Automation of the tutoring process in online environments through the analitycs of learning

Abstract

This article presents the preliminary results of an investigation project about new classroom technologies in online learning, whose main objective is the design of a new technological tool in enviroment online, through personalized tutoring. The model has six elements: access and permanence, usability, activity and participation, communication, internal trajectory and personal interaction dynamics. It is based on the principle of learning analytics with the support of big data, and is focused on elements of learning with formative and evaluative components. As part of the model, a acceptability matrix was elaborated with statistical functions that show signalized indications, alerting the tutor about the performance of each student typified as superior, high, basic, low and very low. When applying the matrix in pilot tests, it was conceived as an instrument of approach between tutor and student that allows a personalized follow-up and its result is reflected in an increase in academic performance.