Crowdsourcing mode-based learning activity flow approach to promote subject ontology generation and evolution in learning

ABSTRACT

Subject ontology can help implement the structured organization of knowledge for online learners and thus plays an important role in the learning process. However, building ontologies by experts is time-consuming, and the adaptation of such ontologies to different contexts might be a problem. Crowdsourcing, which allows users to build and refine ontologies during their learning process, has exhibited potential for solving this problem. In this study, a crowdsourcing mode-based learning activity flow approach is proposed to guide the learning of online learners while promoting the generation and evolution of subject ontologies using the learners’ contributions to the learning content. This flow makes full use of the learners’ wisdom during the learning process to promote self-regulated learning as well as the generation and evolution of the ontology. Based on the proposed approach, a learning support system was developed and an experiment conducted involving a Chinese lesson on “The Liangzhou Poem”. In the experiment, student participants built 722 triples, of which 584 evolved as formal items in the subject ontology. Moreover, all learners were able to construct a well-organized knowledge graph. Students in both high- and low-scoring groups contributed valuably to the knowledge generation and evolution of the subject ontology. Furthermore, while the widths of the knowledge constructed by students in high- and low-scoring groups were similar, their depths were substantially different. During this process, the crowdsourcing-based activity flow system achieved satisfactory technique acceptance, which means that the proposed approach and system are useful for the effective generation of subject ontologies while helping learners acquire knowledge.

KEYWORDS:

• Subject ontology generation and evolution
• crowdsourcing
• learning activity flow
• learning mode
• learning cell knowledge community system

Acknowledgements

This research was supported by the Philosophy and Social Sciences Research of the Chinese Ministry of Education, under projects number 16JZD043. We thank Saad Anis, PhD, from Liwen Bianji, Edanz Editing China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Qi Wang is a doctoral student in Beijing Normal University and his research interest includes knowledge graph, semantic web, mobile learning and learning resource management.

Guozhu Ding is a lecture in Guangzhou University and his research interest includes knowledge graph, semantic web and mobile learning.

Shengquan Yu is a professor in Beijing Normal University and his research interest includes mobile learning, the integration of curriculum and information technology and regional educational informationization.

ORCID

Qi Wang http://orcid.org/0000-0001-9006-9914

Additional information

Funding

This work was supported by Philosophy and Social Sciences Research of the Chinese Ministry of Education [grant number 16JZD043].