Search results for: site-based learning

Authors: Arturo Tobias Calizon Jr.

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The study investigated the practices in critical reflection being employed in teaching and learning mathematics in public and private schools for students to achieve perspective transformation in psychological, convictional and behavioral dimensions. There were 1,969 senior high school and college student-respondents selected at random from 33 schools. Process reflection is most commonly practiced in both public and private schools. Convictional dimension of perspective transformation is most frequently achieved. There is no significant difference in practices of process reflection between senior high school and college students. However, there is a significant difference in perspective transformation in behavioral dimension achieved by students from public and private schools. Also, there are significant differences in psychological, convictional and behavioral dimensions of perspective transformation achieved by senior high school and college students. There is a high and significant relationship between critical reflection practices and perspective transformation of students. The researcher concludes that there are teaching strategies that facilitate critical thinking, and there are learning activities that alter perspective of students about mathematics as an abstract field. The researcher further concludes that consistent use of appropriate teaching and learning activities could bring about perspective transformation in students with success.

Keywords: critical reflection, perspective transformation, process reflection, convictional dimension, teaching and learning mathematics

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Authors: Jeff Dickie

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This research paper combined grounded coding and research questions with the objective to investigate conflict resolution in the classroom. The students’ answers concerning teaching were coded according to phrasal meanings which revealed concepts. These concept codes then became input data into theoretical frameworks. The investigation indicated two conflicts: whether the information was valid and whether to make the study effort which was discussed as perceptions of teacher’s competence in helping to learn. The relevant factors in helping to learn were predominately emotional. These factors were important in the negotiation process to develop relationships. Information validity seemed to be the motivator to begin and participate effectively with the learning process. In effect, confidence in the learning negotiation process with the focus towards relationship building with the subject matter seemed to be the motivator to make the study effort.

Keywords: coding, confidence, competence, conflict resolution, risk, trust, relationship building

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Authors: Gabrielle Brand, Christopher Etherton-Beer

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The learning landscape of higher education environment is changing, with an increased focus over the past decade on how educators might begin to cultivate reflective skills in health professions students. In addition, changing professional requirements demand that health professionals are adequately prepared to practice in today’s complex Australian health care systems, including responding to changing demographics of population ageing. To counteract a widespread perception of health professions students’ disinterest in caring for older persons, the authors will report on an exploratory, mixed method research study that used photographs, narrative and small group work to enhance medical and nursing students’ reflective learning experience. An innovative photo-elicitation technique and reflective questioning prompts were used to increase engagement, and challenge students to consider new perspectives (around ageing) by constructing shared storylines in small groups. The qualitative themes revealed how photographs, narratives and small group work created learning spaces for reflection whereby students could safely explore their own personal and professional values, beliefs and perspectives around ageing. By providing the space for reflection, the students reported how they found connection and meaning in their own learning through a process of self-exploration that often challenged their assumptions of both older people and themselves as future health professionals. By integrating cognitive and affective elements into the learning process, this research demonstrates the importance of embedding visual methodologies that enhance reflection and transformative learning. The findings highlight the importance of integrating the arts into predominantly empirically driven health professional curricula and can be used as a catalyst for individual and/or collective reflection which can potentially enhance empathy, insight and understanding of the lived experiences of older patients. Based on these findings, the authors have developed ‘Depth of Field: Exploring Ageing’ an innovative, interprofessional, digital reflective learning resource that uses Prezi Inc. software (storytelling tool that presents ideas on a virtual canvas) to enhance students’ reflective capacity in the higher education environment.

Keywords: narrative, photo-elicitation, reflective learning, qualitative research

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Authors: Catherine Mary Abou-Zaid

