Gonzalo Lorenzo

Department of Development Psychology and Teaching, University of Alicante, Spain // glledo@ua.es

Alejandro Lorenzo-Lledó

Department of Didactics and School Organisation, University of Granada, Spain // alorenzolledo@ugr.es 

Isabel Góméz-Barreto

Department of Pedagogy, University of Castilla la Mancha, Spain // isabelmaria.gomez@uclm.es

María Teresa Bejarano-Franco

Department of Pedagogy, University of Castilla la Mancha, Spain // mariateresa.bejarano@uclm.es

Andrea Cerdán-Chacón

Department of Development Psychology and Teaching, University of Alicante, Spain // andrea.cerdan@ua.es 

ABSTRACT: 

In recent years, robotics has emerged as a powerful and appealing instrument for autistic children, who struggle with communication. This study aims to analyze the improvement in social communication and interaction derived from the intervention with the NAO robot. A quantitative methodological approach has been adopted together with a quasi-experimental design based on a paired pre-test–post-test model. The participants were sixteen autistic children at the curriculum competence level of kindergarten (three-year-olds) and second grade and classified as ASD levels 1 and 3. The intervention consisted of eleven sessions, in which activities such as identifying the cause of a mood in a social context were proposed. The main instruments used for data collection were the modified checklist from the Early Start Denver Model (ESDM) and a field journal. The results showed substantial improvements in expressive communication, joint attention behavior, and social skills during interactions with both adults and peers. Cohen’s d values, close to or greater than 0.8, support these differences, indicating that the improvements observed are both visible and meaningful. Therefore, it can be concluded that the NAO robot may be a valuable tool for developing expressive communication, joint attention behavior, and social skills in interactions with both adults and peers. Consequently, it is recommended to progressively incorporate it into school environments to address these dimensions where autistic children require support. For future studies, it would be advisable to increase the sample size to potentially achieve more substantial improvements in other dimensions where no significant improvements were found.

Keywords: 

Robotics, NAO, Autism, Communication, Social interaction  

Xin Gong

College of Education, Capital Normal University, China // Gongxinjyjs@163.com

Shufan Yu

Department of Chinese and Bilingual Studies, Faculty of Humanities, The Hong Kong Polytechnic University, Hong Kong, China // yushufan1993@gmail.com

Xiu Guan

Institute of Education, Tsinghua University, China // guanx23@mails.tsinghua.edu.cn

Ailing Qiao

College of Education, Capital Normal University, China // Qiaoal@126.com

ABSTRACT: 

Prior studies have mainly focused on testing collaborative programming learning (CPL) patterns while neglecting the exploration of the dynamic evolution of social epistemic interaction patterns among different groups. Studying the social and epistemic network nature of learner interaction is crucial to understanding the CPL process. This study aims to explore the social epistemic interaction patterns and their evolutionary path among different groups. In this quasi-experimental design, 51 high school students were randomly allocated into 17 groups. Content analysis was used to analyze online collaborative conversations and interaction contents in the early, middle, and later periods of CPL. Social epistemic network and cluster analyses revealed three interaction patterns. The results showed that groups in cluster 1 were composed of core roles, which exhibited a multi-center balanced collaboration pattern (MBCP), and their social epistemic interaction levels showed a continuous upward trend; groups in cluster 2 included core, semi-core, and edge roles, respectively, and demonstrated a hierarchical center-led coordination pattern (HLCP) that initially gained but later declined in social epistemic interaction levels; groups in cluster 3 included core and edge roles, and displayed a single-center feedback cooperation pattern (SFCP), which remained consistently low in social epistemic interaction levels. Our findings emphasize the importance of CPL’s social epistemic interactions. By recognizing these patterns, educators can better facilitate meaningful student interactions, fostering deeper learning and social development.

