Xiangping Cui

Institute of Higher Education, Lanzhou University, China // cuixp@lzu.edu.cn

Jiangming Qian

Institute of Higher Education, Lanzhou University, China // qjm52413@zju.edu.cn

Juan Xu

School of Humanities, Communication University of China, China // xujuan@cuc.edu.cn

Shiping Li

Institute of Higher Education, Lanzhou University, China // shipli@lzu.edu.cn

Jun Shen

School of Computing and Information Technology, University of Wollongong, Australia // jshen@uow.edu.au

ABSTRACT: 

Digitalization of textbooks constitutes a pivotal aspect of the digital transformation of education, playing an indispensable role in the development of a modern education system. Currently, the construction of digital textbooks in higher education lacks a shareable and reusable model. Crowdsourcing is an innovative concept that leverages collective wisdom to improve the quality of resources, transcending organizational boundaries and facilitating the flow of knowledge. This study adopts the design-based research paradigm and comprehensively applies a variety of research methods to construct and implement a crowdsourcing-based construction model for digital textbooks in higher education. The model includes a knowledge subsystem, a process subsystem, and an organizational subsystem, covering crowdsourcing participants such as professionals, ordinary participants and artificial intelligence. The digital textbook developed based on this framework has demonstrated significant user satisfaction in terms of content, teaching, technical design, evaluation, opportunities for deep learning, accessibility, and security. It is expected that the construction model of the digital textbooks in high education built in this study can further enrich the research outcomes of digital textbooks, and provide a model reference for digital textbook construction in higher education.

Keywords: 

Digital textbooks, Crowdsourcing, Generative artificial intelligence, Model construction

Haojun Li

School of Education, Zhejiang University of Technology, China // 424389370@qq.com

Yaohan Chen

School of Education, Zhejiang University of Technology, China // 1635058484@qq.com

Weixia Liao

Wuhan First Vocational Education Center, China // 359579851@qq.com

Xuhui Wang

Yuyao Fourth Vocational and Technical School, China // zgdlhj@zjut.edu.cn

ABSTRACT: 

In the process of collaborative learning, effective grouping is the key to improving learning efficiency. A well-structured collaborative learning group can markedly improve the learning efficiency of both individuals and the group as a whole. Nonetheless, current grouping methods for collaborative learning frequently fall short of a comprehensive assessment of students’ knowledge proficiency, thereby failing to guarantee that group members’ knowledge structures are mutually complementary. Therefore, a grouping model combining knowledge complementarity and dynamic diagnosis is proposed. The grouping model is divided into five layers. Firstly, the learning objective analysis layer deconstructs the teaching content into several core knowledge points. On this basis, the dynamic knowledge diagnosis layer uses the improved deep knowledge tracking model DKVMN-KT to dynamically diagnose students’ mastery of core knowledge points. Then, in the personalized grouping layer, K-means algorithm is used to cluster the personalized grouping results. Furthermore, teachers were involved in the manual fine-tuning of the grouping results. Finally, the model forms a closed-loop feedback link through a deep interactive optimization layer to promote the iterative optimization of the collaborative learning grouping model. The experimental results indicate that the collaborative learning grouping model is helpful to effectively group students at the level of knowledge structure, and can improve the efficiency of collaborative learning, In order to guarantee more equitable and diverse grouping outcomes, this methodology promotes positive interplay among students, enabling them to mutually learn from one another and thereby enhance their comprehension of diverse knowledge points effectively.

Keywords: 

Collaborative learning, Knowledge mastery state diagnosis, Collaborative learning grouping model

Chi-Jung Sui

Research Center for Testing and Assessment, National Academy for Educational Research, Taiwan // chrosui@gmail.com

Chun-Yen Chang

Graduate Institute of Science Education, National Taiwan Normal University, Taiwan // Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taiwan // Department of Earth Sciences, National Taiwan Normal University, Taiwan // Graduate School of Education, Chung Yuan Christian University, Taiwan // Department of Biology, Universitas Negeri Malang, Indonesia // changcy@ntnu.edu.tw

ABSTRACT: 

