Search results

For several decades, calligraphy has been popular among people in Japan, China, and even in the world. Traditionally, a teacher teaches how to write letters on a paper with a brush, and a student will imitate them by referring to the model letters. However, if a teacher is not available, this method will not be applicable either. This study aims to propose a calligraphy learning assistant system (CLAS) using projection mapping, which allows a student to learn calligraphy by him/herself.

By following the letter writing video of a teacher that is directly projected on the paper, a student is able to learn the stroke order and writing speed in addition to the letter shape. Moreover, the letter portion practice function is incorporated in CLAS to allow a learner to repeat practicing hard portions of each letter.

For evaluations, the authors implemented CLAS using Raspberry Pi and open-source software and asked students to use it. The results confirmed that CLAS is effective in improving calligraphy skills of novice students.

With CLAS, a student can practice calligraphy using a conventional brush, ink and paper at a desk while looking at the model letter writing of a teacher projected on the paper using projection mapping.

With rapid increase of Android devices and application systems, there is a strong demand for Android application programmers. A lot of schools are offering Android programming courses to meet this demand. However, Android programming can be different from the conventional one because it needs interactive functions through interfaces with users, which makes the study more difficult. This paper aims to propose an Android Programming Learning Assistance System, namely, APLAS, to assist the Java-based Android programming study and education.

By adopting the test-driven development method, APLAS is designed to achieve independent learning without the presence of teachers. Using JUnit and Robolectric, the answers from the students are automatically marked in APLAS. To cover extensive materials in Android programming, APLAS offers four stages where each stage involves several topics.

To evaluate the effectiveness of APLAS, we implemented the Unit Converter assignment that covers the first two topics, namely, Basic user interface in the first stage and basic activity in the second stage. Through solving the assignment, it is expected to learn basic application development. Forty novice students of an IT department in Indonesia were asked to solve both topics separately.

The results show that APLAS is useful and helpful for the self-study of Android programming, as they could complete codes with good execution performances.

To advance Java programming educations, the authors have developed a Java Programming Learning Assistant System (JPLAS) as a web application system. JPLAS provides fill-in-blank problems for novices to study the grammar and basic programming skills through code reading.

To select the blank elements with grammatically correct and unique answers from a given Java code, the authors have proposed the graph-based blank element selection algorithm. Then, the code for this problem should be highly readable to encourage novice students to study it. Because code readability can be improved by proper names for identifiers, the authors have also proposed the naming rule testing algorithm to identify codes with correct names.

In this paper, the authors present a fill-in-blank problem workbook by collecting suitable Java codes from textbooks and Web sites and applying these algorithms with several extensions. The workbook consists of 16 categories with a considerable number of problems that follow the conventional learning order of Java programming.

The proper set of ready-made fill-in-blank problems is effective in enhancing the usability of JPLAS both for teachers and students. For the preliminary evaluation, the authors assign a few problems to students. In coming semesters, the authors will use this workbook in the course to verify the adequacy of the proposal for novices.

The purpose of this paper is to investigate how learning solely via an assistance system influences work performance compared with learning with a combination of an assistance system and additional training. While the training literature has widely emphasised the positive role of on-the-job training, particularly for groups that are often underrepresented in formalised learning situations, organisational studies have stressed the risks that emerge when holistic process knowledge is lacking and how this negatively affects work performance. This study aims at testing these negative effects within an experimental design.

This paper uses a laboratory experimental design to investigate how assistance-system-guided learning influences the individuals’ work performance and work satisfaction compared with assistance-system-guided learning combined with theoretical learning of holistic process knowledge. Subjects were divided into two groups and assigned to two different settings. In the first setting, the participants used the assistance systems as an orientation and support tool right at the beginning and learned the production steps exclusively in this way. In the second setting, subjects received an additional 10-min introduction (treatment) at the beginning of the experiment, including detailed information regarding the entire work process.