Keywords: 

Collaborative programming learning, Social epistemic network analyses, Interaction patterns, Evolutionary path

Yicheng Sun

School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, China // sunyicheng@stu.xaut.edu.cn

Yi Wang

School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, China // School of Art and Design, Xi’an University of Technology, China // wy2005@xaut.edu.cn

Hanbo Yang

School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, China // yanghanbo@stu.xaut.edu.cn

Richard Suen

Faculty of Management, Shenzhen MSU-BIT University, China // 2120240147@smbu.edu.cn

ABSTRACT: 

With increasing student enrollment in higher education, timely and effective analysis of student feedback has become essential for course evaluation and instructional improvement. Traditional methods often struggle with complex feedback containing mixed sentiments and lack the adaptability to accommodate evolving feedback entries. To address these limitations, we propose a novel framework that integrates Large Language Models (LLMs) with an adaptive template-based cache for comprehensive feedback analysis. By leveraging the semantic understanding capabilities of LLMs and a dynamic caching mechanism, our approach continuously updates and refines templates in response to new feedback themes. It employs hierarchical matching for fine-grained classification and uses In-Context Learning (ICL) to identify semantically relevant templates, thereby enhancing summary generation while minimizing redundancy. The Feedback Summary Generation component synthesizes sentiment trends and category-level distributions into actionable reports to support data-driven decisions in educational settings. Empirical results demonstrate the superiority of our framework in terms of summary quality, information coverage, sentiment classification, and processing efficiency, offering a robust and adaptable solution for feedback analysis across diverse educational environments.

Keywords: 

Student feedback, Large language model, Adaptive template-based caching, In-context learning, Cache matching, Cache updating

Ashraf Sadat Ahadzadeh

School of Communication, Xiamen University Malaysia, Malaysia // ashrafsadat.ahadzadeh@xmu.edu.my

Shin Ling Wu

School of Psychology, Faculty of Medical and Life Sciences, Sunway University, Malaysia // shinling_wu@hotmail.com

Changsong Wang

School of Communication, Xiamen University Malaysia, Malaysia // cswang@xmu.edu.my

ABSTRACT: 

ChatGPT is the most cutting-edge AI language model with the potential to transform education. Building on the Theory of Planned Behaviour (TPB), this study aims to investigate how attitude, subjective norms, perceived behavioural control influence the intention to use ChatGPT for academic purposes, aiming to expand upon the existing literature. Furthermore, drawing upon the Cognitive Moral Development Theory, it explores the moderating effect of ethical judgments on the relationship between TPB constructs and the intention to use ChatGPT for academic purposes. A cross-sectional survey was utilised to gather 311 responses from university students studying in Malaysia. We discovered that the intention to use ChatGPT was positively influenced by TPB constructs while negatively affected by ethical judgments that discourage the use of ChatGPT for academic purposes. Furthermore, these ethical judgments did not attenuate the association between attitude and intention. Individuals with higher scores on ethical judgments disfavouring ChatGPT exhibit a weaker link between perceived behavioural control and their intention to use this technology. Ethical criticisms of ChatGPT usage strengthens the link between subjective norms and intention. These findings provide practical implications for incorporating generative AI tools in education, underscoring the ethical utilisation of these technologies, aiming to leverage their advantages while minimising their potential drawbacks. The lack of established causal links between variables in the study suggests the use of experimental designs in future research. Moreover, investigating ethics as a complex concept and fusing ideas from psychology and education may provide a deeper understanding of ChatGPT adoption.

Keywords: 

ChatGPT, Ethical judgments, Attitude, Subjective norms, Perceived behavioural control

Zongmin Yue

Shaanxi University of Science and Technology, China // Yuezongmin@sust.edu.cn

Xiaoyu Xu

Shaanxi University of Science and Technology, China // 17315381558@163.com

ABSTRACT: 

Student academic prediction aids educators in better understanding and supporting student learning. Educational Data Mining (EDM) and its algorithms are valuable tools for addressing this issue. While numerous models have been applied in EDM, most operate on small and medium-sized datasets. The baseline model Support Vector Machines with Sequential Minimum Optimization (SMO-SVM) excels with such datasets. However, few studies accurately and efficiently process large-scale datasets. In this study, we utilized the educational dataset from the Open University of the United Kingdom to construct prediction models for students’ “final_result.” Leveraging LASSO, XGboost, Deep Neural Networks (DNN), Random Forests, and the Baseline Model Support Vector Classifier, we conducted feature selection and classification phases. Additionally, the Synthetic Minority Over-sampling Technique (SMOTE) addressed data imbalance. Experimental results indicate that the proposed Least Absolute Shrinkage and Selection Operator - Random Forest (LASSO-RF) model is effective in predicting student performance on large-scale datasets. Furthermore, it effectively identifies students at risk of failure with an accuracy of nearly 80%, surpassing baseline models such as Support Vector Classifier and LASSO - Deep Neural Network (LASSO-DNN) in both balanced and unbalanced datasets. This demonstrates Random Forest’s ability to handle such data, enabling educators to provide accurate guidance to students at risk of dropout or failing to graduate.