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This quantitative study discusses the concerns with the self-directed learning (SDL) skills of the undergraduate nursing students in a medical university in Bahrain. The nursing undergraduate student SDL study was conducted taking all 4 years and compiling data collected from the students themselves by survey questionnaire. The aim of the study is to understand and change the attitudes of self-directed learning among the undergraduate students. The SDL of the undergraduate student nurses has been noticed to be lacking and motivation to actually perform without supervision while out-with classrooms are very low. Their use of the resources available on the virtual learning environment and also within the university is not as good as it should be for a university student at this level. They do not use them to their own advantage. They are not prepared for the transition from high school to an academic environment such as a university or college. For some students it is the first time in their academic lives that they have faced sharing a classroom with the opposite sex. For some this is a major issue and we as academics need to be aware of all issues that they come to higher education with. Design Methodology: The design methodology that was chosen was a quantitative design using convenience sampling of the students who would be asked to complete survey questionnaire. This sampling method was chosen because of the time constraint. This was completed by the undergraduate students themselves while in class. The questionnaire was analyzed by the statistical package for social sciences (SPSS), the results interpreted by the researcher and the findings published in the paper. The analyzed data will also be reported on and from this information we as educators will be able to see the student’s weaknesses regarding self-directed learning. The aims and objectives of the research will be used as recommendations for the improvement of resources for the students to improve their SDL skills. Conclusion: The results will be able to give the educators an insight to how we can change the self-directed learning techniques of the students and enable them to embrace the skills and to focus more on being self-directed in their studies rather than having to be put on to a SDL pathway from the educators themselves. This evidence will come from the analysis of the statistical data. It may even change the way in which the students are selected for the nursing programme. These recommendations will be reported to the head of school and also to the nursing faculty.

Keywords: self-directed learning, undergraduate students, transition, statistical package for social sciences (SPSS), higher education

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Authors: Adedoyin Opeyemi Osokoya

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The study assesses the influence of social media on perceived learning outcome of agricultural science students in tertiary institutions in Oyo state, Nigeria. The four-stage sampling procedure was used to select participants. All students in the seven tertiary institutions that offer agriculture science as a course of study in Oyo State was the population. A university, a college of agriculture and a college of education were sampled, and a department from each was randomly selected. Twenty percent of the students’ population in the respective selected department gave a sample size of 165. Questionnaire was used to collect information on respondents’ personal characteristics and information related to access to social media. Data were analysed using descriptive statistics, chi-square, correlation, and multiple regression at the 0.05 confidence level. Age and household size were 21.13 ± 2.64 years and 6 ± 2.1 persons respectively. All respondents had access to social media, majority (86.1%) owned Android phone, 57.6% and 52.7% use social media for course work and entertainment respectively, while the commonly visited sites were WhatsApp, Facebook, Google, Opera mini. Over half (53.9%) had an unfavourable attitude towards the use of social media for learning; benefits of the use of social media for learning was high (56.4%). Removal of information barrier created by distance (x̄=1.58) was the most derived benefit, while inadequate power supply (x̄=2.36), was the most severe constraints. Age (β=0.23), sex (β=0.37), ownership of Android phone (β=-1.29), attitude (β=0.37), constraints (β =-0.26) and use of social media (β=0.23) were significant predictors of influence on perceived learning outcomes.

Keywords: use of social media, agricultural science students, undergraduates of tertiary institutions, Oyo State of Nigeria

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Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

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Authors: Rachid Ed-Dali, Khalid Elasri

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Mass media seem to provide a rich content for language acquisition. Exposure to television, the Internet, the mobile phone and other technological gadgets and devices helps enrich the student’s lexicon positively as well as negatively. The difficulties encountered by students while learning and acquiring second languages in addition to their eagerness to comprehend the content of a particular program prompt them to diversify their methods so as to achieve their targets. The present study highlights the significance of certain media channels and their involvement in language acquisition with the employment of the Natural Approach to further grasp whether students, especially secondary and high school students, learn and acquire errors through watching subtitled television programs. The chief objective is investigating the deductive and inductive relevance of certain programs beside the involvement of peripheral learning while acquiring mistakes.

Keywords: errors, mistakes, Natural Approach, peripheral learning, solecism

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Authors: Sarah Lister, Pauline Palmer

