Moodle Predicta: A Data Mining Tool for Student Follow Up

Igor Moreira F

elix

, Ana Paula Ambr

osio

, Priscila Silva Neves

Joyce Siqueira

and Jacques Duilio Brancher

Instituto de Inform

atica, Universidade Federal de Goi

as, Goi

ania, Brazil

Departamento de Computac¸

ao, Universidade Estadual de Londrina, Paran

a, Brazil

Keywords:

Educational Data Mining, Moodle, Prediction, Tool, Virtual Learning Environment.

Abstract:

Educational data mining (EDM) aims to ﬁnd useful patterns in large volumes of data from teaching/learning

environments, increasing academic results. However, EDM requires previous and deep knowledge of data

mining methods and techniques, involving several computing paradigms, preprocessing and results’ interpre-

tation. In this paper, Moodle Predicta, an educational data mining desktop tool is presented. This software is

developed in Java and enables non-expert data mining users to enjoy beneﬁts from EDM, within the Moodle

system. Divided in two modules, Moodle Predicta allows: (i) visualization of Moodle courses data; and (ii)

predict students’ performance.

1 INTRODUCTION

With the advances in information and communica-

tion technologies (ICT), distance learning has grown,

allowing people in remote locations to access cour-

ses with better educational content, where students

can make their own agenda, schedules and deﬁne the

time spent studying (Zacharis, 2015). Furthermore,

distance education has allowed institutions to incre-

ase the number of students (Garc

ıa-Saiz and Zorrilla,

2012), (Olama et al., 2014).

In Brazil, distance learning registered 1.2 mil-

lion enrollments in 2006. By 2015 this number

had tripled, presenting more than 3.8 million enrol-

lments, distributed in 25,000 courses, taught semi or

totally at a distance (ABED – Associac¸

ao Brasileira

de Educac¸

ao a Dist

ancia, 2015). This is a global mo-

vement. According to a 2013 report of the Online

Learning Consortium’s Survey of Online Learning, in

the USA, over 7.1 million students were taking at le-

ast one online course, and the number of students ta-

king at least one online course has continued to grow

at a rate far in excess of overall enrollments (Allen

and Seaman, 2014). These courses range from clas-

ses in free programs and from face to face programs,

to complete graduation and professionals master de-

grees (Marquez-Vera et al., 2013).

However, in distance learning there is a huge gap

between the number of new students and the num-

ber of graduates. In Brazil, in 2014, online courses

presented a 25% dropout rate (ABED – Associac¸

Brasileira de Educac¸

ao a Dist

ancia, 2015). Roughly,

950,000 students did not ﬁnish the course they star-

ted. This generates losses to public and private insti-

tutions, because the costs to conduct the course is not

reduced with the decrease in the number of partici-

pants.

Students have pointed out that the main cause for

dropout is the lack of time to study and to participate

in activities (Pierrakeas et al., 2004). Considering that

students often have a job or do domestic activities, it

is hard to keep up their routine of learning. Further-

more, students have justiﬁed that dropout is related to

the difﬁculty to adapt to the distance learning metho-

dology and to the tools used, such as the virtual le-

arning environment (VLE) used (ABED – Associac¸

Brasileira de Educac¸

ao a Dist

ancia, 2015), (Pierra-

keas et al., 2004).

VLE is a software used in the distribution of on-

line courses available in the internet, offering sup-

port to several teaching activities that may go from

the delivery of pedagogical content to monitoring stu-

dents’ progress. To do that, VLE has many tools,

including: forums, chats, pedagogical resources and

quizzes (Romero et al., 2013). Among the available

VLEs, there are some that are free and open source

software (Moodle and Canvas) and others that are

private (Blackboard and Desire2Learn) (EDUCAUSE

Center for Analysis and Research, 2014).

While offering support, these VLEs register many

Félix, I., Ambrósio, A., Neves, P., Siqueira, J. and Brancher, J.

Moodle Predicta: A Data Mining Tool for Student Follow Up.

DOI: 10.5220/0006318403390346

In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 1, pages 339-346

ISBN: 978-989-758-239-4

339

details of student’s behavior inside the platform, for

example: activities visualized, answered and their

grades. A part of this information is available for ana-

lysis inside the virtual environment, through summary

statistics and reports of student’s participation in: fo-

rums, chats and other available resources, allowing

the analysis of the cohort or of a given student (Ro-

mero and Ventura, 2010).

