The impact of humanoid robots on students' computational thinking

Writing is a fundamental skill linked closely with academic achievement, day‐to‐day communication, formal negotiations, and more. However, due to their lack of contextual experience, learning to write has been a demanding and complex cognitive process for most learners. As a result, learners struggle to exhibit positive learning behaviours and cognitive engagement in writing, let alone embrace deep and autonomous learning. To solve these problems, in the present study, a spherical video‐based virtual reality (SVVR) learning environment is developed to provide students with a contextual learning experience. Moreover, a peer feedback strategy is used to guide students in deep learning in writing. A quasi‐experimental study was conducted to verify the effectiveness of the proposed approach. A total of 79 students from one primary school in southeast China were recruited. The students were assigned to either the experimental group (EG) exposed to the peer‐feedback‐based SVVR (PF‐SVVR) approach or the control group (CG) exposed to the conventional SVVR (C‐SVVR) approach. The results show that PF‐SVVR had more positive effects than C‐SVVR in terms of students' writing performance, cognitive engagement and autonomous learning tendency. In addition, the PF‐SVVR approach was found to be more beneficial for triggering deep learning in writing than the C‐SVVR approach. This study further found that students in the PF‐SVVR group tended to exhibit less disorderly behaviours than those in the C‐SVVR group. Our study contributes to the prior literature by exploring the educational potential of the PF approach in the context of SVVR‐enabled writing learning. Practitioner notes What is already known about this topic Peer feedback approach provides opportunities for writing learners to regulate their longer‐term cognitive and behavioural competencies. Spherical video‐based virtual reality (SVVR) not only provides an authentic experiential learning context for learners, but it also significantly reduces the cost and the need for the high‐tech capabilities of traditional VR. What this paper adds A peer feedback‐based SVVR approach is proposed to promote students in learning to write. It was found that students' quality of learning to write can be improved by applying the PF‐SVVR approach in writing courses. Implications for practice and/or policy It is worth promoting the application of the peer feedback strategy and SVVR in school settings. The PF‐SVVR approach is useful for promoting young writing learners' cognitive engagement, autonomous learning tendency and deep learning. Further investigations on the effects of employing the PF‐SVVR approach in writing, with anonymous strategies in the process of giving or receiving peer feedback, are expected.

This study integrated the augmented reality (AR) technology into the “Coding Ocean” board game to provide players with real-time simulation of ship paths and learning scaffolds. Combined with Scratch block-based programming, an interactive learning environment is developed to assist elementary school students in learning coding skills from the unplugged board game to enhance their computational thinking concepts. The AR board game is focused on the programming concepts of sequential, and/or and loop. Through the process of treasure hunting, the basic concepts of computational thinking can be developed, i.e., abstraction, problem decomposition, pattern recognition and algorithmic thinking. In order to investigate the learning effectiveness of the AR board game on computational thinking and programming skills, a number of 51 third graders from an elementary school were recruited as research samples. The experimental group (n = 26) used the AR board game and the control group (n = 25) used the traditional board game for game-based learning. The experimental results indicate: (1) the learning effectiveness of computational thinking for the experimental group was significantly higher than that of the control group; (2) the learning achievement of the block-based programming skills for the experimental group was significantly higher than that of the control group; (3) the cognitive load of the experimental group was significantly lower than that of the control group. The AR technology can combine the unplugged board games with plugged learning modules to assist students in game-based learning, which is useful for enhancing computational thinking abilities while reducing the cognitive load.

This research investigates the implementation and challenges of incorporating computational thinking (CT) into science education. This research aims to develop a comprehensive framework to improve computational thinking skills in science education through the implementation of a deep learning curriculum, also integrated with computer programming, which leads to enhancing analytical and problem-solving skills for students to tackle real-world problems. This research used a systematic literature review with the PRISMA technique. Data sources used in this research are indexed by the Crossref database and then analyzed using the VOSviewer program. By examining 133 articles included in total, the research identifies key factors that affect effective teaching in science classrooms to enhance computational thinking. Through a series of case studies and empirical analysis, the study highlights obstacles faced in this educational approach. Findings suggest that while deep learning and computer programming integrated into science classrooms can influence the improvement of computational thinking skills and students’ understanding of scientific concepts and their application, challenges such as limited resources are addressed. The proposed framework offers practical strategies for policymakers and educators, especially science educators, in designing learning to overcome these challenges, aiming to prepare students for a technology-driven future better.

