AUTOMATED PERSONALIZED FEEDBACK FOR INTERACTIVE LEARNING APPLICATIONS
In various embodiments, actions taken on student devices by students using an interactive educational resource are analyzed to determine the students' likelihood of educational success and, if the likelihood is less than a threshold, the students are notified of deficiencies in their actions.
1 . A method of providing automated personalized feedback in an interactive learning application, the method comprising:
(a) distributing an interactive educational resource over a network to a plurality of student devices each associated with a student;
(b) receiving, at a server from the student devices as the resource is used on the devices, signals indicative of actions taken on the student devices with respect to the educational resource, the actions being predictive of success with the resource;
(c) for each of the student devices, computationally determining, at the server, a likelihood of educational success with the resource for the student associated with the student device by computationally processing the received signals corresponding to each student device with a machine-learning model relating actions to educational success with material contained in the resource; and
(d) if the likelihood is less than a threshold, (i) identifying at least one deficiency in an action associated with failure of the likelihood to meet the threshold and (ii) electronically communicating a first type of notification of the deficiency to the student.
2 . The method of claim 1 , wherein the notification is a message appearing to originate with an instructor.
3 . The method of claim 1 , wherein, for each of the student devices, steps (c) and (d) occur during use of the resource on the student device.
4 . The method of claim 1 , wherein the actions comprise at least one of (i) a number of annotations made, (ii) a scrolling pattern, or (iii) an amount of time spent reading.
5 . The method of claim 1 , further comprising, for each of the student devices:
monitoring an effect of the first type of notification on rectifying the deficiency; and
if the deficiency is not subsequently rectified, subsequently sending a second type of notification different from the first type.
6 . The method of claim 1 , wherein the at least one deficiency comprises a plurality of deficiencies, and further comprising, for each of the student devices:
sending different types of notifications for different deficiencies;
monitoring an effect of each of the types of notification on rectifying the associated deficiencies; and
subsequently sending notifications of the type having the best effect.
7 . The method of claim 1 , wherein the machine-learning model comprises a regression model.
8 . The method of claim 7 , wherein the regression model comprises a logistic regression model.
9 . An educational system comprising:
a plurality of student devices for executing an interactive educational resource received over a network, the student devices being configured to send signals indicative of actions taken on the student devices with respect to the educational resource, the actions being predictive of success with the resource;
a server in electronic communication with the student devices, the server comprising a communication module and being configured to (i) receive the signals from the student devices, (ii) for each of the student devices, computationally determine a likelihood of educational success with the material for a student associated with the student device by computationally processing the received signals corresponding to each student device with a machine-learning model relating actions to educational success with material contained in the resource, and (iii) if the likelihood is less than a threshold, identify at least one deficiency in an action associated with failure of the likelihood to meet the threshold and electronically communicate, via the communication module, a first type of notification of the deficiency to the student.
10 . The system of claim 9 , wherein the communication module is configured to send the notification in the form of a message appearing to originate with an instructor.
11 . The system of claim 9 , wherein the actions comprise at least one of (i) a number of annotations made, (ii) a scrolling pattern, or (iii) an amount of time spent reading.
12 . The system of claim 9 , wherein the server is further configured to, for each of the student devices:
monitor an effect of the first type of notification on rectifying the deficiency; and
if the deficiency is not subsequently rectified, subsequently send a second type of notification different from the first type.
13 . The system of claim 9 , wherein the server is further configured to, for each of the student devices:
send different types of notifications for different deficiencies;
monitor an effect of each of the types of notification on rectifying the associated deficiencies; and
subsequently send notifications of the type having the best effect.
14 . The system of claim 9 , wherein the machine-learning model comprises a regression model.
15 . The system of claim 14 , wherein the regression model comprises a logistic regression model.