Human Teacher Research Articles

Peran Guru dalam Pembentukan Karakter Peserta Didik

Education is the formation of character. Teachers have an important role in forming the basic character of students from an early age. Student knowledge and understanding of a material does not make students proficient in everyday life. Mastery of good skills and attitudes is an indicator of the success of a lesson as well as the success of educational goals. Teachers with good character have the opportunity to produce students with better character. Mastery of the method, and mastery of the substance of the material alone is not enough for the teacher. Being a human teacher and having compassion for his students as well as being an ethical mentor are important steps that a teacher must have to produce students with good character. This study aims to see (1) how the teacher's role as a model in the formation of student character, (2) how the challenges faced by the teacher as a model in the formation of student character. The method used in this study is a qualitative description in the form of literature studies, the internet. The results of this study indicate that teachers have complex duties and responsibilities towards achieving educational goals, teachers are not only required to master the knowledge to be taught, have knowledge and teaching skills, teachers are also required to have morals, character and personality that can be role models for students. The character values embedded in the teacher's role are religious, honest, tolerant, disciplined, hard work, creative, and responsible. This is in accordance with the character values according to the Government in the Research and Development agency, the Curriculum Center of the Ministry of National Education.

Literacy and STEM Teachers Adapt AI Ethics Curriculum

This article examines the ways secondary computer science and English Language Arts teachers in urban, suburban, and semi-rural schools adapted a project-based AI ethics curriculum to make it better fit their local contexts. AI ethics is an urgent topic with tangible consequences for youths’ current and future lives, but one that is rarely taught in schools. Few teachers have formal training in this area as it is an emerging field even at the university level. Exploring AI ethics involves examining biases related to race, gender, and social class, a challenging task for all teachers, and an unfamiliar one for most computer science teachers. It also requires teaching technical content which falls outside the comfort zone of most humanities teachers. Although none of our partner teachers had previously taught an AI ethics project, this study demonstrates that their expertise and experience in other domains played an essential role in providing high quality instruction. Teachers designed and redesigned tasks and incorporated texts and apps to ensure the AI ethics project would adhere to district and department level requirements; they led equity-focused inquiry in a way that both protected vulnerable students and accounted for local cultures and politics; and they adjusted technical content and developed hands-on computer science experiences to better challenge and engage their students. We use Mishra and Kohler’s TPACK framework to highlight the ways teachers leveraged their own expertise in some areas, while relying on materials and support from our research team in others, to create stronger learning experiences.

College Classroom Diversity as a Source of Scholarly Learning for Teachers

The international college classroom provides a rich setting for inclusive education. And while the vital role of teachers is acknowledged in the literature on inclusive education, most research is focused on student learning and on teacher-training. We reverse this lens and ask what benefits inclusive practices have for the teachers involved. This under-researched impact of inclusive teaching practices points to an untapped potential for academic scholarship. In Study 1, by analyzing their syllabi, we established that the college teachers in this study use inclusive practices. In Study 2, we interviewed these teachers about their teaching practices and show that the diverse classroom is a source of scholarly learning for them. We report differences that depend on the discipline taught: teachers in social sciences and humanities more often than those in sciences utilize and benefit from students’ diverse perspectives in the classroom. Recommendations for research and implications for practice are discussed.

Exploring the Role of ChatGPT as a Facilitator for Motivating Self-Directed Learning Among Adult Learners

This study explores the potential of ChatGPT as a virtual tutor to facilitate self-directed learning (SDL) among adult learners in asynchronous online contexts. Although SDL has been identified as a critical skill, factors such as the lack of skills to find resources and the absence of a supportive learning environment could impede adult learners’ ability to engage in SDL. By harnessing the power of AI, ChatGPT can assist adult learners in setting learning goals, locating available resources, designing personalized learning plans, monitoring their performance, and reflecting on their learning experiences, ultimately leading to the effective completion of SDL. This study also discusses several existing limitations and challenges, including the need for instructors to provide guidance on creating prompts to use ChatGPT effectively, the possibility of learners becoming reliant on ChatGPT, the significance of institutions to provide policies of proper use of ChatGPT, and the necessary for AI developers to continuously improve the algorithms and data used by the system to minimize the likelihood of providing incorrect and irrelevant information. Additionally, some questions are raised regarding the balance between the use of ChatGPT and engagement with human instructors to ensure optimal learning outcomes. This study aims to enlighten adult educators and practitioners towards the integration of ChatGPT in asynchronous online learning to facilitate adult learning.

