaluating, and selecting data sources with significant quality according to the need has become a costly task for users since the sources do not provide information about their quality. Traditional data quality control methods are based on user experience or business rules limiting performance and slowing down the process with less than desirable accuracy. Using advanced machine learning algorithms, it is possible to take advantage of computational resources to overcome challenges and add value to companies and users. In this study, machine learning is applied to data quality analysis on different datasets, seeking to compare the performance of the techniques according to the dimensions of quality assessment. As a result, we could create a ranking of approaches used, besides a system that is able to carry out automatically, data quality assessment.

Keywords: machine learning, data quality, quality dimension, quality assessment

Procedia PDF Downloads 150

Authors: S. A. N. Danushka, T. A. Weerasinghe

Abstract:

The diverse necessities of instruction could be addressed effectively with the support of new dimensions of ICT integrated learning such as blended learning –which is a combination of face-to-face and online instruction which ensures greater flexibility in student learning and congruity of course delivery. As blended learning has been the ‘new normality' in education, many experimental and quasi-experimental research studies provide ample of evidence on its successful implementation in many fields of studies, but it is hard to justify whether blended learning could work similarly in the delivery of technology-teacher development programmes (TTDPs). The present study is bound with the particular research uncertainty, and having considered existing research approaches, the study methodology was set to decide the efficient instructional strategies for flipped classroom learning in TTDPs. In a quasi-experimental pre-test and post-test design with a mix-method research approach, the major study objective was tested with two heterogeneous samples (N=135) identified in a virtual learning environment in a Sri Lankan university. Non-randomized informal ‘before-and-after without control group’ design was employed, and two data collection methods, identical pre-test and post-test and Likert-scale questionnaires were used in the study. Selected two instructional strategies, self-directed learning (SDL) and self-regulated learning (SRL), were tested in an appropriate instructional framework with two heterogeneous samples (pre-service and in-service teachers). Data were statistically analyzed, and an efficient instructional strategy was decided via t-test, ANOVA, ANCOVA. The effectiveness of the two instructional strategy implementation models was decided via multiple linear regression analysis. ANOVA (p < 0.05) shows that age, prior-educational qualifications, gender, and work-experiences do not impact on learning achievements of the two diverse groups of learners through the instructional strategy is changed. ANCOVA (p < 0.05) analysis shows that SDL is efficient for two diverse groups of technology-teachers than SRL. Multiple linear regression (p < 0.05) analysis shows that the staged self-directed learning (SSDL) model and four-phased model of motivated self-regulated learning (COPES Model) are efficient in the delivery of course content in flipped classroom learning.

Keywords: COPES model, flipped classroom learning, self-directed learning, self-regulated learning, SSDL model

Procedia PDF Downloads 200

Authors: Sujit K. Basak, Marguerite Wotto, Paul Bélanger

Abstract:

This paper presents a conceptual framework on the critical success factors of e-learning implementation in higher education, derived from an in-depth survey of literature review. The aim of this study was achieved by identifying critical success factors that affect for the successful implementation of e-learning. The findings help to articulate issues that are related to e-learning implementation in both formal and non-formal higher education and in this way contribute to the development of programs designed to address the relevant issues.

Keywords: critical success factors, e-learning, higher education, life-long learning

Procedia PDF Downloads 365

Authors: Ahmed Torad

Abstract:

Background: Knee osteoarthritis (OA) is a prevalent musculoskeletal condition characterized by pain and functional impairment. While various rehabilitation approaches have been employed, the effectiveness of high-intensity interval training (HIIT) compared to traditional rehabilitation exercises remains unclear. Objective: This randomized controlled trial aimed to compare the effects of HIIT and traditional rehabilitation exercises on pain reduction, functional improvement, and quality of life in individuals with knee OA. Methods: A total of 120 participants diagnosed with knee OA were randomly allocated into two groups: the HIIT group (n=60) and the traditional rehabilitation group (n=60). The HIIT group participated in a 12-week supervised program consisting of high-intensity interval exercises, while the traditional rehabilitation group followed a conventional physiotherapy regimen. Outcome measures included visual analog scale (VAS) pain scores, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the Short Form-36 Health Survey (SF-36) at baseline and after the intervention period. Results: Both groups showed significant improvements in pain scores, functional outcomes (WOMAC), and quality of life (SF-36) after 12 weeks of intervention. However, the HIIT group demonstrated superior pain reduction (p<0.001), functional improvement (p<0.001), and physical health-related quality of life (p=0.002) compared to the traditional rehabilitation group. No significant differences were observed in mental health-related quality of life between the two groups. Conclusion: High-intensity interval training appears to be a more effective rehabilitation approach than traditional exercises for individuals with knee osteoarthritis, resulting in greater pain reduction, improved function, and enhanced physical health-related quality of life. These findings suggest that HIIT may represent a promising intervention strategy for managing knee OA and enhancing the overall well-being of affected individuals.

Keywords: knee osteoarthritis, high-intensity interval training, traditional rehabilitation exercises, randomized controlled trial, pain reduction, functional improvement, quality of life

Procedia PDF Downloads 77

Authors: Siti Rossidatul Munawaroh, Siti Khusnul Khowatim

Abstract:

This study is determined to find how is the influence of family attention of students in provides guidance of the student learning. The increasing of student’s learning motivation can be increased made up in various ways, one of them are through students social guidance in their relation with the family. The family not only provides the matter and the learning time but also be supervise for the learning time and guide his children to overcome a learning disability. The character of physics subject in their science experiences at junior high schools has demanded that student’s ability is to think symbolically and understand something in a meaningful manner. Therefore, the reinforcement of the physics learning motivation is clearly necessary not only by the school are related, but the family environment and the society. As for the role of family which includes maintenance, parenting, coaching, and educating both of physically and spiritually, this way is expected to give spirit impulsion in studying physics subject in order to increase student learning achievements.

Keywords: physics subject, the influence of family attention, learning motivation, the Student care

Procedia PDF Downloads 430

Authors: Farshad Negintaji, Hamid Reza Zeraat Pisheh, Mahshid Ghanea, Zahra Khalifeh, Mohammad Bagher Rahami

Abstract:

This study has been examined the anatomical components of mosques with the approach of attending to Islamic wisdom and investigated the distinction between the anatomical design of mosques (traditional and modern) by considering the category of perception in Islamic architecture. To this end, this article by reviewing the theoretical and empirical literature of mosques' anatomy and the role of anatomy on the architectural design of Iranian mosques by examining the quantitative and qualitative indicators and in order to understand and identify the anatomy of mosques, indicators such as: entrance, portico, minarets, domes, bedchamber and pool have been used. The aim of this study has been to investigate materials, the functional properties, technology, sizes and fitness of (traditional and modern) mosques. For this purpose, a questionnaire was prepared in which the anatomical and spiritual elements of the mosque shape have been questioned. Research is related to field and is of descriptive, analytical and inferential type and quantitative and qualitative indicators have been examined.

Keywords: Islamic wisdom, Islamic architecture, mosque anatomy, the minaret, dome, bedchamber, entrance, pool, perception

Procedia PDF Downloads 429

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

Abstract:

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

Keywords: collaborative learning, communicative approach, culture, interaction, interculturalism, target language, virtual partnership

Procedia PDF Downloads 130

Authors: Xinyu Wang

Abstract:

In the context of the rapid development of Internet technology and the increasing prevalence of online social media, this study investigates the impact of digital communication on social learning. Through three behavioral experiments, we explore both affective and cognitive social learning in online environments. Experiment 1 manipulates the content of experimental materials and two forms of feedback, emotional valence, sociability, and repetition, to verify whether individuals can achieve online emotional social learning through reinforcement using two social learning strategies. Results reveal that both social learning strategies can assist individuals in affective, social learning through reinforcement, with feedback-based learning strategies outperforming frequency-dependent strategies. Experiment 2 similarly manipulates the content of experimental materials and two forms of feedback to verify whether individuals can achieve online knowledge social learning through reinforcement using two social learning strategies. Results show that similar to online affective social learning, individuals adopt both social learning strategies to achieve cognitive social learning through reinforcement, with feedback-based learning strategies outperforming frequency-dependent strategies. Experiment 3 simultaneously observes online affective and cognitive social learning by manipulating the content of experimental materials and feedback at different levels of social pressure. Results indicate that online affective social learning exhibits different learning effects under different levels of social pressure, whereas online cognitive social learning remains unaffected by social pressure, demonstrating more stable learning effects. Additionally, to explore the sustained effects of online social learning and differences in duration among different types of online social learning, all three experiments incorporate two test time points. Results reveal significant differences in pre-post-test scores for online social learning in Experiments 2 and 3, whereas differences are less apparent in Experiment 1. To accurately measure the sustained effects of online social learning, the researchers conducted a mini-meta-analysis of all effect sizes of online social learning duration. Results indicate that although the overall effect size is small, the effect of online social learning weakens over time.

Keywords: online social learning, affective social learning, cognitive social learning, social learning strategies, social reinforcement, social pressure, duration

Procedia PDF Downloads 49

Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

Abstract:

Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

Procedia PDF Downloads 178

Authors: Irfan Ahmed Rind

Abstract:

Science education has recently become the top priority of government of Pakistan. Number of schemes has been initiated for the improvement of science teaching and learning at primary and secondary levels of education, most importantly training in-service science teachers on inquiry based teaching and learning to empower students and encourage creativity, critical thinking, and innovation among them. Therefore, this approach has been promoted in the recent continuous professional development trainings for the in-service teachers. However, the follow ups on trained science teachers and educators suggest that these teachers fail to implement the inquiry based teaching and learning in their classes. In addition, these trainings also fail to bring any significant change in students’ science content knowledge and understanding as per the annual national level surveys conducted by government and independent agencies. Research suggests that science has been taught using scientific positivism, which supports objectivity based on experiments and mathematics. In contrary, the inquiry based teaching and learning are based on constructivism, which conflicts with the positivist epistemology of science teachers. It was, therefore, assumed that science teachers struggle to implement the inquiry based teaching approach as it conflicts with their basic epistemological beliefs. With this assumption, this research aimed to (i) understand how science teachers conceptualize the nature of science, and how this influence their understanding of learning, learners, their own roles as teachers and their teaching strategies, (ii) identify the conflict of science teachers’ epistemological beliefs with the inquiry based teaching approach, and (iii) find the ways in which science teachers epistemological beliefs may be developed from positivism to constructivism, so that they may effectively use the inquiry based teaching approach in teaching science. Using qualitative case study approach, thirty six secondary and higher secondary science teachers (21 male and 15 female) were selected. Data was collected using interviewed, participatory observations (sixty lessons were observed), and twenty interviews from students for verifications of teachers’ responses. The findings suggest that most of the science teacher were positivist in defining the nature of science. Most of them limit themselves to one fix answer that is provided in the books and that there is only one 'right' way to teach science. There is no room for students’ or teachers’ own opinion or bias when it comes to scientific concepts. Inquiry based teaching seems 'no right' to them. They find it difficult to allow students to think out of the box. However, some interesting exercises were found to be very effective in bringing the change in teachers’ epistemological beliefs. These will be discussed in detail in the paper. The findings have major implications for the teachers, educators, and policymakers.

Keywords: science teachers, epistemology, metacognition, inquiry based teaching

Procedia PDF Downloads 151

Authors: Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui

Abstract:

Recommender systems have become increasingly popular in recent years, and are utilized in numerous areas. Nowadays many web services provide several information for users and recommender systems have been developed as critical element of these web applications to predict choice of preference and provide significant recommendations. With the help of the advantage of deep learning in modeling different types of data and due to the dynamic change of user preference, building a deep model can better understand users demand and further improve quality of recommendation. In this paper, deep neural network models for recommender system are evaluated. Most of deep neural network models in recommender system focus on the classical collaborative filtering user-item setting. Deep learning models demonstrated high level features of complex data can be learned instead of using metadata which can significantly improve accuracy of recommendation. Even though deep learning poses a great impact in various areas, applying the model to a recommender system have not been fully exploited and still a lot of improvements can be done both in collaborative and content-based approach while considering different contextual factors.

