Search results for: learning attitudes

Authors: Usama Shahid

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Be it written documents, news columns, or students' essays, verifying the content can be a time-consuming task. Apart from the spelling and grammar mistakes, the proofreader is also supposed to verify whether the content included in the essay or document is relevant or not. The irrelevant content in any document or essay is referred to as off-topic text and in this paper, we will address the problem of off-topic text detection from a document using machine learning techniques. Our study aims to identify the off-topic content from a document using Echo state network model and we will also compare data with other models. The previous study uses Convolutional Neural Networks and TFIDF to detect off-topic text. We will rearrange the existing datasets and take new classifiers along with new word embeddings and implement them on existing and new datasets in order to compare the results with the previously existing CNN model.

Keywords: off topic, text detection, eco state network, machine learning

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Authors: Tatyana Gavrilova, Vadim Onufriev

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Knowledge-based systems’ design requires the developer’s owning the advanced analytical skills. The efficient development of that skills within university courses needs a deep understanding of main pitfalls and drawbacks, which students usually make during their analytical work in form of visual modeling. Thus, it was necessary to hold an analysis of 5-th year students’ learning exercises within courses of 'Intelligent systems' and 'Knowledge engineering' in Saint-Petersburg Polytechnic University. The analysis shows that both lack of system thinking skills and methodological mistakes in course design cause the errors that are discussed in the paper. The conclusion contains an exploration of the issues and topics necessary and sufficient for the implementation of the improved practices in educational design for future curricula of teaching programs.

Keywords: knowledge based systems, knowledge engineering, students’ errors, visual modeling

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Authors: Calvin Rong, Regina Agassian, Joel Hernandez, Mindy Engle-Friedman

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This study assessed strategies to stimulate engagement with future environmental needs. 32 participants were randomly assigned to one of three conditions which involved imagining and drawing: 1) a generic person in current life, 2) one’s self in current life or 3) one’s self in the future. Participants before and after the intervention indicated connectedness to their selves 50 years in the future on an adapted Future Self-Continuity Scale. A significant interaction (p = .03) showed no difference in connectedness into one’s future self in the control group, a decrease in connectedness in those who imagined themselves in the present and an increase in connectedness in those who imagined themselves in the future. Results suggest attention to one’s present life circumstances may interfere with one’s connection with future environmental issues but imagining one’s future life may stimulate actions that result in future environmental protection.

Keywords: environmental psychology, future priming, climate change, global warming

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Authors: Amah Nnachi Lofty, Oyefeso Olufemi, Ibiam Udu Ama

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New and more powerful communications technologies continue to emerge at a rapid pace and their uses in education are widespread and the impact remarkable in the developing societies. This study investigates Mobile Collaboration Learning Technique (MCLT) on learners’ outcome among students in tertiary institutions of developing nations (a case of Nigeria students). It examines the significance of retention achievement scores of students taught using mobile collaboration and conventional method. The sample consisted of 120 students using Stratified random sampling method. Three research questions and hypotheses were formulated, and tested at a 0.05 level of significance. A student achievement test (SAT) was made of 40 items of multiple-choice objective type, developed and validated for data collection by professionals. The SAT was administered to students as pre-test and post-test. The data were analyzed using t-test statistic to test the hypotheses. The result indicated that students taught using MCLT performed significantly better than their counterparts using the conventional method of instruction. Also, there was no significant difference in the post-test performance scores of male and female students taught using MCLT. Based on the findings, the following recommendations was made that: Mobile collaboration system be encouraged in the institutions to boost knowledge sharing among learners, workshop and trainings should be organized to train teachers on the use of this technique and that schools and government should formulate policies and procedures towards responsible use of MCLT.

