A Moodle Component for Data Visualization with Applied Learning

Analytics for Students

M. Barbachano-Chiu

, V. Menéndez-Domínguez

and L. Curi-Quintal

Universidad Autónoma de Yucatán, Mérida, Mexico

Keywords: Learning Analytics, Data Visualization, e-Learning, Moodle, Plugin, Component, Students.

Abstract: One of the main characteristics of e-learning platforms, such as Moodle, is the registration, monitoring, and

storage of the interaction of its users with the published resources, access, and times of entry and exit to them.

This information certainly occupies a lot of space and, since its analysis requires prior knowledge by teachers,

this information is often forgotten and even eliminated. For this, through Learning Analytics, whose main

objective is to apply an intelligent use to data produced by students to predict, evaluate and optimize their

learning, we have proposed the development of a Moodle component that allows, with the help of data

visualization, to present different results obtained through different learning analytics techniques to evaluate

the academic performance of a student or a group enrolled in a course through parameterization, visual and

quantitative support by the tool.

1 INTRODUCTION

In recent years there has been an increasing interest in

educational data analytics, which is not a new area of

research as in previous years it has been referred to as

learning analytics (LA). Learning analytics has been

described as an emerging field where sophisticated

analytical tools are used to improve learning and

education (Koedinger et al., 2010), especially in

learning management systems (Menéndez and

Castellanos, 2014).

These tools provide increasingly large amounts of

data, which can be analysed to easily address

questions that were previously not feasible, involving

the identification of differences between groups of

students or unusual student behaviours (Romero,

Ventura, Pechenizkiy and Baker, 2012). Importantly,

the use of this information and analytical modelling

allows the prediction of student progress and

performance, and the ability to act on that

information.

Specifically, there are four categories of learning

analytics (Romero and Ventura, 2020):

 Descriptive (What happened?)

 Predictive (What happens next?)

https://orcid.org/0000-0002-2712-579X

https://orcid.org/0000-0003-3587-1263

https://orcid.org/0000-0002-8184-9473

 Diagnostic (Why did it happen?)

 Prescriptive (Do this to improve)

These will be detailed through the following

proposal, where we sought to cover each of these

categories given the vast amount of information that

Moodle stores as well as the need of teachers for tools

that offer them support to support their courses and

their students’ learning.

This paper presents a proposal for the analysis

of the data stored in the logs generated by the Moodle

platform. The educator through a Moodle component

(plugin) of block type developed in PHP will be able

to observe, through the visualization of data, the

information collected corresponding to the

interaction time of the students with a resource within

a Moodle course.

The teacher will be able to visualise the time

spent by his or her group or specific students on the

course or expected resource through quantitative data

and graphs that visually demonstrate the relationship

that the students have had with the resources (What

happened?), as well as visualising through a line

graph the time spent by each student per day on the

course, thus determining patterns according to the

days (What happens next?).

Barbachano-Chiu, M., Menéndez-Domínguez, V. and Curi-Quintal, L.

A Moodle Component for Data Visualization with Applied Learning Analytics for Students.

DOI: 10.5220/0011125500003182

In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 1, pages 533-542

ISBN: 978-989-758-562-3; ISSN: 2184-5026

533

Also across the graphics of plugin is possible

observing multiple reasons why some students get

better grades or which resources are preferred by

students (Why did this happen?), and of course, it is

considered important to offer some dynamicity to the

teacher to introduce a limit parameter according to the

number of accesses and average time so that the

plugin shows which students are above or below the

limit, showing in green those who exceed it and in red

those who do not, thus discovering who to offer more

support and even which resources (Do this to

improve).

This is how, through this proposal, it will be

possible to identify patterns that students follow,

allowing the progress of the group to be monitored.

Section 2 includes a review of similar works and

the most important concepts. Section 3 presents an

overview of the proposed plugin in terms of its

architecture and functionalities. Section 4 showed a

case study applying the plugin in a Moodle Course.

