Computers & Education最新文献

{"title":"From automation to thinking: The role of AGI in discourse analysis of computer-supported collaborative learning based on computational grounded theory","authors":"Tien-Chih Chang ,&nbsp;Alice R.P. Li ,&nbsp;Chia-Yu Wang ,&nbsp;John J.H. Lin","doi":"10.1016/j.compedu.2026.105579","DOIUrl":"10.1016/j.compedu.2026.105579","url":null,"abstract":"<div><div>Analyzing the complex dialogue central to computer-supported collaborative learning is crucial for understanding learning processes, yet remains a significant challenge for educational researchers due to the labor-intensive nature of manual coding and the semantic limitations of traditional computational methods. Recent advancements have highlighted the potential of Large Language Models (LLMs) to move beyond mere automation, demonstrating an ability for inference without task-specific data that is characteristic of artificial general intelligence. To harness this potential, this study introduced and evaluated a human-AI collaborative framework (CGT-LLM) that integrates LLMs into computational grounded theory. Specifically, CGT-LLM focuses on learning analytics for rich discursive data. Applied to dialogue from a climate change collaborative simulation game, the framework was evaluated against a supervised bidirectional encoder representations from transformers (BERT) baseline. The performance of the framework approached human expert-level performance in categories related to explicit instructions, numerical data, or direct statements of intent crucial to game objectives, while also demonstrating promising capability in identifying more abstract and less obvious themes. The findings demonstrate that the researcher's role in computational grounded theory remains critical, particularly in exploring data diversity during the discovery phase, and making final interpretive judgments for abstract themes during the classification phase. This framework thus positions LLMs as a valuable assistant rather than as a replacement for human expertise, providing educators and researchers with a tool to gain deeper, more scalable insights into collaborative learning processes, and offering potential to inform the design of timely pedagogical interventions.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"247 ","pages":"Article 105579"},"PeriodicalIF":10.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative artificial intelligence augments social interactivity and learning outcomes: Advancing the framework of a scaffolded human–GenAI shared agency","authors":"Yi-Chen Juan ,&nbsp;Yuan-Hsuan Lee ,&nbsp;Jiun-Yu Wu","doi":"10.1016/j.compedu.2026.105564","DOIUrl":"10.1016/j.compedu.2026.105564","url":null,"abstract":"<div><div>Generative Artificial Intelligence (GenAI) functions not merely as a tool but an active collaborator in human knowledge construction; however, the Human-GenAI interaction dynamics is still underexplored. This study investigates Human-GenAI interaction profiles, the network interactivity and profile differences within a statistics learning community, as well as the underlying mechanisms linking Human-GenAI interaction to learning performance. We designed the Human–GenAI Inquiry and Problem-Solving Scaffold to foster shared agency between twenty-eight graduate students and GenAI across seven homework assignments in a sixteen-week advanced statistics course. Analytical approaches included <em>k</em>-modes clustering, social network analysis, and Partial Least Squares Structural Equation Modeling, complemented by case studies of interaction profiles. Three distinct Human-GenAI interaction profiles were identified: Human-GenAI collaborators, Peer collaborators with GenAI assistance, and Individual learners with late GenAI adoption. The network interactivity becomes cohesive with GenAI occupying the central hub role within the learning community. The models then demonstrate unique pathways through which Human-GenAI interaction influences learning performance, via degree centrality (number of direct connections) and peer nomination as helpers. The case studies highlight GenAI’s capability to augment human roles, encouraging deeper inquiry, expanding the depth of peer discussion, or promoting the exploration of diverse problem-solving strategies. These findings add value to theory and practice by providing empirical evidence for the framework of a scaffolded Human-GenAI shared agency, offering pedagogical implications to foster active student participation and cultivate learner agency within the symbiotic Human–GenAI partnership.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"247 ","pages":"Article 105564"},"PeriodicalIF":10.