Activity tracking allows teachers and tutors to fol-

low up and monitor student’s performance and even-

tually detect who is risking dropout, failing the course

or has some learning difﬁculty (Bogar

ın et al., 2014).

However, many cohorts contain a large number of stu-

dents, so teachers have difﬁculty in dealing with all

the data properly. Although some information is avai-

lable in several system generated reports, teachers of-

ten have difﬁculty interpreting them. Furthermore,

these reports state facts but do not interpret the re-

sults, trying to identify students in danger of failure,

leaving this analysis to the teachers.

With the goal of analyzing this data automati-

cally, different techniques have been used. A promi-

sing approach is the use of data mining (DM) (Da-

nubianu, 2015) (Sharma and Mavani, 2011), (e Ri-

cardo Araujo e Douglas Detoni, 2015), (Moradi et al.,

2014). DM allows the abstraction of relevant infor-

mation stored in databases. By being relieved of the

task of analysis and prediction, teachers can focus on

the pedagogical aspects of teaching, leaving the iden-

tiﬁcation of at-risk students to data mining.

This paper presents a Java desktop tool for stu-

dent follow up, called Moodle Predicta. The system

connects to Moodle databases, selecting tables accor-

ding to user requirements, and prepares the data to be

analyzed in WEKA data mining software (Hall et al.,

2009), that is integrated with the tool. After data pre-

processing, Moodle Predicta presents a summary of

the results in a visualization module. A second com-

ponent of the system is used to predict students’ ﬁnal

performance. Based on selected data the tool indica-

tes if a student may be at risk of failure or dropout.

This paper presents the research associated to the

deﬁnition of Moodle Predicta and its implementation,

and is organized as follows. Section 2 introduces the

Moodle virtual environment; section 3 presents rela-

ted work and the main topics in Educational Data Mi-

ning associated to the project; section 4 introduces the

Moodle Predicta tool; section 5 represent conclusion

and future work.

2 Moodle

Moodle (Modular Object-Oriented Dynamic Lear-

ning Environment) has more than 93 million users

distributed in 71,000 registered environments in 231

countries (Moodle.org, 2016b). Today it is the most

widely used open source virtual learning environ-

ment for distance education around the world (EDU-

CAUSE Center for Analysis and Research, 2014).

Moodle enables educators and institutions to cre-

ate and manage effective online learning communi-

ties, development ﬂexible and comprehensive online

courses and experiences. Moodle’s modular design

makes it easy to create new courses, adding con-

tent that will engage learners. Moodle was designed

and developed oriented by the social constructionist

pedagogical methodology, where new knowledge is

constructed when students interact with the content

(Moodle.org, 2016a).

Moodle logs record and keep track of what resour-

ces students have accessed, modiﬁed, created and re-

moved, logging every click that students and teachers

make in navigation. However, it offers a limited log

viewing module that is built into the system. Logs

can be ﬁltered by course, participant, period and re-

source. If interested, teachers can use these logs to

determine who has been active in the course, what

they did, and when they did it. For quizzes and acti-

vities, the score and elapsed time are available, and a

comparative analysis of each student with the others

(Bapu et al., 2015).

The Moodle virtual learning environment registers

three different user interactions associated to distance

learning (Moore, 1989):

• Student-student interactions: are related to the

exchanges between the students enrolled in the

course. For example: chats and massages in

forums or workgroups (Agudo-Peregrina et al.,

2012).

• Student-teacher interactions: are related to the

participation of teachers in which students per-

ceive a teacher’s proximity through online pre-

sence. Example: messages from teachers to stu-

dents answering questions about course topics

(Agudo-Peregrina et al., 2012).

• Student-content interactions: these occur when

students make use of many of the content resour-

ces, such as textbooks, documents, research ma-

terials, videos and other learning contents. In

Moodle, they are usually associated to brow-

sing and accessing different resources, tasks, etc.

(Agudo-Peregrina et al., 2012).

Moodle environment does not store logs as a sim-

CSEDU 2017 - 9th International Conference on Computer Supported Education

340

ple text ﬁles. It registers the logs, and all information

in a relational database. It is possible to use different

management systems, like MySQL, Oracle, Access

and others (Moodle.org, 2016b).

Moodle allows teachers to get full reports on the

activities of a unique student, or of all students for a

speciﬁc activity or resource. This is useful to check if

everyone has done a certain task or spent time online

within some speciﬁc resource.