Finally, Section 5 mentions its importance and future

work.

2 PATTERN IDENTIFICATION

PLUGIN FOR THE MOODLE

PLATFORM

Moodle plays the role of a platform that integrates the

different possibilities offered by the network, with a

view to its use in education. Because it is a free

software tool that feeds back on the work done by

multiple institutions and participants collaborating in

a network, educators have the possibility of freely

accessing and incorporating into their subjects

multiple modules or components created by other

users through elements called plugins of various types

(Moodle, 2022). The main ones are described in

Table 1. There are currently 1653 plugins that have

been developed by 929 developers and grouped into

47 categories (Moodle, 2022).

Moodle enables non-classroom learning for

students, facilitating virtuality, and is considered an

e-learning tool (Iker, 2008). The e-learning concept

has a pedagogical and technological duality, the first

refers to the fact that these systems should not be mere

containers of digital information, but that this

information should be transmitted following

pedagogically defined models and patterns to meet

the challenges of these new contexts. The second,

technological, refers to the fact that the entire

teaching and learning process is based on software

applications, mainly developed in Web environments

(García, 2005).

Table 1: Main Moodle components.

Type Description

Activity

An activity is something that a

student will do that interacts with

other students and/or the teacher.

Block

Blocks are elements that can be

added to the left or right column or in

the centre of any page in Moodle.

Registration

These modules are used to augment

ways of handling user registrations.

Authentication

The authentication process in Moodle

is initiated when a user clicks the

to access a protected page.

Repository

The repository plugin allows Moodle

to pull in content from external

repositories.

Filters

Filters are a way to automatically

transform content before it is

displayed.

Given the above, the possibility of using

technology with a focus on assessment and the use of

tools such as learning analytics will allow teachers to

have a better understanding of students' online

interactions. With the use of technology, tracking

learners from any device accessing a course, as well

as course resources, will allow for the collection of

information necessary for the presentation of results.

The use of the Moodle platform allows users to add a

plugin as required in a controlled environment.

Nowadays multiple plugins offer among their

features the ability to provide the time a user has spent

on a course, obtain the number of times a user has

visited a resource, track the whole group based on

their times and even qualitative summaries of the

actions performed by the students; some of the

plugins with these features are "Moodle Course

Dedication" (CICEI ULPGC, 2020), "Use Stats"

(Fremaux, 2018) and GISMO (Mazza and Milani,

2004).

To convert data from an e-learning system such

as Moodle into useful information for making

decisions and answering questions in an educational

context, a process called educational data mining is

presented. This data analysis presents a challenge in

devising how to effectively mine these large amounts

of information, looking for meaningful patterns and

presenting them to teachers and students in a useful

way (Kay, Koprinska and Yacef, 2010).

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534

As we encounter the term "learning analytics", we

also encounter the term "data mining", whose

application in e-learning systems consists of an

iterative cycle in which knowledge enters a loop of

the system, and in this way will guide, facilitate, and

improve learning, not only by converting data into

knowledge but also by filtering mined knowledge for

decision making. Data mining in e-learning

environments consists of 4 steps: collecting data, pre-

processing data, applying data mining, and finally

interpreting, evaluating and displaying the results

(Romero, Ventura and García, 2008).

Learning analytics has similarities with data

mining (Baek and Doleck, 2020) of course, also

differences but specifically the process to follow is

similar (Figure 1). Regarding the previous process,

data collection consists of obtaining information on

the use that users make of the e-learning platform, in

our case Moodle; then we proceed to pre-process the

data with the aim of cleaning and transforming this

information into a format that allows it to be analysed

and synthesised. The purpose of this process is to

observe the derived result that gives meaning to the

knowledge of interest to the user and allows to obtain

results that can be presented to them attractively and

understandably, allowing decision making in search

of learning improvement.

Figure 1: General process of learning analytics.

