Learning Purposes Research Articles

Methodological characteristics in technology-mediated task-based language teaching research: Current practices and future directions

Abstract In the past two decades, the synergistic relationship among task-based language teaching (TBLT), instructed second language acquisition, and computer-assisted language learning has gained increasing interest.Technology-mediated TBLT combines these three research domains by integrating the use of technology with task-based approaches for second language (L2) learning purposes. Since the emergence of this framework, empirical studies have increasingly explored the incorporation of tasks with technology-mediated settings for L2 learning and teaching purposes. To understand the methodological characteristics of technology-mediated TBLT research to date, we conducted a systematic search and reviewed 254 technology-mediated TBLT studies published between 2000 and 2022 in peer-reviewed journals and book chapters. These studies were coded for methodological features, research foci, and types of technology. We further examined the role of technologies in task performance to identify their effectiveness in creating authentic tasks. The findings revealed that technology-mediated TBLT research investigated a rather limited scope of contexts, learner groups, and linguistic features, with little attention paid to evaluating the quality of task outcomes. The types of technology used were skewed toward computer-mediated communication. The results also showed that studies examined various interactional features, and the majority reported both quantitative and qualitative data. Furthermore, technologies were integrated into task design to create meaningful language use contexts. Based on these findings, we share suggestions for future technology-mediated TBLT research.

КУРС «УЧИМ РУССКИЙ ВМЕСТЕ. ПАДЕЖИ. (А1)» В ОБУЧЕНИИ ИНОСТРАННЫХ СТУДЕНТОВ

The article describes the course “Learning Russian together. Cases (A1)”, presented on the educational platform Stepik, its place in the educational space, novelty is presented, the purpose of learning and objectives are defined; Finally, the results of working with the course are presented. This course is a system of effective materials for teaching and supervising foreign students in the Russian language. The described online course allows you to perform all the main methodological functions: reference and information; demonstration, simulator function, controlling; imononic; modeling. The course has been tested for the 2022-2023 academic year on life models + online + offline. The course can be integrated into the educational process in the form of separate lessons or special modules that help introduce new material, reinforce it, or monitor it both online and offline.

The potential of ChatGPT technology in education: advantages, obstacles and future growth

<span lang="EN-US">Information and communication technology is becoming increasingly prevalent in our daily lives, with interactive communication modes such as social networks and instant messaging reaching unprecedented popularity. These tools are now widely utilised in various academic and research institutions by both faculty and students for communication and distance learning purposes. <a name="_Hlk155360394"></a>Chat generative pre-trained transformer (ChatGPT) and artificial intelligence tools hold the potential to revolutionise the way that students obtain knowledge and support. ChatGPT is a cutting-edge language technology capable of constructing intelligent, coherent texts, making it a valuable tool for writing and communication across different fields, including education. However, universities that incorporate ChatGPT as a teaching tool must address concerns regarding plagiarism and academic integrity. This investigation focuses on the advantages and obstacles of applying ChatGPT technology in the education field and its potential for future development. Findings reveal that through careful consideration of the ethical dilemmas and issues, academic institutions can leverage the maximum potential of ChatGPT to provide a more accessible, successful, and personalised learning experience for learners. The development prospects for ChatGPT appear promising, given its potential to grow and enhance its capabilities through on-going research and innovation.</span>

Improve Question Classification Genetic Algorithm Based Feature Selection and Convolution Neural Network

Natural Language Processing (NLP) approaches play a crucial role in classifying inquiries and comprehending human language in diverse applications. A Question Answering System (QAS) consists of three components are question processing, information retrieval, and answer selection. Question Answering Systems (QASs) are a distinct form of information retrieval. The most crucial aspect of QAS is deciding on the question type since it influences the other sections following. However, an important question-answering system requires a prominent question classification system. In the past, there are different methods to solve this problem, such as rule-based learning, and hybrid approaches. However, the problem with these methods is that the rules require a lot of effort to create and are very limited. In this study, the utilization of genetic algorithm and deep neural network techniques enhances the quality control problem-solving process. This research utilizes the UIUC dataset. This collection comprises 5452 questions designed for learning purposes and an additional 500 questions specifically intended for assessment. The suggested solution involves converting each query into a matrix, with each row representing the Word2vec of a word. Subsequently, a Genetic Algorithm (GA) is employed to identify the most optimal features. Ultimately, a Convolutional Neural Network is utilized for classification, yielding a remarkable accuracy of 98.2% in our experimentation with the question dataset.