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Research findings presented within this paper form part of a larger scale collaboration between academics at Manchester Metropolitan University and a technology company. The overarching aims of this project focus on developing a series of game-based resources to promote the teaching of aspects of mathematics through a second language (L2) in primary schools. This study explores the potential of game-based learning (GBL) as a dynamic way to engage and motivate learners, making learning fun and purposeful. The research examines the capacity of GBL resources to provide a meaningful and purposeful context for CLIL. GBL is a powerful learning environment and acts as an effective vehicle to promote the learning of mathematics through an L2. The fun element of GBL can minimise stress and anxiety associated with mathematics and L2 learning that can create barriers. GBL provides one of the few safe domains where it is acceptable for learners to fail. Games can provide a life-enhancing experience for learners, revolutionizing the routinized ways of learning through fusing learning and play. This study argues that playing games requires learners to think creatively to solve mathematical problems, using the L2 in order to progress, which can be associated with the development of higher-order thinking skills and independent learning. GBL requires learners to engage appropriate cognitive processes with increased speed of processing, sensitivity to environmental inputs, or flexibility in allocating cognitive and perceptual resources. At surface level, GBL resources provide opportunities for learners to learn to do things. Games that fuse subject content and appropriate learning objectives have the potential to make learning academic subjects more learner-centered, promote learner autonomy, easier, more enjoyable, more stimulating and engaging and therefore, more effective. Data includes observations of the children playing the games and follow up group interviews. Given that learning as a cognitive event cannot be directly observed or measured. A Cognitive Discourse Functions (CDF) construct was used to frame the research, to map the development of learners’ conceptual understanding in an L2 context and as a framework to observe the discursive interactions that occur learner to learner and between learner and teacher. Cognitively, the children were required to engage with mathematical content, concepts and language to make decisions quickly, to engage with the gameplay to reason, solve and overcome problems and learn through experimentation. The visual elements of the games supported the learning of new concepts. Children recognised the value of the games to consolidate their mathematical thinking and develop their understanding of new ideas. The games afforded them time to think and reflect. The teachers affirmed that the games provided meaningful opportunities for the learners to practise the language. The findings of this research support the view that using the game-based resources supported children’s grasp of mathematical ideas and their confidence and ability to use the L2. Engaging with the content and language through the games led to deeper learning.

Keywords: CLIL, gaming, language, mathematics

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Authors: Mona Hoyng

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In times of digitalization nowadays, the use of games became a crucial new way for digital game-based learning (DGBL) in higher education. In this regard, the development of a gameful experience (GE) among students is decisive when examining DGBL as the GE is a necessary precondition determining the effectiveness of games. In this regard, the purpose of this study is to provide deeper insights into the GE and to empirically investigate whether and how these meaningful learning experiences within games, i.e., GE, among students are created. Based on the theory of experience and flow theory, a double-mediation model was developed considering instructional support, group engagement, and flow as determinants of students’ GE. Based on data of 337 students taking part in a business simulation game at two different universities in Germany, regression-based statistical mediation analysis revealed that instructional support promoted students’ GE. This relationship was further sequentially double mediated by group engagement and flow. Consequently, in the context of DGBL, meaningful learning experiences within games in terms of GE are created and promoted through appropriate instructional support, as well as high levels of group engagement and flow among students.

Keywords: gameful experience, instructional support, group engagement, flow, education, learning

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Authors: N. Hammid, F. Bouarab-Dahmani, T. Berkane

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Since its appearance, the MOOC (massive open online course) is gaining more and more intention of the educational communities over the world. Apart from the current MOOCs design and purposes, the creators of MOOC focused on the importance of the connection and knowledge exchange between individuals in learning. In this paper, we present a conceptual model for massive open online blended courses where teachers over the world can collaborate and exchange their experience to get a common efficient content designed as a MOOC opened to their students to live a better learning experience. This model is based on disciplines’ concepts capitalization and the detection of the obstacles met by their students when faced with problem situations (exercises, projects, case studies, etc.). This detection is possible by analyzing the frequently of semantic errors committed by the students. The participation of teachers in the design of the course and the attendance by their students can guarantee an efficient and extensive participation (an important number of participants) in the course, the learners’ motivation and the evaluation issues, in the way that the teachers designing the course assess their students. Thus, the teachers review, together with their knowledge, offer a better assessment and efficient connections to their students.

Keywords: massive open online course, MOOC, online learning, e-learning

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Authors: Ahmad Salman

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Electronic Response Systems such as Kahoot, Poll Everywhere, and Google Classroom are gaining a lot of popularity when surveying audiences in events, meetings, and classroom. The reason is mainly because of the ease of use and the convenience these tools bring since they provide mobile applications with a simple user interface. In this paper, we present a case study on the effectiveness of using Electronic Response Systems on student participation and learning experience in a classroom. We use a polling application for class exercises in two different technology-oriented classes. We evaluate the effectiveness of the usage of the polling applications through statistical analysis of the students performance in these two classes and compare them to the performances of students who took the same classes without using the polling application for class participation. Our results show an increase in the performances of the students who used the Electronic Response System when compared to those who did not by an average of 11%.

