Analysis of principles knowledge representation in information systems led to the necessity of improving the structuring knowledge. It is caused by the development of software component and new possibilities of information technologies. The article combines methodological aspects of structuring knowledge and effective usage of information resources which are designed for the scientific and methodological study of practical recommendations in Digital learning implementation. It is shown at the paper that one of the most important problems, which face the processing of knowledge or construction systems, is knowledge representation. The main idea of the paper is increasing the efficiency at the university learning process on the basis of a possible applications and rational structuring knowledge. The proposed concept of using information resources of Digital Learning is based on the idea of using the principles of abstraction, encapsulation, modularity, hierarchy, typing, concurrency preservation and implementation in such stages of this process as algorithmic support of knowledge structuring and structured transfer of knowledge to students. The topic of the article is unique as we try to see Digital learning in multidisciplinary aspects: education, mathematics, statistics. The article combines a theoretical approach to structuring knowledge that is based on the integrated usage of fuzzy semantic network, theory of predicates, Boolean functions, theory of complexity of network structures and practical aspects and ways of construction Digital Learning Systems at the University.

Target subject is a module called ‘AI Technology’, which applied the ideas of blended learning. Firstly, lecture-style teaching was conducted with presentation slides in order to explain the contents of a textbook. Secondly, students were required to do exercises and quizzes. By using the last eight weeks, they were asked to create presentation slides outside a class to introduce the up-to-date topics on artificial intelligence. These slides were mutually evaluated among them so that they developed their own slides based on the feedback before the tenth week of the course for the second round of mutual evaluations. Questionnaires concerning students’ understanding technical terms of the field and consciousness-raising towards competence were also conducted before and after the programs. The learning effects of a module in ‘AI Technology’ are compared with my previous research outcome of the module, ‘Artificial Intelligence’. The reasons of difference between both modules are discussed. This paper reports their results.

Studies have acknowledged computational thinking (CT) as an efficient approach for problem-solving particularly required in digital workplaces. This research aims to identify indicators for a holistic CT assessment instrument for undergraduate students. A three-round fuzzy Delphi study has been conducted to gain comprehensive opinions and consensus from undergraduate lecturers of computer science disciplines and experts from the information technology industry. In round 1, the experts judged a set of predefined indicators describing CT skills and attitudes identified from the literature, while rounds 2 and 3 focused on variables selection. The consensus was achieved on holistic CT, and the indicators are teamwork, communication, spiritual intelligence, generalization, problem-solving, algorithmic thinking, evaluation, abstraction, decomposition, and debugging. Results demonstrate the importance of attitudes in the process of solving a problem and suggest higher education institutions to consider holistic CT in preparing qualified future graduates. Many CT studies focused only on the skills of CT. This study outlines the assessment indicators that consider both CT skills and attitudes, particularly at the undergraduate level.

In the last decade, learning from computer-supported collaborative technologies has been combined with social media (SM) and this has gotten a lot of attention. Also, there is a growing body of literature that suggests that SM is gaining a lot of attention because it has the perceived pedagogical affordances that could be used as a potential tool for teaching and learning. These perceived pedagogical affordances allow people to interact, communicate, collaborate and share resources among others. Most of the studies published on SM in education have focused on higher education (colleges and universities) with a relatively small body of literature on secondary education. Despite the wide use of SM in education, its benefits are still not clear across studies. We conducted a systematic literature review using the EBSCOhost database. Screening of abstracts and full texts resulted in the selection of 10 papers for the review. Seven approaches to using SM in learning in high schools have been identified: (1) interaction, (2) information dissemination, (3) communication, (4) collaboration, (5) teaching, learning, and resource sharing, (6) socialization, and (7) entertainment. Most of the articles claimed that the educational use of SM has a strong positive effect on social skills, but the evidence presented was rather weak. Subject-specific outcomes were not in focus in using SM in education. All studies followed a constructivist philosophical perspective. Based on this we provide a theory-based scenario for using SM in learning social skills and subject-specific outcomes.

