Learning Techniques Research Articles

The Optimal Learning Cocktail for Placebo Analgesia: A Randomized Controlled Trial Comparing Individual and Combined Techniques

This study investigated for the first time the effects of individual and combined application of 3 learning techniques (verbal suggestions, classical conditioning, and observational learning) on placebo analgesia and extinction.Healthy participants (N = 206) were assigned to 8 different groups in which they were taught through either a verbal suggestion, a conditioning paradigm, a video observing someone, or any combination thereof that a placebo device (inactive transcutaneous electric nerve stimulation [TENS]) was capable of alleviating heat pain, whereas one group did not (control). Placebo analgesia was quantified as the within-group difference in experienced pain when the placebo device was (sham) ‘activated’ or ‘inactivated’ during equal pain stimuli, and compared between groups.Placebo analgesia was induced in groups with 2 or 3 learning techniques. Significantly stronger placebo analgesia was induced in the combination of all 3 learning techniques as compared to the individual learning techniques or control condition, underlining the additional contribution of 3 combined techniques. Extinction did not differ between groups. Furthermore, pain expectancies, but not state anxiety or trust, mediated placebo analgesia.Our findings emphasize the added value of combining 3 learning techniques to optimally shape expectancies that lead to placebo analgesia, which can be used in experimental and clinical settings. PerspectiveThis unique experimental study compared the individual versus combined effects of 3 important ways of learning (verbal suggestions, classical conditioning, and observational learning) on expectation-based pain relief. The findings indicate that placebo effects occurring in clinical practice could be optimally strengthened if healthcare providers apply these techniques in combination.

Exploring Switzerland’s Land Cover Change Dynamics Using a National Statistical Survey

Timely and reliable Land Use and Cover change information is crucial to efficiently mitigate the negative impact of environmental changes. Switzerland has the ambitious objective of being a sustainable country while remaining an attractive business location with a high level of well-being. However, this aspiration is hampered by increasing pressures that are significantly impacting the environment and putting serious demands on land. In the present study, we used the national Land Cover (LC) dataset, named ArealStatistik, produced by the Federal Statistical Office, to explore the spatiotemporal patterns of Land Cover in Switzerland, providing a comprehensive assessment of land cover change at the national scale. Results indicate that, in general, Switzerland has undergone small, spatially dispersed, dynamic, and gradual change trends, with high rates of transition between low growing Brush Vegetation and forest LC classes in recent years. These pixel-level trends are more important in the lower altitude plateau and Jura regions, while greater changes in the spatial configuration of LC are observed in the alpine regions. However, findings also suggest that identifying drivers and understanding the rate of change are limited by the spatial resolution and temporal update frequency of the ArealStatistik. The ability to understand these drivers would benefit from a high-resolution annual LC dataset. Such a data product can be produced using the ArealStatistik together with dense satellite data time-series and Machine/Deep Learning techniques.

Big data and machine learning and the impact on education

In this paper we mention deep learning. Deep learning can be used to improve computing service provider revenue in the mobile blockchain network or to use a machine to study standard mobile detection tasks (e.g., activity recognition). Using deep neural networks can compare results with learning techniques in more common use. Also, we see the deep learning for massive amounts of data and deep learning for a wide variety of data and high-velocity data. Beyond all applications Big Data and Machine learning can be used to improve education, and its practices and procedures.

Digital-Based Islamic Religious Education (IRE) Learning Model at Senior High School

