Learning Analytics: A Way to Monitoring and Improving Students'

Learning

Jose Manuel Azevedo

, Cristina Torres

, Ana Paula Lopes

and Lurdes Babo

Polytechnic of Porto (P.PORTO) / ISCAP (PORTUGAL) and CEOS.PP, Rua Jaime Lopes de Amorim, Porto, Portugal

Polytechnic of Porto (P.PORTO) / ISCAP (PORTUGAL), Rua Jaime Lopes de Amorim, Porto, Portugal

Keywords: Learning Analytics, Google Analytics, Moodle, Mathematics, Higher Education, Student Engagement,

Course Dedication.

Abstract: This paper focus on the potential contributes of Learning Analytics in the improvement students learning. The

analysis of student’s data collected from Virtual Learning Environments is important to ascertain student’s

engagement. This paper presents the analysis of data collected in the ambit of MatActiva project. Data was

analysed with Google Analytics, Course Dedication and Moodle Reports. Promising results were obtained.

1 INTRODUCTION

Virtual Learning Environments (VLE) are widely

used in all degrees of teaching. A huge number of

educators find that VLE can improve the teaching

process. Especially in large universities courses VLE

can reach a great amount of students in different

geographical areas. It is undeniable that the number

of resources increased sharply but, as pointed out

Dyckhoff et al. (2012), the use of VLE by itself does

not improve the teaching process and today the

challenge is to create more motivating and engaging

learning materials and activities.

After preparing and providing online materials, it

is crucial to assess changing student’s behaviours and

performances.

The enormous quantity of data produced

surrounding the interactions in VLE provides the

basis material to a new research field, called Learning

Analytics, that has emerged in the last years. The

focus of interest is how this data can be used to

improve teaching and learning (Greller and Ebner,

2014).

In this paper it is intended to analyse the

MatActiva Moodle site, in order to check students’

engagement in several courses, since this is a relevant

issue in the project.

This paper is structured in the following way: in

section 2 we define Learning Analytics concept and

some tools used in this paper are summarized.

Section 3 describes the profile of MatActiva site,

in section 4 we present some questions driving the

research focus in this paper. Data collection is

exhibited and data analysed throughout section 5.

Section 6 draws conclusions based on the research,

and in the 7 and last section, we try to pull together

some of the key points made in previous sections.

2 LEARNING ANALYTICS

In any new research area many new terms introduced

must be clarified in order to achieve greater

understanding and therefore a better

contextualization of the problem.

In the literature there is some confusion between

the concepts of Learning Analytics (LA) and

Academic Analytics (AA) (Sclater, Peasgood and

Mullan, 2016). These terms are commonly used but

without clarity as to what the words are intended to

mean. LA focus on the learning process whereas AA

reflects the role of data analysis at an institutional

level (Long and Siemens, 2014). To better clarify this

difference, Siemens and Long (2011) present some

details and a comprehensive list comparing LA and

AA (Table 1).

According to the Society for Learning Analytics

Research (SoLAR - www.solaresearch.org),

“learning analytics is the measurement, collection,

analysis, and reporting of data about learners and their

contexts, for the purpose of understanding and

Azevedo, J., Torres, C., Lopes, A. and Babo, L.

Learning Analytics: A Way to Monitoring and Improving Students’ Learning.

DOI: 10.5220/0006390106410648

In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 1, pages 641-648

ISBN: 978-989-758-239-4

641

optimizing learning and the environments in which it

occurs”. This definition appeared in 2011 in the 1

International Conference on Learning Analytics &

Knowledge (LAK). Similarly Harmelen and

Workman (2012, p. 34) define Learning Analytics as

”the application of analytic techniques to analyse

educational data, including data about learner and

teacher activities, to identify patterns of behaviour

and provide actionable information to improve

learning and learning-related activities”. According

to these authors the first use of analytics in education

goes back to 1995 in a study that examined student

retention and performance. However, it appears that

widespread interest in analytics has only been

increasing since 2007 (Harmelen and Workman,

2012).

Table 1: Differences between Learning Analytics and

Academic Analytics and the respective beneficiaries

(Siemens and Long (2011)).

TYPE OF

ANALYTICS

LEVEL OR OBJECT

OF ANALYSIS

WHO

BENEFITS?

