Search results for: module based teaching and learning

Authors: Hy Dang, Bo Mei

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The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: classification, machine learning, time representation, stock prediction

Procedia PDF Downloads 151

Authors: Mukhtarkhanova Ainagul Madievna, Imanbek Karakoz

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The development and implementation of active forms and methods of extracurricular work in biology are carried out, assistance to people in determining sustainable interests in a particular field of science, expanding pupil's knowledge and horizons during in–depth study of program issues that go beyond the curriculum where considered, but accessible to the understanding of people; the development of interest in the subject, independence, creative activity. The purpose of this study is to open interest through extracurricular activities for teaching biology. The purpose of the presented work is to arouse people's interest in biology lessons in the modern world through extracurricular activities. First, the level of people was determined in the traditional style of teaching lessons, for which special tests and evaluation criteria were compiled. The level of pupils' knowledge of biology was determined by a questionnaire. Samples of extracurricular work were presented, and practices were conducted in the educational process. At the next stage, the level of pupils after the experiment was compared. The results of the work were discussed, and recommendations were developed.

Keywords: extracurricular activities, modern world, teaching, method, excursions, laboratory experiments, science fairs

Procedia PDF Downloads 108

Authors: Yossiri Ariyakul, Piyakiat Insom, Poonyawat Sangiamkulthavorn, Takamichi Nakamoto

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In this paper, the method of optimization algorithm for sensor network comprised of movable sensor nodes which can be used for odor source localization was proposed. A sensor node is composed of an odor sensor, an anemometer, and a wireless communication module. The odor intensity measured from the sensor nodes are sent to the processor to perform the localization based on optimization algorithm by which the odor source localization map is obtained as a result. The map can represent the exact position of the odor source or show the direction toward it remotely. The proposed method was experimentally validated by creating the odor source localization map using three, four, and five sensor nodes in which the accuracy to predict the position of the odor source can be observed.

Keywords: odor sensor, odor source localization, optimization, sensor network

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Authors: Xu Jiaqiao

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Text sentiment analysis is an important branch of natural language processing. This technology is widely used in public opinion analysis and web surfing recommendations. At present, the mainstream sentiment analysis methods include three parts: sentiment analysis based on a sentiment dictionary, based on traditional machine learning, and based on deep learning. This paper mainly analyzes and compares the advantages and disadvantages of the SVM method of traditional machine learning and the Long Short-term Memory (LSTM) method of deep learning in the field of Chinese sentiment analysis, using Chinese comments on Sina Microblog as the data set. Firstly, this paper classifies and adds labels to the original comment dataset obtained by the web crawler, and then uses Jieba word segmentation to classify the original dataset and remove stop words. After that, this paper extracts text feature vectors and builds document word vectors to facilitate the training of the model. Finally, SVM and LSTM models are trained respectively. After accuracy calculation, it can be obtained that the accuracy of the LSTM model is 85.80%, while the accuracy of SVM is 91.07%. But at the same time, LSTM operation only needs 2.57 seconds, SVM model needs 6.06 seconds. Therefore, this paper concludes that: compared with the SVM model, the LSTM model is worse in accuracy but faster in processing speed.

Keywords: sentiment analysis, support vector machine, long short-term memory, Chinese microblog comments

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Authors: Akshat Kumar, Vidushi

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This paper identifies 27 KOIs, 26 of which are currently classified as candidates and one as false positives that have a high probability of being confirmed. For this purpose, 11 machine learning algorithms were implemented on the cumulative kepler dataset sourced from the NASA exoplanet archive; it was observed that the best-performing model was HistGradientBoosting and XGBoost with a test accuracy of 93.5%, and the lowest-performing model was Gaussian NB with a test accuracy of 54%, to test model performance F1, cross-validation score and RUC curve was calculated. Based on the learned models, the significant characteristics for confirm exoplanets were identified, putting emphasis on the object’s transit and stellar properties; these characteristics were namely koi_count, koi_prad, koi_period, koi_dor, koi_ror, and koi_smass, which were later considered to filter out the potential KOIs. The paper also calculates the Earth similarity index based on the planetary radius and equilibrium temperature for each KOI identified to aid in their classification.