Keywords: 

Machine learning, Student academic prediction, Educational data mining, Unbalanced dataset problem, Resampling techniques

Kuay-Keng Yang

National Pingtung University, Taiwan // kuaykeng@gmail.com

Huann-shyang Lin

National Sun Yat-sen University, Taiwan // huannlin@mail.nsysu.edu.tw

Thomas J. Smith

Northern Illinois University, USA // tjsmith@niu.edu

Ling Lee

National Sun Yat-sen University, Taiwan // s7113774@gmail.com

Zuway-R Hong

Chung Shan Medical University, Taiwan // a3803429@gmail.com

ABSTRACT: 

Although computer-based assessment has been widely used in a variety of educational communities, limited studies have focused on the effects of computer-based contextual assessment (CBCA) on elementary school children’s scientific competency. We explain in this study how a CBCA portal was developed and opened citywide to students in grades 4-6 as an out-of-classroom learning resource available during their winter break. Impacts of the CBCA portal use during 4th and 5th grades on their subsequent 6th grade scientific competency were investigated among a sample of N = 1409 Taiwanese students. Twenty volunteer teachers were trained in a professional learning community (PLC) to develop four CBCA test units for each of three grade levels (grades 4, 5, and 6). Rasch analysis was used to examine the psychometric properties of the data resulting from the 6th-grade instrument. Results from multilevel modeling suggested that increased prior engagement with the CBCA portal had positive, statistically significant effects on 6th grade scientific competency. Additionally, a moderating effect of student gender was observed on the competency of designing and evaluating scientific inquiry, where the impact of prior engagement with the portal was significantly greater for females than for males; in addition, the SES of school/community was a statistically significant, positive predictor of scientific competency. The results suggest that the CBCA portal can serve as a non-threatening learning resource to increase scientific competency particularly for female students with more frequent engagement in the portal. Discussion of the results, instruments, and research design, are provided, as well as suggestions for future studies.

Keywords: 

Computer-based assessment, Contextual assessment, Elementary school students, Gender differences, Scientific competency

Chien-Huey Sophie Chang

Department of Special Education, National Taiwan Normal University, Taiwan // sofchang@gapps.ntnu.edu.tw

Cheng-Tai Li

Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taiwan // ctli@mail.ntust.edu.tw

Hui-Chuan Chuang

Department of Special Education, National Taiwan Normal University, Taiwan // springhighbaby@gmail.com

Huei-Tse Hou

Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taiwan // hthou@mail.ntust.edu.tw

ABSTRACT: 

This study aimed to use a game-based learning (GBL) module with contextual scaffolding and reading pen technology for older adults with visually impairments (VI) to assist them in learning travel knowledge and related skills. This study applied a quasi-experimental design to investigate participants’ learning effectiveness, motivation, flow state, and behavior patterns. Correlations between the experimental group’s learning effectiveness, motivation, and flow state were also investigated. We recruited 32 older adults with VI with an average age of 65 years old. This study used the Treasure Taiwan Board Game with contextual scaffolding comprised of two gameplays. The first was a collaboration game; the second was an intergroup competitive game. The results revealed that the GBL module with the contextual scaffolding mechanism could facilitate participants’ learning effectiveness, motivation, and flow state. The correlation analysis showed that more active task engagement was associated with higher learning motivation. In the first gameplay, the results of the sequential analysis indicated that collaborative discussions and interpersonal interactions led to positive emotional behaviors. Furthermore, the reading pen scaffolding enhanced overall learning effectiveness. In the second gameplay, the inter-group competition mechanism promoted actively utilizing reading pens for learning knowledge, peer interaction, and group interaction. Additionally, it facilitated active thinking and strategic adjustment among groups.