Learning

Analytics

Course-level: social

networks, conceptual

development,

discourse analysis,

"intelligent

curriculum"

Learners,

faculty

Departmental:

predictive modelling,

patterns of

success/failure

Learners,

faculty

Academic

Analytics

Institutional: learner

profiles, performance

of academics,

Knowledge flow

Administrators,

funders,

marketing

Regional

(state/provincial):

comparisons between

systems

Funders,

administrators

National and

International

National

governments,

educational

authorities

Siemens (2012) highlights the potential of LA to

dramatically impact the existing models of education

and to generate new insights into what works and

what does not work in teaching and learning.

ECAR (2015) strengthens this idea saying that LA

allows to move into an era where we can predict

student learning outcomes leading us to the concept

of predictive learning analytics. In US there are

several published results supporting the educational

success, in quite a lot of Higher Education Institutions

(HEI), either private or public. This source first

presented in a Panel by Norris and Baer (2012) and

after in EDUCAUSE online (2013) with a complete

report about 58 pages by the same authors.

With LA we can see which resources are more

often used and if offering documents/tests and other

related topics could be interesting for students

(Recker, Walker and Lawless, 2003; Bourkoukou et

al., 2016). We also could provide courses that match

the learning style of learners and “Several educational

theories and studies agree that learners learn easier

when their learning styles match with the teaching

style” (Graf et al., 2009, p. 1280).

Beside the above information, it is important to

know what the goals are, when using LA. Davenport,

Harris and Morison (2010) suggest that we have to

answer some questions, presented in Table 2.

Table 2: Questions that the research using Analytics would

answer.

Past Present Future

Information

What

happened?

What is

happening

now?

What will

happen?

Insight

How and

why did it

happen?

What’s the

next best

action?

What’s the

best/worst that

can happen?

2.1 Google Analytics

Google Analytics (GA) is a tracking application

offered by Google that tracks and reports website’s

traffic. It started in 2005 (Hasan, Morris and Probets,

2009) and the first studies that used is focused on the

analysis of e-commerce sites. This application

generates statistics about the visits and the user

interactions and allows users to export report data in

to MS Excel© format. Most of the data in GA, that

we analyse, are number of sessions and Average

Sessions. A “session” is a group of interactions that

take place on your website within a given time frame

and an “Average session duration” is the total

duration of all sessions (in seconds) divided by the

number of sessions (support.google.com/analytics/).

In GA and with some adaptions for an Educational

Website, there are some questions suggested to

explore such us: “Which pages do people visit?”,

“How does this change with date and time?”, “Where

do visitors come from geographically?” and “Is my

site user-friendly?”

A2E 2017 - Special Session on Analytics in Educational Environments

642

2.2 Moodle Reports

Moodle, especially in the last versions, has a lot of

reports and statistics that can substitute in some

aspects Google Analytics. There are many reports

that could be obtained, but in this work we emphasise

the following topics: Activity report, Course

participation and logs.

In Siemens (2011) opinion a learning

management system like Moodle or Desire2Learn

captures a significant amount of data, including time

spent on a resource, frequency of posting, number of

logins, etc. This data is fairly similar to the data that

we get from the Google Analytics. However, GA

gives some results that it is not necessary to reanalyse

or to do extra effort to extract knowledge.

2.3 Course Dedication

This is a Moodle plugin that allows us to see and

analyse the estimate dedication time of the students

for each topic and activity included in Moodle

Courses. More detail information can be found here:

https://moodle.org/plugins/block_dedication or in the

authors site: http://www.cicei.com.

3 MatActiva PROFILE

The MatActiva site has been developed in

ISCAP/P.PORTO in order to improve learning and

increase the levels of success in the mathematics

subject areas. Initially, this site covered two

programmes namely Accounting and International

Commerce but it is still been extended to others.

The project was started in 2007 to help students,

especially in first year (home students, distance

students, ERASMUS students, Lifelong Learners)

who have weak Math preparation, poor consolidated

basis on the subject and different backgrounds,

wanting to improve their performance and success

(Azevedo et al., 2009).

The general objective of this project is to increase

student’s Mathematics literacy and their rate of

success in Mathematics, taking into account that this

subject is an important component in all educational

systems. The project attends a large number of

students by helping them developing transversal

skills that could be useful in their active life.