Contemporary learning trends lead to digital implementation. Digitalization of learning has implications for the effectiveness and efficiency of achieving learning objectives. Demand and need for implementing digital-based learning in Moral and Islamic Religious Education (Pendidikan Agama Islam dan Budi Pekerti, PAI dan BP) because the students are millennials. Digital-based learning design includes objectives, teaching materials, digital media, methods, and evaluation (assessment tools). This study aimed to find a digital-based learning model for PAI and BP subjects at senior high schools. This type of research was qualitative. Observation, interviews, and document studies carried out techniques of data collection. Data was collected through various settings, sources, and methods. Data analysis techniques used data condensation, information disclosure, and conclusion. The study results showed that the digital-based Islamic religious learning approach was scientific and student-centered; Islamic religious learning used Google Classroom, assisted by WhatsApp. Teaching materials were designed as power points, pdf books, and videos. The digital learning model with a student-centered approach has several indicators, including interactive digital learning strategies, lecture methods, assignments, simulations, inquiry, and Islamic religious learning-specific scenarios, namely habituation, exemplary, praise, and threats. Learning techniques use WhatsApp, connected to other social media such as Facebook and Instagram. Learning tactics through humor, singing, rhetoric, and surprise. The implications of implementing digital-based learning models can create a learning atmosphere that encourages students to be more interactive, foster creativity, and develop independent learning.

Learning Speaking Problems in Online Learning During Covid-19 Outbreak for ESP Learners

Because of the pandemic, Indonesian government implemented new policies in the education system. Previously, education and training took place in the luring situation on a regular basis, particularly in higher education institutions, and now it is entirely daring. The current study aims to examine students' issues in speaking for ESP contexts during the COVID-19 circumstance when they were learning the English language by the online situation. The descriptive qualitative design was used in this study. The information was gathered using questionnaire in the help of Google form media and also interviews with 40 students from the fourth semester of the Management Informatics Diploma Major at Piksi Input Serang Polytechnic in Banten. Based on this study, the research findings highlighted a variety of issues for students. Six issues emerged during distance learning in the Speaking course. The first issue that arose was students' learning equipment and supporting facilities. The second issue was dealing with students' accessibility restrictions to Online Video Conference. The third issue was students' concerns on the lecturer's materials during an online lesson. The fourth issue was the students' approach to completing the lecturer's task or assignment. The use of online streaming videos as a media of learning resources also had a role in the fifth and final issue that emerged during distance learning amid COVID-19 pandemic situations is students' limited cell signal and internet quota. The findings can be used to further enhance and improve online teaching and learning techniques. Further research into how students deal with obstacles is encouraged.

THE INFLUENCE OF TEACHER'S TEACHING STYLE AND LEARNING DISCIPLINE ON STUDENTS' COGNITIVE ABILITY AT SMP NEGERI 3 MOTOLING TIMUR

This study aims to determine the effect of the teacher's teaching style and learning discipline on the cognitive abilities of students at SMP Negeri 3 Motoling Timur. This test strategy is a learning technique with an example of 75 individuals. The procedure used in collecting information is a survey strategy (poll) and documentation. The results of the examination were dissected using various recurrence direct examinations, basic connection examinations and various connection examinations. The results showed: (1) There is an influence between the Teacher's Teaching Style on the Cognitive Ability of students of SMP Negeri 3 Motoling Timur by 44.89% with t count > t table = 7.71 > 2.04 and F count > F table.
 Keywords: Teacher's Teaching Style, Learning Discipline, Students' Cognitive Ability

Peningkatan Motorik Kasar Pada Anak Usia Dini Berbasis Belajar Sambil Bermaian Melalui Permainan Tradisional Engklek di PAUD Kober Al-Maarif

Introduction: Early age is a golden opportunity for children to learn, so it is called the golden age. Early childhood education is a form of education that focuses on laying the foundation for physical growth and development (fine and gross motor coordination). the learning techniques used must make children happy, so the method used is learning-based education while playing and the Engklek game is one of the traditional games that can be used.
 Purpose: The purpose of this activity is to implement play-based learning through the medium of traditional crank games to improve gross motor development in early childhood at PAUD Kober Al-Maarif.
 Method: The method used in health promotion is education based on learning while playing. This health promotion was carried out at PAUD Kober Al-Maarif Lakbok with a total of 35 participants with a series of activities through 4 stages, namely 1) selection of targets and places, 2) preparation of facilities and infrastructure, 3) implementation of activities, 4) evaluation.
 Results: based on the results of the evaluation carried out by the learning while playing method, it gave a positive response to the participants. the participants enjoyed the series of events from start to finish with joy. Through this activity, children are more able to explore their imagination and bring out their creativity and talents.
 Conclusion: This activity can be carried out as a game to train gross motor skills in early childhood by using the traditional crank game as media. In addition, not only gross motor skills but also exploring other learning that can be included in playing activities by recognizing numbers and letters and counting.