However, just as we find similarities, it is possible to

identify five key distinctions between data mining

and learning analytics (Calvet and Pérz, 2015). These

are:

 Discovery: In data mining (DM) researchers are

interested in automated discovery, and human

judgement is a tool for this; in learning analytics

(LA) it is the opposite, harnessing human

judgement is the goal.

 Reduction and holism: DM reduces systems to

components and explores them and their

relationships, while LA seeks to understand

whole systems.

 Origins: LA seeks intelligent curricula and

outcome prediction, while DM would rely on

educational software and student modelling.

 Adaptation and customisation: DM performs

automated adaptation, while LA informs and

trains instructors and students.

 Techniques and methods: DM employs

techniques and methods of classification,

clustering, relationship mining, while LA focuses

on an analysis of learner success, concepts,

models of meaning, among many others.

In this sense, graphs provide an excellent approach to

exploring information, as well as being essential for

presenting results. On the other hand, developments

in computer graphics have managed to provide great

benefits such as increased quality and accuracy when

making these graphs. That said, data visualization is

defined as a relatively new term, as it expresses an

idea of involving more than just the representation of

data in graphical form, but that information should be

revealed satisfactorily, concerning quality and

different types of information (Chen, Hardle and

Unwin, 2008).

In the last couple of years, students have started

using Internet to study and due to the easy availability

of online courses, student online activities have

considerably increased which in turn has generated

huge amount of data (Roy and Narayan, 2017).

And given that today, there is no solid theoretical

framework for the application of learning analytics in

learning managers, the term adaptive learning

emerges as a result of joint work between educators,

designers and programmers (Daniel, Cano and

Cervera, 2015).

This is why we propose the application of the term

"learning analytics" in an automatic way through a

plugin adapted to Moodle, which allows educators to

make decisions based on the data collected by the e-

learning system regarding the interaction of students

with the platform and presented in an easy-to-

interpret interface so that the teacher can apply

adaptive learning according to their group and

students.

3 PROPOSAL

The proposal focuses on the development of a

Moodle plugin (Version 3.6.6) open source, which

allows the collection, preparation and transformation

of data, as well as their analysis through an automatic

and intuitive process to obtain results that help

educators to understand the usage patterns of their

students, based on their interaction with the course

resources. This leads to the presentation of graphs that

A Moodle Component for Data Visualization with Applied Learning Analytics for Students

535

show the patterns that users follow in a session,

generated from the data obtained from the interaction

with the various resources proposed by the educator.

Another feature of this tool arises since each

teacher has different parameters for grading,

observing progress and/or interpreting results

according to the subject taught, this Moodle plugin

seeks to offer the ability to distinguish students based

on different parameters such as time and number of

accesses to the course. Given that the teacher can

indicate a point of separation for the tool to

distinguish students, the tool also offers quantitative

and visual characteristics to decide on this

differentiating parameter and its value.

3.1 Development Methodology

To develop the plugin, an incremental software

development process based on SCRUM (Schwaber

and Sutherland, 2011) was carried out through the

following stages:

 Backlog: The requirements (functionalities) to be

possessed by the component, the results to be

obtained and how they are deployed to users are

defined.

 First Sprint Backlog: The requirements for the

first sprint of 2 weeks are assigned, then we

continue with the first sprint where we define the

architecture necessary to develop a plugin

according to the Moodle specifications (Moodle,

2022) and it is observed that the one that

corresponds most is the "Block", as well as the

use of the Moodle API "Data Manipulation API".

 Second Sprint BackLog: The beginning of the

development is defined to generate the necessary

files for the proper functioning of the component.

 Third Sprint Backlog: The start of the tests is

determined, as well as the determination of the

validity of the data obtained by the component,

concerning a course and compared with the

results of the calculations obtained manually.

 Finally, the loading of the resulting component

on the server of the Faculty of Mathematics of

the Autonomous University of Yucatan,

specifically in the course called "Human-

Computer Interaction (Group B)" - 2019 of the

Bachelor's Degree in Software Engineering with

a group of 28 students.

