Construction and Practical Exploration of Innovation and Entrepreneurship Education System in Universities From the Perspective of Competition

Mingyi Zhou

Journal of Cases on Information Technology2026https://doi.org/10.4018/jcit.402399article
ABDC C
Weight
0.50

What the paper says

In order to solve the structural problems in innovation and entrepreneurship education in colleges and universities, such as the disconnection between competition and teaching, the mismatch of resources, and the lag of evaluation, this study designed and empirically evaluated an intelligent-driven platform for the integration of competition and teaching. The platform takes information technology as the core, integrates the functions of data collection, artificial intelligence–assisted teaching, graph sample and aggregation team matching, local interpretable model-agnostic explanations/Shapley additive explanations interpretable recommendation, and blockchain certificate storage and constructs a closed-loop ecology of teaching–competition–incubation–evaluation. This study contributed a high-fidelity implementation case of educational technology and put forward reusable methodologies such as extract, transform, load middleware, micro-service architecture, and human–computer collaborative optimization, which provided a systematic technical path and practical enlightenment for the digital transformation of complex educational systems.

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https://doi.org/https://doi.org/10.4018/jcit.402399

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@article{mingyi2026,
  title        = {{Construction and Practical Exploration of Innovation and Entrepreneurship Education System in Universities From the Perspective of Competition}},
  author       = {Mingyi Zhou},
  journal      = {Journal of Cases on Information Technology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/jcit.402399},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.