Colour Codes Method Digitalization in edX E-learning Platform

Laura Dzelzkaleja

1,2

and Zanis Timsans

Riga Technical University, Faculty of E-sudy Technologies and Humanities, Kronvalda str.1, Riga, Latvia

Riga Technical University, Cesis Affiliate, Piebalgas str.3, Cesis, Latvia

Keywords: Colour Codes, edX E-learning Platform, Learning Analytics, Learning Evaluation, E-learning Software, Open

Code, IBM SPSS Modeler.

Abstract: Evaluation and improvement of the e-learning process are getting more and more attention. There are tools

and methods available, but none of them is a solution to everything. A new Colour code method for

understanding students’ learning process has been introduced in the previous papers. The method uses three

colour codes: red for "problem", yellow for "work in progress" and green for "job done", and previous research

in classroom showed that this system works. The next step was taken to implement the method in the

computer-based learning management system (LMS). For this purpose, edX learning platform was chosen. In

this paper, an overview of edX learning platform and e-learning course evaluation methods and strategies is

given, as well as a description of method and implementation. The main conclusions are that edX platform is

suitable for the new tool and code block generation, as well as for sharing this code with others; the colour

buttons worked as predicted, the data are gathering and gives an insight of which learning items get the most

attention by learners, rating by the usage of colour buttons. 45% of students that attended e-learning course at

least once, used colour buttons as well. The most used button was the green button “done” (67.2% of all

pushes), the second largest was orange “process” (25.4%), but the least used was red “problem” button (7.4%).

Mostly the codes were used in online tests (75%), but some part went to peer assessment (9%), informative

learning materials (8%) and homeworks (5%).

1 INTRODUCTION

Organizations nowadays focus very much on the

production and distribution of information and

knowledge, in what has been termed the Knowledge

Age (Soares, 2013). Especially important it gets in the

education field which is all about knowledge.

E-learning is also one of the areas that attracts the

most research and development funding. If this

investment is to be maximised, it is imperative that

we generate robust models for the evaluation of e-

learning and tools which are flexible in use but

consistent in results. Student examination is a

powerful indicator of the effectiveness of e-learning,

it is not the only one (Attwell, 2006).

Possibility to evaluate and improve the learning

process is getting more and more topical. It is spoken

a lot about the educational system and change of

paradigms. New and digital age based educational

paradigms are emerging (e.g., connectivism

(Siemens, 2005)). But the question about how to find

out, what improvements are needed in the learning

process and content, is still actual.

It is always hard to make changes in the existing

system, especially in such an inert system as

education. And change is difficult, complex, and

risky because it has unintended side effects. Effects

of change ricochet through systems via interactions

between its parts (Mitra, 2008). That is why people

are intuitively rather cautious with any kind of change

in the educational field because the consequences are

often not observed right away and too many other

systems depend on the educational system to

experiment with it blindly since knowledge is the

chief resource in our economy (Drucker, 2000).

In order to improve educators possibilities to

make improve the quality of a learning course, a new

Colour code system was introduced in previous

research in a face-to-face learning environment. The

system is meant for understanding students’ learning

process dynamics and find the black spots more

quickly.

Dzelzkaleja, L. and Timsans, Z.

Colour Codes Method Digitalization in edX E-learning Platform.

DOI: 10.5220/0006806601650172

In Proceedings of the 10th International Conference on Computer Supported Education (CSEDU 2018), pages 165-172

ISBN: 978-989-758-291-2

165

2 COLOUR CODE TOOL

IMPLEMENTATION IN A

LEARNING PLATFORM

The method could be referred as a Benchmarking

model by Attwell (2006). “There have been several

attempts to generate sets of criteria for quality

assuring e-learning. However, these tend to be

skewed towards proposing quality standards for e-

learning systems and software which often disregard

key variables in the wider learning environment or are

based on criteria associated with evaluating

traditional learning processes (and which disregard

the technology) or criteria associated with measuring

learner achievement through traditional pedagogies.

An additional problem is that the designers of these

benchmarking systems are often locked into a

particular model of e-learning which limits their

transferability” (Attwell, 2006).

2.1 Colour Code Method

A new approach, presented in the authors’ previous

papers (Dzelzkaleja (2016), Dzelzkaleja (2017)) for

continuously evaluating learning process in real time

was presented. In this paper, the method is further

analysed with the data gained from the first

experimental observations.

The main principle of the method is as follows:

there are three colour codes which are used by a

learner to show the teacher the progress in every

moment of the learning process.