This research aimed to develop a web-based application for teacher candidate competence instruments to prepare professional teachers in the Industrial Revolution 4.0 (IR 4.0) era. Teacher candidate competencies consist of pedagogical, professional, social, and personality competences. This is a research and development with 8 stages, involving the development of instrument grids/ construct, focus group discussions, instrument item development, instrument validation, manual instrument testing, application development, application assessment by experts, application trial, and final revision of the application. The initial focus group discussions involved 9 experts, while the instrument validation involved 35 experts consisting of 21 experts for pedagogical and professional competences, 7 experts for social competences, 7 experts for the personality competences, and 4 media experts. The trial involved a total of 107 Mathematics, Indonesian, and English student teacher candidates. Expert validation was analyzed using the Aiken formula; application effectiveness and readability were described based on expert judgment, and discrimination tests on the results of social and personality competence tests between the study programs used the Multivariate Analysis of Variants. The results showed that there were no differences in social and personality competences between Mathematics, Indonesian, and English prospective teachers. The developed instruments for pedagogical, professional, personality, and social competences were deemed valid. The application has met the readability aspect and is scored well by experts with an average assessment rating of  .78. These results suggest that the application can be used by the government as a solution to assess teacher candidate competences in the IR 4.0 era.

The teaching and learning in Indonesian elementary schools focus both on students’ concept mastery and character development.  Teachers are encouraged to implement a learning model that integrates character values and yet promote learning interest.  This study was aimed at investigating the mathematics learning interest of grade three elementary school students through the integration of thematic learning with character education, referred to as the character-integrated thematic learning model. Using a quasi-experimental pretest-posttest control group design, this study involved 70 students and employed a questionnaire to obtain data, which were analyzed using descriptive and inferential statistical techniques. Descriptively, the average scores of students' learning interest before and after the implementation of the character-integrated thematic learning model are respectively 117.54 and 140.69 with the gain index of 0.44 in the fair category. While score obtained for thematic learning model are 116.11 and 120.23 with the gain index of 0.07 in the low category. The results of the statistical inference analysis using the independent sample t-test were obtained t-count of 4.98 > t-table of 1,667. This indicates that there has been a significant increase in students’ learning interest scores with the implementation of character-integrated thematic learning model. Thus, this learning model can be applied to pay attention to the development of student’s character which has an impact on increasing student’s learning interest.

One of the issues that hinder the students’ learning progress is the inability to construct an epistemological explanation of a scientific phenomenon. Four-tier multiple-choice (hereinafter, 4TMC) instrument and Partial-Credit Model were employed to elaborate on the diagnosis process of the aforementioned problem. This study was to develop and implement the four-tier multiple-choice instrument with Partial-Credit Model to evaluate students’ learning progress in explaining the conceptual change of state of matter. This research applied a development research referring to the test development model by Wilson. The data were obtained through development and validation techniques on 20 4TMC items tested to 427 students. On each item, the study applied diagnostic-summative assessment and certainty response index. The students’ conceptual understanding level was categorized based on the combination of their answer choices; the measurement generated Partial-Credit Model for 1 parameter logistic (IPL) data. Analysis of differences was based on the student level class using Analysis of Variants (One-way ANOVA). This study resulted in 20 valid and reliable 4TMC instruments. The result revealed that the integration of 4TMC test and Partial-Credit Model was effective to be treated as the instrument to measure students’ learning progress. One-way ANOVA test indicated the differences among the students’ competence based on the academic level. On top of that, it was discovered that low-ability students showed slow progress due to the lack of knowledge as well as a misconception in explaining the Concept of Change of State of Matter. All in all, the research regarded that the diagnostic information was necessary for teachers in prospective development of learning strategies and evaluation of science learning.

The article aims to develop a machine-learning algorithm that can predict student’s graduation in the Industrial Engineering course at the Federal University of Amazonas based on their performance data. The methodology makes use of an information package of 364 students with an admission period between 2007 and 2019, considering characteristics that can affect directly or indirectly in the graduation of each one, being: type of high school, number of semesters taken, grade-point average, lockouts, dropouts and course terminations. The data treatment considered the manual removal of several characteristics that did not add value to the output of the algorithm, resulting in a package composed of 2184 instances. Thus, the logistic regression, MLP and XGBoost models developed and compared could predict a binary output of graduation or non-graduation to each student using 30% of the dataset to test and 70% to train, so that was possible to identify a relationship between the six attributes explored and achieve, with the best model, 94.15% of accuracy on its predictions.