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtual reality serious games for promoting environmental systems thinking and pro-environmental policy support","authors":"Joseph G. Guerriero ,&nbsp;Pejman Sajjadi ,&nbsp;Janet K. Swim ,&nbsp;Alexander Klippel ,&nbsp;Jamie DeCoster ,&nbsp;Mahda M. Bagher","doi":"10.1016/j.compedu.2026.105575","DOIUrl":"10.1016/j.compedu.2026.105575","url":null,"abstract":"<div><div>Virtual reality (VR) serious games can expose people to environmental processes they would not otherwise experience. This can make topics in environmental science more concrete to learners, improving learning outcomes and downstream behaviors related to environmental sustainability. In a randomized experiment (<em>N</em> = 189), we examined the effectiveness of a VR serious game designed to teach people about a topic in environmental science—the Critical Zone—by comparing it to a non-VR version of the game and to a static presentation of the same information on a website. Although the VR serious game promoted greater spatial presence and feelings of awe (which, in turn, translated to feeling more connected with nature), these effects did not translate to improved learning outcomes and pro-environmental policy support as we hypothesized across two separate models. Yet, exploratory analyses revealed a very small but significant indirect pathway by which the VR serious game promoted systems thinking about the Food-Energy-Water (FEW) nexus and pro-environmental policy support: VR (vs other learning formats) led to increases in a sense of spatial presence, then to perceived learning effectiveness, then to FEW systems thinking, and, finally, to pro-environmental policy support. Our results shed light on the mixed effect of VR and spatial presence on learning outcomes discussed in the wider literature on VR in education. Although the original hypotheses were largely unsupported, by exploring and highlighting pathways from learning formats to outcomes, we demonstrate the potential of VR for promoting learning and pro-environmental policy support.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"247 ","pages":"Article 105575"},"PeriodicalIF":10.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effects of critical thinking intervention on reliance behaviors, problem-solving quality, and creativity during human-Generative AI collaborative learning","authors":"Chenyu Hou ,&nbsp;Gaoxia Zhu ,&nbsp;Yanzhi Liu ,&nbsp;Vidya Sudarshan ,&nbsp;Josephine Leng Leng Chong ,&nbsp;Fannie Yifan Zhang ,&nbsp;Michael Yong Heng Tan ,&nbsp;Yew Soon Ong","doi":"10.1016/j.compedu.2026.105576","DOIUrl":"10.1016/j.compedu.2026.105576","url":null,"abstract":"<div><div>As Generative AI becomes increasingly used in various educational contexts, understanding how students engage with these tools during collaborative problem-solving is critical. While prior research suggests that critical thinking is essential in human-AI problem-solving, few studies have examined how instructional interventions, targeting critical thinking, might shape their reliance behaviors and collaborative outcomes. This study investigates the effects of a critical thinking intervention embedded in a problem-based learning (PBL) environment where students are engaged with Generative AI. The intervention combined strategies that foster critical thinking, including authentic instruction, structured dialogue, and AI-supported peer mentoring, aiming to promote students' thoughtful engagement and improve problem-solving performance. Participants (N = 226) were assigned to experimental (with critical thinking interventions) or comparison (without critical thinking interventions) conditions. We used pre- and post-surveys to measure participants' trust, critical thinking, and AI reliance behaviors, and group reports and chat histories to assess their problem-solving quality and creativity. Results revealed that the intervention did not produce significant improvement in self-reported critical thinking, possibly due to the short intervention duration. However, the intervention led to a marginal reduction in students' thoughtless use of Generative AI and significantly reduced the direct adoption of AI-generated content. Notably, students in the intervention condition produced more creative solutions, demonstrating higher levels of originality and idea density in their group reports. These findings suggest that <em>how</em> students use Generative AI is critical, especially when it is almost impossible to control <em>whether</em> they use it or not. The study highlights the importance of designing interventions that cultivate students’ critical thinking to support creative human-AI problem-solving.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"247 ","pages":"Article 105576"},"PeriodicalIF":10.