3D reconstruction of unlimited-size real-world porous media by combining a BicycleGAN-based multimodal dictionary and super-dimension reconstruction

Three-dimensional reconstruction from a single two-dimensional image is important in the field of numerical simulation of porous media (including sandstones, composite, and alloys). Owing to limitations of computer storage space, the algorithm itself, and GPU memory, traditional algorithms and increasingly popular deep-learning algorithms cannot reconstruct complete real-size core images. To solve this problem, we propose a generative adversarial network dictionary-based super-dimension (GANDB-SD) algorithm that uses a BicycleGAN model to establish eight multimodal mappings of small-size structures. These mapping relations are used as the mapping dictionary when the super-dimension reconstruction framework is used to reconstruct the real core images without size limitation. Based on this algorithm, we further propose a three-dimensional co-occurrence matrix function as the loss function for the first time and propose a diversity preservation strategy to solve the pattern repetition problem during reconstruction. We performed an experiment to reconstruct a 5123 image, in which the morphological parameters and reconstruction time of different algorithms were compared. Results show that the algorithm has advantages in reconstruction accuracy and time. We further reconstructed 10003 real cores, and the statistical and morphological similarities between the reconstructed and target structures proved that the proposed algorithm is competent in the task of reconstructing real-world cores with unlimited size. We remark that our approach opens the door to a new methodology that incorporating machine learning/deep learning techniques into conventional super-dimension methods to address 3D reconstruction of unlimited-size real-world porous media issues in a wide range of related fields.

Edge-HetIoT defense against DDoS attack using learning techniques

The heterogeneous nature of the internet-of-thing (IoT) is gaining popularity and, simultaneously, faces rising security issues. The distributed denial of service (DDoS) attack is the most significant security threat addressed in the research. The research proposes edge-heterogeneous IoT (HetIoT) centric defense IDS that aids the HetIoT infrastructure in detecting and blocking victim traffic near the network edge. The Edge-HetIoT defense IDS helps to address significant issues such as performance and security due to proximity to the local network. The research focuses on six learning techniques, including five machine learning (ML) classifiers, namely, ID3, NB, RF, LR, and AdaBoost, and the proposed deep learning (DL)-based hybrid model (i.e., CNN+LSTM). These learning techniques are trained and tested using the real-time benchmark-dataset CICDDoS2019 and consider binary and multiclass (14 classes) classification. The performance is analyzed and evaluated against six classifiers to determine which classification model performs best in detecting and classifying various DDoS attacks. The proposed DL-based hybrid model outperforms when compared against ID3, NB, RF, LR, and AdaBoost. The proposed DL-based hybrid model successfully detects and classifies MSSQL, NetBIOS, TFTP, NTP, Syn, and Portmap attacks with 100% precision, recall, and f1-score. The overall weighted average precision, recall, and f1-score for the proposed DL-based hybrid model are 92%, 89%, and 90%, respectively.

Spatial federated learning approach for the sentiment analysis of stock news stored on blockchain

Sentiment analysis can be a useful tool in predicting stock market trends, as it allows us to gauge the overall sentiment towards a particular stock or company. When combined with news stored in a blockchain, sentiment analysis can provide a more accurate and trustworthy representation of the market. The aim of this paper is to study the spatial news and external events which disrupt the stock market movement as well as news analytics techniques to understand the impact of news by spatial sentiments on stock market movement. For the prediction of stock market trading decisions, a novel ensemble technique consisting of spatial federation of deep learning algorithms, machine learning algorithms and dictionary based approach is proposed to maintain privacy and geographic diversity. These learning techniques are federated into five groups and these groups are ranked as per the prediction accuracy of these models. A bit 1 or 0 is assigned for each federation thus creating a 5 bit pattern which can be used to predict stock market trading decision as strong buy, buy, hold, strong sell, sell.