 “Red” is used to show that the task is not clear

or difficulties have appeared during the

process, some assistance is needed (in the form

of tutour or some extra learning materials);

 “Orange” is used whenever the task is being

done and everything is clear – no need for

assistance;

 “Green” is used when the learner has finished

the task or isn’t doing anything.

The colours have been chosen based on the

traffic-lights colour coding, since these colours are

recognizable for almost every person andgives and

opportunity to intuitivelly guess the meaning of the

buttons – red as a something that slows down or stops,

green s something that allows you to go the next place

and is connected to pleasurable associations, and

orange (yellow in some cases) being something in the

middle of both previous.

In case of distant learning, the codes need to be

installed so that the learner could click on the

appropriate colour on the screen conveniently in

every moment of the learning process.

There are other widely used methods for getting a

feedback. A nice example of giving a feedback are

voting systems expressing sentiment such as “like”

and “dislike” which is already a popular way of

showing your opinion of a picture, video, text etc. in

social media platforms. Coursera learning platform

offers possiblity to rate each learning object with

“like” (thumbs up symbol) and “dislike” (thumbs

down symbol), as well as report a problem (flag

symbol), visually they are represented with a dark

contour with no filling. In the case of such voting

systems, students have to make a decision whether

they like or dislike the material, and it can be

connected to extra cognitive pressure, since there are

only too possibilities to chose from and nothing in the

middle. In Moodle there is a block available, that

provides a possibility for a teacher and the student to

follow the progress of the student with the help of a

colourful Progress Bar, that shows how much of the

course materials are already viewed or completed and

identifies students at risk of completing the course.

The proposed Colour code method in this paper

differs from already available methods because it

doesn’t ask from the user to make a decision whether

they like every single learning item, but just ask to

record, when and how their learning situation

changes. So basically, it is meant to provide

continuous data rather than discrete data. For

example, it can show that from time x till time y the

student has been in the learning process, from time y

till time w the student struggles with a problem, in

time y the student presses “Done” button and from

time y till time z the student has a break, in the

moment z the student presses “In process” button and

starts another learning session until time a etc.

2.2 edX Learning Platform

The Internet and related web technologies do offer

great solutions for presenting, publishing and sharing

learning content and information, as is the case in

many other areas. Special software called Learning

Management System (LMS) is generally used in most

institutions providing web-based learning.

Nowadays, various LMS are used as a supporting tool

in electronic education. A great number of LMSs,

both commercial and open source, are widely used for

educational and training purposes. Most universities

combine the forms of learning, using one of the

commercial or open-source LMSs. They tend to use

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products such as Claroline, Fle3, ILIAS, MS Class

Server, WebCT, Eden, Enterprise Knowledge

Platform, LearningSpace, eAmos, eDoceo, Uniforms,

uLern, Aspen, Oracle iLearnin, NETOPIL School and

Moodle (Balogh, 2013).

edX learning platform is not among the most

popular learning platforms in the world Toren (2015),

Mauri (2017), Burns (2014), Laurinavicius (2017),

Getting Smart (2017), nevertheless, its influence is

growing and is mentioned in research articles like de

Lera, (2012) and Banday (2014). edX was launched

only in the spring of 2012 by Massachusetts Institute

of Technology (MIT) and Harvard University and is

supported by Google. Despite the short time of

existence, in the end of 2016, it had already ~ 10

million users (Goulart, 2016). In comparison Moodle,

which is the biggest LMS, have about 122 million

users (Moodle, 2016) and Coursera learning platform,

that has the biggest course catalogue amongst MOOC

providers, has 23 million registered users. So edX is

coming in as a powerful player.

EdX provides university-level courses and the

topics are mostly associated with science (Fenton,

2015 ). In 2015 Riga Technical University launched

their own open source edX learning platform in which

the Colour code method was initially deployed.

The strengths of edX according to Fenton (2015)

is a large catalog of online higher education courses;

enroll in either self-paced or timed classes, ranging

between four and twelve weeks; open source platform

(Open edX) enables developers to build and share

assessment modules, so it is possible to make changes

to the appearance of the learning platform, and to add

an additional functionality in form of “xBlocks”

which are components of edX architecture and

connects different sources (Open edX , 2017)

students may audit or pursue Honor Certificates both

for a fee or for free; video transcripts available.

Weaknesses of this platform are that students have

little contact with their professors; outside the

sciences, edX catalogue has gaps; discussion forums

are comparably less convenient (Fenton, 2015 ).