Keywords: education, communication, learning, mobile collaboration, technology

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Authors: Krisztina Bohacs, Klaudia Markus

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To author’s best knowledge we are in absence of studies on cognitive program evaluation and we are certainly short of programs that prove to have high effect sizes with strong retention results. The purpose of our study was to investigate the effectiveness of a comprehensive cognitive training program, namely BrainRx. This cognitive rehabilitation program target and remediate seven core cognitive skills and related systems of sub-skills through repeated engagement in game-like mental procedures delivered one-on-one by a clinician, supplemented by digital training. A larger sample of children with learning disability were given pretest and post-test cognitive assessments. The experimental group completed a twenty-week cognitive training program in a BrainRx center. A matched control group received another twenty-week intervention with Feuerstein’s Instrumental Enrichment programs. A second matched control group did not receive training. As for pre- and post-test, we used a general intelligence test to assess IQ and a computer-based test battery for assessing cognition across the lifespan. Multiple regression analyses indicated that the experimental BrainRx treatment group had statistically significant higher outcomes in attention, working memory, processing speed, logic and reasoning, auditory processing, visual processing and long-term memory compared to the non-treatment control group with very large effect sizes. With the exception of logic and reasoning, the BrainRx treatment group realized significantly greater gains in six of the above given seven cognitive measures compared to the Feuerstein control group. Our one-year retention measures showed that all the cognitive training gains were above ninety percent with the greatest retention skills in visual processing, auditory processing, logic, and reasoning. The BrainRx program may be an effective tool to establish long-term cognitive changes in case of students with learning disabilities. Recommendations are made for treatment centers and special education institutions on the cognitive training of students with special needs. The importance of our study is that targeted, systematic, progressively loaded and intensive brain training approach may significantly change learning disabilities.

Keywords: cognitive rehabilitation training, cognitive skills, learning disability, permanent structural cognitive changes

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Helen Zhang

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Accurate segmentation of the airways from CT scans is crucial for early diagnosis of lung cancer. However, the existing airway segmentation algorithms often rely on thin-slice CT scans, which can be inconvenient and costly. This paper presents a set of machine learning-based 3D super-resolution algorithms along heterogeneous dimensions to improve the resolution of thicker CT scans to reduce the reliance on thin-slice scans. To evaluate the efficacy of the super-resolution algorithms, quantitative assessments using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural SIMilarity index) were performed. The impact of super-resolution on airway segmentation accuracy is also studied. The proposed approach has the potential to make airway segmentation more accessible and affordable, thereby facilitating early diagnosis and treatment of lung cancer.

Keywords: 3D super-resolution, airway segmentation, thin-slice CT scans, machine learning

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Authors: RAM PAL SINGH, VIKASH CHAUDHARY, MONIKA VERMA

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In this paper, we proposed the implementation of optimization based Extreme Learning Machine (ELM) for watermarking of B-channel of color image in discrete wavelet transform (DWT) domain. ELM, a regularization algorithm, works based on generalized single-hidden-layer feed-forward neural networks (SLFNs). However, hidden layer parameters, generally called feature mapping in context of ELM need not to be tuned every time. This paper shows the embedding and extraction processes of watermark with the help of ELM and results are compared with already used machine learning models for watermarking.Here, a cover image is divide into suitable numbers of non-overlapping blocks of required size and DWT is applied to each block to be transformed in low frequency sub-band domain. Basically, ELM gives a unified leaning platform with a feature mapping, that is, mapping between hidden layer and output layer of SLFNs, is tried for watermark embedding and extraction purpose in a cover image. Although ELM has widespread application right from binary classification, multiclass classification to regression and function estimation etc. Unlike SVM based algorithm which achieve suboptimal solution with high computational complexity, ELM can provide better generalization performance results with very small complexity. Efficacy of optimization method based ELM algorithm is measured by using quantitative and qualitative parameters on a watermarked image even though image is subjected to different types of geometrical and conventional attacks.

Keywords: BER, DWT, extreme leaning machine (ELM), PSNR

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Authors: Chhavi Rana, Sanjay K Jain

