@inProceedings{
 title = {Research on the construction and application of individual learner model},
 type = {inProceedings},
 year = {2018},
 identifiers = {[object Object]},
 keywords = {Evaluation model,Individual learner model,Learning evaluation},
 pages = {88-92},
 volume = {131},
 publisher = {Elsevier B.V.},
 id = {a9f1921f-a8e2-3274-ae07-043bc9f10c84},
 created = {2020-02-03T15:25:15.313Z},
 accessed = {2020-02-03},
 file_attached = {false},
 profile_id = {66be748e-b1e3-36e1-95e1-5830d0ccc3ca},
 group_id = {ed1fa25d-c56b-3067-962d-9d08ff49394c},
 last_modified = {2020-02-03T15:25:52.300Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 folder_uuids = {b6de18da-82e2-4c7f-ab8c-6131d929f4d1},
 private_publication = {false},
 abstract = {In the background of extensive attention and research on education big data and learning analysis. The model construction method, the statistical analysis method, the empirical method, the inductive method and the deductive method are adopted in this research to probe in depth the problem of learning effect evaluation based on individual learner model: the learning assessment framework for individual learner model is studied, and the evaluation model of learning effect and evaluation index system are summarized of each sub-model, an empirical study is conducted. Compared with the actual learning effect of the learner, the method proposed in this paper is effective [1].},
 bibtype = {inProceedings},
 author = {Jun-Min, Ye and Song, Xu and Da-Xiong, Luo and Zhi-Feng, Wang and Peng-Wei, Huang and Chen, Xu},
 booktitle = {Procedia Computer Science}
}

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