Search results for: learning approaches

Authors: Chi H. Lin, Chi C. Lin, Chih L. Hsieh

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Motor Imagery (MI) alone or combined with action observation (AO) has been shown to enhance motor performance and skill learning. The most effective way to combine these techniques has received limited scientific scrutiny. In the present study, we examined the effects of simultaneous (i.e., observing an action whilst imagining carrying out the action concurrently), alternate (i.e., observing an action and then doing imagery related to that action consecutively) and synthesis (alternately perform action observation and imagery action and then perform observation and imagery action simultaneously) AOMI combinations on improving golf putting performance and learning. Participants, 45 university students who had no formal experience of using imagery for the study, were randomly allocated to one of four training groups: simultaneous action observation and motor imagery (S-AOMI), alternate action observation and motor imagery (A-AOMI), synthesis action observation and motor imagery (A-S-AOMI), and a control group. And it was applied 'Different Experimental Groups with Pre and Post Measured' designs. Participants underwent eighteen times of different interventions, which were happened three times a week and lasting for six weeks. We analyzed the information we received based on two-factor (group × times) mixed between and within analysis of variance to discuss the real effects on participants' golf putting performance and learning about different intervention methods of different types of combined action observation and motor imagery. After the intervention, we then used imagery questionnaire and journey to understand the condition and suggestion about different motor imagery and action observation intervention from the participants. The results revealed that the three experimental groups both are effective in putting performance and learning but not for the control group, and the A-S-AOMI group is significantly better effect than S-AOMI group on golf putting performance and learning. The results confirmed the effect of motor imagery combined with action observation on the performance and learning of golf putting. In particular, in the groups of synthesis, motor imagery, or action observation were alternately performed first and then performed motor imagery, and action observation simultaneously would have the best effectiveness.

Keywords: motor skill learning, motor imagery, action observation, simulation

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Authors: James P. Takona

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In a world that is experiencing sharp digital transformations guided by digital technologies, the potential of technology to drive transformation and evolution in the higher is apparent. Higher education is facing a paradigm shift that exposes susceptibilities and threats to fully online programs in the face of post-Covid-19 trends of commodification. This historical moment is likely to be remembered as a critical turning point from analog to digital degree-focused learning modalities, where the default became the pivot point of competition between higher education institutions. Fall 2020 marks a significant inflection point in higher education as students, educators, and government leaders scrutinize higher education's price and value propositions through the new lens of traditional lecture halls versus multiple digitized delivery modes. Online education has since tiled the way for a pedagogical shift in how teachers teach and students learn. The incremental growth of online education in the west can now be attributed to the increasing patronage among students, faculty, and institution administrators. More often than not, college instructors assume paraclete roles in this learning mode, while students become active collaborators and no longer passive learners. This paper offers valuable discernments into the threats, challenges, and opportunities of a massive digital revolution in servicing degree programs. To view digital instruction and learning demands for instructional practices that revolve around collaborative work, engaging students in learning activities, and an engagement that promotes active efforts to solicit strong connections between course activities and expected learning pace for all students. Appropriate digital technologies demand instructors and students need prior solid skills. Need for the use of digital technology to support instruction and learning, intelligent tutoring offers great promise, and failures at implementing digital learning may not improve outcomes for specific student populations. Digital learning benefits students differently depending on their circumstances and background and those of the institution and/or program. Students have alternative options, access to the convenience of learning anytime and anywhere, and the possibility of acquiring and developing new skills leading to lifelong learning.