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All sides of every education debate agree that quality learning happens when knowledgeable, caring teachers use sound pedagogy. Many deliberations of pedagogy make the fault of considering it as principally being about teaching. There has been lot of research about how to build a positive climate for learning, improve student curiosity, and enhance classroom association. However, these things can only be facilitated when teachers are equipped with better teaching techniques that use sound and accurate pedagogy. Pedagogy is the science and art of education. Its aims range from the full development of the human being to skills acquisition. In India, a project named Mission 10 x has been started by an esteemed IT Corporation Wipro as a faculty development programme (FDP) that particularly focus on elements that facilitated teachers in developing curriculum and new pedagogies that can lead to improvement in student engagement. This paper presents a study of these FDPs and examines (1) the parameters that help teachers in building new pedagogies (2) the extent to which appropriate usage of pedagogy is improved after the conduct of Mission 10 x FDPs, and (3) whether institutions differ in terms of their ability to convert usage of improved pedagogy into academic performance via these FDPs. The sample consisted of 2,236 students at 6 four-year engineering colleges and universities that completed several FDPs during 2012-2014. Many measures of usage of better pedagogy were linked positively with such FDPs, although some of the relationships were weak in strength. The results suggest that the usage of pedagogy were more benefited after conducting these FDPs and application of novel approaches in conducting classes.

Keywords: student engagement, critical thinking; achievement, student learning, pedagogy

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Authors: Brandon Foggo, Nanpeng Yu

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Power distribution circuits undergo frequent network topology changes that are often left undocumented. As a result, the documentation of a circuit’s connectivity becomes inaccurate with time. The lack of reliable circuit connectivity information is one of the biggest obstacles to model, monitor, and control modern distribution systems. To enhance the reliability and efficiency of electric power distribution systems, the circuit’s connectivity information must be updated periodically. This paper focuses on one critical component of a distribution circuit’s topology - the secondary transformer to phase association. This topology component describes the set of phase lines that feed power to a given secondary transformer (and therefore a given group of power consumers). Finding the documentation of this component is call Phase Identification, and is typically performed with physical measurements. These measurements can take time lengths on the order of several months, but with supervised learning, the time length can be reduced significantly. This paper compares several such methods applied to Phase Identification for a large range of real distribution circuits, describes a method of training data selection, describes preprocessing steps unique to the Phase Identification problem, and ultimately describes a method which obtains high accuracy (> 96% in most cases, > 92% in the worst case) using only 5% of the measurements typically used for Phase Identification.

Keywords: distribution network, machine learning, network topology, phase identification, smart grid

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Authors: Ali Almadan, Anoop Krishnan, Ajita Rattani

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Facial recognition is a widely adopted technology in surveillance, border control, healthcare, banking services, and lately, in mobile user authentication with Apple introducing “Face ID” moniker with iPhone X. A lot of research has been conducted in the area of face recognition on datasets captured by surveillance cameras, DSLR, and mobile devices. Recently, face recognition technology has also been deployed on body-worn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic so far, without the availability of any publicly available datasets with a sufficient sample size. This paper aims to advance research in the area of face recognition using body-worn cameras. To this aim, the contribution of this work is two-fold: (1) collection of a dataset consisting of a total of 136,939 facial images of 102 subjects captured using body-worn cameras in in-door and daylight conditions and (2) evaluation of various deep-learning architectures for face identification on the collected dataset. Experimental results suggest a maximum True Positive Rate(TPR) of 99.86% at False Positive Rate(FPR) of 0.000 obtained by SphereFace based deep learning architecture in daylight condition. The collected dataset and the baseline algorithms will promote further research and development. A downloadable link of the dataset and the algorithms is available by contacting the authors.

Keywords: face recognition, body-worn cameras, deep learning, person identification

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Authors: Theresia El Khoury, Georges Badr, Amir Hajjam El Hassani, Stéphane N’Guyen Van Ky

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The widespread adoption of machine learning and artificial intelligence across different domains can be attributed to the digitization of data over several decades, resulting in vast amounts of data, types, and structures. Thus, data processing and preparation turn out to be a crucial stage. However, applying these techniques to S1000D standard-based data poses a challenge due to its complexity and the need to preserve logical information. This paper describes SALERNO, an S1000d AppLicability dEpended pRocessiNg mOdel. This python-based model analyzes and converts the XML S1000D-based files into an easier data format that can be used in machine learning techniques while preserving the different logic and relationships in files. The model parses the files in the given folder, filters them, and extracts the required information to be saved in appropriate data frames and Excel sheets. Its main idea is to group the extracted information by applicability. In addition, it extracts the full text by replacing internal and external references while maintaining the relationships between files, as well as the necessary requirements. The resulting files can then be saved in databases and used in different models. Documents in both English and French languages were tested, and special characters were decoded. Updates on the technical manuals were taken into consideration as well. The model was tested on different versions of the S1000D, and the results demonstrated its ability to effectively handle the applicability, requirements, references, and relationships across all files and on different levels.