As a not-for-profit open source platform, edX

encourages developers to contribute to their Open

edX initiative, and, thanks to that access, developers

have made edX course assessment, while still

imperfect, the most versatile currently available. Its

courses are supplied by some of the world's most

prestigious institutions, including UC Berkeley,

Boston University, as well as both founding

institutions. But edX is not meant as a replacement of

a traditional university. While participants can audit

classes and earn proof of enrollment for free, edX

does not offer the credentialing of a traditional

university. Typically, learners are adults interested in

sciences, but that is expanding, thanks to a high

school initiative and professional education program

(Fenton, 2015).

To call edX courses "online courses" is somewhat

misleading. EdX courses possess features of online

education, including discussion forums (often

moderated by faculty and teaching assistants);

machine-graded multiple-choice assessments; self

and peer assessments; and, of course, video lectures

(typically divided into segments of twenty minutes or

less). However, unlike an online course at my home

institution, edX courses usually do not require

prerequisites, and anyone can join at any time before

the course ends. This open invitation can be a boon,

as it invites all sorts of non-traditional students with

different perspectives; however, by the same token, it

also means that instructors cannot take for granted

certain levels of competency (Fenton, 2015 ).

2.3 E-learning Course Assessment and

Evaluation

Some summary on assessment, evaluation and

feedback can be found in previous work (Dzelzkaleja

(2016), Dzelzkaleja (2017)). We found EU

handbook Evaluating E-learning A Guide to the

Evaluation of E-learning (Attwell, 2006) as a very

comprehensive, structured and useful guide towards

evaluating a course.

The evaluation methods and tools differ widely.

What they do have in common is that they recognise

the importance of evaluation and many propose that

evaluation should be an integral part of any e-learning

initiatives or development. In this regard, they tend to

lean toward a management model of evaluation; the

primary aim of the evaluation is to provide feedback

to influence e-learning implementation and future

development (Attwell, 2006).

Firstly, there are many online data gathering

instruments for assessing, typically, the user interface

characteristics of software (e.g. student perception

questionnaires) and secondly, there are devices to

record and analyze usage by duration and frequency

of log-in, pages accessed, user profile etc. The next

question that logically arises is - why do we need

another evaluation method in e-learning platform?

The reason is that the available assessment and

evaluation methodologies do not provide enough

information about the reasons of user behaviour in the

real time constantly, many of these are sophisticated

in their design and ingenuity but lack guidance on

interpretation and analysis (Attwell, 2006).

Colour Codes Method Digitalization in edX E-learning Platform

167

We will try to sum up the assessment and

evaluation possibilities available in different learning

platforms and what type of data is available from

them. Let us divide data into two parts: 1) data

connected to user grades and course curricula

acquisition; and 2) data connected with course

quality, learner satisfaction and user behaviour in the

course.

With the 1st set of data, it is more or less clear

because it is easy to track and follow learners’ test

results. However, the 2nd set of data demands some

specific knowledge in programming and (big) data

analysis, that not every course developer has, and

usually the course developers and learners don’t have

an access to the raw data. So there is a need for user-

friendly data visualisations. Some good examples,

where this is available, is Google Analytics Tool.

Another drawback for the 2nd set of data is that

only a limited number of data types is gathered, and

that data may not reflect the reasons of user behaviour

or do not show the patterns of the behaviour

comprehensively enough. So complementary data

gathering is being researched broadly in recent years.

Technologies like eye movement tracking, facial

expression recognition, voice tonality recognition,

pose and gesture recognition have been tested and

implemented. But these often demand to use a

computer built-in camera, which means some loss of

privacy and may leave users feeling uncomfortable,

in some cases an additional equipment is needed such

as eye tracking hardware and software which also can

be costly (Landowska, (2017), Rezende (2017)).

It leaves us with mainly two lower cost and easier

to implement possibilities: a) research mouse

movement patterns on the screen and/or b) implement

additional data gathering opportunities on the screen.

In the first case, mouse movement analysis can offer

us possibilities to make course visual design

accordingly and to make several different visual

designs for the course according to the learner’s type.

But this kind of design is hard to duplicate from

course to course. In the second case, there is a need to

implement additional blocks for feedback gathering

on the screen.

2.4 Description of Method and

Implementation

The Colour code method acts in a similar way to

voting systems - giving the user possibility to express

its process evaluation by clicking the appropriate

button but in addition, it also attaches chunks of

valuable metadata with each click.

Figure 1: Screenshot of EdX with buttons.

The set of colour coded buttons are fixed on the

screen in the same place at all times and is visible only

at course content (Figure 1). When appropriate button

is clicked information of the current state is recorded

and sent to a database where all the data is gathered

(Figure 3).

Figure 2: Button states.