This study provides evidence that learners provided with prior process knowledge achieve a better understanding of the work process leading to higher levels of productivity, quality and work satisfaction. At the same time, the authors found evidence for differences among workers’ ability to process and apply this additional information. Subjects with lower productivity levels faced more difficulties processing and applying additional process information.

Methodologically, this study goes beyond existing research on assistance systems by using a laboratory experimental design. Though the external validity of this method is limited by the artificial setting, it is a solid way of studying the impact of different usages of digital assistance systems in terms of training. Further research is required, however, including laboratory experiments with larger case numbers, company-level case studies and analyses of survey data, to further confirm the external validity of the findings of this study for the workplace.

This study provides some first evidence that holistic process knowledge, even in low-skill tasks, has an added value for the production process. This study contributes to firms' training policies by exploring new, digitalised ways of guided on-the-job training and demonstrates possible training benefits for people with lower levels of (initial) abilities and motivation.

This study indicates the advantage for companies and societies to invest in additional skills and training and points at the limitations of assistance systems. This paper also contributes to training policies by exploring new, digitalised ways of guided on-the-job training and demonstrates possible training benefits for people with lower levels of (initial) abilities and motivation.

This study extends existing research on digital assistance systems by investigating their role in job-related-training. This paper contributes to labour sociology and organisational research by confirming the importance of holistic process knowledge as opposed to a solely task-oriented digital introduction.

University distance learning is benefiting from the intensive use of Internet resources. The variety of information and services offered, together with different kinds of students, are impeding efficient use of this medium. In order to alleviate these problems and other associated ones, a Web site is being developed to facilitate personalised access to these resources. This site is based on three essential elements: new teaching materials to make the most of the medium, alternative teaching organisation adapted to the requirements of the different protagonists (students, lecturers and tutors) and an interactive system, WebDL, which facilitates personalised and adaptive access to information and communication requirements to each and every user at each and every moment. To date, the resources and material for three computer studies courses are being developed at Universidad Nacional de Educación a Distancia (UNED), whose delivery depends on the Artificial Intelligence Department.

To advance Java programming educations, the authors have developed a Web-based Java programming learning assistant system (JPLAS). It offers the element fill-in-blank problem (EFP) for novice students to study Java grammar and basic programming skills by filling in the missing elements in a source code. An EFP instance can be generated by selecting an appropriate code, and applying the blank element selection algorithm. As it is expected to cover broad grammar topics, a number of EFP instances have been generated. This paper aims to propose a recommendation function to guide a student solving the proper EFP instances among them.

This function considers the difficulty level of the EFP instance and the grammar topics that have been correctly answered by the student, and is implemented at the offline answering function of JPLAS using JavaScript so that students can use it even without the Internet connections.

To evaluate the effectiveness of the proposal, 85 EFP instances are prepared to cover various grammar topics, and are assigned to a total of 92 students in two universities in Myanmar and Indonesia to solve them using the recommendation function. Their solution results confirmed the effectiveness of the proposal.

The concept of the difficulty level for an EFP instance is newly defined for the proper recommendation, and the accuracy in terms of the average numbers of answer submission times among the students is verified.

This paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices.

A bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed.

Research on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning.

This study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers’ roles and teaching strategies.

This study aims to present the value trace problem (VTP) for Python programming self-study, by extending the works for Java programming learning assistant system. In total, 130 VTP instances are generated using Python codes in textbooks and websites that cover basic/advanced grammar topics, fundamental data structures and algorithms and two common library usages. Besides, assisting references on Python programming topics related to the VTP instances are introduced to assist novice learners in solving them efficiently.

PyPLAS offers the VTP to study grammar topics and library usage through code reading. A VTP instance asks a learner to trace the actual values of important variables or output messages in the given source code. The correctness of any answer is checked through string matching.

The applications to 48 undergraduate students in Myanmar and Indonesia confirm the validity of the proposal in Python programming self-studies by novice learners.

The applications to 48 undergraduate students in Myanmar and Indonesia confirm the validity of the proposal in Python programming self-studies by novice learners.