Keywords: Kepler objects of interest, exoplanets, space exploration, machine learning, earth similarity index, transit photometry

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Authors: Dragana Gnjatovic

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Assessment is a burning topic in education policy and practice due to measurement-driven neoliberal agendas of quality and standardisation of assessment practice through high stakes standardised testing systems that are now influencing early childhood education. This paper presents a collaborative, arts-informed action research project which places children at the centre of their learning, with assessment as an integral part of play-based learning processes. It aims to challenge traditional approaches to assessment that are often teacher-led and decontextualised from the processes of learning through exploring approaches where children's voices are central, and their creative arts expressions are used to assess learning and development. The theoretical framework draws on Vygotsky's sociocultural theory and Freire's critical pedagogy, which indicate the importance of socially constructed reality where knowledge is the result of collaboration between children and adults. This reality perceives children as competent agents of their own learning processes. An interpretive-constructivist and critical-transformative paradigm underpin collaborative action research in a three to five-year-old setting, where creative methods like storytelling, play, drama, drawing are used to assess children's learning. As data collection and analysis are still in process, this paper will present the methodology and some data vignettes, with the aim of stimulating discussion about innovation in assessment and contribution of the collaborative enquiry in the field of Early Childhood Education and Care.

Keywords: assessment for learning, creative methodologies, collaborative action research, early childhood education and care

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Authors: M. A. Rahman, A. O. Enikuomehin

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Sharing commitment to reach a common goal in research by harnessing available resources from two or more parties can simply be referred to as collaborative research. Asides from avoiding duplication of research, the benefits often accrued from such research alliances include time economy as well as expenses reduction in completing such studies. Likewise, it provides an avenue to produce a wider horizon of scientific knowledge sequel to gathering of skills, knowledge and resources. In institutions of higher learning and research institutes, it often gives scholars an opportunity to strengthen the teaching and research capacity of their various institutions. Between industries and institutions, collaborative research breeds promising relationship that could be geared towards addressing different research problems such as producing and enhancing industrial-based products and services, including technological transfer. For Nigeria to take advantage of this collaboration, different issues like licensing of technology, intellectual property right, confidentiality, and funding among others, which could arise during this collaborative research programme, are identified in this paper. An important tool required to achieve this height in developing economy is the use of appropriate computer model. The paper highlights the costs of the collaborations and likewise stresses the need for evaluating the effectiveness and efficiency of such collaborative research activities and proposes an appropriate computer model to assist in this regard.

Keywords: collaborative research, developing country, computerization, model

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Authors: Eric Bates

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Higher education is recognised as being of critical importance in Ireland and has been linked as a vital factor to national well-being. Statistics show that Ireland has one of the highest rates of higher education participation in Europe. However, student retention and progression, especially in Institutes of Technology, is becoming an issue as rates on non-completion rise. Both within Ireland and across Europe student retention is seen as a key performance indicator for higher education and with these increasing rates the Irish higher education system needs to be flexible and adapt to the situation it now faces. The author is a Programme Chair on a Level 6 full time undergraduate programme and experience to date has shown that the first year undergraduate students take some time to identify themselves as a group within the setting of a higher education institute. Despite being part of a distinct class on a specific programme some individuals can feel isolated as he or she take the first step into higher education. Such feelings can contribute to students eventually dropping out. This paper reports on an ongoing initiative that aims to accelerate the bonding experience of a distinct group of first year undergraduates on a programme which has a high rate of non-completion. This research sought to engage the students in dynamic interactions with their peers to quickly evolve a group sense of coherence. Two separate modules – a Research Module and a Communications module - delivered by the researcher were linked across two semesters. Students were allocated into random groups and each group was given a topic to be researched. There were six topics – essentially the six sub-headings on the DIT Graduate Attribute Statement. The research took place in a computer lab and students also used the library. The output from this was a document that formed part of the submission for the Research Module. In the second semester the groups then had to make a presentation of their findings where each student spoke for a minimum amount of time. Presentation workshops formed part of that module and students were given the opportunity to practice their presentation skills. These presentations were video recorded to enable feedback to be given. Although this was a small scale study preliminary results found a strong sense of coherence among this particular cohort and feedback from the students was very positive. Other findings indicate that spreading the initiative across two semesters may have been an inhibitor. Future challenges include spreading such Initiatives College wide and indeed sector wide.