The visual and interactive design of the buttons is

based on basic principles of user interaction and

experience design guidelines, thus implementing

associated colours and playful animation to

encourage more frequent interactions from users

(Figure 2). Buttons are equipped with an anti-spam

feature as well as feedback message letting the user

know if click was recorded successfully.

As mentioned previously, data gathered from this

method is complementary to already existing user

data in edX platform. The type of the button pushed

records the process stage at the current time (Done,

Process, Problem (Figure 2)), and can be subject to

change during the learning process. With every click

of the button wide range of other parameters is

recorded. These parameters are as follows: 1) what

type of button is clicked, 2) time when it’s clicked, 3)

username, 4) course name, 5) course id, 6) section

title that is currently being viewed, 7) video timer (if

it’s available), and 8) page URL.

Scripting languages, such as javascript, HTML

and CSS, and programming languages, such as

Python, have been used to develop the plug-in.

Currently, it is not a ready-made solution and is in its

early stages of development. The finished product

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will be packed into edX xBlock, which will be

available then to anyone, as it will be open-source.

Current set-up requires jQuery code injection into

edX document object model (DOM) which then

renders buttons and communicates with a database.

Python programming language was used to set up the

database server.

Figure 3: System diagram.

edX has a built-in data analytics feature, but it is

not sufficient enough and this is why Colour coded

method has been implemented.

A research of Schumacher (2018) shows that one

of the most desirable features that students expect

from learning analytics is a production of

personalized analyses of their learning activities. And

Colour code method is aiming to do just that.

As it is seen in Figure 3, there are two separate

databases for collecting learner’s behavioural data

from the platform. Both databases are separated in

order to not temper with EdX core setup. Both EdX

and colour coded button method have adapted

MySQL database, thus all data can be exported in

different formats and analysed with chosen data

analysis tool.

In this paper, a colour code button block

implementation in edX is presented and tested. The

next step is to convert the data into visually appealing

and understandable form for both learners and course

developers. This would engage learners to use the

codes more often since they will be able to see the

changes and patterns of their behaviour in their

profile. For course developers, however, this

information will immediately give a feedback on the

quality of course material and student’s learning pace

as well as help to uncover possible issues associated.

3 RESULTS AND DISCUSSION

In this particular paper, a preliminary data analysis

was done, using IBM SPSS Modeler. This is a tool

that allows to process a big amount of raw data from

a database and predict the future events with a help of

models.

As an experimental group was chosen Riga

Technical University 1st Bachelor course students. The

learning subject was Entrepreneurship. Registered

students’ number was 106, they were split into groups,

but the learning curricula was the same for all. The

experiment took part in the autumn of 2017 and the

database was active and started to record from October

19. The last data were mined from December 14.

Learning form was blended learning - some part

of the course material was to be acquired through edX

learning platform. In the beginning of the course, the

learners were introduced to the edX learning

platform, since it was a new platform for them,

together with the Colour code method, and were

asked to use the buttons voluntarily to help the

research. For this experiment group, there weren’t a

personalized data visualizations of their activities

available in edX since this experiment was mainly

targeted to test the system, debug it, see how the data

is gathered in the database and get a feedback from

students afterwards.

The experiment is still continuing, but there are

some preliminary results available. First of all, the

system works as it should - the colour buttons are

there and the data is gathered when a colour button is

pressed. Quantitative data were gathered during the

experiment. After the data were gathered and primary

data processing was made, qualitative analysis was

made as well.

Not all of the students used the buttons: 34 valid

student IDs were recorded, and it is 32% of all 106

students. It should be noted as well that 31 of all the

students haven’t logged in the online course at all, so

the actual proportion of those who used buttons and

logged in the course was 45% of all 75 students who

used course at least once.

The green button “done” was pushed the most

times (67.2% of all pushes), the second largest was

orange “process” (25.4%), but the least, to our

surprise, red “problem” button was pushed (7.4%).

Graphically it is displayed in Figure 4.

Colour Codes Method Digitalization in edX E-learning Platform

169

Figure 4: Proportion of pushed colours.

Mostly the codes were used in online tests (75%),

but some part went to peer assessment (9%),

informative learning materials (8%) and homework

(5%), as Figure 5 shows.

Figure 5: Code usage by learning material type.

Figure 6: Buttons pushed in different learning material

types.

If we analyse the gathered data about button pushes

by learning material type (Fig.6.), it can be seen that in

tests, peer review tasks and homework “Done” green

button has been pushed the most, but it differs for

learning items (meant the informative materials, not

tests, homework and peer-review), and the biggest

proportion here is orange “In process” button, “Done”

being only the second and no problem button pushed

at all. It can be seen that the “Problem” button hasn’t

been pushed in the Peer review tasks as well. Buti n the

Homework no “In process” data has been recorded.