Keywords: Interactive Learning, Classroom Technology, Electronic Response Systems, Polling Applications, Learning Evaluation

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Authors: Tracy B.E. Omorogiuwa

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Understanding the role of social work in improving societal well-being cannot be separated from the place of field education, which is an integral aspect of social work education. Field learning provides students with knowledge and opportunities to experience solving issues in the field and giving them a clue of the practice situation. Despite being a crucial component in social work curriculum, field education occupies a large space in learning outcome, given the issues and challenges pertaining to its purpose and significance in the society. The drive of this paper is to provide insight on the specific ways in which field education has been conceived, realized and valued in the society. Emphasis is on the significance of field instruction; the link with classroom learning; and the structure of field experience in social work education. Given documented analysis and experience, this study intends to contribute to the development of social work curriculum, by analyzing the pattern, issues and challenges fronting the social work field education in the University of Benin, Nigeria.

Keywords: challenges, curriculum, field education, social work education

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Authors: Linghui Meng, James Atlas, Deborah Munro

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There is an increasing demand for prosthetics capable of mimicking natural limb movements and hand gestures, but precise movement control of prosthetics using only electrode signals continues to be challenging. This study considers the implementation of machine learning as a means of improving accuracy and presents an initial investigation into hand gesture recognition using models based on electromyographic (EMG) signals. EMG signals, which capture muscle activity, are used as inputs to machine learning algorithms to improve prosthetic control accuracy, functionality and adaptivity. Using logistic regression, a machine learning classifier, this study evaluates the accuracy of classifying two hand gestures from the publicly available Ninapro dataset using two-time series feature extraction algorithms: Time Series Feature Extraction (TSFE) and Convolutional Neural Networks (CNNs). Trials were conducted using varying numbers of EMG channels from one to eight to determine the impact of channel quantity on classification accuracy. The results suggest that although both algorithms can successfully distinguish between hand gesture EMG signals, CNNs outperform TSFE in extracting useful information for both accuracy and computational efficiency. In addition, although more channels of EMG signals provide more useful information, they also require more complex and computationally intensive feature extractors and consequently do not perform as well as lower numbers of channels. The findings also underscore the potential of machine learning techniques in developing more effective and adaptive prosthetic control systems.

Keywords: EMG, machine learning, prosthetic control, electromyographic prosthetics, hand gesture classification, CNN, computational neural networks, TSFE, time series feature extraction, channel count, logistic regression, ninapro, classifiers

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Authors: Dorit Alt, Nirit Raichel

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Educational practice is continually subjected to renewal needs, due mainly to the growing proportion of information communication technology, globalization of education, and the pursuit of quality. These types of renewal needs require developing updated instructional and assessment practices that put a premium on adaptability to the emerging requirements of present society. However, university instruction is criticized for not coping with these new challenges while continuing to exemplify the traditional instruction. In order to overcome this critical inadequacy between current educational goals and instructional methods, the LLAF consortium (including 16 members from 8 countries) is collaborating to create a curricular reform for lifelong learning (LLL) in teachers' education, health care and other applied fields. This project aims to achieve its objectives by developing, and piloting models for training students in LLL and promoting meaningful learning activities that could integrate knowledge with the personal transferable skills. LLAF has created a practical guide for teachers containing updated pedagogical strategies and assessment tools based on the constructivist approach for learning. This presentation will be limited to teachers' education only and to the contribution of a pre-pilot research aimed at providing a scale designed to measure constructivist activities in higher education learning environments. A mix-method approach was implemented in two phases to construct the scale: The first phase included a qualitative content analysis involving both deductive and inductive category applications of students' observations. The results foregrounded eight categories: knowledge construction, authenticity, multiple perspectives, prior knowledge, in-depth learning, teacher- student interaction, social interaction and cooperative dialogue. The students' descriptions of their classes were formulated as 36 items. The second phase employed structural equation modeling (SEM). The scale was submitted to 597 undergraduate students. The goodness of fit of the data to the structural model yielded sufficient fit results. This research elaborates the body of literature by adding a category of in-depth learning which emerged from the content analysis. Moreover, the theoretical category of social activity has been extended to include two distinctive factors: cooperative dialogue and social interaction. Implications of these findings for the LLAF project are discussed.

