[Websensors-EAD] School Dropout Prediction Report

Websensors-EAD tool analyzes student access data in online distance learning environments such as Moodle, MOOCS, etc. The goal is to identify dropout patterns and organize students into four dropout risk groups: critical, high, medium, and low. Websensors-EAD uses regularization in heterogeneous networks [1] as a machine learning method to analyze school dropout patterns.

Websensors-EAD is part of the Websensors Analytics research project, developed at the Laboratory of Scientific Computing (Lives) of the Federal University of Mato Grosso do Sul (UFMS), Três Lagoas, Brazil.

The Websensors-EAD source code is free for academic purposes. For private purposes, contact the project team.

Data preparation

The input data of the Websensors-EAD is the students access log of the online learning environment system. The data should be tabulated in a CSV format, using the character ";" as a delimiter. The data must have five columns: access identifier (unique), date or timestamp, student id, course id, and resource name.

The data directory contains an example of an input file.

In practice, Websensors-EAD analyzes students log as a clickstream graph. A clickstream is a list of all the educational resources viewed by a student (e.g. videos, tasks, foruns, quiz, etc), presented in the order the resources were viewed, also defined as the succession of mouse clicks that each student makes. An example of a clickstream graph is shown in the figure below.

Example of a Clickstream Graph (From [2])

Configuration

Edit the unsupervised-dropout-prediction.py file and change the following lines.

# student patterns file

config['clickstream']='data/students-patterns.csv'

config['courses']='data/courses.csv'

config['students']='data/students.csv'

# machine learning configuration

config['net_regularizer_path_size']=100

config['net_regularizer_max_iterations']=100

config['min_students_by_course']=10

If you want Websensors-EAD to send a PDF school dropout risk report (via e-mail), change the config['mail_report'] setting to True and informs your smtp configuration.

# notification parameters

config['mail_report']=False

config['mail_from']='abc@gmail.com'

config['mail_to']='123@gmail.com'

config['mail_smtp']='smtp.gmail.com: 587'

config['mail_pass']='12345'

config['mail_text']="Dear administrator,\n\nI'm attaching the School Dropout Prediction Report.\n\nI suggest the application of mitigation policies for students classified in the 'critical' and 'high' risk group.\n\nRegards,\nWebsensors-EAD AI Bot.\n[I'm a bot. Do not answer this message.]"

Running Websensors-EAD

To run Websensors-EAD, install the numpy, pandas and matplotlib libraries for Python 3.

• pip install numpy
• pip install pandas
• pip install matplotlib

Run the unsupervised-dropout-prediction.py script:

• python unsupervised-dropout-prediction.py

It is recommended to schedule periodic execution (e.g. weekly) of this script, for example, using the Cron tool in Linux environments.

Websensors-EAD generates a PDF report organizing students into four dropout risk groups. An example report is available in the report.pdf file.

References

[1] SHI, Chuan et al. A survey of heterogeneous information network analysis. IEEE Transactions on Knowledge and Data Engineering, v. 29, n. 1, p. 17-37, 2017.

[2] GEIGLE, Chase; ZHAI, ChengXiang. Modeling mooc student behavior with two-layer hidden markov models. In: Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale. ACM, 2017. p. 205-208.