Keywords: first year experience, student retention, group work, presentation workshops

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Authors: César Garza

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In supervised binary classification and regression problems, it is well-known that learnability is equivalent to a uniform convergence of the hypothesis class, and if a problem is learnable, it is learnable by empirical risk minimization. For the general learning setting of unsupervised learning tasks, there are non-trivial learning problems where uniform convergence does not hold. We present here the task of learning centers of mass with an extra feature that “activates” some of the coordinates over the unit ball in a Hilbert space. We show that the learning problem is learnable under a stable RLM rule. We introduce a family of distributions over the domain space with some mild restrictions for which the sample complexity of uniform convergence for these problems must grow logarithmically with the dimension of the Hilbert space. If we take this dimension to infinity, we obtain a learnable problem for which the uniform convergence property fails for a vast family of distributions.

Keywords: statistical learning theory, learnability, uniform convergence, stability, regularized loss minimization

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Authors: Mohammad Rostami, Mohammad Reza Forghani, Elahe Neshat, Fatemeh Yaghoobi

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An Ad Hoc network consists of wireless mobile equipment which connects to each other without any infrastructure, using connection equipment. The best way to form a hierarchical structure is clustering. Various methods of clustering can form more stable clusters according to nodes' mobility. In this research we propose an algorithm, which allocates some weight to nodes based on factors, i.e. link stability and power reduction rate. According to the allocated weight in the previous phase, the cellular learning automaton picks out in the second phase nodes which are candidates for being cluster head. In the third phase, learning automaton selects cluster head nodes, member nodes and forms the cluster. Thus, this automaton does the learning from the setting and can form optimized clusters in terms of power consumption and link stability. To simulate the proposed algorithm we have used omnet++4.2.2. Simulation results indicate that newly formed clusters have a longer lifetime than previous algorithms and decrease strongly network overload by reducing update rate.

Keywords: mobile Ad Hoc networks, clustering, learning automaton, cellular automaton, battery power

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Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

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In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

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Authors: Donghee Noh, Seonhyeong Kim, Junhwan Choi, Heegon Kim, Sooho Jung, Keunho Park

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In this paper, we describe an image post-processing method to enhance the performance of the parcel segmentation method using deep learning-based aerial images conducted in previous studies. The study results were evaluated using a confusion matrix, IoU, Precision, Recall, and F1-Score. In the case of the confusion matrix, it was observed that the false positive value, which is the result of misclassification, was greatly reduced as a result of image post-processing. The average IoU was 0.9688 in the image post-processing, which is higher than the deep learning result of 0.8362, and the F1-Score was also 0.9822 in the image post-processing, which was higher than the deep learning result of 0.8850. As a result of the experiment, it was found that the proposed technique positively complements the deep learning results in segmenting the parcel of interest.