Keywords: constructivist learning, higher education, mix-methodology, lifelong learning

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Authors: Hao Li, Guy Y. Cornejo Maceda, Yiqing Li, Jianguo Tan, Marek Morzynski, Bernd R. Noack

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We propose an automated analysis of the flow control behaviour from an ensemble of control laws and associated time-resolved flow snapshots. The input may be the rich database of machine learning control (MLC) optimizing a feedback law for a cost function in the plant. The proposed methodology provides (1) insights into the control landscape, which maps control laws to performance, including extrema and ridge-lines, (2) a catalogue of representative flow states and their contribution to cost function for investigated control laws and (3) visualization of the dynamics. Key enablers are classification and feature extraction methods of machine learning. The analysis is successfully applied to the stabilization of a mixing layer with sensor-based feedback driving an upstream actuator. The fluctuation energy is reduced by 26%. The control replaces unforced Kelvin-Helmholtz vortices with subsequent vortex pairing by higher-frequency Kelvin-Helmholtz structures of lower energy. These efforts target a human interpretable, fully automated analysis of MLC identifying qualitatively different actuation regimes, distilling corresponding coherent structures, and developing a digital twin of the plant.

Keywords: machine learning control, mixing layer, feedback control, model-free control

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Authors: Pi-Lan Yang

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It is controversial which type of gloss condition (i.e., gloss language or gloss position) is more effective in second or foreign language (L2) vocabulary learning. The present study compared the performance on learning ten English words in the conditions of L2 English reading with no glosses and with glosses of Chinese equivalents/translations and L2 English definitions at the side of a page and at an attached sheet for low-intermediate Chinese-speaking learners of English, who were technology university students in Taiwan. It is found first that the performances on the immediate posttest and the delayed posttest were overall better in the gloss condition than those in the no-gloss condition. Next, it is found that the glosses of Chinese translations were more effective and sustainable than those of L2 English definitions. Finally, the effects of L2 English glosses at the side of a page were observed to be less sustainable than those at an attached sheet. In addition, an opinion questionnaire used also showed a preference for the glosses of Chinese translations in L2 English reading. These results would be discussed in terms of automated lexical access, sentence processing mechanisms, and the trade-off nature of storage and processing functions in working memory system, proposed by the capacity theory of language comprehension.

Keywords: glosses of Chinese equivalents/translations, glosses of L2 English definitions, L2 vocabulary learning, L2 English reading

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Authors: Jeff Chak-Fu Wong, Tony Chun Yin Yip

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The transition from secondary school to university seems exciting for many first-year students but can be more challenging than expected. Enabling instructors to know students’ learning habits and styles enhances their understanding of the students’ learning backgrounds, allows teachers to provide better support for their students, and has therefore high potential to improve teaching quality and learning, especially in any mathematics-related courses. The aim of this research is to collect students’ data using online surveys, to analyze students’ factors using learning analytics and educational data mining and to discover the characteristics of the students at risk of falling behind in their studies based on students’ previous academic backgrounds and collected data. In this paper, we use correlation-based distance methods and mutual information for measuring student factor relationships. We then develop a factor network using the Minimum Spanning Tree method and consider further study for analyzing the topological properties of these networks using social network analysis tools. Under the framework of mutual information, two graph-based feature filtering methods, i.e., unsupervised and supervised infinite feature selection algorithms, are used to analyze the results for students’ data to rank and select the appropriate subsets of features and yield effective results in identifying the factors affecting students at risk of failing. This discovered knowledge may help students as well as instructors enhance educational quality by finding out possible under-performers at the beginning of the first semester and applying more special attention to them in order to help in their learning process and improve their learning outcomes.

Keywords: students' academic performance, correlation-based distance method, social network analysis, feature selection, graph-based feature filtering method

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Authors: Aya Salama

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Digital Twin is an emerging research topic that attracted researchers in the last decade. It is used in many fields, such as smart manufacturing and smart healthcare because it saves time and money. It is usually related to other technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, Human digital twin (HDT), in specific, is still a novel idea that still needs to prove its feasibility. HDT expands the idea of Digital Twin to human beings, which are living beings and different from the inanimate physical entities. The goal of this research was to create a Human digital twin that is responsible for real-time human replies automation by simulating human behavior. For this reason, clustering, supervised classification, topic extraction, and sentiment analysis were studied in this paper. The feasibility of the HDT for personal replies generation on social messaging applications was proved in this work. The overall accuracy of the proposed approach in this paper was 63% which is a very promising result that can open the way for researchers to expand the idea of HDT. This was achieved by using Random Forest for clustering the question data base and matching new questions. K-nearest neighbor was also applied for sentiment analysis.