In this study we focus our research on the case analysis of an eleven-year-old boy and his close relationship with technology, specifically robotics. The methodology of the study is experimental in nature, with the aim of improving the subject's attention span through robotics, thereby favouring his educational process and, consequently, his overall development. To this end, the attitudes, and aptitudes that this technological tool has provided the subject with are evaluated over a period of four years. Three data collection instruments were selected: questionnaire, interview, and observation. Among the conclusions we highlight, on the one hand, that the older the age and the greater the interest in robotics, the greater the individual's attention span and greater psychomotor coordination, increasing the improvement in the educational process and in their daily life. On the other hand, robotics is an effective way of orienting knowledge towards the personal and educational sphere and can provide advantages in integral development.  

Modern technology is necessary and important for improving the quality of education. While machine learning algorithms to support students remain limited. Thus, it is necessary to inspire educational scholars and educational technologists. This research therefore has three main targets: to educate the holistic context of rural education management, to study the relationship of continuing education at the upper secondary level, and to construct an appropriate education program prediction model for high school students in a rural school. The data for research is the academic achievement data of 1,859 students from Manchasuksa School at Mancha Khiri District, Khon Kaen Province, Thailand, during the academic year 2015-2020. Research tools are separated into 2 sections. The first section is a basic statistical analysis step, it composes of frequency analysis, percentage analysis, mean analysis, and standard deviation analysis. Another section is the data mining analysis phase, which consists of discretization technique, XGBoost classification technique (Decision Tree, Gradient Boosted Trees, and Random Forest), confusion matrix performance analysis, and cross-validation performance analysis. At the end, the research results found that the reasonable distribution level of student achievement consisted of four clusters classified by academic achievement. All four clusters were modeled on predicting academic achievement for the next generation of students. In addition, there are four success models in this research. For future research, the researcher aims to develop an application to facilitate instruction for learners by integrating prediction models into the mobile application to promote the utilization of modern technology.

Learning evaluation is a complex task, and this study illustrates the Constructive Assessment of Learning as a complementary alternative to evaluate the computational cognition schema construction. The authors designed a mental representational task under the Chronometric Constructive Cognitive Learning Evaluation Model. Control and experimental groups performed a conceptual definition task based on the Natural Semantic Network technique (NSN). They defined ten target concepts related to computational cognition theory, using verbs, nouns, and adjectives as definers. Afterward, the participants rated the conceptual quality of each definer one by one, on a scale of one to ten; the higher the rating, the greater the quality of the definer to define the target concept. The results indicate that the NSN technique was sensitive to measuring and discriminating cognitive changes in knowledge structures produced by the specific learning of computational cognition theory. In contrast, the learning of broad psychology subjects produced general cognitive changes without organization related to the specific learning of the evaluated course. Data showed more sophisticated cognitive change patterns on the evaluated schema in the experimental group than in the control group. The findings of this study suggest that cognitive assessment techniques can be helpful in the formative assessment of learning and provide clear indicators of students' knowledge management skills.

This study aimed to compare and examine the effectiveness of interactive STEM learning and paper-and-pencil STEM learning in terms of mathematical literacy skills of elementary school students. This research is of a quasi-experimental type with a non-equivalent pretest-posttest control group design. Sampling was carried out on the elementary school populations in Bengkulu and South Sumatra Provinces in two stages. In the first stage, schools in rural and urban areas were selected, and in the second, classes in each school were randomly selected. The selected sample consisted of fifth-grade students of the Public Elementary School of Terawas, Musi Rawas, with an experimental class A (n = 20) and an experimental class B (n = 19), as well as fifth-grade students of the Public Elementary School of Bengkulu City, with an experimental class A (n = 25) and an experimental class B (n = 22). Data collection was conducted using mathematical literacy skills tests in reference to the PISA and Minimum Competency Assessment (level 1–3). Data analysis was performed using descriptive and inferential statistics; it employed an independent t-test for the comparative testing and an N-gain test for testing the effectiveness of STEM learning. The results showed that there were differences in math literacy skills between interactive STEM and paper-and-pencil STEM for students in urban schools, but not significantly different for students in rural schools. General STEM learning was effective in increasing the literacy of elementary school students, and interactive STEM in particular demonstrated the highest level of effectiveness in the urban school.