3.2 Initial Learning Analytics

A few years ago, Gašević, Dawson and Rogers

(Gašević, Dawson and Rogers, 2016) defined which

variables, concerning the characteristics of the

Moodle learning management system, can provide

representative numbers in tools such as quizzes,

feedback, lessons and chats. They considered other

elements such as forums, course logins, resources and

assignments that can simply be used to measure

"login" or "non-login".

Our proposal takes into consideration the time that

users spend on the resources as an essential element

for the analysis of the information, to observe patterns

that allow us to relate students' grades to the time

spent on the resources and activities. The aim is also

to observe which interaction sequences lead to an

improvement in learning in the course, whether team

members spend the same amount of time on a

resource, among many other elements. In the end, it

is observed whether there is a correlation between the

amount of time spent on a course or resource and the

final performance of a student (Kadoic and Oreski,

2018).

Another advantage of the characteristics of the

environment where the plugin will be executed is to

create an interface that allows different educators

(from different areas) the ability to interact with it,

without requiring prior knowledge of the

development of the plugin. So far, the component

allows to know 6 different metrics per group and

student for the analysis of information and

presentation of results:

 The average time of the group enrolled in a

course taking as a time element, from the time a

learner enters the course on the platform until

he/she leaves the course to enter a different

course or logout.

 The average time of the group enrolled in a

course taking as a time element the sum of the

time intervals per day considering from the time

the learner joined the course and logged out or

joined a different course.

 The average time of resource use by the group.

 The average time of the group's use of the

resource by taking as a time element the sum of

the group's intervals on that resource per day.

 The average time of a pupil's use of the resource

by taking as a time element the sum of the pupil's

intervals on that resource per day.

 The average time per day spent by learners on the

course taking as an element of time from entering

the course to leaving the course or logging out.

Results obtained allow educators to predict a

possible path between the different resources used by

students, as well as the progress of each student

regarding the assignment of a project or the feedback

of a test or quiz (Trcka, Pechenizkiy and Aalst, 2010).

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This plugin facilitates the task of educators in

analysing large volumes of information through

learning analytics and enables improved decision

making. By automating this time-consuming and

complicated process, it avoids the possibility of

complex results that can lead to misunderstandings

and incomplete understandings.

Moreover, since data visualization facilitates the

correct interpretation of results and the goal of

learning analytics is to provide clear information to

teachers given the patterns found in the data, both can

work together to provide visualizations to represent

complex quantitative data for teachers to interpret in

various graphs to support improved student learning

and adaptive learning processes for students.

3.3 Component Architecture

Software Architecture is a high-level representation

of the structure of a system or application that

describes the parts that comprise it, the interactions

between them, the patterns that supervise its

composition and the restrictions when applying these

patterns (Pressman, 2006). In general, such a

representation is in terms of a collection of

components and the interactions that take place

between them.

The implemented architecture is called Model-

View-Controller (MVC) (Leff and Rayfield, 2001),

which contemplates a three-layer structure,

separating interface, behaviour and data. This

simplifies the implementation and enables the

reusability of the developed components. In the

architecture, each layer is made up of a collection of

components, which, when executed individually or

together, enable the required processes to be

performed.

This architecture is limited to the API provided by

Moodle for the development of new block plugins, as

presented in the following Figure 2:

As mentioned above, it was decided to develop a

block plugin because of some crucial points, such as:

 The result set must be always at the teacher’s

fingertips.

 The plugin is required to display course

information, so its position is of utmost

importance to relate the information displayed by

Moodle (resources) to the information calculated

by the plugin.

 The plugin should be easily accessible to avoid

inconvenience or require specialised knowledge

to access it.

When the user interacts with one of the views, the

controller detects the change and requests the data

from one of the already defined models provided by

Moodle through its Data Manipulation API, which

were abstracted and manipulated to return results

relevant to this plugin.

