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In China, private-owned cooperatives are becoming increasingly involved in agricultural production. In order to find the key characteristics of smallholders’ social networks after the appearance of cooperatives and better organize different farmland operators, this study completed a field survey of 114 smallholders who adopted farmland trusteeship service of a private-owned cooperative in China and applied the social network analysis to reveal the following results. (1) Compared to the theoretical ideal value, smallholders’ social networks showed low network density, efficiency, and little relevancy. (2) In the social network of mechanical-sharing, neighbor, kinship, and labor-sharing relationships, some isolated nodes existed, but no isolated nodes are found in the synthetic network. (3) The mechanical-sharing relationship among smallholders was stronger than the other relationships. (4) Machinery owners, farmers whose plots are on the geometric center and experienced older farmers showed higher centralities in the network, but village cadres did not. (5) The centralities and QAP correlation coefficients among different networks inside the cooperative were lower than that inside a single village. As a result, this paper confirmed that the ability of cooperatives to organize farmers’ social networks is not ideal. Farmers’ trust of farmland to a cross-village cooperatives does not help them to form a larger social network than their villages. In the future, the answer to the question of “who will farm the land” will still lie with the professional farmers and highly autonomous cooperatives.
In China, private-owned cooperatives are becoming increasingly involved in agricultural production. In order to find the key characteristics of smallholders’ social networks after the appearance of cooperatives and better organize different farmland operators, this study completed a field survey of 114 smallholders who adopted farmland trusteeship service of a private-owned cooperative in China and applied the social network analysis to reveal the following results. (1) Compared to the theoretical ideal value, smallholders’ social networks showed low network density, efficiency, and little relevancy. (2) In the social network of mechanical-sharing, neighbor, kinship, and labor-sharing relationships, some isolated nodes existed, but no isolated nodes are found in the synthetic network. (3) The mechanical-sharing relationship among smallholders was stronger than the other relationships. (4) Machinery owners, farmers whose plots are on the geometric center and experienced older farmers showed higher centralities in the network, but village cadres did not. (5) The centralities and QAP correlation coefficients among different networks inside the cooperative were lower than that inside a single village. As a result, this paper confirmed that the ability of cooperatives to organize farmers’ social networks is not ideal. Farmers’ trust of farmland to a cross-village cooperatives does not help them to form a larger social network than their villages. In the future, the answer to the question of “who will farm the land” will still lie with the professional farmers and highly autonomous cooperatives.
(1) learning efficiency is recognized as the ultimate goal of online education, as it is related to the quality of online education and the cognitive development of students and is influenced by social interactions. This study explores the mediating roles of social presence and learning engagement in the relationship between social interaction and online learning efficiency, addressing gaps in prior studies that have not yet identified the underlying mechanisms. (2) students from three middle schools (N = 344; Mage = 13.61; 56.68% women) completed self-report questionnaires regarding social interaction, social presence, learning engagement, and learning efficiency. (3) the study findings reveal significant serial mediation effects of social presence and learning engagement in the relationship between learner–instructor and learner–learner interactions and learning efficiency. Specifically, while the indirect effect of learner–instructor interaction through social presence alone (indirect effect = 0.08, 95% CI = [−0.00, 0.17]) was not significant, the pathways through learning engagement (indirect effect = 0.18, 95% CI = [0.11, 0.26]) and the combined mediation through both social presence and learning engagement (indirect effect = 0.06, 95% CI = [0.03, 0.09]) were statistically significant. Similarly, for learner–learner interaction, the indirect effects through social presence (indirect effect = 0.09, 0.17) and learning engagement (indirect effect = 0.17, 95% CI = [0.11, 0.24]) were significant, as was the serial mediation through both mediators (indirect effect = 0.07, 95% CI = [0.04, 0.11]). (4) social presence and learning engagement played crucial mediating roles in the links between social interactions and online learning efficiency, and the predictive efficacy of learner–learner and learner–instructor interactions on online learning efficiency was found to be unequal.
This article aims to explore the learning characteristics of online learners within the smart education framework, with a specific emphasis on how they might use Internet of Things (IoT) technologies to improve their educational experience. The term "online learning" refers to the process of acquiring knowledge via electronic means, most often the global web. Online education, e-learning, web-based learning, and computer-assisted learning all share this term. The challenging characteristics of such online learners for students are technical issues, lack of motivation, and slow loading times in online courses. Hence, in this research, the Internet of Things-empowered Smart Education (IoT-SE) Framework has been improved for online learners for students by leveraging IoT tech that tracks how learners interact with learning resources and their environment. This paper aims to revolutionize web-based education through tailored instructions targeting individuals' unique needs and fads as availed by the IoT-SE system. This paper offers evaluation parameters such as level of engagement among learners, retention rates on knowledge acquired while studying e-courses, and satisfaction from an online program. Besides overcoming limitations associated with conventional e-learning approaches, such systems like IoT-SE technology promise more effective pedagogy and student satisfaction for online learners.
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