Cooperative learning techniques for improving English of future security sector officers

The article is devoted to one of the urgent problems of teaching security sector cadets to speak English. Objective. In order to improve the level of formation of skills and abilities in speaking, we offer the use of cooperative techniques and methods of teaching speaking, precisely: Quick, quick trade, World Café Method, Timed Pair Share, and others. Results. We have justified, developed and used these techniques in the context of training sessions to teach lexical items and the competence in speaking for security sector cadets according to STANAG – Standardized Levels of English Proficiency. The purpose of cooperative learning is to perform tasks for teaching speaking in groups, pairs, quarters. Cooperative learning contributes to better assimilation of speech material. In addition, the teacher’s teaching activity is transferred into the cadet’s educational activity. Through cooperative learning cadets learn to work in a team, support each other, correct and monitor each other when needed. The teacher takes the role of the observer. Thus, cadets acquire skills, abilities and competences in speaking and the ability to cooperate, interact face-to-face, and think critically. Conclusions. Using cooperative methods of teaching speaking, cadets become more motivated to speak in English, everyone is a full participant in a monologue or polylogue, taking into account inclusiveness. Various methods of cooperative learning can be used at different stages for different purposes. In particular, the World Café method can be used at the final stage of learning a topic, when all cadets know the vocabulary of the topic and grammatical forms. The Quiz, quiz trade method is used at the stage of language material activation, to learn grammatical forms, vocabulary and the definitions of the words.

DigiTala – Moodle-sovellus suullisen kielitaidon automaattiseen arviointiin

This article presents an online tool developed by the DigiTala research project in 2022. The tool is the first one to provide comprehensive automated assessment of spontaneous L2 Finnish and Swedish speech. The Moodle-based tool records and analyzes L2 learners’ speech automatically and provides holistic as well as analytic feedback to learners. In addition to self-regulated learning purposes, the tool can be used to support human assessment in high-stakes tests and language courses. We discuss the benefits and weaknesses of the tool and automated speaking assessment in general from the perspectives of low-resourced languages as well as developers and users.

Treading water: new data on the impact of AI ethics information sessions in classics and ancient language pedagogy

Abstract Over 2023, many universities and policy organisations in the higher education (HE) sector are working to create guiding principles and guidelines for the use of generative artificial intelligence (AI) in HE Teaching and Learning (T&L). Despite these guidelines, students remain unsure if and how they should use AI. This article discusses the AI information sessions held over the Autumn 2023 term in the Department of Classics at the University of Reading, which aimed to provide students with the knowledge and tools to make informed judgements about using AI in their studies. These sessions discussed the benefits and drawbacks of generative AI, highlighting training data, content policy, environmental impact, and examples of potential uses. Staff and student participants were surveyed before and after these information sessions to gather their opinions surrounding AI use. Although at least 60% of participants had previously used generative AI, 80% of participants were apprehensive of or against using generative AI tools for learning purposes following the AI information sessions. By providing staff and students with the ethical considerations surrounding generative AI, they can make an informed judgement about using AI in their work without misplaced faith or excessive fear.

How do we measure? Perspectives for learning, steering, and accountability when measuring the utilisation of sports facilities

ABSTRACT Purpose/rationale We discuss how sports facility managers can use the concepts of bookings, usage, and attendance for learning, steering/controlling, and accountability purposes. Design/methodology/approach First, we present different approaches to the performance measurement of sports facilities and show how using data on bookings, usage, and attendance can illustrate the public value created. Using data collected from 321 indoor multi-use sports halls in Denmark, we then analyse data on bookings, usage, and accountability. Findings We conclude that bookings, usage, and attendance can be used for learning. However, when it comes to steering and accountability, it is primarily data on usage that are relevant. Practical implications Managers can use data on usage to work with learning, steering/controlling, and accountability. Research contribution Research has shown that managers do not sufficiently use data in their daily practice, and if they do use data, they primarily use data on bookings. Based on the data in this study, we suggest how sports facility managers can use data on usage and attendance for learning, steering, and accountability purposes. Originality value The research article combines theoretical insights with empirical findings to shed light on the complexities of measuring and managing the utilisation of sports-for-all facilities.