DIDACTIC POTENTIAL OF HUMANITIES IN DEVELOPING TRANSFORMATIVE COMPETENCIES AMONG COMPUTER ENGINEERING AND INFORMATION TECHNOLOGY UNDERGRADUATES

In the recent decades harnessing the advanced information technologies and their tremendous potential to improve the quality of life for people all over the world is becoming an urgent need than a luxury. Knowing full well that as an innovative student-centred learning paradigm Education 4.0 creates favourable prerequisites for developing valuable professional skills, abilities and knowledge among Computer Engineering and Information Technology undergraduates, the researchers assumed that it also might be effective in preparing them for challenges of the contemporary world through developing their transformative competencies while studying the humanities. The paper aims to reveal the didactic potential of humanities for developing Computer Engineering and Information Technology undergraduates’ transformative competencies. The researchers carried out an exploratory study in five Ukrainian universities using an online survey conceived and developed by a team of researchers. A total of 275 respondents selected by means of a purposeful sampling method took part in the survey. The study found that humanities (1) are more likely to apply various learning activities which can be done both individually and in groups than technical disciplines and (2) have much broader potential for developing all key constructs of transformative competencies among Computer Engineering and Information Technology undergraduates than technical disciplines. The results enabled the researchers to formulate recommendations for university teachers of humanities on how to develop transformative competences among Computer Engineering and Information Technology undergraduates. The combination of challenge-based learning (CBL) and activity-based learning (ABL) is being considered as a major methodological approach of Education 4.0 for developing transformative competences among Computer Engineering and Information Technology undergraduates. Scientific novelty of research consists in revealing and describing the didactic potential of humanities for developing Computer Engineering and Information Technology undergraduates’ transformative competencies.

Technology-Enhanced Language Learning: A Meta-Analysis Study On English Language Teaching Tools

This meta-analysis study explores the effectiveness of technology-enhanced language learning in the context of English language teaching. Through a comprehensive review of relevant literature, various research studies were examined to gain insights into the impact and potential of technology in language learning. The findings highlight that the use of new technologies positively contributes to learners' language learning skills and enhances their overall proficiency. Mobile learning and ICT tools have been identified as effective resources that facilitate English language learning by providing accessible and efficient learning environments. Additionally, the research indicates that technology-supported language learning is as effective as learning with human teachers, indicating the potential of technology to offer personalized and tailored learning experiences. However, it is crucial to provide teachers with proper training and support to confidently integrate technology into their teaching practices. Educational institutions should offer comprehensive training programs and allocate sufficient time for teachers to enhance their technological skills and confidently utilize educational technologies. These findings underscore the importance of technology in enhancing language learning outcomes and emphasize the need for ongoing support and professional development for teachers in integrating technology into language teaching.

Personalization in Graphically Rich E-Learning Environments for K-6 Mathematics

This report describes a randomized controlled study that compared personalization of educational content based on neural networks to personalization by human experts. The study was conducted in a graphically rich online learning environment for elementary school mathematics, in which N = 135 fourth- and sixth-grade students learn via mathematical applets. The performance of students who followed the algorithm's recommendations was compared to that of students who followed an a-priori sequence constructed by the experts. While the algorithm only considered students' performance on past problems when recommending new problems, the human experts also took into consideration other factors related both to content and to the graphical interface. The findings reveal no significant differences in performance between the two groups, suggesting that the algorithm was as successful in preparing the students as human teachers. Herein we discuss the different mechanisms used to prepare each of the groups for the learning tasks and highlight the importance of the user interface in that process. Specifically, we find that applets involving supportive interactions, in which students' interactions were intended to help solve the problem but were optional, represented students' pre-knowledge, while applets entailing required interactions did not. We contribute to the field of personalization in education with new evidence of the advantages of a content sequencing algorithm—based on collaborative filtering ranking and implemented via a neural network—in a graphically rich environment as tested in authentic classrooms.