The challenge was innovating the teaching and

learning process, exploring VLE as a pedagogical

resource to create more motivating and engaging

learning materials and activities.

Engaging materials and temporal flexibility

supporting the individual and collaborative study of

students, has been of great importance. The

development of this project was crucial, especially for

working students and for students in geographical

areas away from school, who cannot always attend

classes and timely access to the support materials. In

addition, for students with a weak preparation and

without consolidated basis this project was

determinant to encourage and give them more self-

confidence. Communication abilities among fellow

students and among students and teachers increased.

The MatActiva project is intuitive and provides a

set of useful functionalities according to the subjects

taught in mathematics department, such as Algebra,

Calculus, Statistics, Financial Mathematics, etc...

offering support materials and promoting

self-learning.

In the initial menu we can find eight topics:

 About us – Information about the project.

 Mathematics Zero – An area supporting students

who have difficulties and gaps in basic

mathematics. Video lectures followed by a set

of proposed exercises related with the issue

presented in the video are available in this topic.

They can also find and solve online diagnostic

tests to validate prerequisites skills necessary for

different subjects.

 Learning – In this topic students can find

interactive eBooks, tables of formulae, working

sheets about topics related with contents taught

in several mathematic courses. They can find as

well links to pages about mathematical subjects.

 Tests – Here students can find and solve online

diagnostic tests, evaluation tests with multiple

choice or true/false questions. A large bank of

questions originates a series of self-evaluation

tests, which the student can solve and submit,

taking conscience of their level of knowledge.

 Doubts – A forum where students can put their

doubts online and receive answers giving by a

teacher. More students are willing to participate

in an asynchronous forum than are willing to

speak up in class because forums are

asynchronous and students can take their time

composing a reply.

 MathChallenge – One-year competition open to

all ISCAP/P.PORTO community with a set of 6-

7 challenges/problems to stimulate the students’

interest in mathematic problems. Learning

mathematics should also be done through

activities research and discovery. The

challenges reinforce the motivation, thinking

and communication.

Learning Analytics: A Way to Monitoring and Improving Students’ Learning

643

 ERASMUS – Multiple choice tests in English to

support ERASMUS students.

 ETC – Provides curiosities, cartoons, contests

and games to test math skills.

A question bank of more than 1 100 questions

divided into categories according to the four subjects

– Algebra, Calculus, Statistics and Financial

Mathematics – support the multiple choices tests

available in this topic. These tests were constructed

according to the guidelines proposed in Torres et al

(2011) and Haladyna (2004). Furthermore, feedback

is provided for each question, allowing students to see

the proposed solution, step by step.

It is important to find out levels of students’

engagement in the several materials provided.

4 METHOD

In this study, we analyse the students’ data using new

instruments installed in the project web site. Those

instruments were Google Analytics, Moodle reports

and Course Dedication, which were installed in

Moodle platform. Data was collected from the topics

described in section 3. We use Google Analytics to

retrieve data related to the number of sessions and

average time of sessions of the students. Dynamic

Table by Excel© was used to generate the graphs.

We also used Moodle Reports and Course

Dedication to generate some of the results.

The data was extracted and analysed to better help

our students at ISCAP/P.PORTO. We did

quantitative analyses of the data.

The research questions were the following: i) how

deep do visitors navigate in the website pages? ii) are

some of the contents more interesting than others? iii)

it is important the day of the week when we want to

propose problems/tasks to be solved in a limited time

period? iv) have the goals been reached? v) are the

materials available enough for the purpose for which

they were created?

All those questions guided a reflection around

students’ engagement and behaviour and intend to

answer to a broader question about the effectiveness

of the project.

5 MONITORING STUDENTS'

LEARNING

First, we started by characterize the students. In

Figure 1 we can see that majority of students access

our Project in our area, Porto, followed by a

considerable number of students whose geographical

area cannot be identified. The next city is Vila Nova

de Gaia, which borders Porto. In fourth, appears

Lisbon which is at a distance of 300 km from Porto.

This seems a little strange to us, and it can be related

with the IP (Internet Protocol) assignment made by

the Internet Service Provider (ISP). The following

cities are within a radius of 50/70 Km away fro

Porto. The remaining cities are apart from more than

100 Km from Porto, but the number is residual.

Figure 1: Accesses to MatActiva from major cities.