Keywords: 

Board game-based learning, Reading pen technology, Contextual scaffolding, Older adults with visually impairments

Yanyi Wu

School of Public Affairs, Zhejiang University, China // Institute of China’s Science, Technology and Education Policy, Zhejiang University, China // yanyi.wu@hotmail.com

Xinyu Lu

School of Public Affairs, Zhejiang University, China // Institute of China’s Science, Technology and Education Policy, Zhejiang University, China // 12422079@zju.edu.cn

Chenghua Lin

School of Public Affairs, Zhejiang University, China // Institute of China’s Science, Technology and Education Policy, Zhejiang University, China // chlin@zju.edu.cn

ABSTRACT: 

Artificial intelligence (AI) integration challenges traditional definitions and assessments of interdisciplinary competence. Addressing the limitations of existing instruments that inadequately capture crucial human-AI dynamics, ethical considerations, and adaptive capabilities, this study develops and validates the Interdisciplinary Competence in AI-Enabled Learning (ICAIL) scale. Grounded in a synthesis of theories on interdisciplinarity, dynamic capabilities, and human-AI collaboration, the scale was refined through expert review and pilot testing, then validated with 872 students from Chinese universities using Exploratory and Confirmatory Factor Analyses. Results confirmed a robust five-dimensional structure: Knowledge Connectivity, Critical Interdisciplinary Analysis, AI-Driven Innovation, Collaborative Problem Solving, and Adaptive Transfer. The scale demonstrated high internal consistency reliability and excellent model fit, effectively measuring key competencies vital in AI-rich environments, such as critically evaluating AI outputs, engaging in iterative co-creation with AI, and strategically adapting tool usage. The findings suggest interdisciplinary competence in the AI era is best understood as a dynamic interplay between human critical agency and AI affordances. The validated ICAIL scale provides a valuable tool for educators and researchers to assess learning outcomes, inform the design of AI-enhanced pedagogies, and foster ethically responsible, adaptive learners prepared for complex socio-technical challenges. It advances both measurement methodology and the theoretical understanding of interdisciplinary learning in the age of AI.

Keywords: 

Interdisciplinary competence, AI-enabled learning, Scale validation, Educational technology, Human-AI collaboration

Shiyao Wang

College of Preschool Education, Capital Normal University, China // Szwsy2020@163.com

Lin Song

Faculty of Education, Northeast Normal University, China // Songl347@nenu.edu.cn

Yanming Liu

School of Public Health and Preventive Medicine, Monash University, Australia // Liam.Liu@monash.edu

ABSTRACT: 

With the assistance of deep learning, our study explores teacher-child conversations in a multi-dimensional way within different contexts (e.g., circle time, playtime, shared book reading time) in early childhood education (ECE) classrooms. In the ECE context, children’s interactions with teachers play a crucial role in supporting their language development; however, manually transcribing teacher-child conversations is both prohibitive and labour intensive. An analysis model for the observation is to detect important indicators in teacher-child conversations and then better support key dimensions of teacher practice for children’s vocabulary development. The accuracy of this model may make it possible to find out the linguistic forms (e.g., questions, comments, prompts), even types of utterances (e.g., open prompts, closed prompts), teacher feedback, and their link with children’s vocabulary gains and how teachers’ application of these strategies vary across contexts. The research finds out that our analysis model is more sensitive to indicators relevant to acoustic features (e.g., multi-turn) than those metrics related to semantics (e.g., requests and comments). Findings also indicate that the analysis model proved effective reliability in identifying elicitation and extension strategies based on its acoustic features using deep learning. Key findings could benefit to identify teachers’ practical practices in real ECE classrooms and assist them to better reflect on qualified teacher-child conversations.

Keywords: 

Teacher-child conversations, Preschool education, Deep learning, Analysis model, YAMNet

Sami Algouzi

Department of English, College of Languages and Translation, Najran University, Saudi Arabia // sami.algouzi@hotmail.com

Mohd Nazim

Department of English, College of Languages and Translation, Najran University, Saudi Arabia // nazimspeaking@yahoo.co.in

Ali Abbas Falah Alzubi

Department of English, College of Languages and Translation, Najran University, Saudi Arabia // aliyarmouk2004@gmail.com

ABSTRACT: 

