Esta pesquisa indagativa tuvo por finalidad determinar la relación que existe entre la usabilidad de la plataforma Classroom y aprendizaje de Google-Apps de los servidores de la UGEL N°05-Lima;2020. Desde una perspectiva psicométrica, se realizó una indagación descriptivo-correlacional con diseño no experimental (corte transversal). La unidad de análisis fueron 15 servidores del Equipo de capacitación del Área de Recursos Humanos de esta institución, a quienes se les suministró el cuestionario de apreciación. Para la prueba de hipótesis se consideró la prueba Rho de Spearman, ya que los resultados no seguían una distribución normal, lo que permitió inducir que existe asociación significativa entre ambas variables. Finalmente, los estadígrafos alcanzaron un valor de 0,987 para el coeficiente de Cronbach y uso instrumental.

“Unit short-term memory (LSTM) is a type of recurrent neural network (RNN) whose sequence-based models are being used in text generation and/or prediction tasks, question answering, and classification systems due to their ability to learn long-term dependencies. The present research integrates the LSTM network and dropout technique to generate a text from a corpus as input, a model is developed to find the best way to extract the words from the context. For training the model, the poem ““La Ciudad y los perros““ which is composed of 128,600 words is used as input data. The poem was divided into two data sets, 38.88% for training and the remaining 61.12% for testing the model. The proposed model was tested in two variants: word importance and context. The results were evaluated in terms of the semantic proximity of the generated text to the given context.“

Unit short-term memory (LSTM) is a type of recurrent neural network (RNN) whose sequence-based models are being used in text generation and/or prediction tasks, question answering, and classification systems due to their ability to learn long-term dependencies. The present research integrates the LSTM network and dropout technique to generate a text from a corpus as input, a model is developed to find the best way to extract the words from the context. For training the model, the poem "La Ciudad y los perros" which is composed of 128,600 words is used as input data. The poem was divided into two data sets, 38.88% for training and the remaining 61.12% for testing the model. The proposed model was tested in two variants: word importance and context. The results were evaluated in terms of the semantic proximity of the generated text to the given context.