Keywords: big data, decision making, deep learning, recommender system

Procedia PDF Downloads 480

Authors: Pennapa Palapin

Abstract:

This research sought to study the traditional local wisdom and study the promotion and dissemination of traditional local wisdom in order to find the forms of promotion and dissemination of traditional local wisdom to create occupations among the elderly at Nonmueng Community, Muang Sub-District, Baan Dung District, UdonThani Province. The criterion used to select the research sample group was, being a person having a role involved in the promotion and dissemination of traditional local wisdom to create occupations among the elderly at Nonmueng Community, Muang Sub-District, Baan Dung District, UdonThani Province; being an experienced person whom the residents of Nonmueng Community find trustworthy; and having lived in Nonmueng Community for a long time so as to be able to see the development and change that occurs. A total of 16 people were selected. Data was gathered as a qualitative study, through semi-structured in-depth interviews. The collected data was then summarised and discussed according to the research objectives. Finally, the data was presented in a narrative format. Results found that the identifying traditional local wisdom of the community (which grew from the residents’ experience and beneficial usage in daily life, passed down from generation to generation) was the weaving of cloth and basketry. As for the manner of promotion and dissemination of traditional local wisdom, the skills were passed down through teaching by example to family members, relatives and others in the community. This was done by the elders or elderly members of the community. For the promotion and dissemination of traditional local wisdom to create occupations among the elderly, the traditional local wisdom should be supported in every way through participation of the community members. For example, establish a museum of traditional local wisdom for the collection of traditional local wisdom in various fields, both in the past and at present. This would be a source of pride for the community, in order to make traditional local wisdom widely known and to create income for the community’s elderly. Additional ways include exhibitions of products made by traditional local wisdom, finding both domestic and international markets, as well as building both domestic and international networks aiming to find opportunities to market products made by traditional local wisdom.

Keywords: traditional local wisdom, occupation, elderly, community

Procedia PDF Downloads 304

Authors: Jolanta Jonak

Abstract:

All students demonstrate various learning preferences and learning styles that range from visual, auditory to kinesthetic preferences. These learning preferences are further impacted by individual cognitive profiles hat characterizes itself in linguistic strengths, logical- special, inter-or intra- personal, just to name a few. Students from culturally and linguistically diverse backgrounds (CLD) have an increased risk of being misunderstood by many school systems and even medical personnel. Students with disability, specifically Autism, are faced with another layer of learning differences. Research indicates that large numbers of students are not provided the type of education and types of supports they need in order to be successful in an academic environment. Multiple research findings indicate that significant numbers of school staff self-reports that they do not feel adequately prepared to work with students with disability and different learing profiles. It is very important for the school staff to be educated about different learning needs of students with autism spectrum disorders. Having the knowledge, school staff can avoid unnecessary referrals for office referrals and avoid inaccurate decisions about restrictive learning environments. This presentation will illustrate the cognitive differences in students with autism, how to recognize them, and how to support them through Differentiated Instruction. One way to ensure successful education for students with disability is by providing Differentiated Instruction (DI). DI is quickly gaining its popularity in the Unites States as a scientific- research based instructional approach for all students. This form of support ensures that regardless of the students’ learning preferences and cognitive learning profiles, they have an opportunity to learn through approaches that are suitable to their needs. It is extremely important for the school staff, especially school psychologists who often are the first experts to be consulted by educators, to be educated about differences due to learning preference styles and differentiation needs.