Innovative Teaching and Learning Techniques in Nursing

The novice demand for nursing education in this era is to help reduce the competition among the students, promote cooperation and boost a healthy study environment by embracing contemporary teaching methods. In the modern teaching methods, the learner is the primary target. These methods are more activity-based and centre the learner’s mind; which involves them entirely in the process of learning. From a passive listener, the students move toward active participants. A cocktail of all the various methods of teaching should be carried out in the curriculum implementation in nursing academic institutions.

Comparative Review of Sculpture Programmes in Kwame Nkrumah University of Science and Technology and University Of Education, Winneba

The desire for Ghana's economic liberation through skill-oriented education to raise the required human capital for economic transformation makes Sculpture or Art essential to the Education system of Ghana and around the world. However, the majority of students who graduate from or complete the Sculpture section of Kwame Nkrumah University of Science and Technology and the University of Education, Winneba, deviate from practising Sculpture or Art into other fields. The effectiveness of any educational system in terms of learner academic performance is dependent on the quality of teaching and learning techniques, material resources, human resources, and the transition or history of the program or section. The study was designed to review the Sculpture programmes at Kwame Nkrumah University of Science and Technology (KNUST) and the University of Education, Winneba (UEW). Data elicited through questionnaires, interviews, and observation of the participants were mainly analyzed thematically and explicitly presented in percentages, tables, and photographs with detailed textual explanations. A total of 105 students, 7 lecturers, 3 non-teaching staff, and 2 heads of the department participated in the study. The KNUST Sculpture Programme has been run for 59 years and the UEW Sculpture Programme for 27 years in the same infrastructure inherited from colleges with considerably lower standards. The university administration and the government should come to an agreement to collaborate on improving the infrastructure, facilities, and equipment of both programmes.

Complete Integration of Team Project-Based Learning Into a Database Syllabus

Team project-based learning (TPBL) combines two learning techniques: 1) project-based learning (PBL) and 2) teamwork. This combination leverages the learning outcomes of both methods and places students in a real work situation where they must develop and solve a real project while working as a team. TPBL has been used in two advanced database subjects in Jaume I University (UJI)’s Computer Science degree program. This learning method was used for four years (academic years from 2018/2019 to 2021/2022) with positive outcomes. This study presents the project development, which includes teamwork formation, activities, timetable, and exercised learning competencies (both soft and specific). Further, the project’s results were evaluated from three different perspectives: 1) teamwork evaluation by teammates; 2) students’ opinions on the subject and project; and 3) subject final grades.

Bioorthogonal Metabolic Labeling Experiments to Introduce Undergraduate Students to Interdisciplinarity at the Interface of Chemistry and Biology

Chemists have always designed and deployed cross-disciplinary approaches that interface chemistry with other disciplines to proffer solutions to many human problems. We designed a laboratory experiment that implements chemistry concepts in a biological model to prime undergraduate students to appreciate interdisciplinarity as an approach to solving complex problems in contemporary society. The experiments can also be implemented with graduate students transitioning to chemical biology research. The experiment introduces students to classical and emerging chemistry and molecular biology techniques─mammalian cell culture, bioorthogonal metabolic labeling, azide–alkyne cycloaddition reaction (“click” chemistry), microplate photometry, gel electrophoresis, and far-western blot analysis─that are routinely used in research at the interface of chemistry and biology. The students cultured a Chinese hamster ovary cell line (CHO-K1) and metabolically labeled the cellular sialoglycans with azide groups. Next, they used a click chemistry reaction to bioconjugate a fluorogenic molecule to the labeled sialoglycans, enabling the detection of the glycans using microplate photometry or far-western blot analysis. The experiment created a context for a first-hand demonstration of bioorthogonal chemical reaction, a concept novel to most undergraduates with traditional chemistry (organic, inorganic, physical chemistry, and analytical) background. The experience allows the students to apply and connect organic chemistry, bioanalytical chemistry, and molecular biology concepts while learning techniques such as microplate reading, SDS PAGE, and western blot imaging that are indispensable to the growing fields of drug discovery, chemical toxicology, chemical biology, and molecular biology. This experiment is ideal for preparing and motivating students to pursue future research opportunities at the interface of chemistry and biology.