City Matters! A Dual-Target Cross-City Sequential POI Recommendation Model

Existing sequential POI recommendation methods overlook a fact that each city exhibits distinct characteristics and totally ignore the city signature. In this study, we claim that city matters in sequential POI recommendation and fully exploring city signature can highlight the characteristics of each city and facilitate cross-city complementary learning. To this end, we consider the two-city scenario and propose a d ual-target c ross-city s equential P OI r ecommendation model (DCSPR) to achieve the purpose of complementary learning across cities. On one hand, DCSPR respectively captures geographical and cultural characteristics for each city by mining intra-city regions and intra-city functions of POIs. On the other hand, DCSPR builds a transfer channel between cities based on intra-city functions, and adopts a novel transfer strategy to transfer useful cultural characteristics across cities by mining inter-city functions of POIs. Moreover, to utilize these captured characteristics for sequential POI recommendation, DCSPR involves a new region- and function-aware network for each city to learn transition patterns from multiple views. Extensive experiments conducted on two real-world datasets with four cities demonstrate the effectiveness of DCSPR .

Re-thinking housing inspection and regulation: using 360 technology as a new approach in environmental health practice training

Front-line professions such as environmental health require practitioners to demonstrate competence in practice-based skills typically gained only through on-the-job experience. However, practice opportunities have been more scarce, owing mainly to austerity but also to the impact of the COVID-19 pandemic, so necessitating novel approaches to teaching and learning. 360 technology has recently emerged as a possible solution, but its potential for use in an environmental health setting, such as housing inspection and regulation, is virtually unknown. This study involved the use of a 360-degree camera to record a simulated walkthrough and mock inspection of a two-storey house. These ‘photospheres’ were demonstrated to under- and postgraduate students, who were then surveyed online to explore their attitudes towards the use of this technology as a training method. Analysis of the questionnaire responses revealed four self-reported themes: experience of housing regulation and enforcement practice and training; experiences of 360 technology in housing inspection practice and enforcement; attitudes towards using 360 technology versus traditional photograph and filming methods for experiential learning, training and practice purposes; and attitudes towards the application of 360 technology for experiential learning and training purposes in housing inspection enforcement and regulation. The results indicated that students found this a helpful and flexible tool, particularly valuing its immersive nature, both for initial training and continuing professional development (CPD) in housing inspections and interventions. It has wider applicability to other frontline professional practices and requires further development to help shape new forms of training.

Bayesian inference of graph-based dependencies from mixed-type data

Mixed data comprise measurements of different types, with both categorical and continuous variables, and can be found in various areas, such as in life science or industrial processes. Inferring conditional independencies from the data is crucial to understand how these variables relate to each other. To this end, graphical models provide an effective framework, which adopts a graph-based representation of the joint distribution to encode such dependence relations. This framework has been extensively studied in the Gaussian and categorical settings separately; on the other hand, the literature addressing this problem in presence of mixed data is still narrow. We propose a Bayesian model for the analysis of mixed data based on the notion of Conditional Gaussian (CG) distribution. Our method is based on a canonical parameterization of the CG distribution, which allows for posterior inference of parameters indexing the (marginal) distributions of continuous and categorical variables, as well as expressing the interactions between the two types of variables. We derive the limiting Gaussian distributions, centered on the correct unknown value and with vanishing variance, for the Bayesian estimators of the canonical parameters expressing continuous, discrete and mixed interactions. In addition, we implement the proposed method for structure learning purposes, namely to infer the underlying graph of conditional independencies. When compared to alternative frequentist methods, our approach shows favorable results both in a simulation setting and in real-data applications, besides allowing for a coherent uncertainty quantification around parameter estimates.

The use of AI Chatbots in higher education: the problem of plagiarism

Background: The use of ChatGPT in the learning process is becoming a common practice. Researchers identify opportunities to improve the learning process using AI tools. At the same time, there are many unresolved problems and threats from the use of ChatGPT. These include unreliable information, false information, lack of references to primary sources, lack of intellectual property protection, and especially the problem of plagiarism in academic texts. Objectives: The purpose of the study is to summarise the results of published research on the benefits and threats of using ChatGPT in higher education and to analyse the experience of using AI to write academic assignments by university students in compliance with the requirements of academic integrity. Methods: A survey was conducted among Kyiv National Economic University named after Vadym Hetman (KNEU) students about their experience of using ChatGPT in performing academic tasks and the degree of satisfaction with this tool. Results: The survey involved 58 KNEU students. We have analysed how satisfied students are with using ChatGPT for different learning purposes. Students are most satisfied with using ChatGPT to quickly find information and translate texts. The majority of respondents said that ChatGPT does not always provide accurate and reliable information. Students also pointed to the problem of violating academic integrity when using ChatGPT to complete their assignments. Conclusions: The study shows the general advantages and disadvantages of using ChatGPT in higher education. Particular attention should be paid to the level of borrowing in academic texts prepared with the help of ChatGPT.