An Effective Language Convention Model Based on Deep Structured Learning and Natural Language Processing for Higher Education

Language translation is highly needed in the increasingly networked world. The writing of English essays is an essential competency for higher education students. For non-English students in China, this is especially important. They often represent large numbers of Chinese English as second language students. Therefore this paper introduced Deep Structured Learning with Natural language processing (DSL-NLP) has been proposed to convert the Chinese language to the English language for student learning. An automatic input tool for writing assistance will be beneficial in building the proposed DSL-NLP method. The relationship between textual attributes and input from human teachers and provide characteristics feedback system. This study explored natural language processing (NLP) to determine the interpretative actions required for a successful understanding of the literary text. NLP attempts to assist in achieving consistency in applied linguistics, improving classroom communication, optimizing learner attitudes and motivation, raising personality, facilitating personal development, and even changing students' perspectives toward life. Thus the experimental results show the DSL-NLP improves student learning in the English language, enhances student performance in education, and effectively increases the student career

Nline Tutoring System "Kak Eto Rabotaet: Algoritmy"

The article describes the features and architecture of intelligent online tutor "Kak eto rabotaet: Algoritmy", which is based on modelling subject-domain concepts on the comprehension level of Bloom's taxonomy. It is designed for learning basic algorithmic structures — sequences, selection statements, and loops — using the task of building an execution trace of the given algorithm. The tutor analyzes the student's solution step by step and displays explanatory messages for every error right after it happens; the error messages include the violated domain rules and their consequences in the specific situation. It can also hint the next correct step with textual explanation of why it is correct. It allows reliable learning of new concepts without control of each exercise from the human teachers which significantly increases the number of exercises learners can perform during a course. A control-flow diagram of the algorithm can be visualized; when the student makes an error, it is displayed with a red line on the diagram. The tutor is based on the formal model of subject domain implement using Apache Jena rules over RDF graph. Teachers can create learning problems using block-based interface and them, receiving links that can be sent to their students. Students can also use the tutor on their own to explore examples of interest to them.

O022 Real-time artificial intelligence instructor vs expert instruction in teaching of expert level tumour resection skills – a randomized controlled trial

Abstract Introduction Competency-based approach in surgical training still lacks objective quantifiable methodologies to assess surgical technical skills and train residents, a limitation that can be addressed with the adaptation of artificial intelligence (AI). In this randomized controlled, the efficacy of learning by a real-time intelligent instruction system was compared to learning with in-person human instructor-mediated training. Methods The study was ethics approved. Ninety-eight medical students performed five virtually simulated brain tumour resections, randomly allocated into three feedback groups: (1) no-real-time feedback, (2) real-time intelligent instruction, and (3) in-person human instruction. The first task was considered as baseline performance, done with no feedback. Group-1 received expert benchmark feedback only after each procedure. Group-2 was instructed in real-time by the AI system. After each task, the students were shown their error-video clips generated by this system alongside the expert-level demonstrations relating to each error. Group-3 was instructed by human instructors during the tasks. After each task, instructors summarized the areas of improvement and demonstrated how to expertly perform the tumour resections. Participant data in all tasks were scored by the AI system to assess learning. Results Students in Group-2 and Group-3 significantly improved their performance score by the third and second task, respectively (p<0.01, p=0.01), compared to the baseline performance. Group-2 achieved significantly higher scores than Group-3 in the final/fifth task (p<0.01). Conclusion AI-powered systems may increase efficiency in learning by providing objective, and action-oriented real-time feedback. Such systems may aid the shift towards competency-based surgical curricula.

How Do We Perceive Our Trainee Robots? Exploring the Impact of Robot Errors and Appearance When Performing Domestic Physical Tasks on Teachers’ Trust and Evaluations

To be successful, robots that can learn new tasks from humans should interact effectively with them while being trained, and humans should be able to trust the robots’ abilities after teaching. Typically, when human learners make mistakes, their teachers tolerate those errors, especially when students exhibit acceptable progress overall. But how do errors and appearance of a trainee robot affect human teachers’ trust while the robot is generally improving in performing a task? First, an online survey with 173 participants investigated perceived severity of robot errors in performing a cooking task. These findings were then used in an interactive online experiment with 138 participants, in which the participants were able to remotely teach their food preparation preferences to trainee robots with two different appearances. Compared with an untidy-looking robot, a tidy-looking robot was rated as more professional, without impacting participants’ trust. Furthermore, while larger errors at the end of iterative training had a greater impact, even a small error could significantly reduce trust in a trainee robot performing the domestic physical task of food preparation, regardless of the robot’s appearance. The present study extends human–robot interaction knowledge about teachers’ perception of trainee robots, particularly when teachers observe them accomplishing domestic physical tasks.

Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT

Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT

Learning data teaching strategies via knowledge tracing

Teaching plays a fundamental role in human learning. Typically, a human teaching strategy involves assessing a student’s knowledge progress for tailoring the teaching materials to enhance the learning progress. A human teacher can achieve this by tracing a student’s knowledge over essential learning concepts in a task. Albeit, such a teaching strategy is not well exploited yet in machine learning as current machine teaching methods tend to directly assess the progress of individual training samples without paying attention to the underlying learning concepts in a learning task. In this paper, we propose a novel method, called Knowledge Augmented Data Teaching (KADT), which can optimize a data teaching strategy for a student model by tracing its knowledge progress over multiple learning concepts in a learning task. Specifically, the KADT method incorporates a knowledge tracing model to dynamically capture the knowledge progress of a student model in terms of latent learning concepts. We further develop an attention-pooling mechanism to distill knowledge representations of a student model with respect to class labels, which enables to develop a data teaching strategy on critical training samples. We have evaluated the performance of the KADT method on four different machine learning tasks, including knowledge tracing, sentiment analysis, movie recommendation, and image classification. The KADT method consistently outperforms the state-of-the-art methods on all these tasks.

Multimodal Resources and Students’ Motivation in English for Specific Purposes

The aim of the research was to analyze and generalize the educational potential of multimodal resources in enhancing students’ motivation in learning English for Specific Purposes. The study was conducted following the theories of digital humanities pedagogy and multimodal pedagogy. It had a descriptive cross-sectional quantitative-based (pedagogical experiment; survey; questionnaire) and qualitative-based (observation of the participants in the educational process; comparison and generalization of pedagogical experience on the problem under research; analysis of the data collected in a statistically accurate manner; monographic method – to interpret the results obtained in a coherent, logical approach) design. The significance of the findings can redound, on the one hand, to modernizing the educational processes of English for Specific Purposes based on the adequate integration of multimodal resources and, on the other, contributing to efficiently developing university students’ multimodal communicative competence. The data collected can be exploited in further research into the issue of increasing the motivation of non-philological students to learn English for professional use. In the course of the pedagogical experiment, effective ways of perceiving and processing the information by students were identified, as well as the advantage of integrating multimodal resources into teaching English for Special Purposes. The scope for further study lies in the in-depth research into forming multimodal communicative competence of technical students in learning English for Specific Purposes.

Otherwise than teaching by artificial intelligence

Abstract Advances in digital technology are changing the methods of teaching and learning. In the course of this stream of changes, the emergence of artificial intelligence (AI) has become a topic of great interest and concern. The development of teaching AIs (artificial intelligences that teach), which can provide adaptive and personalized dialogue with students, has shifted the relationship between teachers and students, and has raised new questions regarding the role of teachers. The purpose of this paper is to rethink the meaning of the act of teaching, without denying its variety, and to reflect on the role of teachers in the relatively unfamiliar situation created by the emergence of AI teaching machines. To this end, I attempt, first, to re-examine the nature of teaching by considering two possible orientations: teaching as a return to homogeneity and teaching as exposure to heterogeneity. My purpose is to consider the characteristics and limitations of the teaching implemented by AI and to discuss the role of human teachers in overcoming these limitations. Second, I seek to reveal what it is for the teacher to speak, which is often understood as the most important aspect of teaching, again by way of two characterizations: the closed conversation, which converges on a conclusion, and the open conversation, oriented towards ethical responsibility. In doing this, I shall examine critically some characteristics of language used by AI teaching machines and suggest alternatives that might constitute openness to an ethical conversation. In furtherance of this, I shall consider the account of teaching that is central to the philosophy of Emmanuel Levinas.

Technology integration in emergency remote teaching: teachers' self-efficacy and sense of success.

Sense of success and self-efficacy regarding technology integration in teaching are among the most important factors that influence teachers' well-being and professional development, and may have a substantial impact on student learning. In this quantitative study (N = 735K-12 teachers in Israel), we explored the factors contributing to sense of success in emergency remote teaching and self-efficacy for integrating technology in teaching following the experience of teaching during COVID-19 days. We use decision-tree models to look at nuanced relations. Overall, our findings highlight the crucial-albeit not surprising-role of experience in teaching with technology as an important factor that promotes sense of success and self-efficacy. Going beyond this factor, we emphasize that emotional difficulties in times of emergency may serve as an important risk factor, and that taking a leading role in school may serve as an important protective factor. We also found an advantage to STEM and Language teachers, compared with Social Sciences and Humanities teachers. Following our findings, we conclude with a set of recommendations that could enhance school-based teaching and learning at large.