Keywords: digi̇tized learning, digital education, collaborative work, high education, online education, digitize delivery

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Authors: Mirjana Ruppel, Rajendra Persad, Amit Bahl, Sanja Dogramadzi, Chris Melhuish, Lyndon Smith

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Multimodal image registration is a profoundly complex task which is why deep learning has been used widely to address it in recent years. However, two main challenges remain: Firstly, the lack of ground truth data calls for an unsupervised learning approach, which leads to the second challenge of defining a feasible loss function that can compare two images of different modalities to judge their level of alignment. To avoid this issue altogether we implement a generative adversarial network consisting of two registration networks GAB, GBA and two discrimination networks DA, DB connected by spatial transformation layers. GAB learns to generate a deformation field which registers an image of the modality B to an image of the modality A. To do that, it uses the feedback of the discriminator DB which is learning to judge the quality of alignment of the registered image B. GBA and DA learn a mapping from modality A to modality B. Additionally, a cycle-consistency loss is implemented. For this, both registration networks are employed twice, therefore resulting in images ˆA, ˆB which were registered to ˜B, ˜A which were registered to the initial image pair A, B. Thus the resulting and initial images of the same modality can be easily compared. A dataset of liver CT and MRI was used to evaluate the quality of our approach and to compare it against learning and non-learning based registration algorithms. Our approach leads to dice scores of up to 0.80 ± 0.01 and is therefore comparable to and slightly more successful than algorithms like SimpleElastix and VoxelMorph.

Keywords: cycle consistency, deformable multimodal image registration, deep learning, GAN

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Authors: Junbeom Lee, Jaehyuck Cho, Wookyeong Jeong, Jonghan Won, Jungmin Hwang, Youngseok Song, Taikyeong Jeong

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This paper investigates and analyzes meta-learning technologies that use multiple-cameras to monitor and check abnormal behavior in people in real-time in the area of healthcare fields. Advances in artificial intelligence and computer vision technologies have confirmed that cameras can be useful for individual health monitoring and abnormal behavior detection. Through this, it is possible to establish a system that can respond early by automatically detecting abnormal behavior of the elderly, such as patients and the elderly. In this paper, we use a technique called meta-learning to analyze image data collected from cameras and develop a commercial product to determine abnormal behavior. Meta-learning applies machine learning algorithms to help systems learn and adapt quickly to new real data. Through this, the accuracy and reliability of the abnormal behavior discrimination system can be improved. In addition, this study proposes a meta-learning-based abnormal behavior detection system that includes steps such as data collection and preprocessing, feature extraction and selection, and classification model development. Various healthcare scenarios and experiments analyze the performance of the proposed system and demonstrate excellence compared to other existing methods. Through this study, we present the possibility that camera-based meta-learning technology can be useful for monitoring and testing abnormal behavior in the healthcare area.

Keywords: artificial intelligence, abnormal behavior, early detection, health monitoring

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Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

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Authors: Dalila Souilem, Salma Boumiza, Abdelkarim Abdelkader

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The Clarifier Dialogue Interface (CDI) is a part of an online teaching system based on human-machine communication in learning situation. This interface used in the system during the learning action specifically in the evaluation step, to clarify ambiguities in the learner's response. The CDI can generate patterns allowing access to an information system, using the selectors associated with lexical units. To instantiate these patterns, the user request (especially learner’s response), must be analyzed and interpreted to deduce the canonical form, the semantic form and the subject of the sentence. For the efficiency of this interface at the interpretation level, a set of substitution operators is carried out in order to extend the possibilities of manipulation with a natural language. A second approach that will be presented in this paper focuses on the object languages with new prospects such as combination of natural language with techniques of handling information system in the area of online education. So all operators, the CDI and other interfaces associated to the domain expertise and teaching strategies will be unified using FRAME representation form.

Keywords: dialogue, e-learning, FRAME, information system, natural language

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Authors: Javad Abdi, Azam Famil Khalili

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Modeling emotions have attracted much attention in recent years, both in cognitive psychology and design of artificial systems. However, it is a negative factor in decision-making; emotions have shown to be a strong faculty for making fast satisfying decisions. In this paper, we have adapted a computational model based on the limbic system in the mammalian brain for control engineering applications. Learning in this model based on Temporal Difference (TD) Learning, we applied the proposed controller (termed BELBIC) for a simple model of a submarine. The model was supposed to reach the desired depth underwater. Our results demonstrate excellent control action, disturbance handling, and system parameter robustness for TDBELBIC. The proposal method, regarding the present conditions, the system action in the part and the controlling aims, can control the system in a way that these objectives are attained in the least amount of time and the best way.