With the emergence of big data, cloud computing, and other technologies, artificial intelligence (AI) technology has set off a new wave in the field of education. The application of AI technology to deep learning in university teachers' teaching and students' learning processes is an innovative way to promote the quality of teaching and learning. This study proposed the deep learning-based assessment to measure whether students experienced an improvement in terms of their mastery of knowledge, development of abilities, and emotional experiences. It also used comparative analysis of pre-tests and post-tests through online questionnaires to test the results. The impact of technology on teachers' teaching and students' learning processes, identified the problems in the teaching and learning processes in the context of the application of AI technology, and proposed strategies for reforming and optimizing teaching and learning. It recommends the application of software and platforms, such as Waston and Knewton, under the orientation of AI technology to improve efficiency in teaching and learning, optimize course design, and engage students in deep learning. The contribution of this research is that the teaching and learning processes will be enhanced by the use of intelligent and efficient teaching models on the teachers' side and personalized and in-depth learning on the students' side. On the one hand, the findings are helpful for teachers to better grasp the actual conditions of in-class teaching in real time, carry out intelligent lesson preparations, enrich teaching methods, improve teaching efficiency, and achieve personalized and precision teaching. On the other hand, it also provides a space of intelligent support for students with different traits in terms of learning and effectively improves students' innovation ability, ultimately achieving the purpose of “artificial intelligence + education.”

Educational Robotics (ER) is a tool that can favor teamwork, stimulate creativity, and the ability to solve students' problems through the process of assembling and programming robots. In this sense, ER has been used to improve educational practices. There are several initiatives that seek to stimulate Computational Thinking (CT) to stand out. Although ER being used to develop CT, there are still no guidelines based on scientific studies that support this use. The objective of this Research Full Paper is to identify: a) the main pedagogical approaches are applied in classes with ER focused on developing students' CT; b) the central difficulties are encountered in classes with ER focused on developing students' CT; c) the CT skills are presents in classes with ER focused on developing students' CT; d) the instruments are used to assess students' CT during classes with ER; e) the students' profile who participated in classes with ER focused o on developing students' CT; f) the educational robotics technologies are adopted in classes focused on developing students' CT. To achieve these goals, we planned and carried out systematic mapping of the literature. The results show that, through ER, CT is approached through the teaching of programming languages in a collaborative way in Early Childhood and High School. The most CT skills observed are related to programming, such as sequencing, algorithm, loop, generalization, decomposition, and debugging. The main instruments for assessing students are tests, and the most significant difficulty is the limitation of class hours. Finally, LEGO® is the most used technology in class.

To accelerate the deep application of deep learning in text data processing, an English statistical translation system is established and applied to the question answering of humanoid robot. Firstly, the model of machine translation based on recursive neural network is implemented. A crawler system is established to collect English movie subtitle data. On this basis, an English subtitle translation system is designed. Then, combined with sentence embedding technology, the Particle Swarm Optimization (PSO) algorithm of meta-heuristic algorithm is adopted to locate the defects of translation software. A translation robot automatic question and answer interactive module is constructed. Additionally, the hybrid recommendation mechanism based on personalized learning is built using blockchain technology. Finally, the performance of translation model and software defect location model is evaluated. The results show that the Recurrent Neural Network (RNN) embedding algorithm has certain effect of word clustering. RNN embedded model has a strong ability to process short sentences. The strongest translated sentences are between 11 and 39 words long, while the weakest translated sentences are between 71 and 79 words long. Therefore, the model must strengthen the processing of long sentences, especially character—level input. The average sentence length is much longer than word-level input. The model based on PSO algorithm shows good accuracy in different data sets. This model averages better performance on Tomcat, standard widget toolkits, and Java development tool datasets than other comparison methods. The average reciprocal rank and average accuracy of the weight combination of PSO algorithm are very high. Moreover, this method is greatly affected by the dimension of the word embedding model, and the 300-dimension word embedding model has the best effect. To sum up, this study proposes a good statistical translation model for humanoid robot English translation, which lays the foundation for intelligent interaction between humanoid robots.

