Geodesic
Improving the efficiency of the learning process using the Markov model
Itogi nauki i tehniki. Sovremennaâ matematika i eë priloženiâ. Tematičeskie obzory, Proceedings of the All-Russian Scientific Conference «Differential Equations and Their Applications» dedicated to the 85th anniversary of Professor M. T. Terekhin. Ryazan State University named for S. A. Yesenin, Ryazan, May 17-18, 2019. Part 2, Tome 186 (2020), pp. 116-122

Voir la notice de l'article provenant de la source Math-Net.Ru

We consider a Markovian model of learning consisting of three states: the state of knowledge transfer, the training state, and the state of knowledge control. Taking into account of training time within this model allows one to obtain additional information about competencies of trainees, to estimate the values of latent parameters, and to improve the learning process.
Keywords: training system, latent parameter, training, testing, mathematical model, Markov model, differential entropy, Rasch model. 
@article{INTO_2020_186_a14,
     author = {V. I. Serbin},
     title = {Improving the efficiency of the learning process using the {Markov} model},
     journal = {Itogi nauki i tehniki. Sovremenna\^a matematika i e\"e prilo\v{z}eni\^a. Temati\v{c}eskie obzory},
     pages = {116--122},
     publisher = {mathdoc},
     volume = {186},
     year = {2020},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/INTO_2020_186_a14/}
}
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V. I. Serbin. Improving the efficiency of the learning process using the Markov model. Itogi nauki i tehniki. Sovremennaâ matematika i eë priloženiâ. Tematičeskie obzory, Proceedings of the All-Russian Scientific Conference «Differential Equations and Their Applications» dedicated to the 85th anniversary of Professor M. T. Terekhin. Ryazan State University named for S. A. Yesenin, Ryazan, May 17-18, 2019. Part 2, Tome 186 (2020), pp. 116-122. http://geodesic.mathdoc.fr/item/INTO_2020_186_a14/