Smart Classroom Research Articles

Smart Classrooms: Mengintegrasikan IoT dan AI untuk Pembelajaran yang Lebih Interaktif dan Terukur

This study examines the impact of integrating the Internet of Things (IoT) and Artificial Intelligence (AI) in Smart Classrooms on interactivity, personalization, and learning evaluation. Using a descriptive qualitative method, data were collected through in-depth interviews, participatory observation, and document analysis in schools implementing this technology. The findings show that IoT and AI enhance student and teacher interaction, enable personalized learning tailored to individual needs, and provide more measurable and data-driven evaluations. Although challenges in technology adaptation and data security were identified, this study affirms that Smart Classrooms have the potential to become a more effective and responsive model of future education, aligned with technological advancements in education. These findings are supported by relevant educational theories, such as Vygotsky’s social learning theory and Bloom’s data-driven evaluation. They are further reinforced by related literature demonstrating that these technologies can significantly enhance the quality of education.

Has the smart classroom revolutionized general education in Chinese universities? A comparative analysis of its influence path with that of traditional multimedia classrooms

ABSTRACT Although general education in China undertakes an important mission to balance the overspecialization of higher education and cultivate wise citizens, its realization is facing challenges. The emergence of the smart classroom (SC) has introduced new concepts to transform general education. However, the impact of SCs on student approaches to learning (SAL) and learning outcomes and the differences from the traditional multimedia classroom (TMC) have not yet been explored. Therefore, this study proposes a 3P learning system model to examine the relationships between physical instructional environments, teacher instruction, SAL, and the development of generic skills. A study survey was completed by 596 students from three Chinese universities, and the results indicated that the cultivation of generic skills was better in SCs than in TMCs. This primarily stemmed from the enhanced student engagement resulting from the multifaceted teaching functions present in SCs. In addition, several pieces of evidence suggested that the impact of general education teacher instruction was not as significant as anticipated in SCs. These findings provide valuable direction for reforming general education and improving teacher instruction.

Research on English Classroom Teaching Interactive Strategy for Higher Vocational Students in smart Classroom Environment

This study conducted experimental teaching of vocational college English listening and speaking courses in both smart classroom and simple multimedia classroom environments. Using an improved version of the Flipped Classroom Interaction Analysis System (SCIAS) and questionnaires, supplemented by S-T analysis as a complementary research method, the study conducted a comparative analysis of classroom records in terms of classroom structure, emotional atmosphere, interactive behaviors between teachers and students, interpersonal interactions, and interactions between teachers, students, and media technology. The results showed that compared to traditional classrooms, teaching in a smart classroom environment is more conducive to stimulating students' initiative, leading to deeper and more active classroom interactions.

A Dynamic Position Embedding-Based Model for Student Classroom Complete Meta-Action Recognition.

The precise recognition of entire classroom meta-actions is a crucial challenge for the tailored adaptive interpretation of student behavior, given the intricacy of these actions. This paper proposes a Dynamic Position Embedding-based Model for Student Classroom Complete Meta-Action Recognition (DPE-SAR) based on the Video Swin Transformer. The model utilizes a dynamic positional embedding technique to perform conditional positional encoding. Additionally, it incorporates a deep convolutional network to improve the parsing ability of the spatial structure of meta-actions. The full attention mechanism of ViT3D is used to extract the potential spatial features of actions and capture the global spatial-temporal information of meta-actions. The proposed model exhibits exceptional performance compared to baseline models in action recognition as observed in evaluations on public datasets and smart classroom meta-action recognition datasets. The experimental results confirm the superiority of the model in meta-action recognition.

Self-regulated learning strategies, self-efficacy, and learning engagement of EFL students in smart classrooms: A structural equation modeling analysis

Smart classrooms have become increasingly prevalent in the domain of foreign language education. However, students' EFL learning in smart classrooms, especially their self-regulated learning (SRL) strategies, self-efficacy, and learning engagement during the process, remains understudied. Given the significant impact of these factors on students' academic achievement, this study applied a quantitative method to first clarify their respective factor structures, on the basis of which the links between the three were examined via structural equation modeling (SEM). Participants were 1107 undergraduates from different universities in China who had engaged in EFL learning in smart classrooms. The factor analysis revealed a two-facet structure of students’ SRL strategies (goal-planning strategies and help-seeking and self-evaluation strategies), a similar dual-component structure of their self-efficacy (self-efficacy of communicating with instructors and self-efficacy of task completion), and a four-dimensional structure of their engagement (cognitive engagement, behavioral engagement, social engagement, and emotional engagement) during EFL learning in smart classrooms. Moreover, the SEM results showed that SRL strategies did not directly predict learning engagement, while self-efficacy fully mediated the relationship between SRL strategies and learning engagement. The implications and limitations of these findings were also discussed.