This study investigates the integration of technology acceptance and pedagogical deep learning in Islamic Religious Education (IRE), where deep learning is conceptualized as a pedagogical approach that emphasizes meaningful understanding, reflective thinking, and enjoyable engagement, rather than artificial intelligence-based deep learning. Although previous studies have extensively examined technology acceptance and deep learning independently, empirical studies integrating the Technology Acceptance Model (TAM) with pedagogical deep learning in the context of Islamic education are still limited. To address this gap, this study uses a quantitative survey design involving 321 teachers and students selected through simple random sampling from a population of 1,287 participants. Data were collected using a validated Likert scale questionnaire (Cronbach's Alpha = 0.874) and analyzed through descriptive statistics and simple linear regression using SPSS version 25. The results of the study indicate that acceptance of TAM-based learning media has a significant and positive effect on the implementation of pedagogical deep learning (R² = 0.542, t = 12.884, p <0.001), showing that 54.2% of the variance in deep learning practices is explained by technology acceptance. Descriptive analysis shows that Perceived Usefulness recorded the highest average score among TAM constructs (M = 4.26), while Meaningful Learning emerged as the most dominant dimension in pedagogical learning (M = 4.23). Theoretically, this study expands TAM by positioning technology acceptance as a driver of deep, reflective, and value-oriented pedagogical learning in Islamic education. Practically, these findings provide evidence-based guidance for educators and schools in designing technology-supported IRE that promotes meaningful, attentive, and enjoyable learning experiences.

There has been substantial research undertaken on the integration of computational thinking (CT) in K-12 mathematics education in recent years, particularly since 2018 when relevant systematic reviews were conducted on the topic. Many empirical studies in this area have yet to elaborate clearly and explicitly on how CT may support mathematics learning, or otherwise, in CT-based mathematics activities. Addressing this research gap, we conducted a systematic review on the integration of CT in K-12 mathematics education with a focus on CT-based mathematics instruction and students learning under such instruction. The Web of Science database was searched for in terms of studies published from 2006 to 2021, from which 24 articles were selected to provide illustrations of CT-based mathematics instruction and related student learning, and they were further analyzed according to education levels and contexts, programming tools, learning outcomes in CT and mathematics, and the mutual relationship between CT and mathematics learning. Among the results, this review found that geometrized programming and student-centered instructional approaches were facilitators of productive learning in CT and mathematics. Moreover, CT-based mathematics learning entails an interactive and cyclical process of reasoning mathematically and reasoning computationally, which can occur when: (1) applying mathematics to construct CT artefacts; (2) applying mathematics to anticipate and interpret CT outputs; and (3) generating new mathematical knowledge in parallel with the development of CT. The findings contribute to an in-depth understanding of what, and how, CT-based mathematics instruction impacts student learning in K-12 contexts.

Interactive multimedia is one of the most popular types of learning media and provides a significant increase in the success of the learning process. The achievement of mathematics learning objectives can be seen based on the level of students' computational thinking abilities and learning motivation. Therefore, this research was conducted to develop interactive multimedia that can improve students' computational thinking skills and learning motivation. This development research uses the ADDIE (Analysis, Design, Development, Implementation and Evaluation) model. The sample for this research was class IX students at Tunas Karya Batangkuis Middle School. The research results show that the interactive multimedia developed meets the criteria of being valid, practical and effective in improving students' computational thinking skills and learning motivation. Interactive multimedia obtained a validity value of 3.6 with very good criteria. Interactive multimedia obtained a practicality score of 3.4 (very good category) from the response questionnaire and 3.2 (good category) from the learning implementation questionnaire. The effectiveness of interactive multimedia can also be seen from several aspects, including students' classical completion level which reached 92%, achievement of learning objectives by 80%, very positive student responses, and good levels of student activity. Apart from that, the use of interactive multimedia also succeeded in increasing computational thinking abilities by 0.54 (medium category) and student learning motivation by 0.65 (medium category).