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative AI: A double-edged sword for creative thinking learning — Evidence from facial expressions and fNIRS","authors":"Xinheng Song ,&nbsp;Yue Zhang ,&nbsp;Zhaolin Lu ,&nbsp;Linci Xu ,&nbsp;Hengheng Shen","doi":"10.1016/j.compedu.2026.105578","DOIUrl":"10.1016/j.compedu.2026.105578","url":null,"abstract":"<div><div>With the widespread integration of generative AI tools into educational contexts, understanding their influence on learners’ cognitive and emotional processes has become increasingly critical. While AI holds potential for enhancing creativity, its double-edged impact on neurocognitive and emotional processes still requires further investigation. This study investigates the impact of generative AI-based learning tools on the creative thinking learning process. Participants were divided into two groups: a generative AI design group and a traditional design group. They completed tasks employing the divergent brainstorming creative method and the structured innovation TRIZ method. During these tasks, both facial expressions and functional near-infrared spectroscopy (fNIRS) data were collected to explore the effects of generative AI-assisted creative thinking education on students’ facial emotional changes and prefrontal cortex (PFC) activation patterns. Expert evaluations were conducted to assess the outcomes of creative thinking. The results indicated that generative AI significantly enhanced creative thinking performance. Facial emotion analysis revealed that, with generative AI assistance, the brainstorming process generated more fear emotions, while the Theory of Inventive Problem Solving (TRIZ) design process produced more happiness emotions. fNIRS data showed that, with generative AI support, the brainstorming process facilitated activation in the right DLPFC, while the TRIZ design process activated both the left and right DLPFC areas. Machine learning classifiers indicated that facial emotion and fNIRS data could serve as effective indicators for assessing creative thinking performance. The CatBoost classifier achieved an accuracy rate of 91.40 %/89.06 % in the two groups. This study focuses on learners’ facial emotions and PFC activity, revealing that while generative AI enhances creative thinking performance, it may also increase negative emotions. The findings call for caution in using generative AI in creativity education to avoid potential negative psychological effects on students, despite its benefits in promoting creative thinking.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"247 ","pages":"Article 105578"},"PeriodicalIF":10.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How explanatory features of AI and time frame reshape adolescents’ decision-making","authors":"Zhuo Shen ,&nbsp;Yinghe Chen ,&nbsp;Jingyi Zhang ,&nbsp;Hengrun Chen","doi":"10.1016/j.compedu.2026.105563","DOIUrl":"10.1016/j.compedu.2026.105563","url":null,"abstract":"<div><div>As AI technologies permeate daily life, adolescents' distinctive cognitive profiles make their decision-making highly sensitive to AI explanation features. The study aimed to examine the underlying mechanisms by which AI's explanatory features and time frame impact adolescents' decision-making. We created an online platform where adolescents interacted with an explainable AI. A preliminary survey identified 10 mathematics-related factors. Experiment 1 involved 158 students (<em>M</em>_age = 13.7) and used a 3 (explanation type: prediction, causal, counterfactual) × 2 (perceived control: high, low) × 2 (perceived reliability: reliable, unreliable) mixed design. Experiment 2 recruited 225 students (<em>M</em>_age = 13.7) and employed a 3 (explanation type) × 2 (time frame: short-term, long-term) mixed design. Decision-making and expectation (expected impact of each factor on math achievement) were the outcomes in both experiments. In Experiment 1, perceived unreliable counterfactual explanations for low-control factors produced the lowest expectation and decision-making probability, whereas predictions and causal explanations did not differ. For high-control factors, perceived reliable counterfactual explanations similarly reduced decision-making probability, although expectation remained constant across explanations. In Experiment 2, predictions and causal explanations led to higher decision-making probability for short-term events than long-term ones, while counterfactuals reversed this pattern. While counterfactual explanations help restore trust and motivate change in distant, uncertain contexts, they can trigger reactance and reduce action when events feel controllable or imminent. Although adolescents cognitively understand causality and time frames, they still struggle to effectively regulate their decisions. AI model explanations should therefore account for the developmental characteristics of adolescents and recognize the dual effects inherent in counterfactual explanations.