Keywords: aeronautics, big data, data processing, machine learning, S1000D

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Authors: M. M. Essa, S. Subash, M. Akbar, S. Al-Adawi, A. Al-Asmi, G. J. Guillemein

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Objective: Transgenic (tg) mice which contain an amyloid precursor protein (APP) gene mutation, develop extracellular amyloid beta (Aβ) deposition in the brain, and severe memory and behavioural deficits with age. These mice serve as an important animal model for testing the efficacy of novel drug candidates for the treatment and management of symptoms of Alzheimer's disease (AD). Several reports have suggested that oxidative stress is the underlying cause of Aβ neurotoxicity in AD. Pomegranates contain very high levels of antioxidants and several medicinal properties that may be useful for improving the quality of life in AD patients. In this study, we investigated the effect of dietary supplementation of Omani pomegranate extract on the memory, anxiety and learning skills along with inflammation in an AD mouse model containing the double Swedish APP mutation (APPsw/Tg2576). Methods: The experimental groups of APP-transgenic mice from the age of 4 months were fed custom-mix diets (pellets) containing 4% pomegranate. We assessed spatial memory and learning ability, psychomotor coordination, and anxiety-related behavior in Tg and wild-type mice at the age of 4-5 months and 18-19 months using the Morris water maze test, rota rod test, elevated plus maze test, and open field test. Further, inflammatory parameters also analysed. Results: APPsw/Tg2576 mice that were fed a standard chow diet without pomegranates showed significant memory deficits, increased anxiety-related behavior, and severe impairment in spatial learning ability, position discrimination learning ability and motor coordination along with increased inflammation compared to the wild type mice on the same diet, at the age of 18-19 months In contrast, APPsw/Tg2576 mice that were fed a diet containing 4% pomegranates showed a significant improvements in memory, learning, locomotor function, and anxiety with reduced inflammatory markers compared to APPsw/Tg2576 mice fed the standard chow diet. Conclusion: Our results suggest that dietary supplementation with pomegranates may slow the progression of cognitive and behavioural impairments in AD. The exact mechanism is still unclear and further extensive research needed.

Keywords: Alzheimer's disease, pomegranates, oman, cognitive decline, memory loss, anxiety, inflammation

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Authors: Jamison Duckworth, Shankarachary Ragi

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Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.

Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands

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Authors: A. Bouabid, B. Bielenberg, S. Ainane, N. Pasha

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In this paper, a team of faculty members of the Petroleum Institute in Abu Dhabi, UAE representing six different courses across General Engineering (ENGR), Communication (COMM), and Design (STPS) worked together to establish a clear developmental progression of learning outcomes and performance indicators for targeted knowledge, areas of competency, and skills for the first three semesters of the Bachelor of Sciences in Engineering curriculum. The sequences of courses studied in this project were ENGR/COMM, COMM/STPS, and ENGR/STPS. For each course’s nine areas of knowledge, competency, and skills, the research team reviewed the existing learning outcomes and related performance indicators with a focus on identifying linkages across disciplines as well as within the courses of a discipline. The team reviewed existing performance indicators for developmental progression from semester to semester for same discipline related courses (vertical alignment) and for different discipline courses within the same semester (horizontal alignment). The results of this work have led to recommendations for modifications of the initial indicators when incoherence was identified, and/or for new indicators based on best practices (identified through literature searches) when gaps were identified. It also led to recommendations for modifications of the level of emphasis within each course to ensure developmental progression. The exercise has led to a revised Sequence Performance Indicator Mapping for the knowledge, skills, and competencies across the six core courses.