However, all this data is usually raw, without any

form of intelligent processing, so users have used dif-

ferent tools for Moodle data analysis. For example

(Danubianu, 2015):

• GISMO – tool for data visualization, that uses log

data from Moodle, allows its edition and produces

graphical information that can be used by teachers

to understand social, cognitive and behavioral stu-

dent interactions;

• MocLog – includes a set of tools, built based on

GISMO, that can be used for the analysis and pre-

sentation of data within Moodle;

• Analytics and recommendation – plug-in avai-

lable in a supplement component to be instal-

led within Moodle, that can be used by teachers

and students for visualization of students’ invol-

vement and to recommend activities.

3 EDUCATIONAL DATA MINING

Educational Data Mining (EDM) is a relatively re-

cent research ﬁeld, that emerges from two converging

trends: the increasing use of VLE in educational in-

stitutions, and the application of data mining techni-

ques to business intelligence processes in organizati-

onal information (Agudo-Peregrina et al., 2012).

Data mining (DM) represents automated disco-

very of implicit and interesting patterns from large

amount of data. DM is an interdisciplinary scien-

tiﬁc area which involves several computing para-

digms: rule induction, decision tree, Bayesian lear-

ning, Neural networks, etc. Largely used data mining

techniques include classiﬁcation, clustering, associa-

tion rule mining, visualization and statistics (Avlija

2016). Developed tools that automatize this process

help teachers analyze and visualize learning data in

order to recognize useful patterns and evaluate the ef-

fectiveness of the course and students’ participation.

Researches have been conducted with data mining

to analyze Moodle data and logs. Some of them are

summarized in Table 1, presenting the data mining al-

gorithms and techniques used, as well as the attributes

considered for students’ prediction.

As in data mining, the educational data mining

process in VLE follows four steps (Avlija

s, 2016),

(Romero et al., 2008b):

• Data collection: while the students use the system,

information is collected and stored in the data-

base. In Moodle, the data is collected in system

logs.

• Preprocessing: after data collection, the data is

transformed into suitable formats for analysis.

Usually software is used for data preprocessing.

• Data mining: with the aim of developing a model

and discovering useful patterns, the appropriate

data mining algorithms are applied at this stage.

• Results evaluation: in this last step, educators

interpret the obtained results and use discovered

knowledge to improve the learning and decision

making process.

Several surveys give a general overview of EDM

research being conducted and DM in VLE is of-

ten mentioned. Romero and Ventura (Romero and

Ventura, 2007), covers researches published between

1995 and 2005, being extended in 2010 (Romero and

Ventura, 2010), Baker and Yacef (Baker and Yacef,

2009), was published in 2009, Jindal and Borah (Jin-

dal and Borah, 2013), covers research published bet-

ween 1998 and 2012, Pe

na-Ayala (Pe

na-Ayala, 2014),

papers published between 2010 and 2013, and Thakar

(Thakar, 2015) (2002-2014), present a state of the art

review in EDM and research trends in this domain.

Luan (Luan, 2002) discusses potential applications of

EDM in higher education.

(Romero et al., 2008b), gives a broad overview of

the use of data mining in the Moodle learning ma-

nagement system. They argue that data mining in

virtual learning environments represents an iterative

process, that allows improvement of the overall lear-

ning and decision making process. In this paper, they

apply educational data mining and give suggestions

to instructors and e-learning administrators on how to

conduct their own research.

(Kotsiantis et al., 2010) proposed a combination

of three algorithms: Naive Bayes, the 1-NN and the

WINNOW, using the voting methodology. (Avlija

2016), after rigorous data preprocessing and discre-

tization, applies Apriori algorithm to generate asso-

ciation rules to predict student performance outco-

mes and identify students who require special atten-

tion from teachers to increase the overall success ra-

tio. Results present 99% accuracy rate.

In (Yoshida et al., 2013), an algorithm was deve-

loped to determine failure of learning, using quiz ans-

wers as input. The result is a class used to determine

if a student is at risk or not. The algorithm is based

Moodle Predicta: A Data Mining Tool for Student Follow Up

341

Table 1: Related work.

Research

Data mining

tecnhique

Data mining algorithms

Attributes

(Neto and Castro, 2015) Association Apriori

Accesses and forum logs,

chats and tasks

(Gu

ercio et al., 2014) Classiﬁcation

Decision trees

(RandomTree and J48)

Interaction logs

and forums

(Sisovic et al., 2016) Clustering K-Means Resources logs

(Sorour et al., 2015) Classiﬁcation Support Vector Machine Students’ comments

(Zorrilla and Garcia-Saiz, 2014) Classiﬁcation

NearestNeighbours, Jrip,

J48 and NaiveBayes

Interaction logs

on statistics analysis, reaching a maximum accuracy

of 88.9%. (Romero et al., 2008a) compared different

data mining methods and techniques to classify stu-

dents based on their Moodle usage data and the ﬁnal

marks obtained in their courses.