Keywords: aerial image, image process, machine vision, open field smart farm, segmentation

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Authors: Mohd Afif Md Nasir, Faizal Amin Nur Yunus, Jamaluddin Hashim, Abd Samad Hassan Basari, A. Halim Sahelan

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The aim of this study is to identify the suitability of Virtual Learning Environment (VLE) in welding simulator application towards Computer-Based Training (CBT) in developing skills upon new students at the Advanced Technology Training Center (ADTEC), Batu Pahat, Johor, Malaysia and GIATMARA, Batu Pahat, Johor, Malaysia. The purpose of the study is to create a computer-based skills development approach in welding technology among new students in ADTEC and GIATMARA, as well as cultivating the elements of general skills among them. This study is also important in elevating the number of individual knowledge workers (K-workers) working in manufacturing industry in order to achieve a national vision which is to be an industrial nation in the year of 2020. The design of the study is a survey type of research which uses questionnaires as the instruments and 136 students from ADTEC and GIATMARA were interviewed. Descriptive analysis is used to identify the frequency and mean values. The findings of the study shows that the welding technology skills have developed in the students as a result of the application of VLE simulator at a high level and the respondents agreed that the skills could be embedded through the application of the VLE simulator. In summary, the VLE simulator is suitable in welding skills development training in terms of exposing new students with the relevant characteristics of welding skills and at the same time spurring the students’ interest towards learning more about the skills.

Keywords: computer-based training (CBT), knowledge workers (K-workers), virtual learning environment, welding simulator, welding technology

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Authors: Y. Kavanagh, N. O'Hara, R. Palmer, P. Lowe, D. Rafferty

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Physical Physics is a physics education methodology for games programfmes that integrates physical activity with movement tracking and modelling. It significantly enhances the learning experience and it is effective in illustrating how physics is core in games design and programming, while allowing students to be active participants and take ownership of the learning process. It has been successfully piloted with undergraduate students studying Games Development.

Keywords: activity, enhanced learning, game development, physics

Procedia PDF Downloads 293

Authors: Zaideth Zobeida Ponce Alonso, Laura Emilia Fierro Lopez, Maria del Rocio Dominguez Gaona

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The present study is part of the Master in Modern Languages at the Universidad Autónoma de Baja California, and it aims to analyze how the sociocultural background might influence the development of learner autonomy in foreign language education in order to propose some strategies to overcome such challenges. Given the lack of research on the sociocultural barriers in learner autonomy in a Mexican context and the need to hear teachers’ voices about this issue, qualitative data was obtained from semi-structured interviews with six language teachers on their perspectives on learner autonomy, its application to the language classroom, and their experiences with Mexican and foreign learners/contexts in order to find out differences regarding learner autonomy. The results suggest three main sociocultural characteristics: preference for an authority figure, tendency towards collectivism, and low tolerance of ambiguity. Finally, nine strategies were proposed in order to help language teachers to deal with such sociocultural characteristics when fostering learner autonomy in the border city of Mexicali, where this study was carried out.

Keywords: learner autonomy, Mexican context, sociocultural influence, teachers' perspectives, teaching strategies

Procedia PDF Downloads 162

Authors: Pei-Chun Chen, Chi-Ting Tseng, Lih-Chi Chen, Kai-Hsiang Yang

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Introduction: The pharmacist is responsible for encouraging adverse drug reaction (ADR) reporting. In a local center in Northern Taiwan, promotion and rewarding of ADR reporting have continued for over six years but failed to bring significant changes. This study aims to find a solution to increase ADR reporting. Research question or hypothesis: We hypothesized that under-reporting is due to the inconvenience of the reporting system. Reports were made conventionally through printed sheets. We proposed that reports made per month will increase if they were computerized. Study design: An ADR reporting platform was established in April 2015, before which was defined as the first stage of this study (January-March, 2015) and after which the second stage. The third stage commenced in November, 2015, after adding a reporting module to physicians prescription system. ADRs could be reported simultaneously when documenting drug allergies. Methods: ADR report rates during the three stages of the study were compared. Effects of the information platform on reporting were also analyzed. Results: During the first stage, the number of ADR reports averaged 6 per month. In the second stage, the number of reports per month averaged 1.86. Introducing the information platform had little effect on the monthly number of ADR reports. The average number of reports each month during the third stage of the study was 11±3.06, with 70.43% made electronically. Reports per month increased significantly after installing the reporting module in November, 2015 (P<0.001, t-test). In the first two stages, 29.03% of ADR reports were made by physicians, as compared to 70.42% of cases in the third stage of the study. Increased physician reporting possibly account for these differences. Conclusion: Adding a reporting module to the prescription system significantly increased ADR reporting. Improved accessibility is likely the cause. The addition of similar modules to computer systems of other healthcare professions may be considered to encourage spontaneous ADR reporting.