Figure 2: Class diagrams of a Block type plugin.

3.4 Component Architecture

Developing a Web application implies an appropriate

selection of standard technologies that are freely

distributable and independent of any execution

platform, which guarantees its portability,

availability, compatibility and efficiency (Pressman,

2006).

The main development technologies involved in

the plugin are listed below. For each of them, brief

information on their role is given (Wandschneider,

2006):

 HTML (HyperText Markup Language) for the

creation of the Web pages that make up the

visible interface of the component.

 CSS (Cascade Style Sheets) for defining the

presentation of a Web page in terms of a specific

format and typography. CSS files are referred to

as style sheets.

A Moodle Component for Data Visualization with Applied Learning Analytics for Students

537

 JavaScript, to dynamically manipulate the

different elements of the interface, allowing new

interaction styles such as AJAX.

 PHP, for the development of software

components, functions and libraries. It is the core

language of the Moodle platform.

 SQL language to manipulate the information in

the Moodle database.

For data visualization, the D3.js library (D3.js,

2020) is used, which allows easy and competent

development of the graphics needed to present the

data using HTML, SVG and CSS. D3.js is a

JavaScript code library for manipulating data-driven

documents, combining powerful visualization

components and a data-driven approach to DOM

(Document Object Model) manipulation.

Another library used for data visualization;

specifically graph generation (Nodes and Edges) is

called Vis.js Community Edition, which is called a

dynamic visualization library for browsers, designed

to be easy to develop, to handle large amounts of

dynamic data and to enable manipulation of and

interaction with the data (Vis.js, 2021). This library

consists of multiple components such as Network,

Timeline, Graph3d, Graph2d and DataSet, however,

the one used in this case is "Network" which allows

displaying dynamic graphs, automatically organized

and customizable.

3.5 Access to the Moodle Log

The plugin interacts with the Moodle database

through the Data Manipulation API using information

from the user enrolments, user, enrol,

logstore_standard_log and course tables (Table 2).

The Data Manipulation API has all the functions

required to access the Moodle database. It is

recommended that these functions are only used for

retrieving or modifying database content, as they

provide a high level of abstraction and ensure that

database manipulation will work with different

database management systems while maintaining

component compatibility for future Moodle upgrades.

Given a large amount of information obtained

from the Moodle database and the processing time to

obtain the metrics as well as the graphs presented, it

has been decided to automatically store certain results

in the browser's LocalStorage, this is a storage space

that allows saving small amounts of data in the

browser (Mozilla, 2020) in this case of the teacher, to

display immediate results (if previously entered).

Table 2: Moodle tables used.

Table Description

user

This table stores student profile

information and in particular the

student's unique identifier.

course

This table stores information

about the course(s), to which the

student belongs, as well as a

unique identifier.

enrol

It has the course identifier,

together with course information

regarding when the course was

registered and when it ends.

logstore_standard_log

Stores all the movements made

by the student on the platform,

together with their respective

date and time.

user_enrolments

This table stores when a user

registers for a course and their

course membership.

3.6 Component Interface

The web application is the visible interface of our

Moodle plugin. In its design, we considered topics of

Information Architecture (Morville, 2003): the

distribution of textual content, the number of graphic

elements, the presence of descriptive menus, the use

of links, among others, as well as the

recommendations and specifications provided by

Moodle (Moodle, 2022). The purpose is to create a

functional and aesthetic system in terms of the user

interface.

Based on the establishment of the objectives and

needs of potential users, profiles that group common

needs, experiences and attributes are characterised.

This allows the definition of archetypes (Hassan,

Iazza and Fernández, 2004) and action scenarios that

contextualise a usage situation. As a result, prototypes

are developed and validated through heuristics and

tests with real users to accept useful proposals.

Finally, the system is implemented for use and

maintenance.