Computational thinking has become a crucial skill for students because it gives them the tools to solve complicated issues using organized methods. Implementing Problem-based learning and Design-based learning in science learning has significantly enhanced students' skills. This research aims to ascertain which of the two models is better for facilitating computational thinking skills among middle school students in science learning. The study used a sample of 69 middle school students selected through random sampling. This research employs a quasi-experimental, multi-group, post-test-only design. The instrument utilized in this research is a computational thinking skills test comprising five questions. The data were analyzed using one-way ANOVA to test the hypotheses. Tukey test results indicate that the mean difference score between the PBL and DBL groups is 1.2609, with a significance value greater than 0.05. The study confirms that Problem-based learning is more effective than Design-based learning in facilitating students' computational thinking skills. However, the difference between the two is not particularly noteworthy. PBL and DBL represent viable pedagogical approaches that can enhance middle school students' computational thinking skills in science learning. Keywords: problem-based learning, design-based learning, computational thinking skill. DOI: http://dx.doi.org/10.23960/jpmipa/v25i3.pp1214-1223

Migrating to an online format, forced by the Covid-19 pandemic, remarkably affected the teaching-learning process in the architecture studio, characterized by providing a rich social environment and permanent interaction among students and tutors. Therefore, this study aimed to identify the opportunities offered by the virtual learning environment to meet the challenge to continue the social learning process that fosters critical-reflexive thinking (CRT) and deep learning (DP) in architecture students at a private university in Lima. This qualitative case study research collected and validated data from the studio tutor reflexive logbook, class observations, 50 questionnaires and 15 interviews to second-year students. The results showed the challenges found and the effect of the new strategies used. Conclusions highlight the opportunities provided by the virtual learning environment for developing CRT and DL; the continuity of social learning and sense of community, reinforced by auto-reflexive and inter-reflexive dialogues; and the strengthening of tutor accompaniment for social, psychological and emotional support.

The University of Queensland, Australia, commenced an innovative Master of Dietetics Studies Program in 2009, using adult learning principles to foster critical thinking. Medical Nutrition Therapy courses consist of 3-hour case study workshops and assigned readings. Two educational interventions, online quizzes and a discussion board, were trialed to enhance supervisor and peer communication and feedback. Student survey, case study assignment, and the examination results were used to compare deep student learning in the same cohort of dietetic students pre- and postintervention and with previous dietetic student cohorts. Data revealed that the online quizzes encouraged completion of assigned readings that helped students work through the case studies, facilitating deep learning. Students wished to continue the online assessment (mean = 4.7 of 5). The discussion board was not well used because students felt that weekly communication with staff and other students was sufficient. Refinements will expand the online question pool and investigate the use of a less-structured communication forum.