Artificial intelligence-based surrogate modeling for computational cost-effective optimization of hydrogen liquefaction process

The chemical processes are inherently complex, and obtaining desired product quality and high energy efficiency by optimizing the design variables is challenging. The progress in computing technologies can be a blessing since machine learning models can process the information and data for learning purposes and eventually help ease the simulation and optimization efforts. Against this backdrop, an artificial neural network-based surrogate model is developed in the present study and applied to the challenging process of hydrogen liquefaction. Optimization of hydrogen liquefaction processes is still an open challenge because of convergence issues and many design variables; only a handful of algorithms are considered successful. One of those algorithms, particle swarm optimization (PSO), is used here to compare computational effort and results accuracy. The percentage error of prediction of the ANN-based surrogate model compared to PSO was 4% for the minimum internal approach temperature, which is usually considered as a constraint, and 0.04% for specific energy consumption considered as the objective function. The optimization of the proposed model significantly reduced the time needed for PSO optimization of the existing model by more than 99.99%. As an extension of the current work, the ‘shallow' neural network model, with one hidden layer used in the present study, can be used to analyze the whole liquefaction process.

DeepCOVID: a deep learning approach for accurate COVID-19 detection in point-of-care lung ultrasound

Sickness still continued to spread through several countries when it first appeared in China. The number of COVID-19 cases is rising daily worldwide, posing a severe threat to the government and the populace. As a result of the virus’s rapid spread, doctors are having trouble recognizing positive cases. It is obvious that computer-based diagnosis must be developed to get results at a reasonable cost. The classic convolutional neural network (CNN) is used for this, utilizing the CT dataset, and the upgraded CNN model is used with the lung ultrasound (LUS) dataset. The CT and LUS COVID imaging datasets are compared in the model. The accuracy of both deep learning models is higher. We customized ResNet50, a pre-trained deep learning architecture, for a web application paradigm. First, we suggest a method for normalizing data that addresses its variability because it is collected in hospitals using various CT scanners and ultrasound machines. Second, we identify COVID-19 patients using U-Net segmentation and classification. The CNN architecture is added for deep learning purposes, and Res-Net 50 offers incredible accuracy.

Systematic Literature Review : The Trend Of Augmented Reality (AR) in Science Teaching and Learning in Primary School / Sorotan Literatur Bersistematik: Trend Realiti Terimbuh (AR) dalam Pembelajaran dan Pemudahcaraan Mata Pelajaran Sains sekolah Rendah

The rapid development of technology worldwide has led to significant changes in various fields, particularly in education. The emergence of Augmented Reality (AR) technology has garnered attention from researchers in assessing its effectiveness in the teaching and learning process. However, only a small number have systematically studied the trends in AR implementation in the teaching and learning of science, especially at the elementary school level. Therefore, this systematic literature review aims to identify the trends in AR in science teaching and learning from 2018 to 2023. This literature review utilizes the Meta-Analysis framework (PRISMA method). A total of 404 articles were identified through screening from the SCOPUS and WOS databases. Following the screening based on predefined criteria, only 16 articles were selected. Subsequently, these articles were analyzed, and the study findings were categorized into seven discussions regarding the use of AR, namely, general aspects, study findings, contributions of the studies, study designs, research instruments, science learning topics, and theories and models. It is hoped that the results of this review will benefit researchers and educators interested in the use of technology, especially AR, for teaching and learning purposes.

Developing and Validating a Rubric for Measuring Skills in Designing Science Experiments for Prospective Science Teachers

The Indonesian government mandates that science teachers must have competence in designing science experiments for learning purposes so that science content can be learned optimally by students while preparing them to have the ability to face the 21st century. This is development research that aims to develop a measurement instrument for science experiment design skills for prospective science teachers that meets good psychometric characteristics. The rubric development procedure refers to the Churches rubric development method, which consists of four stages: define, design, do, and debrief, involving 10 experts (lecturers and teachers) and 124 prospective science teachers as research participants. The results of exploratory and confirmatory factor analysis showed that the analytical rubric developed by measuring ten aspects, namely title, research objectives, relevant theories, variables, materials, equipment and instrumentation, method, an appropriate number of data, references, and systematic and technical writing was valid in content (CVI=.96), valid in construct (GFI=.94; RMSEA=.071; NFI=.99; CFI=1.00; PNFI=.91), and reliable (α=.968). The use of a standardized rubric certainly allows the assessment to provide consistent, accurate, and objective results and helps students understand what competencies they must achieve.