Keywords: adverse drug reactions, adverse drug reaction reporting systems, regional hospital, prescription system

Procedia PDF Downloads 355

Authors: P. Thachitasing, N. Jansawang, W. Rakrai, T. Santiboon

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The aim of this study is to investigate of the comparing the instructional design models between the Think-Pair-Share and Conventional Learning (5E Inquiry Model) Processes to enhance students’ learning achievements and their problem solving skills on stoichiometry issue for concerning the 2-instructional method with a sample consisted of 80 students in 2 classes at the 11th grade level in Chaturaphak Phiman Ratchadaphisek School. Students’ different learning outcomes in chemistry classes with the cluster random sampling technique were used. Instructional Methods designed with the 40-experimenl student group by Think-Pair-Share process and the 40-controlling student group by the conventional learning (5E Inquiry Model) method. These learning different groups were obtained using the 5 instruments; the 5-lesson instructional plans of Think-Pair-Share and STEM Education Method, students’ learning achievements and their problem solving skills were assessed with the pretest and posttest techniques, students’ outcomes of their instructional the Think-Pair-Share (TPSM) and the STEM Education Methods were compared. Statistically significant was differences with the paired t-test and F-test between posttest and pretest technique of the whole students in chemistry classes were found, significantly. Associations between student learning outcomes in chemistry and two methods of their learning to students’ learning achievements and their problem solving skills also were found. The use of two methods for this study is revealed that the students perceive their learning achievements to their problem solving skills to be differently learning achievements in different groups are guiding practical improvements in chemistry classrooms to assist teacher in implementing effective approaches for improving instructional methods. Students’ learning achievements of mean average scores to their controlling group with the Think-Pair-Share Model (TPSM) are lower than experimental student group for the STEM education method, evidence significantly. The E1/E2 process were revealed evidence of 82.56/80.44, and 83.02/81.65 which results based on criteria are higher than of 80/80 standard level with the IOC, consequently. The predictive efficiency (R2) values indicate that 61% and 67% and indicate that 63% and 67% of the variances in chemistry classes to their learning achievements on posttest in chemistry classes of the variances in students’ problem solving skills to their learning achievements to their chemistry classrooms on Stoichiometry issue with the posttest were attributable to their different learning outcomes for the TPSM and STEMe instructional methods.

Keywords: comparisons, students’ learning achievements, think-pare-share model (TPSM), stem education, problem solving skills, chemistry classes, stoichiometry issue

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Authors: Elineuda Do Socorro Santos Picanço Sousa, Ana Kerlly Souza da Costa

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This paper presents the analysis of the information society as a plural, inclusive and participatory society, where it is necessary to give all citizens, especially young people, the right skills in order to develop skills so that they can understand and use information through of contemporary technologies; well as carry out a critical analysis, using and producing information and all sorts of messages and / or informational language codes. This conviction inspired this article, whose aim is to present current trends in the use of technology in distance education applied as an alternative and / or supplement to classroom teaching for Youth and Adults, concepts and actions, seeking to contribute to its development in the state of Amapá and specifically, the Center for Professional of Amapá Teaching Professor Josinete Oliveira Barroso - CEPAJOB.