Keywords: curriculum alignment, horizontal and vertical progression, performance indicators, skill level

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

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Authors: Hana Maresova, Daniel Ecler

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The paper focuses on the use of 3D multi-user virtual environments (MUVE) in language teaching and presents the results of four years of research at the Faculty of Education, Palacký University in Olomouc (Czech Republic). In the form of an experiment, mother tongue language teaching in the 3D virtual worlds Second Life and Kitely (experimental group) and parallel traditional teaching on identical topics representing teacher's interpretation using a textbook (control group) were implemented. The didactic test, which was presented to the experimental and control groups in an identical form before and after the instruction, verified the effect of the instruction in the experimental group by comparing the results obtained by both groups. Within the three components of mother-tongue teaching (vocabulary, literature, style and communication education), the students in the literature group achieved partially better results (statistically significant in the case of items devoted to the area of visualization of the learning topic), while in the case of grammar and style education the respondents of the control group achieved better results. On the basis of the results obtained, we can conclude that the most appropriate use of MUVE can be seen in the teaching of those topics that provide the possibility of dramatization, experiential learning and group involvement and cooperation, on the contrary, with regard to the need to divide students attention between the topic taught and the control of avatar and movement in virtual reality as less suitable for teaching in the area of memorization of the topic or concepts.

Keywords: distance learning, 3D virtual environments, online teaching, language teaching

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Authors: Kyle De Freitas, Margaret Bernard

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Enabling data mining analysis among a wider audience of educators is an active area of research within the educational data mining (EDM) community. The paper proposes a framework for developing an environment that caters for educators who have little technical data mining skills as well as for more advanced users with some data mining expertise. This framework architecture was developed through the review of the strengths and weaknesses of existing models in the literature. The proposed framework provides a modular architecture for future researchers to focus on the development of specific areas within the EDM process. Finally, the paper also highlights a strategy of enabling analysis through either the use of predefined questions or a guided data mining process and highlights how the developed questions and analysis conducted can be reused and extended over time.

Keywords: educational data mining, learning management system, learning analytics, EDM framework

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Authors: Jack R. McKenzie, Peter A. Appleby, Thomas House, Neil Walton

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Cold-start is a notoriously difficult problem which can occur in recommendation systems, and arises when there is insufficient information to draw inferences for users or items. To address this challenge, a contextual bandit algorithm – the Fast Approximate Bayesian Contextual Cold Start Learning algorithm (FAB-COST) – is proposed, which is designed to provide improved accuracy compared to the traditionally used Laplace approximation in the logistic contextual bandit, while controlling both algorithmic complexity and computational cost. To this end, FAB-COST uses a combination of two moment projection variational methods: Expectation Propagation (EP), which performs well at the cold start, but becomes slow as the amount of data increases; and Assumed Density Filtering (ADF), which has slower growth of computational cost with data size but requires more data to obtain an acceptable level of accuracy. By switching from EP to ADF when the dataset becomes large, it is able to exploit their complementary strengths. The empirical justification for FAB-COST is presented, and systematically compared to other approaches on simulated data. In a benchmark against the Laplace approximation on real data consisting of over 670, 000 impressions from autotrader.co.uk, FAB-COST demonstrates at one point increase of over 16% in user clicks. On the basis of these results, it is argued that FAB-COST is likely to be an attractive approach to cold-start recommendation systems in a variety of contexts.

Keywords: cold-start learning, expectation propagation, multi-armed bandits, Thompson Sampling, variational inference

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Authors: Shri Krishna Mishra

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In an educational context, technology can prompt interactive plight only when it is used in conjunction with interactive plight methods. This study, therefore, examines the relationships among components that contribute to higher levels of interactive plight and execution, such as interactive Plight methods, technology, intrinsic motivation and deep learning. 526 students participated in this study. With structural equation modelling, the authors test the conceptual model and identify satisfactory model fit. The results indicate that interactive Plight methods, technology and intrinsic motivation have significant relationship with interactive Plight; deep learning mediates the relationships of the other variables with Execution.

Keywords: searching the relationship among components, contribute to interactive plight, educational execution, intrinsic motivation

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Authors: Shrikrishna Mishra, Badri Yadav

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In this complex society of today where experiences are innumerable and varied, it is not at all possible to present every situation in its original colors hence the opportunities for learning by actual experiences always are not at all possible. It is only through the use of proper audio visual aids that the life situation can be trough in the class room by an enlightened teacher in their simplest form and representing the original to the highest point of similarity which is totally absent in the verbal or lecture method. In the presence of audio aids, the attention is attracted interest roused and suitable atmosphere for proper understanding is automatically created, but in the existing traditional method greater efforts are to be made in order to achieve the aforesaid essential requisite. Inspire of the best and sincere efforts on the side of the teacher the net effect as regards understanding or learning in general is quite negligible.