Deep Learning-Driven Optimization Strategies for Teaching Decisions in Smart Classrooms

With the rapid advancement of information technology, smart classrooms have increasingly become a vital component of modern education. By integrating technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data, smart classrooms provide a smart, efficient educational setting for teachers and students. However, the challenge of fully utilizing these technologies to enhance teaching effectiveness in smart classrooms remains unresolved. Existing research has highlighted the significant potential of deep learning in optimizing teaching decisions. However, its application faces challenges, including insufficient integration of technologies and limited effectiveness in practical implementations. The main focus of this study encompasses three parts: firstly, a biological neural network model targeted at optimizing teaching decisions, which emulates the mechanisms of biological neural networks to efficiently optimize teaching decisions; secondly, an active consciousness teaching decision model within smart classroom settings, which merges deep learning with theories of active consciousness to support dynamic, intelligent teaching decisions; and thirdly, a biologically inspired teaching process coordination network in smart classrooms, designed to optimize and coordinate educational processes based on biological principles. Through these investigations, this study provides both theoretical and practical support for optimizing teaching decisions in smart classrooms, offering significant academic value and practical application prospects.

Perception of Teachers and Students on the Use of Smart Classroom in Teaching and Learning Mathematics for Rwandan Secondary Schools in Nyarugenge District

Perception of Teachers and Students on the Use of Smart Classroom in Teaching and Learning Mathematics for Rwandan Secondary Schools in Nyarugenge District

DNN-Based Resource Recommendation for Ideology Theory Courses Online

Revamped IPE confronts static material constraints and outdated pedagogy, warranting integration of web resources and big data analytics for instructional innovation. Digital IPE adoption in vocational education optimizes online resource use, enhancing teaching effectiveness. Introducing CUPMF, a personalized learning model, we conduct empirical assessments on a large dataset (364,617+ entries) from Smart Classroom's cloud platform and public datasets, reflecting varied IPE scenarios. Comparative experiments against association rule, content-, tag-based, and collaborative filtering algorithms show CUPMF's superiority. It achieves a 11.61% F1 score boost over four alternatives for basic recommendations and outperforms Que Rec by 1.975%. Complexity-wise, CUPMF registers an 11.52% mean F1 score increment over four methods and 1.875% over Que Rec. Proven, CUPMF markedly improves IPE resource recommendation accuracy and efficacy, poised to transform personalized online vocational learning.

Identifying Student Behavior in Smart Classrooms: A Systematic Literature Mapping and Taxonomies

The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) in educational settings has revolutionized the traditional teaching-learning environment, giving rise to the concept of smart classrooms. This transformation not only enhances the monitoring of people and processes but also impacts the dynamics of student behaviors, which are influenced by various factors such as the teaching environment and teacher-student interactions. Recognizing and accurately identifying these behaviors is crucial for educators to implement effective interventions and improve learning outcomes. This study conducts a systematic literature mapping to examine the contemporary landscape of student behavior identification in smart classrooms. The analysis reveals a rich diversity of methodologies and approaches used in this area. Key contributions of this research include the development of taxonomies for 37 technologies deployed, 25 challenges faced, 58 student behaviors observed, as well as the identification of nine subjects benefiting from this data and 10 methodologies for processing behavioral information. By offering an overview of the technological and methodological underpinnings of behavior identification in smart classrooms, this study significantly propels forward the domain of smart classroom research, equipping educators and technologists with a holistic understanding of how to navigate and leverage the complexities associated with monitoring student behavior.

New Evidence on the Influence of Coloured Lighting on Students’ Cognitive Processes

Although there is a large amount of scientific literature on the impact of colour on learning, there is considerably less research on the impact of coloured lighting on learning. Numerous studies have explored this traditional approach, but their results are inconsistent and lack systematic rigour. However, the logical technological evolution towards coloured lighting remains a nascent field, with most research focusing on colour temperature (CCT) rather than coloured lighting per se. Studies such as this one highlight the benefits of coloured LED lighting on students’ cognitive processes, as it is a technology which can overcome the limitations of traditional colour applications by introducing the concept of “dynamic colour” as a key component of smart classrooms that can be integrated into artificial intelligence (AI)-based decision making. This study, conducted in a primary school classroom, employed a quasi-experimental design with a pre-test and a control group, and had a duration of three months. The effect of coloured lighting on students’ cognitive processes, such as attention, impulsivity control and figurative creativity, divided into four dimensions, was investigated. Descriptive, variance-based and comparative analyses of the overall results reveal that coloured light significantly influences cognitive processes, and some results are even generalisable across the variables analysed.