Keywords: artificial neural networks, temporal difference, brain emotional learning based intelligent controller, heating- ventilating and air conditioning

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Authors: Steven Sam, Ximena Schmidt, Hugh Dickinson, Jens Jensen

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The goal of this study is to explore the potential of data science and citizen science approaches to develop an interactive, digital, open infrastructure that pulls together African indigenous agriculture and food systems data from multiple sources, making it accessible and reusable for policy, research and practice in modern food production efforts. The World Bank has recognised that African Indigenous Knowledge (AIK) is innovative and unique among local and subsistent smallholder farmers, and it is central to sustainable food production and enhancing biodiversity and natural resources in many poor, rural societies. AIK refers to tacit knowledge held in different languages, cultures and skills passed down from generation to generation by word of mouth. AIK is a key driver of food production, preservation, and consumption for more than 80% of citizens in Africa, and can therefore assist modern efforts of reducing food insecurity and hunger. However, the documentation and dissemination of AIK remain a big challenge confronting librarians and other information professionals in Africa, and there is a risk of losing AIK owing to urban migration, modernisation, land grabbing, and the emergence of relatively small-scale commercial farming businesses. There is also a clear disconnect between the AIK and scientific knowledge and modern efforts for sustainable food production. The study combines data science and citizen science approaches through active community participation to generate and share AIK for facilitating learning and promoting knowledge that is relevant for policy intervention and sustainable food production through a curated digital platform based on FAIR principles. The study adopts key informant interviews along with participatory photo and video elicitation approach, where farmers are given digital devices (mobile phones) to record and document their every practice involving agriculture, food production, processing, and consumption by traditional means. Data collected are analysed using the UK Science and Technology Facilities Council’s proven methodology of citizen science (Zooniverse) and data science. Outcomes are presented in participatory stakeholder workshops, where the researchers outline plans for creating the platform and developing the knowledge sharing standard framework and copyrights agreement. Overall, the study shows that learning from AIK, by investigating what local communities know and have, can improve understanding of food production and consumption, in particular in times of stress or shocks affecting the food systems and communities. Thus, the platform can be useful for local populations, research, and policy-makers, and it could lead to transformative innovation in the food system, creating a fundamental shift in the way the North supports sustainable, modern food production efforts in Africa.

Keywords: Africa indigenous agriculture knowledge, citizen science, data science, sustainable food production, traditional food system

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Authors: Maria Ahmad, Michael Page, Danë Goodsman

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‘Educational’ debriefing – a construct distinct from clinical debriefing – is used following simulated scenarios and is central to learning and development in fields ranging from aviation to emergency medicine. However, little research into educational debriefing in prehospital medicine exists. This qualitative study explored the facilitation and effects of prehospital educational debriefing and identified obstacles to debriefing, using the London’s Air Ambulance Pre-Hospital Care Course (PHCC) as a model. Method: Ethnographic observations of moulages and debriefs were conducted over two consecutive days of the PHCC in October 2019. Detailed contemporaneous field notes were made and analysed thematically. Subsequently, seven one-to-one, semi-structured interviews were conducted with four PHCC debrief facilitators and three course participants to explore their experiences of prehospital educational debriefing. Interview data were manually transcribed and analysed thematically. Results: Four overarching themes were identified: the approach to the facilitation of debriefs, effects of debriefing, facilitator development, and obstacles to debriefing. The unpredictable debriefing environment was seen as both hindering and paradoxically benefitting educational debriefing. Despite using varied debriefing structures, facilitators emphasised similar key debriefing components, including exploring participants’ reasoning and sharing experiences to improve learning and prevent future errors. Debriefing was associated with three principal effects: releasing emotion; learning and improving, particularly participant compound learning as they progressed through scenarios; and the application of learning to clinical practice. Facilitator training and feedback were central to facilitator learning and development. Several obstacles to debriefing were identified, including mismatch of participant and facilitator agendas, performance pressure, and time. Interestingly, when used appropriately in the educational environment, these obstacles may paradoxically enhance learning. Conclusions: Educational debriefing in prehospital medicine is complex. It requires the establishment of a safe learning environment, an understanding of participant agendas, and facilitator experience to maximise participant learning. Aspects unique to prehospital educational debriefing were identified, notably the unpredictable debriefing environment, interdisciplinary working, and the paradoxical benefit of educational obstacles for learning. This research also highlights aspects of educational debriefing not extensively detailed in the literature, such as compound participant learning, display of ‘professional honesty’ by facilitators, and facilitator learning, which require further exploration. Future research should also explore educational debriefing in other prehospital services.