This allows structuring the whole package with

visual consistency and fosters a sense of stability

within the application, making it easier for novice

users to navigate and explore, speeding up the

execution of tasks.

The contents and descriptions have been

structured for easy reading and understanding (Figure

3). The images used have been limited both in

quantity and variety. The resolution and size were

chosen to allow for a reduction in the waiting time for

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downloading the page without detracting from its

significance.

Figure 3: Average time of group per resource.

Regarding the proposed plugin, this is a type of

component called by Moodle as a "block", which

allows it to be added by the teachers of a course

statically, on the left or right side of where the

resources of the course are presented. This plugin

must be configured within the Moodle administration

panel to be viewed only by teachers, preventing

students from accessing confidential information of

their fellow students.

The plugin acts as an intermediary between the

complex processing of the vast amount of data in the

system and the user (teacher) in need of quantitative

and qualitative data to support decision making.

Because of this, the plugin has been composed of

several views that allow a simple and easy

visualization for the user.

Starting from the main interface, where general

information of the group is displayed such as:

 The average time the group is kept within the

group's resources.

 The average time of the group per day.

 Download of .csv files with the time intervals

of the users per day and course resource.

In this interface (Figure 3), we can observe other

functionalities that we will detail below.

The two "activity detail" buttons show the

following interface, in which we find the average

times of the group per activity (considering as unit of

time since the student enters a resource until he leaves

one and switches to another) and with the other button

the average times of the group per activity per day (in

this one we consider as a unit of time the sum of all

time that a student entered a resource per day), in

these interfaces you can see which are the activities

or resources where the students have spent the most

time (forums, URLs, pages, folders, resources,

wikis).

Figure 4: Main interface.

At this point, we have observed how the tool

through quantitative data shows us information about

the behaviour of the group concerning the resources,

however, taking advantage of the use of data

visualization and the vast amount of information that

Moodle collects, we have been able to develop a

relationship graph, whose objective is to relate the

resources concerning the users of the course,

observing the amount of time spent on each resource

per student.

We can observe in this same interface the filtering

functionality for students, where the teacher places

the minimum value to separate the students in the

network (in red when they are below the minimum

value and in green when it is equal or greater than the

value placed), this can be according to the average

time dedicated to the course or the number of accesses

to it (Figure 4).

A Moodle Component for Data Visualization with Applied Learning Analytics for Students

539

The tool aims to make this task even easier for the

teacher by providing the average time and number of

accesses of the group to have a reference point.

Figure 5: Student filtering functionality by minimum value.

Finally, in the main interface, we find the

"Average time per day" button where we can select a

student from the group to obtain the average time of

the selected student per activity per day as well as the

average time concerning the course (Figure 3).

Since this information is only quantitative and its

interpretation can be complicated, two graphs have

been proposed to allow a better visualization:

Starting from the main interface, where general

information of the group is displayed such as:

 The first is a line graph, where the time in

minutes that the user dedicated per date to the

course is observed (Figure 6)

 The second is the same relationship graph

(Figure 7), however, in this case, the main node

is the student, and the surrounding nodes are

the resources, observing their relationship and

time spent with each one of them.

Figure 6: Line chart with average times per date of a

selected.

As we have seen, all the interfaces present a

similar and intuitive design to guide the user through

the interface, allowing them to observe results

without the complex task of understanding and

analysing the vast amount of data that Moodle stores

in its various databases tables.

Figure 7: Graph of student-resource relationships.

4 CASE STUDY

The software evaluation process considers two types

of assessments to ensure the quality of a product:

verification and validation (Pressman, 2006).

While verification is aimed at verifying that the

product has been developed correctly, validation

evaluates the developed product against the stated

needs, i.e., whether the correct product has been built

according to the initial objectives.

This case study focuses on the validation of the

component, its main objective being to check that the

proposed plugin is consistent with the objectives for

which it has been developed.