4 Moodle PREDICTA

Moodle Predicta is a tool developed in Java that al-

lows users to connect into any version of the Moodle

database, as well as different management systems,

such as MySQL, Oracle, PostgreSQL and others.

Moodle Predicta is divided in two parts: visualization

and prediction modules.

4.1 Visualization Module

The visualization module allows users to have an

overview of student and teacher behavior, interacti-

ons, personal data, and academic performance. By

means of reports containing relevant information, the

module enables teachers to evaluate the course struc-

ture, content and its effectiveness.

Figure 1: Diagram of visualization module.

To bring the processed data to the user, the visu-

alization module is composed of four main steps, in-

volving data preprocessing, algorithms execution and

formatting (Figure 1):

i Data selection – in this step, the data is selected,

according to user requirements.

ii Data transformation – after selection, the data is

gathered and transformed/discretized.

iii Reports execution – in this stage, the reports are

generated.

iv Reports – the reports are presented to the user.

The software was developed for non-expert data

mining users. The conﬁguration requires few inputs

in an easy to use interface. The options selected by the

user are stored in a CSV (Comma Separated Values)

local ﬁle. This allows the software to load the same

conﬁguration in the next execution.

The ﬁrst step in the visualization module execu-

tion is the connection to database. The connection

is established into local or remote servers, according

to users’ requirements. The connection is guided

by Java Database Connectivity (JDBC). Therefore,

any database that implements JDBC API (Application

Programming Interface) should connect successfully.

After the connection is established, all database

schemas in the node are listed. This list is constructed

by a Java method in the JDBC API. The list is presen-

ted to the user, so he can choose the Moodle installa-

tion to be analyzed.

Once the user has selected the desired Moodle da-

tabase, he is asked for a speciﬁc course on next screen

(Figure 3). To make the selection, Moodle Predicta

creates a hierarchical structure of courses, according

to categories and subcategories that are stored in the

selected Moodle database. This structure is the same

presented in the Moodle database schema.

When the course is selected, a new screen is pre-

sented to the user, so he can choose the attributes, as

seen in Figure 4. The selected attributes will compose

the reports, and include: forums, chats, quizzes, logs,

grades, and tasks. Once the user makes the selection,

Moodle Predicta generates a ﬁle as the ﬁnal result.

This ﬁle can be in three different formats:

i HTML (HyperText Markup Language) – format

indicated for data visualization. The ﬁle is gene-

rated according to standard characters encode and

deﬁnitions, and can be opened in any web brow-

ser.

CSEDU 2017 - 9th International Conference on Computer Supported Education

342

Figure 2: Forum data analysis report, in HTML format.

Figure 3: Selection of Moodle course.

ii CSV (Comma Separated Values) – this format is

indicated for data import and export, such as spre-

adsheets and databases.

iii ARFF (Attribute-Relation File Format) – this for-

mat is used by WEKA and KEEL, two widely

used open source data mining software.

The data is gathered by means of several SQL

(Structured Query Language) commands, crossing

different tables, joining scattered information, accor-

ding to the type of report and data to be analyzed.

Then, it is processed and presented in the selected for-

mat.

For example, when the data to be analyzed are

course’s forum, eleven different tables are inclu-

ded in the data collection: course, user, forum,

user enrolments, forum discussions, forum posts,

forum read, forum track prefs, forum queue, fo-

rum subscriptions, and role.

Joining all these tables and gathering their data,

it’s possible with the application of data mining pre-

processing techniques (cleaning, integration, transfor-

mation and reduction), to have a report that can be

understandable by non-expert data mining users, as

shown in Figure 2.

4.2 Prediction Module

The prediction module allows teachers and tutors to

identify students that are not following classes and

may abandon the course before the end, making it

possible to take some preventive action, and bring the

student back into the course. Prediction is based on

behavior, interactions, and performance of students

inside the Moodle environment.

To undertake the students’ prediction perfor-

Moodle Predicta: A Data Mining Tool for Student Follow Up

343

Figure 4: Selection of attributes for analysis.