Keywords: debriefing, prehospital medicine, prehospital medical education, pre-hospital care course

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Authors: C. K. Peter Chuah

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Essentially, the intention of reflective journal is meant for students to develop higher-order thinking skills and to provide a 'space' to make their learning experience and thinking, making and feeling visible, i.e., it provides students an opportunity to evaluate their learning critically by focusing on the rationale behind their thinking, making and feeling. In addition, reflective journal also gets the students to focus on how could things be done differently—the possibility, alternative point of views, and opportunities for change. It is hoped that by getting communication design students to reflect at various intervals, they could move away from mere working on the design project and pay more attention to what they thought they have learned in relation to the development of their design ability. Unfortunately, a closer examination—through content analysis—of the learning journals submitted by a group of design students revealed that most of the reflections were descriptive and tended to be a summary of what occurred in the learning experience. While many students were able to describe what they did, very few were able to explain how they were able to do something critically. It can be concluded that to get design students to reflect is a fairly easy task, but to get them to reflect critically could be very challenging. To ensure that design students could benefit from the use of reflective journal as a tool to develop their critical thinking skills, a more systematic and structured approach to the introduction of critical thinking and reflective journal should be built into the design curriculum to provide as much practice and sufficient feedback as other studio subjects.

Keywords: communication design education, critical thinking, reflection, reflective journal

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Authors: Murtala Sale

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The use of instructional materials is an indispensable tool that enhances qualitative teaching and learning especially at the primary level. Instructional materials are used to facilitate comprehension of ideas in the learners as well as ensure long term retention of ideas and topics taught to pupils. This study examined the relevance of using instructional materials in primary schools in Katsina State, Nigeria. It employed survey design using cluster sampling technique. The questionnaire was used to gather data for analysis, and statistical and frequency tables were used to analyze the data gathered. The results show that teachers and students alike have realized the effectiveness of modern instructional materials in teaching and learning for the attainment of set objectives in the basic primary education policy. It also discovered that reluctance in the use of instructional materials will hamper the achievement of qualitative primary education. The study therefore suggests that there should be the provision of adequate and up-to-date instructional materials to all primary schools in Katsina State for effective teaching and learning process.

Keywords: instructional materials, effective teaching, learning quality, indispensable aspect

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Authors: Sean Paulsen, Michael Casey

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In this work we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data.

Keywords: transfer learning, fMRI, self-supervised, brain decoding, transformer, multitask training

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Authors: Mark Harmon, Abdolghani Ebrahimi, Patrick Lucey, Diego Klabjan

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In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories. Previous approaches to similar problems center on hand-crafting features to capture domain-specific knowledge. Although intuitive, recent work in deep learning has shown, this approach is prone to missing important predictive features. To circumvent this issue, we present a convolutional neural network (CNN) approach where we initially represent the multiagent behavior as an image. To encode the adversarial nature of basketball, we use a multichannel image which we then feed into a CNN. Additionally, to capture the temporal aspect of the trajectories, we use “fading.” We find that this approach is superior to a traditional FFN model. By using gradient ascent, we were able to discover what the CNN filters look for during training. Last, we find that a combined FFN+CNN is the best performing network with an error rate of 39%.