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"248 ","pages":"Article 105563"},"PeriodicalIF":10.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of multimodal learning analytics in computer-supported collaborative learning","authors":"Fan Ouyang ,&nbsp;Xianping Bai","doi":"10.1016/j.compedu.2026.105574","DOIUrl":"10.1016/j.compedu.2026.105574","url":null,"abstract":"<div><div>Multimodal learning analytics (MMLA) has provided new perspectives for computer-supported collaborative learning (CSCL) by capturing multimodal data to explore behavior, social interaction, cognition, regulation, and emotion in CSCL process. However, there are critical challenges in handling multimodal data in CSCL context, such as multimodal data preprocessing methods, selecting suitable analysis methods and tools, and integrating multi-source, multimodal data to represent learning indicators in CSCL process. To fill these gaps, this systematic review constructed a conceptual framework of MMLA in CSCL and provided an overview of the contexts, multimodal data, indicators, data preprocessing methods, analysis methods, and tools, and effects of MMLA applications in CSCL from 2012 to 2024. One hundred fourteen studies articles were included for the final synthesis. Results found that: (1) existing studies primarily focused on groups’ social interactions in CSCL; (2) visual data was commonly adopted in CSCL; (3) the relationships between multimodal data and learning indicators in CSCL included four types, namely One-to-One, Many-to-One, One-to-Many, and Many-to-Many, with particular emphasis on Many-to-One relationships; (4) the most frequently used data preprocessing method was manual coding and extraction, and the utilization of traditional analysis methods (e.g., statistical analysis) had gradually decreased in CSCL, while advanced analysis techniques (e.g., AI algorithms) were gradually gaining traction but were not yet widely adopted; and (5) the application of MMLA in CSCL had positive effects on both learners and instructors, which primarily help instructors comprehensively understanding the CSCL process. Based on the results, this research proposed theoretical, technological, and practical implications to guide future research in the application of MMLA within CSCL contexts.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"247 ","pages":"Article 105574"},"PeriodicalIF":10.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Timing matters! Using delayed signaling to improve experiential learning in procedural VR training","authors":"Jonas De Bruyne ,&nbsp;Charlotte Larmuseau ,&nbsp;Lieven De Marez ,&nbsp;Durk Talsma ,&nbsp;Klaas Bombeke","doi":"10.1016/j.compedu.2025.105553","DOIUrl":"10.1016/j.compedu.2025.105553","url":null,"abstract":"<div><div>‘Learning by doing’, or experiential learning, is increasingly implemented through immersive media such as virtual reality (VR) across domains like education and professional training. Immersive technologies enable dynamic instruction and guidance, but this potential remains underexplored. To support learning, cognitive load theory promotes signaling to reduce cognitive load by guiding attention to essential content, while discovery learning encourages minimal guidance to foster exploration. While the temporal aspect of the signaling principle is underrepresented in literature, this study suggests that striking a balance between the theoretical approaches is possible by delaying additional guidance. This work therefore investigates the impact of delayed signaling on experiential learning in VR, using a VR training module on electrofusion welding that is currently used in industry. When comparing performance after training either with immediate or delayed signaling, the data suggested improved procedural learning when signaling was delayed, with an average improvement of 8% in task completion time (<span><math><mi>p</mi></math></span> <span><math><mo>&lt;</mo></math></span> .05, <em>d</em> = .76). Furthermore, the method with delayed signaling did not increase cognitive load, as measured by self-reports, suggesting that discovery learning in combination with (delayed) guidance does not place undue cognitive demand on participants. The findings stress the – currently underexposed – importance of timing of visual aids through signaling and how they can be used to optimize training effectiveness. The results are interpreted in light of existing learning literature with future directions for adaptive training systems highlighted.