The magnitude of vocabulary, including literary terms, needs a boost for English as a foreign language (EFL) students and Saudi learners draw no exception. Numerous studies have been conducted on EFL students’ vocabulary development using a variety of designs, approaches, methods, and instruments, including a blend of technology and digital media. This study aims to assess the effectiveness of a Blackboard (BB)-mediated flipped classroom instructional model in enhancing EFL students’ vocabulary in the drama classroom context. The researchers employed an experimental approach with two groups (experimental and control). Sixty English major students participated in the study, with one group undergoing an intervention utilizing a BB-mediated flipped classroom model, while the control group received instructions via traditional teaching methods. Data were collected through a set of instruments: a test and semi-structured interviews. The findings revealed a significant improvement in the post-test scores among participants in the experimental group compared to those in the control group. In addition, sustained retention of vocabulary was evident in the delayed test results. Further insights from content analysis revealed that the participants perceived the BB-mediated flipped learning environment to be instrumental in strengthening their drama vocabulary.

Keywords: 

Flipped classroom model, Blackboard, Drama, EFL students’ vocabulary, Literary terms

Xiao-Dong Wei

School of Educational Technology, Northwest Normal University, Lanzhou City, Gansu Province, China // wxd1633@163.com

Lei Wang

Department of Curriculum, Instruction, and Technology, Auburn University at Montgomery, Montgomery, AL, USA // lwang8@aum.edu

Lap-Kei Lee

School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China // lklee@hkmu.edu.hk

Rui-Xue Liu

School of Educational Technology, Northwest Normal University, Lanzhou City, Gansu Province, China // isnow0211@163.com

ABSTRACT: 

Spatial Reasoning (SR) skills are crucial for success in STEM fields and many professional careers. While Virtual Reality (VR) technologies offer promising avenues for developing SR skills across educational levels, current research lacks a structured pedagogical framework to integrate VR into school curricula effectively. To address this gap, this study proposed an educational framework combining Experiential Learning (EL) method with VR technologies to cultivate multifaceted SR skills, including Mental Rotation (MR), Spatial Orientation (SO), and Spatial Visualization (SV). Eighty elementary students were divided into EL-VR and traditional groups. The EL-VR group engaged with the creation of historical artifacts through an EL-based VR curriculum, while the traditional group used methods like teacher presentations and physical 3D shape cards. Data were collected through SR standard tests, learning achievement tests, perceptions of experiential activity scales, and semi-structured interviews. Results indicated that the EL-VR group significantly outperformed the traditional group in overall SR skills and in three dimensions: MR, SO, and SV. Furthermore, the EL-VR group demonstrated higher learning achievements and greater perceptions in experiential activities. These findings highlighted the effectiveness of a structured VR-EL framework for enhancing elementary students’ SR skills and suggested its potential for broader curricular integration.

Keywords: 

Elementary education, Virtual reality, Spatial reasoning skills, Experiential learning

Okan Yetişensoy

Faculty of Education, Bayburt University, Türkiye // okan.yetisensoy@gmail.com

ABSTRACT: 

This research aims to examine the educational potential of AI-assisted chatbots, the popular AI technology of recent years, in middle school disaster education. In this regard, six chatbot characters in human form, each with fictional stories, were developed for various disasters taught within the scope of 5th-grade Social Studies in Türkiye. While the practitioner teacher instructed on disaster-related curricular outcomes in the experimental group through activities that included the use of the relevant chatbots, the same curricular outcomes in the control group were taught using conventional pedagogical methods. The experimental results showed that the disaster knowledge of the experimental group students was significantly higher than that of the control group students. However, no significant difference was observed in students’ disaster awareness levels. In the semi-structured interviews, students expressed that chatbots promoted a more effective, interesting, and student-centered disaster education with their distinctive design and pedagogical features. However, they noted that chatbots had certain inadequacies in developing concrete experiences, as well as psychomotor skills necessary to address disaster risks. Findings from the study indicate that the relevant AI technology is effective in promoting disaster education processes, particularly in facilitating more effective learning. Nevertheless, achieving more complex competencies for mitigating risks requires a higher level of technological advancement. At this point, it is suggested that undertaking initiatives to combine chatbots with extended reality environments will be beneficial in making them a more effective tools in disaster education processes.

Keywords: 

Artificial intelligence, Chatbots, Disaster education, Middle schools, Social studies