Keywords: basketball, computer vision, image processing, convolutional neural network

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Authors: Rosiris Maturo Domingues, Patricia Luissa Masmo, Cibele Cavalheiro Neves, Juliana Dalla Martha Rodriguez

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This article reports the experience of the pedagogical consultants responsible for the curriculum development of Senac São Paulo courses when facing the emergency need to maintain the pedagogical process in their schools in the face of the Covid-19 pandemic. The urgent adjustment to distance education resulted in the improvement of the process and the adoption of new teaching and learning strategies mediated by technologies. The processes for preparing and providing guidelines for professional education courses were also readjusted. Thus, a bank of teaching-learning strategies linked to digital resources was developed, categorized, and identified by their didactic-pedagogical potential, having as an intersection didactic planning based on learning objectives based on Bloom's taxonomy (revised), given its convergence with the competency approach adopted by Senac. Methodologically, a relationship was established between connectivity and digital networks and digital evolution in school environments, culminating in new paradigms and processes of educational communication and new trends in teaching and learning. As a result, teachers adhered to the use of digital tools in their practices, transposing face-to-face classroom methodologies and practices to online media, whose criticism was the use of ICTs in an instrumental way, reducing methodologies and practices to teaching only transmissive. There was recognition of the insertion of technology as a facilitator of the educational process in a non-palliative way and the development of a web curriculum, now and fully, carried out in contexts of ubiquity.

Keywords: technologies, education, teaching-learning strategies, Bloom taxonomy

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Authors: Alshehri Sultan

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Organizational change has remained an important subject for many researchers in the field of organizations theory. We propose the importance of organizational liability of renewal through a model that examines how an organization can overcome potential rigidities in organizational capabilities from learning by changing capabilities. We examine whether an established organization can overcome liability of renewal by changes in organizational capabilities and how the organizational renewal process reflect on the balance between the dynamic aspect of organizational learning as demonstrated by changes in capabilities and the stabilizing aspects of organizational inertia. We found both positive relationship between organizational learning and performance, and between legitimacy and performance. Performance and legitimacy have, however, a negative relationship on the pressure for change.

Keywords: organizational capabilities, organizational liability, liability of renewal, pressure for change

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Authors: Charles Lemckert

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E-Learning and E-Teaching can mean many things to different people. For some, the implication is that all material must be delivered in an E way, while for others it only forms part of the learning/teaching process, and (unfortunately) for some it is considered too much work. However, just look around and you will see all generations learning using E devices. In this study we used different forms of teaching, including E, to look at how students responded to set activities and how they performed academically. The particular context was set around a postgraduate university course where students were either present at a face-to-face intensive workshop (on water treatment plant design) or where they were not. For the latter, students needed to make sole use of E media. It is relevant to note that even though some were at the face-to-face class, they were still exposed to E material as the lecturer did use PC projections. Additionally, some also accessed the associate E material (pdf slides and video recordings) to assist their required activities. Analysis of the student performance, in their set assignment, showed that the actual form of delivery did not affect the student performance. This is because, in the end, all the students had access to the recorded/presented E material. The study also showed (somewhat expectedly) that when the material they required for the assignment was clear, the student performance did drop. Therefore, it is possible to enhance future delivery of courses through careful reflection and appropriate support. In the end, we must remember innovation is not just restricted to E.

Keywords: postgraduate, engineering, assignment, perforamance

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Authors: Bo Ning Lyu

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Blog assisted language learning (BALL) has been trialed by educators in language teaching with the development of Web 2.0 technology. Understanding the development trend of related research helps grasp the whole picture of the use of blog in language education. This paper reviews current research related to blogs enhanced language learning based on bibliometric analysis, aiming at (1) identifying the most frequently used keywords and their co-occurrence, (2) clustering research topics based on co-citation analysis, (3) finding the most frequently cited studies and authors and (4) constructing the co-authorship network. 330 articles were searched out in Web of Science, 225 peer-viewed journal papers were finally collected according to selection criteria. Bibexcel and VOSviewer were used to visualize the results. Studies reviewed were published between 2005 to 2016, most in the year of 2014 and 2015 (35 papers respectively). The top 10 most frequently appeared keywords are learning, language, blog, teaching, writing, social, web 2.0, technology, English, communication. 8 research themes could be clustered by co-citation analysis: blogging for collaborative learning, blogging for writing skills, blogging in higher education, feedback via blogs, blogging for self-regulated learning, implementation of using blogs in classroom, comparative studies and audio/video blogs. Early studies focused on the introduction of the classroom implementation while recent studies moved to the audio/video blogs from their traditional usage. By reviewing the research related to BALL quantitatively and objectively, this paper reveals the evolution and development trends as well as identifies influential research, helping researchers and educators quickly grasp this field overall and conducting further studies.