Keywords: human digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification, clustering

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Authors: María-Dolores Olvera-Lobo, Bryan John Robinson, Juncal Gutierrez-Artacho

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Graduates are equipped with a wide range of generic competencies which complement solid curricular competencies and facilitate their access to the labour market in diverse fields and careers. However, some generic competencies such as instrumental, personal and systemic competencies related to teamwork and interpersonal communication skills, decision-making and organization skills are seldom taught explicitly and even less often assessed. In this context, translator training has embraced a broad range of competencies specified in the undergraduate program currently taught at universities and opens up the learning experience to cover areas often ignored due to the difficulties inherent in both teaching and assessment. In practice, translator training combines two well-established approaches to teaching/learning: project-based learning and genuinely cooperative – or merely collaborative – learning. Our professional approach to translator training is a model focused on and adapted to the teleworking context of professional translation and presented through the medium of blended e-learning. Teamwork-related competencies are extremely relevant, and they require explicit and implicit teaching so that graduates can be confident about their capacity to make their way in professional contexts. In order to highlight the importance of teamwork and intra-team relationships beyond the classroom, we aim to raise awareness of teamwork processes so as to empower translation students in managing their interaction and ensure that they gain valuable pre-professional experience. With these objectives, at the University of Granada (Spain) we have developed a range of classroom activities and assessment tools. The results of their application are summarized in this study.

Keywords: blended learning, collaborative teamwork, cross-curricular competencies, higher education, intra-team relationships, students’ perceptions, translator training

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Authors: Eunice H. Li

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This e-Poster presents how the ‘digital backpack’ was introduced to primary school children in Ivory Coast. The idea of a ‘digital backpack’ was initiated by Mr. Thierry N’Doufou in 2012, who later designed and presented to the rest of the world in September 2014. The motivation behind the backpack was to relieve children of the heavy-weight they carry in their school backpacks. Another motivation was to promote Ivory Coast as a country where all children are brought into the digital era. Thierry N’Doufou regards education as the means by which his nation and the entire African Continent can be developed as a prosperous territory. The ‘digital backpack’ contains the entire curriculum for each class and favours a constructivist approach to learning. The children’s notes and exercises are also included in the pack. Additionally, teachers and parents are able to monitor remotely children’s activities while they are working with the ‘backpack’. Teachers are also able to issue homework, assess student’s progress and manage the student’s coursework. This means that teachers should always think the most appropriate pedagogies that can be used to help children to learn. Furthermore, teachers, parents and fellow students are able to have conversations and discussions by using web portals. It is also possible to access more apps if children would like to have additional learning activities. From the presentation in the e-Poster, it seems reasonable to conclude that the ‘digital backpack’ has potential to reach other-level of education. In this way, all will be able to benefit from this new invention.

Keywords: pedagogy, curriculum, constructivism, social constructivism, distance learning environment, ubiquitous learning environment

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Authors: Yacoub Aljaffery

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As much as we think negatively about social media such as TV and smart phones, there are many positive benefits our society can get from it. Cartoons, for example, are made specifically for children. However, in this paper, we will prove how cartoon videos can have a positive impact on adults, especially college students. Since cartoons are meant to be a good learning tool for children, as well as adults, we will show our audience how they can use cartoon in teaching critical thinking and other language skills.

Keywords: social media, TV, teaching, learning, cartoon movies

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Authors: Cristina Maria Ribeiro Martins Pereira Caridade, Ana Rita Ferreira Morais

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The use of computational tools has become essential in the context of interactive learning, especially in engineering education. In the medical industry, teaching medical image processing techniques is a crucial part of training biomedical engineers, as it has integrated applications with healthcare facilities and hospitals. The aim of this article is to present a teaching-learning simulation tool developed in MATLAB using a graphical user interface for medical image fusion that explores different image fusion methodologies and processes in combination with image pre-processing techniques. The application uses different algorithms and medical fusion techniques in real time, allowing you to view original images and fusion images, compare processed and original images, adjust parameters, and save images. The tool proposed in an innovative teaching and learning environment consists of a dynamic and motivating teaching simulation for biomedical engineering students to acquire knowledge about medical image fusion techniques and necessary skills for the training of biomedical engineers. In conclusion, the developed simulation tool provides real-time visualization of the original and fusion images and the possibility to test, evaluate and progress the student’s knowledge about the fusion of medical images. It also facilitates the exploration of medical imaging applications, specifically image fusion, which is critical in the medical industry. Teachers and students can make adjustments and/or create new functions, making the simulation environment adaptable to new techniques and methodologies.