Keywords: Audio-Visual Aids, the secondary school teaching, complex society, audio

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Authors: Irfan Ahmed Rind

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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

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Authors: Neda Jalaliyoon, Nooh Abu Bakar, Hamed Taherdoost

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The present research recommended balanced scorecard (BSC) framework to appraise the performance of the universities. As the original model of balanced scorecard has four perspectives in order to implement BSC in present research the same model with “financial perspective”, “customer”,” internal process” and “learning and growth” is used as well. With applying fuzzy Delphi method (FDM) and questionnaire sixteen measures of performance were identified. Moreover, with using the analytic network process (ANP) the weights of the selected indicators were determined. Results indicated that the most important BSC’s aspect were Internal Process (0.3149), Customer (0.2769), Learning and Growth (0.2049), and Financial (0.2033) respectively. The proposed BSC framework can help universities to enhance their efficiency in competitive environment.

Keywords: balanced scorecard, higher education, fuzzy delphi method, analytic network process (ANP)

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Authors: Sherry Ann Ganase, Sandra Sookram

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This study investigates the level of awareness to climate change and major factors that influence such awareness in Grande Riviere, Trinidad. Through the development of an Awareness Index and application of a Structural Equation Model to survey data, the findings suggest an Awareness index value of 0.459 in Grande Riviere. These results suggest that households have climate smart attitudes and behaviors but climate knowledge is lacking. This is supported by the structural equation model which shows a negative relationship between awareness and causes of climate change. The study concludes by highlighting the need for immediate action on increasing knowledge.

Keywords: awareness, climate change, climate education, index structural equation model

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Authors: Geir Sigurðsson

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This paper compares ancient Daoist and Confucian approaches to the human body as a locus for learning, edification or personal cultivation. While pointing out some major differences between ancient Chinese and mainstream Western visions of the body, it seeks at the same time inspiration in some seminal Western phenomenological and post-structuralist writings, in particular from Maurice Merleau-Ponty and Pierre Bourdieu. By clarifying the somewhat dissimilar scopes of foci found in Daoist and Confucian philosophies with regard to the role of and attitude to the body, the conclusion is nevertheless that their approaches are comparable, and that both traditions take the physical body to play a vital role in the cultivation of excellence. Lastly, it will be argued that cosmological underpinnings prevent the Confucian li from being rigid and invariable and that it rather emerges as a flexible learning device to train through active embodiment a refined sensibility for one’s cultural environment.

Keywords: body, Confucianism, Daoism, li (ritual), phenomenology

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Authors: Gaye Senbag

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In English language teaching, feedback is considered as one of the main components of writing instruction. Teachers put a lot of time and effort in order to provide learners with written feedback for effective language learning. At Anadolu University School of Foreign Languages (AUSFL) students are given written feedback for their each piece of writing through online platforms such as Edmodo and Turnitin, and traditional methods. However, little is known regarding how learners value and respond to teacher-provided feedback. As the perceptions of the students remarkably affect their learning, this study examines how they perceive the effectiveness of feedback provided by the teacher. Aiming to analyse it, 30 intermediate level (B1+ CEFR level) students were given a questionnaire, which includes Likert scale questions. The results will be discussed in detail.

Keywords: feedback, perceptions, writing, English Language Teaching (ELT)

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Authors: Jiangping Wang

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This presentation describes the design and implementation of PaaS (platform as a service) cloud-based labs that are used in database-related courses to teach students practical skills. Traditionally, all labs are implemented in a desktop-based environment where students have to install heavy client software to access database servers. In order to release students from that burden, we have successfully deployed the cloud-based solution to support database-related courses, from which students and teachers can practice and learn database topics in various database courses via cloud access. With its development environment, execution runtime, web server, database server, and collaboration capability, it offers a shared pool of configurable computing resources and comprehensive environment that supports students’ needs without the complexity of maintaining the infrastructure.