The Application of Audio Scripts for Micro Learning Meaning and Purpose in Enhancing Students' Learning Creativity

The rapid development of technology has an impact on the rapid exchange of information, including in the world of education. Providing provisions for creativity is needed as an effort to prepare graduate students to face rapid changes. This study aims to examine the effectiveness of the application of audio script micro learning meaning and purpose to improve student creativity. This research is an experimental research with a quantitative approach. This study used a pre-experimental design with one class of research samples. Hypothesis testing in this study used a one sample t-test to test the effectiveness of the application of the audio script for micro-learning the meaning and purpose of effective in increasing student learning creativity. The application of audio scripts in micro-learning courses has a positive impact on student creativity as evidenced by an increase in student creativity by 11.2. The increase in student creativity is reinforced by the results of the one samples t test on student creativity data showing a significance value of 0.00, which means that audio script media can improve student creativity skills. Therefore, in this study it can be concluded that the application of audio scripts of micro learning meaning and purpose is effective in increasing student learning creativity.

How secure memorization promotes expression: A longitudinal case study of performing Chopin’s Barcarolle, Op. 60

Experienced musicians can perform reliably from memory because they prepare performance cues (PCs) that allow them to recover when performance is disrupted. We describe a longitudinal case study in which a pianist (the second author) learned and performed Chopin’s Barcarolle, Op. 60, over a 12-year period. The pianist provided PC reports for 28 performances, starting a month before her first public performance. Shortly after each performance, she marked the features of the music that guided her playing (PCs) on a clean copy of the score, distinguishing five types of PC: structural, Schenkerian (tonal elaborations), expressive, interpretive, and basic. Structural PCs predominated at first and were later replaced by expressive and Schenkerian PCs. PCs were more frequent at structural boundaries than at other locations. This boundary effect was larger under typical, compared to atypical performance conditions, and increased over time, suggesting that the main function of PCs was promoting expression rather than guarding against mistakes. Although the pianist’s initial purpose in learning to use PCs was to have a safety net in case of memory failure, she was surprised to find that she enjoyed playing with PCs and believed they helped her to play more expressively.

Target Oriented Dynamic Adaption for Cross-Domain Few-Shot Learning

Few-shot learning has achieved satisfactory progress over the years, but these methods implicitly hypothesize that the data in the base (source) classes and novel (target) classes are sampled from the same data distribution (domain), which is often invalid in reality. The purpose of cross-domain few-shot learning (CD-FSL) is to successfully identify novel target classes with a small quantity of labeled instances on the target domain under the circumstance of domain shift between the source domain and the target domain. However, in CD-FSL, the knowledge learned by the network on the source domain often suffers from the situation of inadaptation when it is transferred to the target domain, since the instances on the source and target domains do not obey the same data distribution. To surmount this problem, we propose a Target Oriented Dynamic Adaption (TODA) model, which uses a tiny amount of target data to orient the network to dynamically adjust and adapt during training. Specifically, this work proposes a domain-specific adapter to ameliorate the network inadaptability issues in transfer to the target domain. The domain-specific adapter can make the extracted features more specific to the tasks in the target domain and reduce the impact of tasks in the source domain by combining them with the mainstream backbone network. In addition, we propose an adaptive optimization method in the network optimization process, which assigns different weights according to the importance of different optimization tasks. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our TODA method.

PENGARUH PENERAPAN METODE TIME GAMES TOURNAMENT (TGT) TERHADAP PENINGKATAN HASIL BELAJAR PESERTA DIDIK PADA MATERI INFAK DAN SEDEKAH DI KELAS V SD IT ULUL ALBAB JATI AGUNG LAMPUNG SELATAN

The purpose of this research is to investigate the influence of the Time Games Tournament (TGT) method on improving students' learning outcomes and their response towards the TGT method. The methodology employed in this study is a quantitative true experimental design, utilizing data collection techniques such as pre-tests and post-tests, as well as a student response questionnaire. Sample selection was conducted using random sampling techniques, with data analysis including Shapiro-Wilk normality tests, hypothesis testing using paired t-tests, and N-Gain analysis. The results of the study indicate that the TGT method significantly enhances students' learning outcomes, as evidenced by the normal distribution of the data. The paired sample t-test yielded a significance value of 0.000 < 0.05, indicating a substantial impact of the TGT method on students' learning outcomes. Additionally, the N-Gain results showed a mean value of 57.6%, suggesting a satisfactory level of effectiveness for instructional purposes. Regarding students' responses to the TGT method, 83% expressed their interest, stating they are "Interested" in using the TGT method for learning purposes. This indicates a positive reception of the method among the student population involved in the study.