Dancing on the inside: A qualitative study on online dance learning with teacher-AI cooperation.

Recent technologies have extended opportunities for online dance learning by overcoming the limitations of space and time. However, dance teachers report that student-teacher interaction is more likely to be challenging in a distant and asynchronous learning environment than in a conventional dance class, such as a dance studio. To address this issue, we introduce DancingInside, an online dance learning system that encourages a beginner to learn dance by providing timely and sufficient feedback based on Teacher-AI cooperation. The proposed system incorporates an AI-based tutor agent(AI tutor, in short) that uses a 2D pose estimation approach to quantitatively estimate the similarity between a learner's and teacher's performance. We conducted a two-week user study with 11 students and 4 teachers. Our qualitative study results highlight that the AI tutor in DancingInside could support the reflection on a learner's practice and helpthe performance improvement withmultimodal feedback resources. The interview results also revealthat the human teacher's role isessential in complementing the AI feedback. We discuss our design and suggest potential implications for future AI-supported cooperative dance learning systems.

Gaussian-process-based robot learning from demonstration

AbstractLearning from demonstration allows to encode task constraints from observing the motion executed by a human teacher. We present a Gaussian-process-based learning from demonstration (LfD) approach that allows robots to learn manipulation skills from demonstrations of a human teacher. By exploiting the potential that Gaussian process (GP) models offer, we unify in a single, entirely GP-based framework, the main features required for a state-of-the-art LfD approach. We address how GP can be used to effectively learn a policy from trajectories in task space. To achieve an effective generalization across demonstrations, we propose the novel Task Completion Index (TCI) for temporal alignment of task trajectories. Also, our probabilistic GP-based representation allows encoding variability throughout the different phases of the task. Finally, we present a method to efficiently adapt the policy to fulfill new requirements and modulate the robot behavior as a function of task variability. This approach has been successfully tested in a real-world application, namely teaching a TIAGo robot to open different types of doors.

Knowledge Tracing: A Survey

Humans’ ability to transfer knowledge through teaching is one of the essential aspects for human intelligence. A human teacher can track the knowledge of students to customize the teaching on students’ needs. With the rise of online education platforms, there is a similar need for machines to track the knowledge of students and tailor their learning experience. This is known as the Knowledge Tracing (KT) problem in the literature. Effectively solving the KT problem would unlock the potential of computer-aided education applications such as intelligent tutoring systems, curriculum learning, and learning materials’ recommendation. Moreover, from a more general viewpoint, a student may represent any kind of intelligent agents including both human and artificial agents. Thus, the potential of KT can be extended to any machine teaching application scenarios which seek for customizing the learning experience for a student agent (i.e., a machine learning model). In this paper, we provide a comprehensive survey for the KT literature. We cover a broad range of methods starting from the early attempts to the recent state-of-the-art methods using deep learning, while highlighting the theoretical aspects of models and the characteristics of benchmark datasets. Besides these, we shed light on key modelling differences between closely related methods and summarize them in an easy-to-understand format. Finally, we discuss current research gaps in the KT literature and possible future research and application directions.

Moving beyond narrow definitions of gene drive: Diverse perspectives and frames enable substantive dialogue among science and humanities teachers in the United States and United Kingdom.

Gene drive is an emerging biotechnology with applications in global health, conservation and agriculture. Scientists are preparing for field trials, triggering debate about when and how to release gene-drive organisms. These decisions depend on public understandings of gene drive, which are shaped by language. While some studies on gene drive communication assume the need to persuade publics of expert definitions of gene drive, we highlight the importance of meaning-making in communication and engagement. We conducted focus groups with humanities and science teachers in the United Kingdom and United States to explore how different media framings stimulated discussions of gene drive. We found diversity in the value of these framings for public debate. Interestingly, the definition favoured by gene drive scientists was the least popular among participants. Rather than carefully curating language, we need opportunities for publics to make sense and negotiate the meanings of a technology on their own terms.