“Chinese core, global trend”: A critical discourse analysis of Chinese civics teachers’ perceptions on global citizenship education

ABSTRACT Globalization has stimulated widespread reforms of traditional citizenship education. Regional sociopolitical and cultural circumstances substantially influence global citizenship education (GCE) discourse. A more in-depth examination of the recontextualization of GCE in educational institutions is required to comprehend its inner power tensions and potential dilemmas. This study investigates how GCE is recontextualized in China’s civics curriculum through a critical discourse analysis of 17 civics teachers’ perceptions, considering the expanding global perspective on national curriculum in recent years. The analysis revealed a nation-centric view of GCE dominated by sociopolitical ideology and Confucian norms, communicated in complicity with localized constructivist teaching and learning approaches that tend to be co-opted as a tool for dissimulating and euphemizing official knowledge standing for nationalism in educational practice. The study discusses the epistemological paradox in Chinese teachers’ perceptions, shedding light on repressed teacher subjectivity and limited educational initiatives in the nationalist-dominated GCE. It advocates a critical and reflective examination of the educational practice of GCE and a better way of speaking GCE that avoids equating it with teaching and learning techniques while ignoring the extent to which these methods have shaken educational relationships and value assumptions.

(451) Human Rights and Sexual Health to Reproductive Health, A Research Project on Massive Open One line Courses (MOOCs)

Abstract Introduction Faced with alarming and persistent reproductive health indicators, the raise in refusal of oral contraception by young people and the decline in access to abortion in some developed countries, United states more precisely, UNESCO Chair is creating a mini MOOC in the MOOC Rights, sexual health in Educations, "What sexual health and human rights bring to reproductive health? Objective To raise awareness and advance knowledge, empower citizens and their practice in the area of human rights in relation to reproductive health. This includes a variety of aspirations: the protection of young people from sexual violence, infertility; the fight against gender inequality, incest, sexual mutilation, sexual exploitation, unsafe abortion. Methods This mini MOOC is composed of 6 mini modules with different formats: • What are the contributions of sexual health to reproductive health? • What are the contributions of human rights to reproductive health? • The desire for a child • Contraception, historical abortion and current issues • Reduction of unwanted pregnancies? • The role of man in reproductive health, a challenge for achieving the 2030 SDGs This mini-MOOC is blended-learning approach, at opening sessions of the MOOC, learners register on a social network (WhatsApp, Fb….) and create private groups to discuss and exchange regularly different materials (testimonies, useful links, files…). This learning technique, which mixes a top down and a horizontal approach, aims at creating a community of learners, a strong group entity that can positively impact public opinion to thrive change. Results Past MOOCs results 2021, 93% of learners were satisfied and would recommend UNESCOs’ MOOC. We have had more than 40,000 learners in total in only two years (2019-2021). More than one thousand members of our community on Facebook dedicated to the MOOC and more than one thousand participants of contests/polls and quizess on social media. Conclusions What we want to do with ISSM is create an intercultural MOOC available in English and/or more your local languages (Spanish?), that promotes reproductive health and human rights and reflects both our values and vision. A process of co-creation taking into account intercultural diversity as well as the United Nations human rights framework and the Sustainable Development Goals (SDGs) Disclosure No

Hybrid modeling for biopharmaceutical processes: advantages, opportunities, and implementation

Process models are mathematical formulations (essentially a set of equations) that try to represent the real system/process in a digital or virtual form. These are derived either based on fundamental physical laws often combined with empirical assumptions or learned based on data. The former has been existing for several decades in chemical and process engineering while the latter has recently received a lot of attention with the emergence of several artificial intelligence/machine learning techniques. Hybrid modeling is an emerging modeling paradigm that explores the synergy between existing these two paradigms, taking advantage of the existing process knowledge (or engineering know-how) and information disseminated by the collected data. Such an approach is especially suitable for systems and industries where data generation is significantly resource intensive while at the same time fundamentally not completely deciphered such as the processes involved in the biopharmaceutical pipeline. This technology could, in fact, be the enabler to meeting the demands and goals of several initiatives such as Quality by design, Process Analytical tools, and Pharma 4.0. In addition, it can aid in different process applications throughout process development and Chemistry, Manufacturing, and Control (CMC) to make it more strategic and efficient. This article focuses on providing a step-by-step guide to the different considerations to be made to develop a reliable and applicable hybrid model. In addition, the article aims at highlighting the need for such tools in the biopharmaceutical industry and summarizes the works that advocate its implications. Subsequently, the key qualities of hybrid modeling that make it a key enabler in the biopharmaceutical industry are elaborated with reference to the literature demonstrating such qualities.