Keywords: blog, bibliometric analysis, language learning, literature review

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Authors: Jarek Krajewski, David Daxberger, Luzi Beyer

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Finding objective and non-intrusive measurements of emotional and psychopathological states (e.g., post-traumatic stress disorder, PTSD) is an important challenge. Thus, the proposed approach here uses Photoplethysmographic imaging (PPGI) applying facial RGB Cam videos to estimate heart rate levels. A pipeline for the signal processing of the raw image has been proposed containing different preprocessing approaches, e.g., Independent Component Analysis, Non-negative Matrix factorization, and various other artefact correction approaches. Under resting and constant light conditions, we reached a sensitivity of 84% for pulse peak detection. The results indicate that PPGI can be a suitable solution for providing heart rate data derived from these indirectly post-traumatic stress states.

Keywords: heart rate, PTSD, PPGI, stress, preprocessing

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Authors: Vanessa Kummer, Maik Meusel

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Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation.

Keywords: demand estimate, missing-data imputation, real estate, unsupervised learning

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Authors: Miranda Lin

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This project examined the impact of a globally focused service-learning project implemented in a multicultural education course in a Midwestern university. This project facilitated critical self-reflection and build cross-cultural competence while nurturing a partnership with two schools that serve students with disabilities in Vietnam. Through a service-learning project, pre-service teachers connected via Skype with the principals/teachers at schools in Vietnam to identify and subsequently develop needed instructional materials for students with mild, moderate, and severe disabilities. Qualitative data sources include students’ intercultural competence self-reflection survey (pre-test and post-test), reflections, discussions, service project, and lesson plans. Literature Review- Global service-learning is a teaching strategy that encompasses service experiences both in the local community and abroad. Drawing on elements of global learning and international service-learning, global service-learning experiences are guided by a framework that is designed to support global learning outcomes and involve direct engagement with difference. By engaging in real-world challenges, global service-learning experiences can support the achievement of learning outcomes such as civic. Knowledge and intercultural knowledge and competence. Intercultural competence development is considered essential for cooperative and reciprocal engagement with community partners.Method- Participants (n=27*) were mostly elementary and early childhood pre-service teachers who were enrolled in a multicultural education course. All but one was female. Among the pre-service teachers, one Asian American, two Latinas, and the rest were White. Two pre-service teachers identified themselves as from the low socioeconomic families and the rest were from the middle to upper middle class.The global service-learning project was implemented in the spring of 2018. Two Vietnamese schools that served students with disabilities agreed to be the global service-learning sites. Both schools were located in an urban city.Systematic collection of data coincided with the course schedule as follows: an initial intercultural competence self-reflection survey completed in week one, guided reflections submitted in week 1, 9, and 16, written lesson plans and supporting materials for the service project submitted in week 16, and a final intercultural competence self-reflection survey completed in week 16. Significance-This global service-learning project has helped participants meet Merryfield’s goals in various degrees. They 1) learned knowledge and skills in the basics of instructional planning, 2) used a variety of instructional methods that encourage active learning, meet the different learning styles of students, and are congruent with content and educational goals, 3) gained the awareness and support of their students as individuals and as learners, 4) developed questioning techniques that build higher-level thinking skills, and 5) made progress in critically reflecting on and improving their own teaching and learning as a professional educator as a result of this project.

