Computers & Education最新文献

{"title":"Measuring different types and domains of AI knowledge: Developing and validating a performance-based scale","authors":"Inbal Klein-Avraham ,&nbsp;Rut Ston ,&nbsp;Osnat Atias ,&nbsp;Ido Roll ,&nbsp;Ayelet Baram-Tsabari","doi":"10.1016/j.compedu.2026.105573","DOIUrl":"10.1016/j.compedu.2026.105573","url":null,"abstract":"<div><div>As artificial intelligence (AI) and generative AI (GenAI) technologies become increasingly integrated into everyday life, the need for validated tools that measure people's knowledge about AI grows. Here, we present the development and validation of a theoretically driven, performance-based scale for assessing AI and GenAI knowledge. The scale is grounded in a two-axial framework. One axis captures three knowledge types: content knowledge (what AI is and where it is encountered), procedural knowledge (how AI systems operate and are used), and epistemic knowledge (what features and construction processes characterize AI outputs). The other axis encompasses three knowledge domains: technology-related knowledge (AI systems), user-related knowledge (users' interaction with AI), and society-related knowledge (the social and ethical implications of AI). Based on an online survey of 800 internet-using adults from Israel, the 26-item scale was evaluated using confirmatory factor analysis, which demonstrated an acceptable model fit. It was further validated through two-stage structural equation modeling and group comparisons. Overall, the scale was found to be both valid and practically insightful: while it reproduces the expected relationships with additional constructs (e.g., trust in GenAI, attitudes toward AI) and expected differences between demographic groups, it also provides nuanced insights on the intricacies of AI knowledge. For example, the scale indicates that the relationship between trust in GenAI and knowledge about AI is grounded in both epistemic and societal knowledge. Thus, this novel tool affords more precise investigations into how different types and domains of AI knowledge relate to perceptions, behaviors, and decision-making in an AI-mediated world.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"247 ","pages":"Article 105573"},"PeriodicalIF":10.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957052","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":"Expressions of learner agency in virtual inquiry: Linking agency and evidence-centered game design","authors":"Jillianne Code ,&nbsp;Kieran Forde ,&nbsp;Rachel Moylan ,&nbsp;Aimee Lutrin ,&nbsp;Zahira Tasabehji ,&nbsp;Rachel Ralph ,&nbsp;Aashay Mehta ,&nbsp;Nick Zap ,&nbsp;Nesrine El Banna","doi":"10.1016/j.compedu.2026.105590","DOIUrl":"10.1016/j.compedu.2026.105590","url":null,"abstract":"<div><div>Game-based learning environments often support exploration but rarely connect learner agency with rigorous, embedded assessment. This study reports on the design and pilot implementation of ALIVE (Agency for Learning in Immersive Virtual Environments), a virtual inquiry environment that integrates the Agency for Learning framework with Evidence-Centered Game Design. Nine middle and high school students completed an ecological investigation that required evidence collection, hypothesis testing, and causal explanation. Data included think-aloud protocols, gameplay logs, and brief feedback questions. Triangulated analyses captured both convergence and divergence between self-reported and observed agency. Learners showed intentional decisions, strategy shifts, and selective delegation to system supports at points of uncertainty. These findings show how aligned competency, evidence, and task models make inquiry actions visible and interpretable. The study also offers a multisource approach for examining expressions of agency within guided digital inquiry. Limitations include the small sample and single-session design. Future work should examine longer-term patterns, broader implementation, and transfer across domains.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"247 ","pages":"Article 105590"},"PeriodicalIF":10.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134173","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":"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}