Keywords: image fusion, image processing, teaching-learning simulation tool, biomedical engineering education

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Authors: Alvyra Galkienė, Ona Monkevičienė

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The full participation of all pupils in the overall educational process is defined by the concept of inclusive education, which is gradually evolving in education policy and practice. It includes the full participation of all pupils in a shared learning experience and educational practices that address barriers to learning. Inclusive education applying the principles of Universal Design for Learning (UDL), which includes promoting students' involvement in learning processes, guaranteeing a deep understanding of the analysed phenomena, initiating self-directed learning, and using e-tools to create a barrier-free environment, is a prerequisite for the personal success of each pupil. However, the sustainability of quality education is affected by the transformation of education systems. This was particularly evident during the period of the forced transition from contact to distance education in the COVID-19 pandemic. Research Problem: The transformation of the educational environment from real to virtual one and the loss of traditional forms of educational support highlighted the need for new research, revealing the individual profiles of teachers using UDL-based learning and the pathways of sustainable transfer of successful practices to non-conventional learning environments. Research Methods: In order to identify individual latent teacher profiles that encompass the essential components of UDL-based inclusive teaching and direct leadership of students' learning, the quantitative analysis software Mplius was used for latent profile analysis (LPA). In order to reveal proven, i.e., sustainable, pathways for the transit of the components of UDL-based inclusive learning to distance learning, latent profile transit analysis (LPTA) via Mplius was used. An online self-reported questionnaire was used for data collection. It consisted of blocks of questions designed to reveal the experiences of subject teachers in contact and distance learning settings. 1432 Lithuanian, Latvian, and Estonian subject teachers took part in the survey. Research Results: The LPA analysis revealed eight latent teacher profiles with different characteristics of UDL-based inclusive education or traditional teaching in contact teaching conditions. Only 4.1% of the subject teachers had a profile characterised by a sustained UDL approach to teaching: promoting pupils' self-directed learning; empowering pupils' engagement, understanding, independent action, and expression; promoting pupils' e-inclusion; and reducing the teacher's direct supervision of the students. Other teacher profiles were characterised by limited UDL-based inclusive education either due to the lack of one or more of its components or to the predominance of direct teacher guidance. The LPTA analysis allowed us to highlight the following transit paths of teacher profiles in the extreme conditions of the transition from contact to distance education: teachers staying in the same profile of UDL-based inclusive education (sustainable transit) or jumping to other profiles (unsustainable transit in case of barriers), and teachers from other profiles moving to this profile (ongoing transit taking advantage of the changed new possibilities in the teaching process).