Keywords: PaaS, database environment, e-learning, web server

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Authors: Annette Cosgrove

Abstract:

The objectives of this research study is to examine the impact of professional development in Technology Enhanced Learning (TEL) and the digitisation of learning in teaching communities across multiple higher education sites in the ATU (Atlantic Technological University *) ( 2020-2025), including the proposal of an evidence based digital teaching model for use in a future pandemic. The research strategy undertaken for this PhD Study is a multi-site study using mixed methods. Qualitative & quantitative methods are being used in the study to collect data. A pilot study was carried out initially , feedback collected and the research instrument was edited to reflect this feedback, before being administered. The purpose of the staff questionnaire is to evaluate the impact of professional development in the area of TEL, and to capture the practitioners views on the perceived impact on their teaching practice in the higher education sector across ATU (West of Ireland – 5 Higher education locations ). The phenomenon being explored is ‘ the impact of professional development in the area of technology enhanced learning and on teaching practice in a higher education institution.’ The research methodology chosen for this study is an Action based Research Study. The researcher has chosen this approach as it is a prime strategy for developing educational theory and enhancing educational practice . This study includes quantitative and qualitative methods to elicit data which will quantify the impact that continuous professional development in the area of digital teaching practice and technologies has on the practitioner’s teaching practice in higher education. The research instruments / data collection tools for this study include a lecturer survey with a targeted TEL Practice group ( Pre and post covid experience) and semi-structured interviews with lecturers.. This research is currently being conducted across the ATU multisite campus and targeting Higher education lecturers that have completed formal CPD in the area of digital teaching. ATU, a west of Ireland university is the focus of the study , The research questionnaire has been deployed, with 75 respondents to date across the ATU - the primary questionnaire and semi- formal interviews are ongoing currently – the purpose being to evaluate the impact of formal professional development in the area of TEL and its perceived impact on the practitioners teaching practice in the area of digital teaching and learning . This paper will present initial findings, reflections and data from this ongoing research study.

Keywords: TEL, DTL, digital teaching, digital assessment

Procedia PDF Downloads 70

Authors: Vareesha Masood, Ammara Gul, Nabeeha Areej, Muhammad Asif Masood, Hamna Imran

Abstract:

The universal use of PDF files has prompted hackers to use them for malicious intent by hiding malicious codes in their victim’s PDF machines. Machine learning has proven to be the most efficient in identifying benign files and detecting files with PDF malware. This paper has proposed an approach using a decision tree classifier with parameters. A modern, inclusive dataset CIC-Evasive-PDFMal2022, produced by Lockheed Martin’s Cyber Security wing is used. It is one of the most reliable datasets to use in this field. We designed a PDF malware detection system that achieved 99.2%. Comparing the suggested model to other cutting-edge models in the same study field, it has a great performance in detecting PDF malware. Accordingly, we provide the fastest, most reliable, and most efficient PDF Malware detection approach in this paper.

Keywords: PDF, PDF malware, decision tree classifier, random forest classifier

Procedia PDF Downloads 91

Authors: Ozge Yalciner

Abstract:

Intercultural communication is crucial in the education of multilingual learners in primary grades, significantly influencing their academic and social development. This study explores how intercultural communication intersects with multilingual education, highlighting the importance of culturally responsive teaching practices. It addresses the challenges and opportunities presented by diverse linguistic backgrounds and proposes strategies for creating inclusive and supportive learning environments. The research emphasizes the need for teacher training programs that equip educators with the skills to recognize and address cultural differences, thereby enhancing student engagement and participation. This study was completed in an elementary school in a city in the Midwest, USA. The data was collected through observations and interviews with students and teachers. It discusses the integration of multicultural perspectives in curricula and the promotion of language diversity as an asset. Peer interactions and collaborative learning are highlighted as crucial for developing intercultural competence among young learners. The findings suggest that meaningful intercultural communication fosters a sense of belonging and mutual respect, leading to improved educational outcomes for multilingual students. Prioritizing intercultural communication in primary education is essential for supporting the linguistic and cultural identities of multilingual learners. By adopting inclusive pedagogical approaches and fostering an environment of cultural appreciation, educators can better support their students' academic success and personal growth.

Keywords: diversity, intercultural communication, multilingual learners, primary grades

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