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"246 ","pages":"Article 105553"},"PeriodicalIF":10.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating learning disruptions: The role of digital learning platforms in student motivation, feedback and emotion","authors":"Negar Monazam-Tabrizi ,&nbsp;Yusuf Kurt ,&nbsp;William il-Kuk Kang","doi":"10.1016/j.compedu.2025.105534","DOIUrl":"10.1016/j.compedu.2025.105534","url":null,"abstract":"<div><div>Crises such as pandemics disrupt the motivational, feedback, and emotional processes that sustain effective learning. Digital learning platforms (DLPs) are often positioned as tools to maintain educational continuity, yet their associations with effective learning in disrupted contexts remains insufficiently understood. This study examines whether DLP use moderates the relationships between low motivation, insufficient feedback, negative emotions, and students' effective learning during crisis-driven remote education. Using a mixed-methods design, quantitative data from 216 UK university students were analysed using partial least squares structural equation modelling (PLS-SEM), and qualitative insights derived from open-ended questionnaire responses were analysed using thematic analysis. Findings indicate that diminished motivation, inadequate feedback, and negative emotions are negatively associated with learning effectiveness during crises. While DLP use was positively associated with effective learning, it did not significantly moderate the relationships between low motivation, insufficient feedback, negative emotions, and students’ effective learning. Qualitative insights suggest that more favourable learner experiences occur when technological affordances are aligned with instructional design, instructor presence and emotional support. The study points to the value of incorporating motivation-enhancing strategies, personalised feedback and affective support within digital learning environments. It contributes to digital pedagogy by highlighting that DLPs relate more strongly to effective learning when their features are integrated with coherent pedagogical design, meaningful social interaction and emotionally supportive practices. These insights extend beyond crisis contexts and offer guidance for designing resilient, inclusive, and human-centred digital learning systems.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"246 ","pages":"Article 105534"},"PeriodicalIF":10.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145730839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative study of expert, AI, and no external feedback on mathematics teacher learning outcomes in reflective practice","authors":"Yimin Ning ,&nbsp;Wee Tiong Seah ,&nbsp;Jihe Chen ,&nbsp;Jinhai Liu ,&nbsp;Peifen Tan","doi":"10.1016/j.compedu.2026.105572","DOIUrl":"10.1016/j.compedu.2026.105572","url":null,"abstract":"<div><div>Feedback is crucial for teacher professional development, yet most studies focus on a single source (e.g., experts or peers). Longitudinal comparisons across sources and analyses of underlying mechanisms are rare. This study therefore investigates how different feedback sources influence teacher learning outcomes. A total of 522 teachers participated in a 13-week professional learning program, including three weeks of reflective practice. Participants were divided into three groups: NF (no external feedback), EF (expert feedback), and AF (AI feedback). Teaching videos, instructional design artifacts, and reflective journals were analyzed to assess outcomes. Teachers in the EF group achieved significantly higher learning outcomes than those in the AF group, who, in turn, outperformed the NF group. These findings underscore the distinctive value of expert feedback in providing depth of insight and contextual sensitivity, while also indicating that AI feedback, although timely and adaptive, cannot fully replace expert judgment. Cross-Lagged Panel Network (CLPN) analysis identified key behaviors and sequences across groups and revealed detailed temporal patterns within behavioral clusters. Qualitative interviews further demonstrated that feedback effectiveness is shaped by the interaction of three dimensions—Time, Object, and Level (TOL)—which form the basis of a framework with 16 elements. The study highlights the complementary strengths of AI adaptability and expert insight, suggesting that multi-source feedback enhances teacher professional development by integrating process support with higher-order regulation.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"246 ","pages":"Article 105572"},"PeriodicalIF":10.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}