Keywords: youth and adults education, Ead. Professional Education, online courses, CEPAJOB

Procedia PDF Downloads 645

Authors: Jonathan A. Sánchez Muñoz, Gustavo Flores Eraña, Juan M. Silva

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Mexican Mezcal industry has reached attention during the last decade due to it has been a popular beverage demanded by North American and European markets, reaching popularity due to its crafty character. Despite its wide demand, productive processes are still made with rudimentary equipment, and there is a lack of evidence to improve kiln energy efficiency. Tec21 is a challenge-based learning curricular model implemented by Tecnológico de Monterrey since 2019, where each formation unit requires an industrial partner. “Problem processes solution” is a formation unity designed for mechatronics engineers, where students apply the acquired knowledge in thermofluids and apply electronic. During five weeks, students are immersed in an industrial problem to obtain a proper level of competencies according to formation unit designers. This work evaluates the competencies acquired by the student through qualitative research methodology. Several evaluation instruments (report, essay, and poster) were selected to evaluate etic argumentation, principles of sustainability, implemented actions, process modelling, and redesign feasibility.

Keywords: applied electronic, challenge based learning, competencies, mezcal industry, thermofluids

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Authors: May George

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Inference plays an important role in the learning process and it can lead to a rapid acquisition of a second language. When learning a non-native language, i.e., a critical language like Arabic, the students depend on the teacher’s support most of the time to learn new concepts. The students focus on memorizing the new vocabulary and stress on learning all the grammatical rules. Hence, the students became mechanical and cannot produce the language easily. As a result, they are unable to predict the meaning of words in the context by relying heavily on the teacher, in that they cannot link their prior knowledge or even identify the meaning of the words without the support of the teacher. This study explores how the teacher guides students learning during the inference process and what are the processes of learning that can direct student’s inference.

Keywords: inference, reading, Arabic, language acquisition

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Authors: L. N. P. Wedikandage, Mohamed Razmi Zahir

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Technology is a global imperative because of its contributions to human existence and because it has improved global socioeconomic relations. As a result, the mobile phone has become the most important mode of communication today. Smartphones, Internet-enabled devices with built-in computer software and applications, are one of the most significant inventions of the twenty-first century. Technology is advantageous to many people, especially those involved in education. It is an important learning tool for today's schoolchildren. It enables students to access online learning platforms and course resources and interact digitally. Senior secondary students, in particular, have some of the most expensive and sophisticated mobile phones, tablets, and iPads capable of connecting to the internet and various social media platforms, other websites, and so on. At present, the use of mobile phones' potential for effective teaching and learning is growing. This is due to the benefits of mobile learning, including the ability to share knowledge without any limits in space or Time and the capacity to facilitate the development of critical thinking, participatory learning, problem-solving, and the development of lifelong communication skills. However, it is yet unclear how mobile devices may affect education and how they may affect opportunities for learning. As a result, the purpose of this research was to ascertain the relationship between mobile phone usage and the academic Performance of secondary-level students at an international school in Sri Lanka. The study's sample consisted of 523 secondary-level students from an international school, ranging from Form 1 to Upper 6. For the study, a survey research design and questionnaires were used. Google Forms was used to create the students' survey. There were three hypotheses tested to find out the relationship between mobile phone usage and academic preference. The findings show that there is a positive relationship between mobile phone usage and academic performance among secondary school students (the number of students obtaining simple passes is significantly higher when mobile phones are being used for more than 7 hours), no relationship between mobile phone usage and academic performance among secondary school students of different parents' occupations, and a relationship between the frequency of mobile phone usage and academic performance among secondary school students.

