Configuraciones de analítica

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El sistema de analítica del aprendizaje de Moodle requiere un poco de configuración inicial antes de que pueda ser usado. Usted puede acceder a Configuraciones de analítica desde Administración del sitio > Analítica > Configuraciones de analítica.

Información del sitio

¡Nueva característica
en Moodle 3.7!

La información del sitio será usada para ayudar a los modelos ae analíica del aprendizaje a que tomen en cuenta las características de la institución. Esta información también es reportada como parte de la recolección de datos del sitio al momento de registrar el sitio. Esto le permite al Cuartel General de Moodle comprender cuales áreas de la analítica del aprendizaje están siendo mpás utilizadas y priorizar apropiadamente los recursos para el desarrollo.

Configurar configuraciones de analítica del aprendizaje

configure settings.png
Las configuraciones para el sistema de Analítica del Aprendizaje de Moodle están configuradas a valores predeterminados razonables, pero vamos a revisarlas. Para configurar y habilitar las configuraciones de Analítica del Aprendizaje de Moodle, acceda al panel de configuraciones de Analítica bajo Administración del sitio/Analítica.

Procesador de predicciones

Selección del procesador de predicciones

Los procesadores de predicciones son los backends del aprendizaje de máquina que procesan los conjuntos de datos generados a partir de las metas y los indicadores calculados y regresan predicciones. El núcleo de Moodle incluye 2 procesadores de predicciones:

    pip install "moodlemlbackend>=1.0.0,<2.0.0"

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Almacén de bitácoras

From Moodle version 2.7 and up, the “Standard logstore” is the default. If for some reason you also have data in the older “legacy logs,” you can enable the Moodle Learning Analytics system to access them instead.

Intervalos de análisis

Intervalos de análisis determine how often insights will be generated, and how much information to use for each calculation. Using proportional analysis intervals allows courses of different lengths to be used to train a single model.

Analysis intervals

Each analysis interval divides the course duration into segments. At the end of each defined segment, the predictions engine will run and generate insights. It is recommended that you only enable the analysis intervals you are interested in using; the evaluation process will iterate through all enabled analysis intervals, so the more analysis intervals enabled, the slower the evaluation process will be.

Directorio de salida de modelos

Models output directory

This setting allows you to define a directory where machine learning backends data is stored. Be sure this directory exists and is writable by the web server. This setting can be used by Moodle sites with multiple frontend nodes (a cluster) to specify a shared directory across nodes. This directory can be used by machine learning backends to store trained algorithms (its internal variables weights and stuff like that) to use them later to get predictions. Moodle cron lock will prevent multiple executions of the analytics tasks that train machine learning algorithms and get predictions from them.

Trabajos agendados

Most analytics API processes are executed through trabajos agendados. These processes usually read the activity log table and can require some time to finish. You can find Train models and Predict models scheduled tasks listed in Administration > Site administration > Server > Scheduled tasks. It is recommended to edit the tasks schedule so they run nightly.

Definiendo roles

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Moodle learning analytics makes use of a number of capabilities. These can be added or removed from roles at the site level or within certain contexts to customise who can view insights.

To receive notifications and view insights, a user must have the analytics:listinsights capability within the context used as the "Analysable" for the model. For example, the Estudiantes en riesgo de abandonar model operates within the context of a course. Insights will be generated for each enrolment within any course matching the criteria of the model (courses with a start date in the past and an end date in the future, with at least one teacher and student), and these insights will be sent to anyone with the listinsights capability in that course. By default, the roles of Profesor, Profesor no-editor, and Mánager (gestor) have this capability.

Some models (e.g. the No teaching model) generate insights at the Site level. To receive insights from these models, the user must have a role assignment at the System level which includes the listinsights capability. By default, this is included in the Rol de Mánager if assigned at the site level.

Note: Site administrators do not automatically receive insight notifications, though they can choose to view details of any insight notifications on the system. To enable site administrators to receive notifications of insights, assign an additional role that includes the listinsights capability to the site administrator at the system level (e.g. the Rol de Mánager).