The Construction and Application of Smart Classrooms in China: A Literature Review Based on 93 Studies

The smart classroom, as a product of advances in information technology, has garnered wide attention in academia in recent years. Using the method of literature review, this article delves into the construction and application of smart classrooms in China on the basis of 93 prior studies retrieved from the database of the China National Knowledge Infrastructure. Research results show that there is a lack of uniform evaluation criteria for the construction of smart classrooms despite the prominent advancements in their systems and facilities, and that in the dimension of applications, there has been growing interest in the alignment between smart classroom technologies and instructional needs but a dearth of research on teaching activity design for smart classroom instruction. Recommendations are also proposed based on the issues detected, with a view to contributing to the widespread deployment and ongoing optimization of smart classrooms.

A model for assessing student satisfaction with smart classroom environment in higher education

A model for assessing student satisfaction with smart classroom environment in higher education

Ignored Subjectivity in the Informational Panopticon: Restricted Classroom Interaction Under Technical Discipline

Purpose This article aims to provide a portrait of classroom interactions supported by information technology and discuss the complex impact of technical elements on teaching and the time–space distance of student subjectivity in a technology-dominated world. Design/Approach/Methods Based on Foucault's panopticism, this study examines a smart classroom in East Asia to observe classroom teaching and learning processes; conducts semi-structured interviews with teachers, students, parents, and other stakeholders; and analyzes the hidden mechanism of the smart classroom on interaction from the perspective of perceivers. Findings The smart classroom has the distinctive structural features of a Panopticon. Artificial intelligence, which coalesces cultural capital through technological authority, has a significant capital advantage over students, producing a false spectacle of efficient learning. Panoramic surveillance causes the “backstage” of students’ roles to be gradually consumed by the “front” and makes students exhibit “make-work” behavior. Refined information management mechanisms produce invisible digital walls that separate interactive groups, making it difficult for students to obtain sufficient information about the gestures and behaviors of their classmates to interact effectively. Originality/Value This study reveals the ignorance of human subjectivity in the orientation of instrumental rationality by understanding the expressions of witnesses.

Student Motivation Analysis Based on Raising-Hand Videos.

In current smart classroom research, numerous studies focus on recognizing hand-raising, but few analyze the movements to interpret students' intentions. This limitation hinders teachers from utilizing this information to enhance the effectiveness of smart classroom teaching. Assistive teaching methods, including robotic and artificial intelligence teaching, require smart classroom systems to both recognize and thoroughly analyze hand-raising movements. This detailed analysis enables systems to provide targeted guidance based on students' hand-raising behavior. This study proposes a morphology-based analysis method to innovatively convert students' skeleton key point data into several one-dimensional time series. By analyzing these time series, this method offers a more detailed analysis of student hand-raising behavior, addressing the limitations of deep learning methods that cannot compare classroom hand-raising enthusiasm or establish a detailed database of such behavior. This method primarily utilizes a neural network to obtain students' skeleton estimation results, which are then converted into time series of several variables using the morphology-based analysis method. The YOLOX and HrNet models were employed to obtain the skeleton estimation results; YOLOX is an object detection model, while HrNet is a skeleton estimation model. This method successfully recognizes hand-raising actions and provides a detailed analysis of their speed and amplitude, effectively supplementing the coarse recognition capabilities of neural networks. The effectiveness of this method has been validated through experiments.

College English Smart Classroom Learning Model Utilizing Data Mining Technology

The integration of Internet technology and the collaborative development of smart classrooms is an essential step for colleges and universities to advance English instruction reform. This study utilized data mining (DM) technology to analyze the learning process in college English smart classrooms. The results indicate that the DM algorithm used in this study outperforms the other two algorithms across all metrics. After conducting 15 experiments, the centrality of the DM algorithm in this study reached 0.58, exceeding the ant colony algorithm's centrality of 0.42. The decision tree algorithm exhibited the lowest centrality, reaching a maximum value of only 0.39. Consequently, the methodology utilized in this study demonstrates a significant centrality within the classroom, indicating its suitability for investigating University English smart classroom learning. Hence, implementing a University English smart classroom learning model utilizing DM technology represents the primary approach to achieving intelligent education.