Keywords: distance education, latent teacher profiles, sustainable transit, UDL

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Authors: Thomas Turner

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Due to the Covid-19 pandemic alternative ways of delivering dental education were required. As a result, many institutions moved teaching online. The impact of this is poorly understood. Is online problem-based learning (PBL) and case-based learning (CBL) effective and is it suitable in the post-pandemic era? PBL and CBL are both types of interactive, group-based learning which are growing in popularity within many dental schools. PBL was first introduced in the 1960’s and can be defined as learning which occurs from collaborative work to resolve a problem. Whereas CBL encourages learning from clinical cases, encourages application of knowledge and helps prepare learners for clinical practice. To evaluate the use of online PBL and CBL. A literature search was conducted using the CINAHL, Embase, PubMed and Web of Science databases. Literature was also identified from reference lists. Studies were only included from dental education. Seven suitable studies were identified. One of the studies found a high learner and facilitator satisfaction rate with online CBL. Interestingly one study found learners preferred CBL over PBL within an online format. A study also found, that within the context of distance learning, learners preferred a hybrid curriculum including PBL over a traditional approach. A further study pointed to the limitations of PBL within an online format, such as reduced interaction, potentially hindering the development of communication skills and the increased time and technology support required. An audience response system was also developed for use within CBL and had a high satisfaction rate. Interestingly one study found achievement of learning outcomes was correlated with the number of student and staff inputs within an online format. Whereas another study found the quantity of learner interactions were important to group performance, however the quantity of facilitator interactions was not. This review identified generally favourable evidence for the benefits of online PBL and CBL. However, there is limited high quality evidence evaluating these teaching methods within dental education and there appears to be limited evidence comparing online and faceto-face versions of these sessions. The importance of the quantity of learner interactions is evident, however the importance of the quantity of facilitator interactions appears to be questionable. An element to this may be down to the quality of interactions, rather than just quantity. Limitations of online learning regarding technological issues and time required for a session are also highlighted, however as learners and facilitators get familiar with online formats, these may become less of an issue. It is also important learners are encouraged to interact and communicate during these sessions, to allow for the development of communication skills. Interestingly CBL appeared to be preferred to PBL in an online format. This may reflect the simpler nature of CBL, however further research is required to explore this finding. Online CBL and PBL appear promising, however further research is required before online formats of these sessions are widely adopted in the post-pandemic era.

Keywords: case-based learning, online, problem-based learning, remote, virtual

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Authors: Chaiwat Waree

Abstract:

The purposes of the research were to develop online lessons to strengthen the learning process of Master's degree students majoring in Curriculum and Instruction at Suan Sunandha Rajabhat University; to achieve the efficiency criteria of 80/80; and to study the satisfaction of students who use online lessons to strengthen the learning process of Master’s degree students majoring in Curriculum and Instruction at Suan Sunandha Rajabhat University. The sample consisted of 40 University students studying in semester 1, academic year 2012. The sample was determined by Purposive Sampling. Selected students were from the class which the researcher was the homeroom tutor. The tutor was responsible for the teaching of learning process. Tools used in the study were online lessons, 60-point performance test, and evaluation test of satisfaction of students on online lessons. Data analysis yielded the following results; 83.66/88.29 efficiency of online lessons measured against the criteria; the comparison of performance before and after taking online lessons using t-test yielded 29.67. The statistical significance was at 0.05; the average satisfaction level of forty students on online lessons was 4.46 with standard deviation of 0.68.

Keywords: online, lessons, curriculum, instruction

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Authors: Omar Alzeley, Sergey Utev

Abstract:

Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

Procedia PDF Downloads 613

Authors: Joseph Gear, Yue Xu, Ernest Foo, Praveen Gauravaran, Zahra Jadidi, Leonie Simpson

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Deep learning methods have been seeing an increasing application to the long-standing security research goal of automatic vulnerability detection for source code. Attention, however, must still be paid to the task of producing vector representations for source code (code embeddings) as input for these deep learning models. Graphical representations of code, most predominantly Abstract Syntax Trees and Code Property Graphs, have received some use in this task of late; however, for very large graphs representing very large code snip- pets, learning becomes prohibitively computationally expensive. This expense may be reduced by intelligently pruning this input to only vulnerability-relevant information; however, little research in this area has been performed. Additionally, most existing work comprehends code based solely on the structure of the graph at the expense of the information contained by the node in the graph. This paper proposes Semantic-enhanced Code Embedding for Vulnerability Discovery (SCEVD), a deep learning model which uses semantic-based feature selection for its vulnerability classification model. It uses information from the nodes as well as the structure of the code graph in order to select features which are most indicative of the presence or absence of vulnerabilities. This model is implemented and experimentally tested using the SARD Juliet vulnerability test suite to determine its efficacy. It is able to improve on existing code graph feature selection methods, as demonstrated by its improved ability to discover vulnerabilities.

Keywords: code representation, deep learning, source code semantics, vulnerability discovery

Procedia PDF Downloads 158

Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

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Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

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Authors: Felicia K. Oluwalola

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This paper examines the relevance of technology on education. It identified the concept of technology on education, bringing real-world learning to the classroom situation, examples of where technology can be used. This study established the fact that technology facilitates students learning compared with traditional method of teaching. It was recommended that the teachers should use technology to supplement, not replace, other instructional modes. It should be used in conjunction with hands-on labs and activities that also address the concepts targeted by the technology. Also, technology should be students centered and not teachers centered.

Keywords: computer, simulation, classroom teaching, education

Procedia PDF Downloads 451