Aim/Purpose: Our study is focused on prototyping, development, testing, and deployment of a new knowledge primitive for the humanoid robot assistant NAO, in order to enhance student visual learning by establishing a human-robot interaction. Background: This new primitive, utilizing a convolutional neural network (CNN), enables real-time recognition of handwritten digits captured by the NAO robot, a humanoid robot assistant developed by SoftBank Robotics. It is equipped with advanced capabilities, including a wide range of sensors, cameras, and interactive features. By integrating the proposed primitive, the NAO robot gains the ability to accurately recognize handwritten digits, contributing to improved student visual learning experiences. Methodology: Our developed primitive consists of the use of a convolutional neural network (CNN) so that the robot is able to recognize the handwriting of the digits present in the input image received in real-time. The NAO robot establishes interaction with the learners through a scenario based on a predefined assignment. In this scenario, NAO captures the digit handwritten by the learner via its camera, recognizes the digit using the deep learning model generated by the MNIST dataset, and announces to the learner the handwritten digit in the input image. The prototype is realized using the concept of a distributed system allowing the distribution of tasks in four different computing nodes. Contribution: Our research makes a significant contribution by equipping the humanoid robot NAO with a cognitive intelligence system through the integration of a new knowledge primitive based on handwriting digit recognition (HWDR). Our approach used to create and implement this primitive in the NAO robot is interesting and innovative, and presents a promising provision for enhancing the visual learning experience of children and young students with special needs, based on the use of distributed systems that divide the work using various components distributed over several nodes, coordinating their efforts to perform tasks more efficiently than a single device besides the NAO robot. Findings: We designed our model using specific parameters and a fully convolutional neural network architecture, which includes three residual depthwise separable convolutions, each followed by batch normalization and ReLU activation. To evaluate the performance of our model, we tested it on the MNIST dataset, where we achieved a remarkable accuracy, F1 score, and recall of 99%. An experiment was conducted to test our implemented primitive and see the effectiveness of this invention for enhancing visual learning in children with special needs. We developed a visual learning strategy based on the creation of engaging activities mediated by the NAO robot in an educational context. The results showed that participants achieved a strong commitment to the NAO robot, appreciating its ability to recognize handwritten digits and highlighting its promising potential to enrich visual learning experiences. Participants expressed a strong preference for teaching methods integrating assistive learning technologies, demonstrating the positive impact of our humanoid assistant robot on improving learning and visual intelligence in an educational environment. Recommendations for Practitioners: Encourage creativity and innovation in the field of robotics and special needs. This can lead to new and effective solutions that improve the lives of students with special needs. Recommendation for Researchers: Test and evaluate the proposed robotics solutions to ensure they are effective and making a positive impact. Use feedback from users, educators, and parents to refine and improve your solutions. Also, ensure that the robotics solutions are accessible to students with a range of abilities. This may involve designing solutions that are adjustable or providing alternative means of access. Impact on Society: As there are several ways to educate, there are multiple forms of learning. With the help of this learning procedure and strategy, the human teacher collaborates with the robot assistance NAO to improve visual learning among students. The findings of this research can serve as an application for the implementation of various pedagogical methods that will assist in meeting the needs of the majority of learners. Future Research: Our future research will concentrate on addressing the educational needs of students with special needs, enabling them to overcome their challenges and reach academic excellence in an inclusive environment. To achieve this goal, we plan to leverage the capabilities of social robots, which have emerged as a significant contributor to the field of human-robot interaction, particularly in facilitating inclusive education. These agents have proven to be effective in providing support to students with special needs, thereby enabling them to receive the education they need to succeed.

The application of deep learning-based learning media as a pedagogical approach that emphasizes meaningful learning and in-depth conceptual understanding in biology learning at the high school level is still relatively limited. This study aims to analyze the application of deep learning-based learning media in biology learning at the high school level and identify relevant media types to improve student motivation and in-depth conceptual understanding. The method used is a literature review by systematically analyzing 14 relevant scientific articles published between 2021-2025. Articles were obtained through Google Scholar, DOAJ, and ScienceDirect databases. The results of the literature review indicate three main patterns. First, deep learning focuses on meaningful, reflective, and contextual learning. Second, interactive digital media integrated with learning models has proven effective in increasing learning motivation, active engagement, and higher-order thinking skills. Third, the implementation of deep learning-based media still faces obstacles such as limited technological infrastructure, low teacher digital literacy, and resistance to conventional learning. The synthesis of these findings confirms that the effectiveness of deep learning-based media is highly dependent on the alignment between media design and the applied pedagogical strategy. Therefore, a learning media development strategy that integrates deep learning principles is needed. Thus, deep learning-based media has the potential to become a strategic tool in realizing meaningful, contextual, and 21st-century biology learning.

Learning to code and the acquisition of computational thinking by young children