Partly the low activity in e-learning platform

could be explained by the fact that it was a blended

learning subject, and the edX learning platform was

new to the learners.

Despite thr fact that buttons were used mostly in

tests, the colours were distributed quite similar

throughout all of the course material types. It could

mean that students perceive the possibility to push the

green “done” button as a reward - a symbol of work

being done. The reasons for red being pushed so little

could be one of three: 1) good quality of the

course/nothing better to compare to, 2) feeling that

there’s no point of pushing red, because tutor would

probably not react/the student feels it’s his/her

responsibility to understand and 3) fear from the tutor

getting angry for judging/fear of getting caught not

pushing the buttons (could explain the high activity

of pushing buttons in tests). These are just a few ideas

that could be useful explaining the results and need a

further research to reveal the true reasons for this.

4 CONCLUSIONS

There are many online data analysis instruments for

assessing available, typically, the user interface

characteristics (e.g. student perception questionnai-

res) and there are devices to record and analyze usage

by duration and frequency of log-in, pages accessed,

user profile etc. But the available assessment and

evaluation methodologies do not provide enough

information about the reasons of user behaviour in the

real time constantly, many of these tools are

sophisticated in their design and ingenuity but lack

guidance on interpretation and analysis.

edX learning platform is not among the most

popular learning platforms in the world but has some

important strong parts, and its influence is growing.

Open source platform Open edX enables developers

to build and share assessment modules, so it is

possible to make changes to the appearance of the

learning platform and to add an additional

functionality in form of “xBlocks” which are

components of edX architecture and connects

different sources. edX encourages developers to

contribute to their Open edX initiative, and, thanks to

that combined efforts, edX platform has growing

number of new features and functions.

The Colour code method acts in a similar way to

voting systems - giving the user possibility to express

its process evaluation by clicking the appropriate

button, but, in addition, it also attaches chunks of

valuable metadata with each click. The Colour code

method, while still imperfect, is very versatile. In order

20%

40%

60%

80%

100%

Tests Peer

review

Learning

items

Homework

In process Done Problem

CSEDU 2018 - 10th International Conference on Computer Supported Education

170

to offer this method for a wider audience of edX users,

it has to go through severe tests and trials with possible

changes and technical upgrades to the current version.

Researchers are aware that current data gathering

approach may not be enough and that even more

advanced content marking approaches are needed.

Data analysis connected to course quality, learner

satisfaction and user behaviour in the course demands

some specific knowledge in programming and (big)

data analysis, that not every course developer has, and

usually the course developers and learners don’t have

an access to the raw data. So there is a need for user-

friendly and easy to interpret data visualisations

solution for both learners and course developers. This

would engage learners to use the codes more often

since they would be able to see the changes and

patterns of their behaviour in their profile. For course

developers, however, this information would

immediately give a feedback on the quality of course

material and student’s learning pace as well as help to

uncover possible issues associated.

The experiment is still continuing, but there are

some preliminary small data set available. The

conclusion is that system works as it should - the

colour buttons are there and the data is gathered when

a colour button is pressed. As mentioned in the

discussion, only part of the students used learning

platform and colour codes. The green button “done”

was pushed the most times (67.2% of all pushes), the

second largest was orange “process” (25.4%), but the

least - red “problem” button (7.4%). Mostly the codes

were used in online tests (75%), but some part went

to peer assessment (9%), informative learning

materials (8%) and homework (5%).

More research is to be made on this to analyse,

what is the correlation between students using the

learning platform and codes - whether they use codes

whenever they use the platform, or only partly.

Another interesting question arises - whether course

design correlates to the codes used for different

learning item types: for example, if 70% of learning

materials in the platform are videos, then will the

codes be used 70% on videos.

In this preliminary analysis descrete data was

analysed – how many pushes and why. In the future

it planned to deepen the analysis so that the time

parameters would be included and some conclusions

about session lengths could be drawn.

ACKNOWLEDGEMENTS

This research has been developed symbiotically with

a support of grant from the European Regional

Development Fund (ERFD/ERAF) project

“Technology Enhanced Learning E-ecosystem with

Stochastic Interdependences - TELECI”, Project

No.1.1.1.1./16/A/154, and international European

Project funded under the FLAG-ERA Joint

Transnational Call (JTC) 2016 FuturICT 2.0. Special

thanks to Jānis Kapenieks (jun.) for allowing to

experiment in his learning course and all the help.

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