Keywords: mobile phone, academic performance, secondary level, international schools

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Authors: Albert Amagyei, Julia Carroll, Amanda R. Amorim Adegboye, Laura Strumidlo, Rosie Kneafsey

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Introduction: Classroom-based learning has persisted as the mainstream model of pre-registration nurse education. This model is often rigid, teacher-centered, and unable to support active learning and the practical learning needs of nursing students. Health Education England (HEE), a public body of the Department of Health and Social Care, hypothesises that blended learning (BL) programmes may address health system and nursing profession challenges, such as nursing shortages and lack of digital expertise, by exploring opportunities for providing predominantly online, remote-access study which may increase nursing student recruitment, offering alternate pathways to nursing other than the traditional classroom route. This study will provide evidence for blended learning strategies adopted in nursing education as well as examine nursing student learning experiences concerning the challenges and opportunities related to using blended learning within nursing education. Objective: This review will explore the challenges and opportunities of BL within pre-registration nurse education from the student's perspective. Methods: The search was completed within five databases. Eligible studies were appraised independently by four reviewers. The JBI-convergent segregated approach for mixed methods review was used to assess and synthesize the data. The study’s protocol has been registered with the International Register of Systematic Reviews with registration number// PROSPERO (CRD42023423532). Results: Twenty-seven (27) studies (21 quantitative and 6 qualitative) were included in the review. The study confirmed that BL positively impacts nursing students' learning outcomes, as demonstrated by the findings of the meta-analysis and meta-synthesis. Conclusion: The review compared BL to traditional learning, simulation, laboratory, and online learning on nursing students’ learning and programme outcomes as well as learning behaviour and experience. The results show that BL could effectively improve nursing students’ knowledge, academic achievement, critical skills, and clinical performance as well as enhance learner satisfaction and programme retention. The review findings outline that students’ background characteristics, BL design, and format significantly impact the success of the BL nursing programme.

Keywords: nursing student, blended learning, pre-registration nurse education, online learning

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Authors: Abd Aziz Bin Abd Shukor, Eftah Binti Moh Hj Abdullah

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This case study aims to identify teachers’ perception as regards integrity in School-Ba sed Assessment (PBS) practice. This descriptive study involved 9 teachers from 4 secondary schools in 3 districts in the state of Perak. The respondents had undergone an integrity in PBS Practice interview using a focused group discussion method. The overall findings showed that the teachers believed that integrity in PBS practice could be achieved by adjusting the teaching methods align with learning objectives and the students’ characteristics. Many teachers, parents and student did not understand the best practice of PBS. This would affect the integrity in PBS practice. Teachers did not emphasis the principles and ethics. Their integrity as an innovative public servant may also be affected with the frequently changing assessment system, lack of training and no prior action research. The analysis of findings showed that the teachers viewed that organizational integrity involving the integrity of PBS was difficult to be implemented based on the expectations determined by Malaysia Ministry of Education (KPM). A few elements which assisted in the achievement of PBS integrity were the training, students’ understanding, the parents’ understanding of PBS, environment (involving human resources such as support and appreciation and non-human resources such as technology infrastructure readiness and media). The implications of this study show that teachers, as the PBS implementers, have a strong influence on the integrity of PBS. However, the transformation of behavior involving PBS integrity among teachers requires the stabilisation of support and infrastructure in order to enable the teachers to implement PBS in an ethical manner.

Keywords: assessment integrity, integrity, perception, school-based assessment

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Authors: Omid Noroozi, Martin Mulder

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The motivational potential of educational games is undeniable especially for teaching topics and skills that are difficult to deal with in traditional educational situations such as argumentation competence. Willingness to argue has an association with student epistemic beliefs, which can influence whether, and the way in which students engage in argumentative discourse activities and critical discussion. The goal of this study was to explore how undergraduate students engage with argumentative discourse activities which have been designed to intensify debate, and whether epistemic beliefs are significant to the outcomes. A pre-test, post-test design was used with students who were assigned to groups of four. They were asked to argue a controversial topic with the aim of exploring various perspectives, and the 'pros and cons' on the topic of 'Genetically Modified Organisms (GMOs)'. The results show that the game facilitated argumentative discourse and a willingness to argue and challenged peers, regardless of students’ epistemic beliefs. Furthermore, the game was evaluated positively in terms of students’ motivation and satisfaction with the learning experience.