The age of most of the students that access ou

site is between 18 and 24, as we can see in

Figure 2

. I

is possible to verify that there are many “older”

students. The main reason for this happen is that in

our School there are several programmes for adult

students, in special students that work during the day

and study during the night (from 18h30 to 23h).

Figure 2: Percentage of sessions in site by age.

Interestingly we observe that although the

students in range 35 to 44 only have 8% of sessions

(

Figure 2

) they spend 25% of the time in each session

(Figure 3). The older students

erhaps have more

difficult in some areas, thus they need more time in

each session to solve some quiz or understand some

concepts.

Geographical

areas

Porto

(notset)

VilaNovadeGaia

Lisbon

Maia

SaoJoaodaMadeira

Matosinhos

Felgueiras

Espinho

SaoCosme

18‐24

56%

25‐34

36%

35‐44

Sessions by ages

A2E 2017 - Special Session on Analytics in Educational Environments

644

Figure 3: Percentage of average sessions duration by

students age.

When we consider the gender, 63% of the

sessions are from females and 37% are from males.

The average session duration is 59% for females an

41% for males, which indicate that females spend

little bit more time in each of the sessions.

Figure 4: Percentage of sessions in site by gender.

Figure 5: Percentage of average sessions duration by

student’s gender.

Figure 6

resents the allocation of the sessions b

topics available in MatActiva. It should be noted tha

the numbers appearing in the graph are the sum o

various activities included in each of the topics, as

described in Section 3. GA shows the number o

sessions by topic. We can observe that “Learning” is

the most accessed topic.

Figure 6: Number of sessions by topics.

Despite that Learning is the topic with the bigges

number of sessions, we can see that the topics Etc an

Mathematics Zero are the ones with more average

time by session. This fact is not surprising and can be

ustified by the type of contents available in each

topic.

Figure 7: Average sessions (in seconds) by topics.

In Figure 8 we can see the number of students’

sessions by month of the year. We can observe tha

January and November are the months with more

students’ accesses to the platform. Surprisingly we

still have accesses during July and August which are

traditionally holidays.

18‐24

44%

25‐34

31%

35‐44

25%

Average session duration

63%

37%

Sessions by gender

female male

59%

41%

Average session duration

female male

05000

Learning

Mathematics Zero

Calculus

MathChallenge

Algebra

Statistics

Doubts

Etc

Erasmus

Sessions by topics

020406080

Learning

Mathematics Zero

Calculus

MathChallenge

Algebra

Statistics

Doubts

Etc

Erasmus

Average session duration by

topics

Learning Analytics: A Way to Monitoring and Improving Students’ Learning

645

Figure 8: Sum of the sessions by month of the year.

In Figure 9, the number of students’ sessions b

day of the week is presented. We can observe tha

students use the platform mostly on weekends an

Mondays.

Figure 9: Sum of the number of sessions by day of the wee

compared with sessions quizzes.

It is possible to see (Figure 10) that in the last three

academic years the number of accesses has grown up.

Other finding (Figure 11) is that in the last years

the accesses to the website by phones and similar

devices also increased. Note that the last academic

year presented in the graph is not complete, since a

moment we write this paper we were only at the

beginning of the summer semester.

Figure 10: Sessions in last 3 academic years.

Figure 11: Traffic by mobile devices.

In Figure 12, the total dedication time (in hours)

for each of the topics is

resented. We can find tha

the Calculus, surpasses with a big difference all the

other topics that we have in MatActiva. Note that in

this graph, the data is from last 4 years.

Figure 12: Dedication time in hours for each topic.

In Table 3 we

resent the number of students tha

accessed each of the materials available in

10000

20000

30000

40000

50000

Sessions by day of the week

All Quizzes

30000

60000

90000

120000

Allusers Tabletsand

Computer

traffic

Mobile

Devices

traffic

Number of sessions in last 3 years

1/09/2014‐31/08/2015 1/09/2015‐31/08/2016

1/09/2016‐6/02/2017

2000

4000

6000

8000

Traffic in last 3 years by mobile

1/09/2014‐31/08/2015 1/09/2015‐31/08/2016

1/09/2016‐6/02/2017

500

1000

1500

2000

Total Dedication (h)

0 20000 40000 60000

Month

Sessions by month

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646

atActiva, considering only the eleven ones with the

highest number of accesses. We can verify tha

quizzes (random tests) are the ones most used with

large advantage.