The rapid advancement of generative AI (GenAI) technologies, such as ChatGPT, has significantly transformed the educational landscape. Previous research highlights the impact of teachers’ beliefs on their professional development and instructional methods. There is a growing need to align teachers’ beliefs with the latest technological advancements. This study investigates the relationships between teachers’ epistemic beliefs and their self-regulated learning under GenAI contexts. With a sample of 687 educators, this research employs exploratory and confirmatory factor analyses to validate the GenAI Epistemic Beliefs Questionnaire and the GenAI Self-regulated Learning Questionnaire. Structural equation modeling reveals that specific dimensions of GenAI epistemic beliefs, particularly the justification for GenAI-generated content and the structure of GenAI-generated content, significantly influence the planning phase of self-regulated learning. Teachers with sophisticated beliefs in justifying GenAI-generated content demonstrate more effective planning strategies. In contrast, those with structured beliefs in GenAI-generated content exhibit a more rigid approach to planning. However, these epistemic beliefs do not directly impact the adaptation phase of self-regulated learning; instead, planning serves as a full mediator, emphasizing the critical role of initial self-regulated learning planning in fostering adaptability. This study underscores the importance of understanding how educators’ beliefs about GenAI influence their self-regulated learning.

Keywords: 

Epistemic beliefs, Self-regulated learning, Generative AI, Structural model

Chien Chou

Institute of Education, National Yang Ming Chiao Tung University, Hsinchu, Taiwan // cchou@nycu.edu.tw

Min-Ling Hung

Teacher Education Center, Ming Chuan University, Taoyuan, Taiwan // mlhong@mail.mcu.edu.tw

Zoe Ruo-Yu Li

UCL Knowledge Lab, Institute of Education, University College London, London, UK // ruo-yu.li.23@ucl.ac.uk

ABSTRACT: 

This study developed and validated a scale for assessing online learning readiness among students aged 10 to 12 years in grades 4 to 6. The scale is called the Online Learning Readiness Scale for Elementary School (OLRS-E). To establish the reliability and validity of the scale, expert insights were gathered using the fuzzy Delphi method, and exploratory and confirmatory factor analyses were conducted on separate samples of 152 and 423 students, respectively. The OLRS-E comprises four key dimensions. Self-directed learning measures students’ motivation and ability to regulate their own learning behaviors. Communication self-efficacy assesses students’ confidence in expressing themselves and interacting in online environments. Family support captures the practical and emotional assistance students receive from caregivers during online learning. Technical support reflects students’ perceived competence in using digital tools and resolving basic technological issues. Among these, technical support yielded the highest score, indicating a high degree of technological readiness among Taiwanese elementary school students, followed by family support and communication self-efficacy. By contrast, self-directed learning yielded the lowest score, indicating a lower degree of self-directed learning readiness among these students. Overall, these findings of student readiness have major implications for parents and teachers regarding online learning efficacy.

Keywords: 

21st century skills/thinking skills, Distance education and online learning, K-12 education, Teacher training

Ali Derakhshan

Department of English Language and Literature, Faculty of Humanities and Social Sciences, Golestan University, Gorgan, Iran // a.derakhshan@gu.ac.ir

Di Zou

Department of English, Head, Centre for English and Additional Languages, Lingnan University, 8 Castle Peak Road, Tuen Mun, New Territories, Hong Kong // dizou@ln.edu.hk

Saeed Khazaie

Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran // saeed.khazaie@gmail.com

ABSTRACT: 

Recent advancements in Artificial Intelligence-powered robots have introduced a variety of educational technologies aimed at accommodating standard varieties of languages. However, the adaptiveness of the knowledge bases of these robots in establishing adaptive language learning at universities remains underexplored. This quasi-experimental study involved 1041 students from 13 academic disciplines with diverse first languages. Utilizing a parallel design, while students in the control group learned medical English skills through non-adaptive AI-powered robots, students in the experimental group learned the same skills through adaptive AI-powered robots. We assessed the effectiveness of learning medical English through Artificial Intelligence-powered robots with(out) adaptive knowledge bases, employing Objective Structured Video Exams for formative assessment. Additionally, the participants shared their perceptions of Artificial Intelligence-powered robots in medical English education through semi-structured interviews. Quantitative findings indicated that students using adaptive Artificial Intelligence-powered robots showed a greater sociolinguistic competence in medical English compared to those learning with non-adaptive robots. The qualitative findings suggested that the students in disciplines requiring direct interaction with patients and healthcare staff expressed a more positive perception of learning medical English through Artificial Intelligence-powered robots. While both groups acknowledged the value of the course, those engaging with adaptive systems demonstrated a great appreciation for the learning process and outcomes. Based on these results, we offer recommendations for researchers on effectively integrating Artificial Intelligence-powered robots into university language education, highlighting the potential for enhanced learning experiences through adaptive technologies.

Keywords: 

Adaptive learning, Artificial intelligence, Medical English, Robots, Sociolinguistic competence