First, the plugin was incorporated into the Moodle

course called "Human-Computer Interaction (Group

B)" of the bachelor’s degree in Software Engineering

offered by the Faculty of Mathematics of the

Autonomous University of Yucatan in 2019. The

number of students enrolled was 28.

The programmed functionalities were validated to

ensure that they were available and did not generate

errors or affect the performance of the Moodle

platform. Subsequently, learning analytics were

generated for the group and some students for testing.

Some examples of the results obtained are presented

below.

The graph is obtained by the following

mathematical expression:

𝑎









𝑏





)

Where:

 a = Time of entry to an external resource of the

course.

 b = Time of entry to a resource in the course.

 n = Number of time slots per pupil per day F.

 i = ith student time slot of day F.

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540

Figure 8: Graph of results generated by a pupil’s

component.

In this graph (Figure 8) we can see on the y-axis

(vertical) the average amount of time a student spends

in the course per day, where on the x-axis (horizontal)

we observe the days on which he/she entered, this is

obtained through the sum of the times per resource

occupied by the students per day (mathematical

expression mentioned above). With this, we can see

on which dates the students spend more time in the

course than on other days.

All this allows us to verify that the proposed

component fulfils its initial purpose, pending the

validation of its usability in an experiment involving

potential users.

5 CONCLUSIONS

Learning analytics can be transparently applied in e-

learning systems by using components to reduce the

need for prior or advanced knowledge. A benefit is

obtained by processing the large amounts of

information that are stored in these platforms, thus

allowing the automation of the process necessary to

analyse the information and present the results

obtained (Romero et al., 2008).

Likewise, these results can be represented by

graphs to identify and observe patterns based on the

progress of the students to determine the results of

one concerning the group, as well as the improvement

of the course through the adaptation of new resources

that improve learning.

Results represented by the graphs provide

teachers with information about the time their

students spend interacting with the resources

previously described, allowing them to know the

behaviour of the group within a Moodle course. With

this, it is possible to continuously monitor the

progress that a student can present over the other

members of the group (Figure 9).

Figure 9: Graph of the students in a course concerning the

used resources.

The plugin provides results that favour the

planning of new strategies, which can support the

high demand for digital activities, to improve student

learning.

In addition to providing useful information for the

teacher, this last network of relationships allows the

introduction of 2 types of parameters to filter the

students:

 Filter by average time

 Filter by number of accesses

With this, it is possible to distinguish those

students who are below average or above a specific

parameter, to be able to offer adaptive learning to

their profile.

The COVID-19 pandemic has encouraged

computer-based activities that reduce people's

presence and education is no exception; distance

education in all its aspects and variations is an

important alternative to maintain a healthy distance.

In this sense, learning management systems become

online spaces where all activity performed by the

learner is evidenced.

As previously mentioned, our proposal extends

Moodle as a tool that offers value to the learning

process, incorporating new functionalities that enable

an easy and interactive understanding of the results

obtained.

By implementing the architecture of a Moodle

plugin, the use of the plugin in different versions of

the platform is made possible, even favouring the

speed of the plugin in terms of its operation. This

means that its use by teachers requires downloading

and adding it to Moodle through the administrator

who manages the platform in their school.

It is worth mentioning that one aspect to improve

is to establish the time that a student is connected in

Moodle, as in some cases the user does not explicitly

log out but closes the browser window directly and

this generates wrong values.

A Moodle Component for Data Visualization with Applied Learning Analytics for Students

541

In addition, in the latest updates, Moodle has

launched a new feature, called Learning Analytics

API, which acts as an open system with a wide variety

of models and indicators like predictors, goals and

intuitions.

The aim is to incorporate these options from the

information stored in the Moodle log and to explore

new visualization techniques and their application to

student groups to evaluate the effectiveness of the

proposal in terms of usability.

ACKNOWLEDGEMENTS

To the Autonomous University of Yucatan for

facilitating the completion of this work.

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