Keywords: argumentation, attitudinal change, epistemic beliefs, dialogue, digital game objectives and theoretical

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Authors: Gabriel Ocheleka Aniedi A. Udo, Patum Wasinda

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The study was carried out to appraise the impact of ICT-based teaching methodologies (video-taped instructions and Power Point presentations) on academic performance and retention of secondary school students in Physics, with particular interest in Newton Laws of Motion. The study was conducted in Cross River State, Nigeria, with a quasi-experimental research design using non-randomised pre-test and post-test control group. The sample for the study consisted of 176 SS2 students drawn from four intact classes of four secondary schools within the study area. Physics Achievement Test (PAT), with a reliability coefficient of 0.85, was used for data collection. Mean and Analysis of Covariance (ANCOVA) was used in the treatment of the obtained data. The results of the study showed that there was a significant difference in the academic performance and retention of students taught using video-taped instructions and those taught using power point presentations. Findings of the study showed that students taught using video-taped instructions had a higher academic performance and retention than those taught using power point presentations. The study concludes that the use of blended ICT-based teaching methods can improve learner’s academic performance and retention.

Keywords: video taped instruction (VTI), power point presentation (PPT), academic performance, retention, physics

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Authors: Benjamin Bobbia, Matthias Picard

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In recent years active learning methodologies based on the representativity of the data seems more promising to limit overfitting. The presented query methodology for regression using the Wasserstein distance measuring the representativity of our labelled dataset compared to the global distribution. In this work a crucial use of GroupSort Neural Networks is made therewith to draw a double advantage. The Wasserstein distance can be exactly expressed in terms of such neural networks. Moreover, one can provide explicit bounds for their size and depth together with rates of convergence. However, heterogeneity of the dataset is also considered by weighting the Wasserstein distance with the error of approximation at the previous step of active learning. Such an approach leads to a reduction of overfitting and high prediction performance after few steps of query. After having detailed the methodology and algorithm, an empirical study is presented in order to investigate the range of our hyperparameters. The performances of this method are compared, in terms of numbers of query needed, with other classical and recent query methods on several UCI datasets.

Keywords: active learning, Lipschitz regularization, neural networks, optimal transport, regression

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Bitewulign Mekonnen

Abstract:

Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Ferdağ Çulhan, Emine Gaye Çontay

Abstract:

Geometric thinking levels created by Van Hiele are used to determine students' progress in geometric thinking. Many studies have been conducted on geometric thinking levels and they have taken their place in teaching curricula over time. It is thought that geometric thinking levels, which have become so important in teaching, can be examined in depth. In order to make an in-depth analysis, it was decided that the most appropriate management was discourse analysis. In this study, the focus is on examining the geometric thinking levels of 8th grade students from a discursive point of view. Sfard (2008)'s "Commognitive" theory will be used to conduct discursive analysis. The "Global Van Hiele Questionnaire" created by Patkin (2014) and translated into Turkish for this research will be used in the research. The "Global Van Hiele Questionnaire" contains questions from the sub-learning domain of triangles and quadrilaterals, circles and geometric objects. It has a wider scope than many "Van Hiele Questionnaires". “Global Van Hiele Questionnaire” will be applied to 8th grade students. Then, the geometric thinking levels of the students will be determined and interviews will be held with two students from each of the 1st, 2nd and 3rd levels. The interviews will be recorded and the students' discourses will be examined. By evaluating the relations between the students' geometric thinking levels and their discourses, it will be examined how much their discourse reflects their level of thinking. In this way, it is thought that students' geometric thinking processes can be better understood.

Keywords: mathematical discourses, commognitive framework, geometric thinking levels, van hiele

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Authors: Sai Maniveer Adapa, Sai Guptha Perla, Adithya Reddy P.

Abstract:

Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection.

Keywords: thyroid tumor diagnosis, ultrasound images, deep learning, machine learning, convolutional auto-encoder, support vector machine

Procedia PDF Downloads 62