Micro-expression recognition based on euler video magnification and 3D residual network under imbalanced sample

A student’s verbal behavior plays a crucial role in education, while nonverbal behavior, such as micro-expressions, significantly improves teaching quality. To address the problem of small facial expression movements, imbalanced data categories, and lack of temporal information in static expressions, a micro-expression recognition method is proposed based on Eulerian Video Magnification (EVM) and a 3D Residual Network (3D ResNet) under imbalanced samples. Firstly, face detection in the Dlib library is used to locate the face in the micro-expression video sample and crop it. Secondly, the EVM is used to magnify the motion features in micro-expressions. Then, the 3D ResNet is used to extract spatio-temporal information from micro-expression video samples, and the Cyclical Focal Loss (CFL) function is introduced in the network training process to solve the class imbalance problem in micro-expression datasets. Finally, the roles of the EVM and the CFL function in recognizing micro-expressions by the 3D ResNet are analyzed. The experimental results on the Spontaneous Micro-expression Database (SMIC) and Chinese Academy of Sciences Micro-expression Database II (CASME II) demonstrate the effectiveness and superiority of this method. The proposed method can assist in teaching evaluation and promote the development of smart classrooms, and further research is needed on the storage and computing of the proposed method on devices.

Exploring Brazilian Teachers’ Perceptions and a priori Needs to Design Smart Classrooms

Exploring Brazilian Teachers’ Perceptions and a priori Needs to Design Smart Classrooms

Interrelationships among College Students' Perceptions of Smart Classroom Environments, Perceived Usefulness of Mobile Technology, Achievement Emotions, and Cognitive Engagement.

Cognitive engagement is a crucial factor that shapes successful learning outcomes, but our understanding of the factors that influence such engagement in the smart classroom context remains limited. This study aims to narrow this research gap by exploring the relationships among college students' perceptions of the smart learning environment, perceived usefulness of mobile technology, achievement emotions, and cognitive engagement. A total of 1293 college students completed an online questionnaire survey, and 1076 valid responses were received. Structural equation modeling was used to analyze the interrelationships among these factors. The results revealed that students' perceptions of the smart classroom environment and perceived usefulness of mobile technology as well as two achievement emotions (pride and anxiety) significantly impact cognitive engagement. Both pride and anxiety act as mediators in the relationships among perceptions of smart classroom environments, the perceived usefulness of mobile technology, and cognitive engagement, in which context the mediating effect of pride is stronger than that of anxiety. These findings have practical implications for instructors, who should focus on implementing strategies that promote positive achievement emotions when students use mobile technology in smart classrooms. Additionally, these findings can inform the design and construction of smart classroom environments. Moreover, our study has limitations due to reliance on online data collection and self-reported data, which may introduce biases and measurement errors. Future research should incorporate multimodal data and advanced technologies for a comprehensive assessment to better understand students' engagement in smart learning environments, while also considering individual factors and the educational context to enhance the effectiveness of mobile technology in supporting students' emotions and achievement.

그린스마트 미래학교의 저학년 교실공간 특성에 관한 연구

In July 2020, the Ministry of Education announced the 'Green Smart Future School' project plan as the representative task of the Korean version of the New Deal. The Green Smart Future School project is a project promoted in the form of 'smart classroom', 'green school', and 'connection with local communities' based on 'space innovation', and 'safety' was added in 'Green Smart Future School 2.0' in 2022. In this study, the characteristics of the changed classroom space were analyzed through the results of the spatial innovation project conducted by the Daegu City Office of Education. Based on these results, the direction of reorganization of classroom space for the lower grades of elementary school for Green Smart Future School was suggested. Therefore, this study is thought to be used as a guide for the creation of a green smart future school in the future.

The Design of Intelligent Classroom Teaching in Table Tennis Forehand Attack

Intelligent classroom is the inevitable requirement of physical education reform to adapt to the development of the situation, and has unique practical value. Using the research method of literature and logical analysis, the wisdom factor is applied to the specific table tennis teaching. Combined with the characteristics of PE curriculum teaching, the smart classroom is used to formulate teaching objectives and the teaching design of pre-class preview, practice in class and consolidation after class, so as to promote students' smart growth and development. In order to provide theoretical support and practical reference for the construction of smart classroom in table tennis teaching, and promote the development and innovation of smart classroom physical education model.