A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening.

Over the past few years, many machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. Only a scoring function that accounts for the interatomic interactions involved in binding can accurately predict binding affinity on unseen molecules. However, many scoring functions make predictions based on data set biases rather than an understanding of the physics of binding. These scoring functions perform well when tested on similar targets to those in the training set but fail to generalize to dissimilar targets. To test what a machine learning-based scoring function has learned, input attribution, a technique for learning which features are important to a model when making a prediction on a particular data point, can be applied. If a model successfully learns something beyond data set biases, attribution should give insight into the important binding interactions that are taking place. We built a machine learning-based scoring function that aimed to avoid the influence of bias via thorough train and test data set filtering and show that it achieves comparable performance on the Comparative Assessment of Scoring Functions, 2016 (CASF-2016) benchmark to other leading methods. We then use the CASF-2016 test set to perform attribution and find that the bonds identified as important by PointVS, unlike those extracted from other scoring functions, have a high correlation with those found by a distance-based interaction profiler. We then show that attribution can be used to extract important binding pharmacophores from a given protein target when supplied with a number of bound structures. We use this information to perform fragment elaboration and see improvements in docking scores compared to using structural information from a traditional, data-based approach. This not only provides definitive proof that the scoring function has learned to identify some important binding interactions but also constitutes the first deep learning-based method for extracting structural information from a target for molecule design.

Classification of Gujarati Articles using Bernoulli Naïve Bayes Classifier and Extra-trees Classifier

On the internet, information technology generated massive amounts of data. Because this data was initially primarily in English, the majority of data mining research was conducted on English text documents. As internet usage grew, so did data in other languages such as Gujarati, Marathi, Tamil, Telugu, and Punjabi, among others. We present a text categorization method based on artificial text summarization of Gujarati Articles in this paper. For the classification of text documents, various learning techniques such as Naïve Bayes, Support Vector Machines, and Decision Trees are available. We gathered articles from various e-newspaper editorials. This paper focuses on a brief review of the various techniques and methods for Gujarati Articles Classification, so that research in Text Classification can be further explored using various classifier algorithms. The dataset, which contains 1604 documents from 8 different categories, is used by the system. The result shows that Stacking Classifier with Bernoulli Naïve Bayes Classifier and Extra-trees Classifier is efficient for Gujarati Articles.

PENGARUH TEKNIK PEMBELAJARAN TREFFINGER TERHADAP KEMAMPUAN BERPIKIR KREATI(KUASI EKSPERIMEN PADA SISWA KELAS X SMA DI BANDUNG)

This research is based on problems according to conditions in schools. The fact is that students look passive when teaching and learning activities take place, and students' creativity is not visible. Efforts are made to encourage students' creative minds by using the Treffinger Technique in learning. This research was conducted to determine the effect of Treffinger learning on students' creative thinking in class X at SMA Kristen 3 Bina Bakti and SMA N1 Parongpong Bandung. In this study, the method used was quasi-experimental, consisting of 56 students for the experimental and 54 for the control classes. The research data was collected through a written exam (pretest and posttest) with 6 essay questions. After analyzing the data using the univariate test, the results show that: 1. It was found that there was a difference between students' creative thinking using finger learning techniques which was higher than students using conventional learning; 2. It was found that there was a difference in the creative thinking of students who used the Trefinger learning technique between Bina Bakti 3 Christian High School and Parongpong 1 Public High School; 3. The type of school and the learning skills used to increase proficiency in creative thinking are separate.