Figure 5: Diagram of prediction module.

mance, the module is composed by ﬁve main steps,

involving data preprocessing, data mining techniques

and formatting (Figure 5):

i Data selection – in this step, the data is selected,

from attributes data describe students’ behavior,

interactions and grades.

ii Data transformation – the data is gathered and

transformed/discretized for data mining.

iii Data mining – in this stage, the data is used in

decision tree models.

iv Results evaluation – the results presented by data

mining are interpreted and evaluated automati-

cally.

v Students at risk – Students at risk are listed to user.

As proposed by (Romero et al., 2008b), and follo-

wed by other researches, as (Chayanukro et al., 2014),

the relevant attributes from students’ behavior for data

mining in Moodle and used by Moodle Predicta, are

presented in Table 2.

Once the course is selected, as seen in Figure 3,

Moodle Predicta prepares the data, in a preproces-

sing phase (cleaning, integration, transformation and

reduction), and generates an ARFF ﬁle. After con-

necting to the WEKA data mining API, the decision

Table 2: Predictors attributes used in Moodle Predicta.

Adapted from (Romero et al., 2008b).

Name Description

course id

Identiﬁcation of

the course

n assignment

# of assignments

submitted

n quiz # of quizzes solved

n quiz a # of quizzes passed

n quiz s # of quizzes failed

n messages

# of messages

sent by chat

n messages ap

# of messages

sent to the teacher

n posts

# of messages

sent to the forum

n read

# of messages

read on the forum

total time assignment

Total time spent

in assignment

total time quiz

Total time spent

on quizzes

total time forum Total time used on forum

mark

Final mark obtained

by student in the course

tree algorithms are executed with standard parameters

for listing of students at risk of failing, presented in

Figure 6.

The WEKA (Waikato Environment for Know-

ledge Analysis) allows researchers access to updated

techniques in machine learning, being recognized as a

landmark system in data mining. With an widespread

acceptance so in academia and business contexts, has

becoming a widely tool for data mining research (Hall

et al., 2009).

Integration with WEKA is done through the API

library. The decision tree algorithms can be called

through speciﬁc classes and methods available in the

“weka.classiﬁers.trees” package

Prediction requires that models be built based on

prior data. This means that each context will have to

build and test their models before prediction is pos-

sible. The decision tree models we used were gene-

rated using training data from biology undergraduate

students from an online course, using predictors attri-

butes presented in Table 2.

The list of students at risk (Figure 6) is deﬁned

by the algorithm using the model generated by the

training data. Students whose behavior, interactions,

and performance, is similar to those students from the

Available at http://weka.sourceforge.net/doc.stable/

Accessed in 12/06/2016.

CSEDU 2017 - 9th International Conference on Computer Supported Education

344

training dataset that have failed will be diﬁned as “at

risk”. Teachers can then follow up on these students

to conﬁrm their situation and take some preventive

measure to bring back the student to the course.

Figure 6: Students at risk (Fictitious personal data for pri-

vacy reasons).

5 CONCLUSION AND FUTURE

WORK

With the advances of information and communica-

tion technologies, new and major challenges are being

created, mainly because of the large amounts of data

about students’ activities, academic results and users’

interactions being stored. However, this data has can

be explored and analyzed by known data mining met-

hods and techniques. These two facts are the basis of

the recent area of research, educational data mining,

that consists of application of data mining technolo-

gies to data collected from educational contexts, with

the aim of discover patterns and useful information.

The data mining processes are difﬁcult and need

previous knowledge to be applied successfully. Mo-

reover, the data needs to be correctly selected, prepa-

red and the result of process requires evaluation and

interpretation. These are barriers that must be over-

come so a greater number of users can beneﬁt from

educational data mining.

In this paper, Moodle Predicta, an easy-to-use tool

was presented. This software enables students follow

up, selecting and preparing the Moodle data for two

modules: (i) the visualization module, that generates

reports for analysis purposes; and (ii) the prediction

module, that integrated to WEKA data mining soft-

ware, uses decision tree models to identify and list

students at risk of dropout or failure.

The tool is implemented in Java, connecting to da-

tabases by means of JDBC API. The data collection is

implemented in SQL queries, taking beneﬁt from the

commands offered by database management systems.

While the data processing is done inside the tool.

Future work will be undertaken in the direction of

using the resulting datasets from the visualization mo-

dule to build effective models their validation. More-

over, other Moodle tables can be joined in the same

reports for more complex analysis.

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