In recent years, educational robotics has gained ground in educational policy around the world, and primary education is no exception. However, there has not yet been a thorough synthesis of methodologically appropriate empirical research on the effects of robotics upon cognitive performance among primary school students, which this paper attempted to do. Following literature screening, a total of eight studies published between 2018 and 2022 with a sample size of 567 children met inclusion criteria and were meta-analyzed. Resultantly, a medium aggregate effect size in favor of robotics experiments emerged (standardized mean difference of .641), which was significantly higher compared to non-robotics learning (p <.01). No between-study heterogeneity was detected. Subgroup analysis revealed a slightly larger overall effect for interventions on first- to third-graders rather than those in grades 4-6. Additionally, the analysis indicates that in order to enhance cognitive abilities in primary students, robotics interventions should be no longer than four weeks and involve robot construction. Based on the findings, implications, and suggestions are outlined for future research and practice.

21st-century learning requires teachers and students to integrate literacy skills, scientific literacy, mathematics, reading, writing, and technology in the learning process. Students must have initiative, discipline, responsibility, confidence, motivation for independent learning, and the ability to think critically about the problems presented. This study aims to determine students' autonomous knowledge and critical thinking abilities (CTA) using mobile learning technology (MLT). This research is a quantitative study involving 83 students from four junior high schools in the city of Mataram. The data collection for independent learning and students' CTA was carried out by giving tests and non-tests to students. The test conducted was a written test in the form of a description of 10 questions covering indicators of CTA. The non-test was conducted by giving a student learning independence questionnaire with as many as 15 statements, including five indicators of learning independence. This quantitative research data analysis uses the Rash modeling application with the help of Ministep software. The analysis results show that the learning independence of male and female students in the four junior high schools obtained a percentage of 77.38% in the “good” category. Each indicator of learning independence accepts a percentage above 70%, which is in the excellent category. Meanwhile, the CTA of male and female students from the four junior high schools obtained 75.28% in the “good” category. Each indicator of CTA also gets a percentage of more than 70%, meaning that each indicator is in a good category.

Online learning has become increasingly popular, making the learning process more attractive. One of the most popular learning media is artificial intelligence (AI). However, students do not accept this technology at all. Therefore, this study examined the factors influencing accounting students' acceptance of AI in learning. The survey was conducted with 147 higher-education students who use AI as a learning medium. The data were analyzed using SmartPLS 4.0 with the partial least square approach. The results showed that perceived usefulness influenced behavioral intention to use and satisfaction. However, perceived ease of use was only significant for satisfaction. Similarly, perceived confidence must be consistent with intention. Although it may influence perceived usefulness, other constructs, such as AI quality and personal innovativeness, can increase students' perceptions of the benefits and convenience of adopting AI in learning. Thus, this study contributes to the development of the technology acceptance model (TAM) and the information systems success model and is helpful to scholars, especially in applying AI in learning. They need to pay attention to the quality of AI, such as the accuracy of the information produced. Thus, the need to control the information from the AI only serves as a reference without requiring you to trust it completely.

This study aimed to investigate the relationship between teacher professional development, quality of lecture design, student engagement, teacher technical skills, pedagogical content knowledge and teacher satisfaction in using Artificial Intelligence (AI)-Powered Facilitator for designing lectures. The study used a non-random sample technique, and 208 participants answered a survey via Google Form after one semester, using a 5-point Likert scale to rate their responses. The structural equation model was used to analyze the data, and six factors were included in the study. The study confirmed hypotheses that teacher professional development, quality of lecture design, student engagement, and pedagogical content knowledge have a positive effect on teacher satisfaction. However, the study also revealed that teacher technical skills have a negative effect on teacher satisfaction, and pedagogical content knowledge has no significant effect. The proposed conceptual model explained 55.7% of the variance in teacher satisfaction Theoretical and practical implications were also discussed. These findings provide insights into the factors that contribute to teacher satisfaction in utilizing AI-Powered Facilitator for designing lectures and could inform the development of effective teacher training programs.