Keywords: global service-learning, teacher education, intercultural competence, diversity

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Authors: Félix David Suárez Bonilla

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A sentiment analysis service to detect polarity (positive, neural, and negative), based on transfer learning, was built using a Spanish version of BERT and applied to tweets written in Spanish. The dataset that was used consisted of 11975 reviews, which were extracted from Google Play using the google-play-scrapper package. The BETO trained model used: the AdamW optimizer, a batch size of 16, a learning rate of 2x10⁻⁵ and 10 epochs. The system was tested using tweets of three presidential pre-candidates from Costa Rica. The system was finally validated using human labeled examples, achieving an accuracy of 83.3%.

Keywords: NLP, transfer learning, BERT, sentiment analysis, social media, opinion mining

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Authors: Divi Sharma

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The emergence of positive behaviour support (PBS) is directly linked to applied behaviour analysis that incorporates evidence-based approaches to addressing ethical challenges and improving autonomy, participation, and the overall quality of life of people living and learning in complex social environments. Its features include lifestyle improvement, collaboration with general caregivers, tracking progress with sound steps, comprehensive performance-based interventions, striving for contextual equality, and ensuring entry and implementation. This document aims to summarize its features with the support of case examples such as involving caregivers to play an active role in behavioural interventions, creating effective interventions within natural practices. Additionally, dealing with lifestyle changes, as well as a wide variety of behavioural changes, develop strong strategies which reduce professional dependence.

Keywords: positive behaviour support, quality of life, performance-based interventions, behavioural changes, participation

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Authors: S. Jonsson, D. Millard, C. Bokhove

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Smartphones are ubiquitous and frequently used as learning tools, which makes the design of educational apps an important area of research. A key issue is designing apps to encourage engagement while maintaining a focus on the educational aspects of the app. Flow experience is a promising method for addressing this issue, which refers to a mental state of cognitive absorption and positive emotion. Flow experience has been shown to be associated with positive emotion and increased learning performance. Studies have shown that immediate feedback is an antecedent to Flow. This experiment investigates the effect of immediate feedback on Flow experience. An app teaching Spanish phrases was developed, and 30 participants completed both a 10min session with immediate feedback and a 10min session with delayed feedback. The app contained a task where the user assembles Spanish phrases by pressing bricks with Spanish words. Immediate feedback was implemented by incorrect bricks recoiling, while correct brick moved to form part of the finished phrase. In the delayed feedback condition, the user did not know if the bricks they pressed were correct until the phrase was complete. The level of Flow experienced by the participants was measured after each session using the Flow Short Scale. The results showed that higher levels of Flow were experienced in the immediate feedback session. It was also found that 14 of the participants indicated that the demands of the task were ‘just right’ in the immediate feedback session, while only one did in the delayed feedback session. These results have implications for how to design educational technology and opens up questions for how Flow experience can be used to increase performance and engagement.

Keywords: feedback timing, flow experience, L2 language learning, mobile learning

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Authors: Manpreet Kaur

Abstract:

When legitimate credit card users are mistakenly labelled as fraudulent in numerous financial delated applications, there are numerous ethical problems. The innovative machine learning approach we have suggested in this research outperforms the current models and shows how to model a data set for credit card fraud detection while minimizing false positives. As a result, we advise using random forests as the best machine learning method for predicting and identifying credit card transaction fraud. The majority of victims of these fraudulent transactions were discovered to be credit card users over the age of 60, with a higher percentage of fraudulent transactions taking place between the specific hours.

Keywords: automated fraud detection, isolation forest method, local outlier factor, ML algorithm, credit card

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Authors: Darwin Junnior Sabino Diego, Brando Burgos Guerrero, Diego Arroyo Villanueva

Abstract:

Additive manufacturing, commonly known as 3D printing, has revolutionized modern manufacturing by enabling the agile creation of complex objects. However, challenges persist in the consistency and quality of printed parts, particularly in their mechanical properties. This study focuses on addressing these challenges through the optimization of printing parameters in FDM technology, using Machine Learning techniques. Our aim is to improve the mechanical properties of printed objects by optimizing parameters such as speed, temperature, and orientation. We implement a methodology that combines experimental data collection with Machine Learning algorithms to identify relationships between printing parameters and mechanical properties. The results demonstrate the potential of this methodology to enhance the quality and consistency of 3D printed products, with significant applications across various industrial fields. This research not only advances understanding of additive manufacturing but also opens new avenues for practical implementation in industrial settings.