In data grids, replication has been regarded as a crucial optimization strategy. Computing tasks are performed on IoT gateways at the cloud edges to obtain a prompt response. So, investigating the data replication mechanisms in the IoT is necessary. Henceforth, a systematic survey of data replication strategies in IoT techniques is presented in this paper, and some suggestions are offered for the upcoming works. In two key classifications, various parameters dependent on the analysis of the prevalent approaches are considered. The pros and cons associated with chosen strategies have been explored, and the essential problems of them have been presented to boost the future of more effective data replication strategies. We have also discovered gaps in papers and provided solutions for them.

Progress in Information Technology (IT) growth has brought the Internet of Things (IoT) into life to take a vital role in our everyday lifestyles. Big IoT-generated data brings tremendous data processing challenges. One of the most challenging problems is data replication to improve fault-tolerance, reliability, and accessibility. In this way, if the primary data source fails, a replica can be swapped in immediately. There is a significant influence on the IoT created by data replication techniques, but no extensive and systematic research exists in this area. There is still no systematic and full way to address the relevant methods and evaluate them. Hence, in the present investigation, a literature review is indicated on the IoT-based data replication from papers published until 2021. Based on the given guidelines, chosen papers are reviewed. After establishing exclusion and inclusion criteria, an independent systematic search in Google Scholar, ACM, Scopus, Eric, Science Direct, Springer link, Emerald, Global ProQuest, and IEEE for relevant studies has been performed, and 21(6 paper analyzed in section 1 and 15 paper analyzed in section 3) papers have been analyzed.

The results showed that data replication mechanisms in the IoT algorithms outperform other algorithms regarding impressive network utilization, job implementation time, hit ratio, total replication number, and the portion of utilized storage in percentage. Although a few ideas have been suggested that fix different facets of IoT data management, we predict that there is still space for development and more study. Thus, in order to design innovative and more effective methods for future IoT-based structures, we explored open research directions in the domain of efficient data processing.

The present investigation encountered some drawbacks. First of all, only certain papers published in English were included. It is evident that some papers exist on data replication processes in the IoT written in other languages, but they were not included in our research. Next, the current report has only analyzed the mined based on data replication processes and IoT keyword discovery. The methods for data replication in the IoT would not be printed with keywords specified. In this review, the papers presented in national conferences and journals are neglected. In order to achieve the highest ability, this analysis contains papers from major global academic journals.

To appreciate the significance and accuracy of the data often produced by different entities, the article illustrates that data provenance is essential. The results contribute to providing strong suggestions for future IoT studies. To be able to view the data, administrators have to modify novel abilities. The current analysis will deal with the speed of publications and suggest the findings of research and experience as a future path for IoT data replication decision-makers.

In general, the rise in the knowledge degree of scientists, academics, and managers will enhance administrators' positive and consciously behavioral actions in handling IoT environments. We anticipate that the consequences of the present report could lead investigators to produce more efficient data replication methods in IoT regarding the data type and data volume.

This report provides a detailed literature review on data replication strategies relying on IoT. The lack of such papers increases the importance of this paper. Utilizing the responses to the study queries, data replication's primary purpose, current problems, study concepts, and processes in IoT are summarized exclusively. This approach will allow investigators to establish a more reliable IoT technique for data replication in the future. To the best of our understanding, our research is the first to provide a thorough overview and evaluation of the current solutions by categorizing them into static/dynamic replication and distributed replication subcategories. By outlining possible future study paths, we conclude the article.

Library assistants were originally considered to be professional librarians in the making, and were trained accordingly. With the expansion of libraries and librarianship, Britain's “apprenticeship” system of qualification gave way to formal library school education, and a new category of “non‐professional staff” was created, of people who were unwilling or unable to proceed to graduate‐level qualification. The development of non‐professional certificates of competence in the UK is described against parallel developments in the US, Canada and Australia; the COMLA training modules are also examined. The theoretical and practical issues surrounding training are discussed, training schemes and qualifications in the four countries analysed, and the relative merits of in‐house training and external certificate programmes argued.