This study sought to investigate the current state of innovative teaching research and identify emerging themes and trends in the field from 2013 to 2023. The Scopus database was searched for the term “innovative teaching,” resulting in 1005 documents. After manual screening, 903 articles were exported in the BibTeX format for further processing in Bibliometrix using three bibliometric analysis types: network analysis, science mapping, and performance analysis. Performance analysis revealed bursts in publication output in 2015 and 2021, with a moderate boost in 2018. Ten top-cited journal papers were identified. The citation rates were low between 2019 and 2021, but there has been an upturn since 2022. The top keywords included simulation and nursing education, and there was a shift in research topics from broad educational concepts to more specific approaches, such as e-learning. Innovative teaching has been predominantly investigated in higher education, particularly in nursing education, with themes like “teaching/learning strategies” suggesting an emphasis on enhancing teaching practices not just through technology infusion. This study can aid educators and researchers in staying current with innovative teaching developments and inform their teaching practices.

The pervasive development and momentous changes of internationalization in higher education have led to the acceleration of research on its key component – the curriculum. However, there has not been any comprehensive analysis of the research status of the internationalization of the curriculum (IoC). To address the gap, this study employs the bibliometric method to construct an intellectual structure of research on the topic. The data, retrieved from the Scopus Database, consisted of 386 publications. The extracted data were then analyzed using citation, co-authorship, and keyword co-occurrence analysis. The results reveal a significant growth in research volume during the last ten years and the domination of Global North in the geographical distribution of publications. Besides, the most prominent authors include those who introduced fundamental knowledge on the topic. The most cited works and the most popular publishing sources focus on various aspects of internationalization of higher education. They also show a multidisciplinary interest in the topic. Finally, concerning newly emerged themes of studies on IoC, “cultural competence” and “internationalization at home” are outstanding keywords. The research findings emphasize IoC as a potential research matter. Hence, this study is recommended as a starting point for future researchers when examining related subjects.

The central focus of this study is exploring the potential of Artificial Intelligence (AI) Chatbots in enhancing digital dialogue for students. The study investigates the key attributes of Chatbots that can contribute to the feasibility of facilitating digital dialogue to improve students' communication skills through discussions and dialogues. The study employed a descriptive method using a questionnaire to gather the perspectives of 35 educational experts on the use of AI Chatbots in digital dialogue skills. This study revealed that using AI Chatbots plays a crucial role in enhancing digital dialogue skills and can be effectively integrated into instructional practices to facilitate meaningful dialogue among students. Finally, the study recommends that educational technology specialists leverage new technologies, such as Al Chatbots to help improve student performance and facilitate digital dialogue in education.

The world eagerly anticipates advancements in AI technologies, with substantial ongoing research on the potential AI applications in the domain of education. The study aims to analyse publications about the possibilities of artificial intelligence (AI) within higher education, emphasising their bibliometric properties. The data was collected from the Scopus database, uncovering 775 publications on the subject of study from 2000 to 2022, using various keywords. Upon analysis, it was found that the frequency of publications in the study area has risen from 3 in 2000 to 314 in 2022. China and the United States emerged as the most influential countries regarding publications in this area. The findings revealed that “Education and Information Technologies” and the “International Journal of Emerging Technologies in Learning” were the most frequently published journals. “S. Slade” and “P. Prinsloo” received the most citations, making them highly effective researchers. The co-authorship network primarily comprised the United States, Saudi Arabia, the United Kingdom, and China. The emerging themes included machine learning, convolutional neural networks, curriculum, and higher education systems are co-occurred with AI. The continuous expansion of potential AI technologies in higher education calls for increased global collaboration based on shared democratic principles, reaping mutual advantages.