Keywords: 3D printing, additive manufacturing, machine learning, mechanical properties

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Authors: Michael Subbey, Kwame Takyi Danquah

Abstract:

Finding a child's exceptional skills and abilities at a young age and nurturing them is a challenging process. The Social Learning Theory (SLT) of Albert Bandura is used to analyze the effects of early talent identification on children's growth and development. The study examines both the advantages and disadvantages of early talent identification and stresses the significance of a moral strategy that puts the welfare of the child first. The paper emphasizes the value of a balanced approach to early talent identification that takes into account individual differences, cultural considerations, and the child's social environment.

Keywords: early talent development, social learning theory, child development, child welfare

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Authors: Woogul Lee, Johnmarshall Reeve

Abstract:

Learners’ perceived autonomy predicts learners’ interest, engagement, and learning. To understand these processes, we conducted an fMRI experiment. In this experiment, participants saw the national flag and were asked to rate how much they freely wanted to learn about that particular national flag. The participants then learned the characteristics of the national flag. Results showed that (1) the degree of participants’ perceived autonomy was positively correlated with the degree of insula activity, (2) participants’ early-trial insula activity predicted corresponding late-trial dorsolateral prefrontal cortex activity, and (3) the degree of dorsolateral prefrontal cortex activity was positively correlated with the degree of participants’ learning about the characteristics of the national flag. Results suggest that learners’ perceived autonomy predicts learning through the mediation of insula activity associated with intrinsic satisfaction and 'pure self' processes.

Keywords: insular cortex, autonomy, self-determination, dorsolateral prefrontal cortex

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Authors: Chandra Prayaga, Aaron Wade, Lakshmi Prayaga, Gopi Shankar Mallu

Abstract:

This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-.

Keywords: machine learning, student success, physics course, grades, synthetic data, CTGAN, gaussian copula CTGAN

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Authors: Angélique Laurent, Marie-Josée Letarte, Jean-Pascal Lemelin, Marie-France Morin

Abstract:

This study aims at exploring teacher-child interactions within learning contexts in public prekindergartens of the province of Québec (Canada). It is based on previous research showing that teacher-child interactions in preschools have direct and determining effects on the quality of early childhood education and could directly or indirectly influence child development. However, throughout a typical preschool day, children experience different learning contexts to promote their learning opportunities. Depending on these specific contexts, teacher-child interactions could vary, for example, between free play and shared book reading. Indeed, some studies have found that teacher-directed or child-directed contexts might lead to significant variations in teacher-child interactions. This study drew upon both the bioecological and the Teaching Through Interactions frameworks. It was conducted through a descriptive and correlational design. Fifteen teachers were recruited to participate in the study. At Time 1 in October, they completed a diary to report the learning contexts they proposed in their classroom during a typical week. At Time 2, seven months later (May), they were videotaped three times in the morning (two weeks’ time between each recording) during a typical morning class. The quality of teacher-child interactions was then coded with the Classroom Assessment Scoring System (CLASS) through the contexts identified. This tool measures three main domains of interactions: emotional support, classroom organization, and instruction support, and10 dimensions scored on a scale from 1 (low quality) to 7 (high quality). Based on the teachers’ reports, five learning contexts were identified: 1) shared book reading, 2) free play, 3) morning meeting, 4) teacher-directed activity (such as craft), and 5) snack. Based on preliminary statistical analyses, little variation was observed within the learning contexts for each domain of the CLASS. However, the instructional support domain showed lower scores during specific learning contexts, specifically free play and teacher-directed activity. Practical implications for how preschool teachers could foster specific domains of interactions depending on learning contexts to enhance children’s social and academic development will be discussed.

Keywords: teacher practices, teacher-child interactions, preschool education, learning contexts, child development

Procedia PDF Downloads 105

Authors: Ammarah Irum, Muhammad Ali Tahir

Abstract:

Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

Procedia PDF Downloads 70