Table 3: The MatActiva activities most accessed.

Materials most viewed in MatActiva

Mathematics Zero Accesses

Video classes and Diagnostic Test 6651

Learning

Support Forms, Lesson Plans and Practice

Problems 4385

Calculus

Random Test - Multiple Choice (Functions) 16784

Random Test - Multiple Choice (Integrals) 19448

Random Test - Multiple Choice (Series) 6854

Statistics

Random Test - Multiple Choice

(Permutations and Combinations) 10806

Random Test - Multiple Choice (Discrete

and Continuous Distributions) 5568

Algebra

Random Test - Multiple Choice (Matrix and

Systems) 7571

Doubts

Algebra 430

MathChallenge

MathChallenge 2014/2015 3177

Erasmus

Tests 55

6 DISCUSSION AND

CONCLUSION

The students that access MatActiva are similar to the

ones of the school where the project was

implemented, being that we have more students in the

ages from 18 to 24 years and more female students. A

bigger proportion of sessions and the average session

duration in this range of students’ age and gender

confirm this statement. It is interesting to verify that,

despite the percentage of the number of sessions in

the platform is only 8% for students aged 35 to 44, the

proportion of average session duration increases

significantly to 25% in this range of years. This can

lead us to the conclusion that they spend more time in

each of the resources than younger students. We

think that this could be done to the fact that these

older students do not study for a long time and have

got more difficulties. Considering gender, the

differences are not so pronounced, however females

spend more time in each of the sessions than males.

Most of the students’ accesses are from cities very

close to Porto, which is also accordingly to the type

of students in the school.

When we look at the students’ preferences, the

topic “Learning” presents the highest number of

sessions, but they spend more time, in average, with

the topics “Mathematics Zero” and “Etc”. This can

be due to the fact that the topic “Mathematics Zero”

is more intended to promote study and it requires a

more detailed and careful approach. This is a very

important topic to support students with more

learning disabilities leading to longer sessions.

The “Learning” topic has got several support forms

and working sheets in pdf format, that can be

downloaded and does not require longer session’s

duration.

We can also verify that students have bigger

dedication time (total hours spended) to the

subject “Calculus”, which is a subject that includes a

lot of quizzes with multiple choice questions. We

consider that this can be the reason for the students

spend a big amount of time in this subject, since this

is a way to preparing the assessment. We consider

that the other materials can be very important in the

students’ learning process, and were very hard to

develop. Thus, we must develop strategies to increase

the use of other types of resources by the students.

Concerning the analyses of the number of session

by month it was interesting to verify that we still have

accesses during the traditional holydays period. This

was a motivation for the teachers that are involved in

the project. The months with the biggest number of

accesses are November and January. With regard

November, after one month of classes, the students

start feeling difficulties and search for these tools that

can help them with their learning process. As for

January, students have exams at the end of that

month, and thus they can also be searching for tools

to help them with their study.

Considering the number of sessions by day of the

week, it was surprising verify that the students use the

platform during weekends. This can be due to the fact

that the students live near Porto but have long voyage

periods during the weeks, thus in the weekends they

stay at home and can have more available time to

dedicate to their study. This suggests that making

quizzes and homeworks available during weekend

may have better acceptance by students.

It was very stimulating to verify that the number

of students using the platform is steadily increasing in

Learning Analytics: A Way to Monitoring and Improving Students’ Learning

647

the last years. The increase in the use of mobile

devices, puts some more challenges, since the site is

not prepared to those types of accesses. Thus, we

should consider adapting the platform. We hope that

this can increase the participation of the students.

7 FINAL REMARKS

Learning Analytics have become popular in the

literature. In this work, we present some instruments,

such as Google Analytics, Course Dedication, and

Moodle Reports, that can help to improve the students

learning, based in this concept.

We described the project that we intended to

improve and that is going on for some years in

ISCAP/P.PORTO.

This paper constitutes an important reflexion for

the members of the project. It was verified that many

of the materials available are not used, and that the

students prefer to access the quizzes.

In the future we are planning to inquiry the

students about the reason for accessing mostly this

type of resources. We also intend to define strategies

to promote the other resources, since we consider that

these are important tools to improve students’

learning. Also, we are planning to implement

predictive analytics, and other analytics resources that

can improve the success of the students.

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