Keywords: special education, autism, differentiation, differences, differentiated instruction

Procedia PDF Downloads 46

Authors: Selvavinayagan Babiharan

Abstract:

This research aims to develop a deep learning platform to enhance mathematics education for secondary students in Sri Lanka. The platform will be designed to incorporate interactive and user-friendly features to engage students in active learning and promote their mathematical skills. The proposed platform will be developed using TensorFlow and Keras, two widely used deep learning frameworks. The system will be trained on a large dataset of math problems, which will be collected from Sri Lankan school curricula. The results of this research will contribute to the improvement of mathematics education in Sri Lanka and provide a valuable tool for teachers to enhance the learning experience of their students.

Keywords: information technology, education, machine learning, mathematics

Procedia PDF Downloads 85

Authors: Saba Rahimi, Ozan Oktay, Javier Alvarez-Valle, Sujeeth Bharadwaj

Abstract:

Successful application of deep learning in medical image analysis necessitates unprecedented amounts of labeled training data. Unlike conventional 2D applications, radiological images can be three-dimensional (e.g., CT, MRI), consisting of many instances within each image. The problem is exacerbated when expert annotations are required for effective pixel-wise labeling, which incurs exorbitant labeling effort and cost. Active learning is an established research domain that aims to reduce labeling workload by prioritizing a subset of informative unlabeled examples to annotate. Our contribution is a cost-effective approach for U-Net 3D models that uses Monte Carlo sampling to analyze pixel-wise uncertainty. Experiments on the AAPM 2017 lung CT segmentation challenge dataset show that our proposed framework can achieve promising segmentation results by using only 42% of the training data.

Keywords: image segmentation, active learning, convolutional neural network, 3D U-Net

Procedia PDF Downloads 156

Authors: Kazeem Oluwatoyin Ajape

Abstract:

The study is concerned with the problem of how to improve the teaching of Arabic as a foreign language in Nigerian Higher Education System. The paper traces the historical background of Arabic education in Nigeria and also outlines the problems facing the language in Nigerian Institutions. It lays down some of the essential foundation work necessary for bringing about systematic and constructive improvements in the Teaching of Arabic as a Foreign Language (TAFL) by giving answers to the following research questions: what is the appropriate medium of instruction in teaching a foreign or second language? What is the position of English language in the teaching and learning of Arabic/Islamic education? What is the relevance of the present curriculum of Arabic /Islamic education in Nigerian institutions to the contemporary society? A survey of the literature indicates that a revolution is currently taking place in FL teaching and that a new approach known as the Communicative Approach (CA), has begun to emerge and influence the teaching of FLs in general, over the last decade or so. Since the CA is currently being adapted to the teaching of most major FLs and since this revolution has not yet had much impact on TAPL, the study explores the possibility of the application of the CA to the teaching of Arabic as a living language and also makes recommendations towards the development of the language in Nigerian Institutions of Higher Learning.

Keywords: Arabic Language, foreign language, Nigerian institutions, curriculum, communicative approach

Procedia PDF Downloads 613

Authors: Yasaswi Palagummi, Sareh Rowlands

Abstract:

Generalised Zero-Shot Learning, often known as GZSL, is an advanced variant of zero-shot learning in which the samples in the unseen category may be either seen or unseen. GZSL methods typically have a bias towards the seen classes because they learn a model to perform recognition for both the seen and unseen classes using data samples from the seen classes. This frequently leads to the misclassification of data from the unseen classes into the seen classes, making the task of GZSL more challenging. In this work of ours, to solve the GZSL problem, we propose an approach leveraging the Shifted Window based Self-Attention in the Swin Transformer (Swin-GZSL) to work in the inductive GSZL problem setting. We run experiments on three popular benchmark datasets: CUB, SUN, and AWA2, which are specifically used for ZSL and its other variants. The results show that our model based on Swin Transformer has achieved state-of-the-art harmonic mean for two datasets -AWA2 and SUN and near-state-of-the-art for the other dataset - CUB. More importantly, this technique has a linear computational complexity, which reduces training time significantly. We have also observed less bias than most of the existing GZSL models.

Keywords: generalised, zero-shot learning, inductive learning, shifted-window attention, Swin transformer, vision transformer

Procedia PDF Downloads 71