From Analytics to Cognition: Expanding the Reach of Data in

Learning

R. Tsoni

, C. Samaras

, E. Paxinou

, C. Panagiotakopoulos

and V. S. Verykios

1,a

School of Science and Technology, Hellenic Open University, Patra, Greece

Department of Primary Education, University of Patras, Patra, Greece

Keywords: Data Mining, Learning Analytics, Hellenic Open University, Big Data Lab.

Abstract: Education constitutes a rapidly changing and challenging environment, therefore, a shift from reporting to

actionable interventions based on data is almost imperative. At the same time, Learning Analytics is

incorporating increasingly advanced tools and methods. Artificial Intelligence and cognitive science allow us

to study in depth the behavior of students, creating patterns and prediction models. All the above summarize

the scope of the newly established Big Data Analytics and Anonymization Laboratory (BAT Lab) in the

Hellenic Open University. This paper presents the vision and the work in progress of the BAT Lab in an

attempt not only to produce interpretable results but also to organize and present these results in a highly

usable way for non-experts. Additionally, PRIME-EDU software, which is the team’s latest work, is presented.

1 INTRODUCTION

1.1 The Era of Data Analytics

Higher Education Institutions are facing major

difficulties which will, at some point, render them

incapable of sustaining their existence. Some of these

problems include outdated curriculum, high rates of

students’ dropouts, and graduates who lack the

necessary skills for the modern job market. This leads

to unqualified workforce and outdated and irrelevant

institutions (DeMillo, and Young, 2015). It is obvious

that changes need to be made.

Contemporary methods to data-driven decision

making include controlled experiments like A/B

testing and the use of questionnaires. However, all

these methods present certain problems. Regarding

experiments, scalability is a big issue, and as far as

questionnaires are concerned, they are not considered

reliable as people’s answers are not always

representative of their true feelings (Lindstrom,

2010). Manual approaches to gathering data incur a

very high cost as well.

It appears that there must be a more suitable

alternative. Living in the Petabyte age equals rapidly

developing technology and more information

gathering than ever before. This has led to the

Work by Vassilios S. Verykios was partially supported by a research grant from New York University Abu Dhabi.

creation of Big Data research area which hopefully

will provide the answers to some of the problems that

society and academia are facing today. In order to do

this, data mining and data analytics are employed, by

using a well-established iterative process for model

building and evaluation. It used to be the case that the

focus of this process was on the practical steps,

whereas it has now become apparent that the pressing

need is to be able to tell a story with the data (Knaflic,

2015). A story that aims to shed light on the issues

raised in education.

1.2 Learning Analytics: An Attempt

for an Efficient and Innovative

Model for Educational

Development

Education, as a living and rapidly transforming

organism, needs a sophisticated method to collect,

analyze and act upon data. Actually, higher levels of

complexity demand more sophisticated ways of

information processing. Business has benefited and

ripped rewards for the insights provided by data

analytics and it would not be an exaggeration to say

that is has revolutionized the way commerce works

today. It would be really useful if education

458

Tsoni, R., Samaras, C., Paxinou, E., Panagiotakopoulos, C. and Verykios, V.

From Analytics to Cognition: Expanding the Reach of Data in Learning.

DOI: 10.5220/0007751904580465

In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 458-465

ISBN: 978-989-758-367-4

stakeholders could find ways, consistent with

pedagogical values and related to the educational

demands, to apply these successful models with the

same extraordinary results. That is all about Learning

Analytics.

Learning Analytics involves analyzing data that has

been collected from different educational contexts and

environments, and from various levels with the view of

discovering patterns. By gaining this knowledge,

decisions can be made and changes can be

implemented which will bring about favorable

outcomes for the students and the institutions.

Hellenic Open University (HOU) is the only Open

University in Greece. It offers exclusively distance and

blended learning courses to mature students of diverse

backgrounds, skills, and qualifications. The unique

nature of this institution means that it confronts a

unique set of problems as far as meeting the needs of

its students is concerned. Finding ways to

accommodate such a diverse population and optimize

the learning experience is certainly a challenge that led

to the creation of the Big Data Analytics and

Anonymization Laboratory (BAT lab).

2 BAT LAB PRESENTATION

2.1 The Objective

BAT lab conducts research in the field of large-scale

data management and analysis, in conjunction with

privacy protection of this data with the view to

understand student behavior and interaction both with

their peers but also with the teaching staff. Its main

purpose is to find ways to delve into the hidden patterns

relating to student behavior, communication,

engagement, performance and faculty guidance.

2.2 Difficulties and Barriers

Both children and adolescents are familiar with the

idea of finding and using digital information. For them,

data is ubiquitous and they pursue the creating, using

and sharing of data. They own smartwatches to

monitor their physical activity, they use social media to

state their emotions and they measure their popularity

by counting “likes” and “views”. Unlike the new

generation, a lot of academic members are still using

conventional methods to get feedback from their

students and theories to predict the efficiency of the

educational material they use. Thus, a significant

barrier is posed by a pre-existing culture or, in some

cases, even a sense of technophobia. So, in order to

obtain solutions, it is imperative that the data is

analyzed in a way that is intuitive and at the same time

more user-friendly, to reveal hidden models or patterns

for prediction and summarization, which are not

initially obvious (Siegel, 2013). A shift from metrics to

analytics and from reporting to actionable

interventions is the next generation of the learning

environment. However, this transition will require a

significant institutional change (Baer, and Campbell,

2012).

Another, more practical, barrier is the fact that almost

all the available NLP (Natural Language Processing)

tools do not support Greek language. Specific tools for

sentiments analysis have been created by Greek

researchers (Agathangelou et al., 2018) and are used by

the team for analyzing students’ fora (see section 3.4).

3 PROJECTS AND ONGOING

RESEARCH

3.1 Outlining the Identity of HOU

HOU is University unique at its scope in Greece, as it

serves the ideal of openness and inclusiveness. This

openness leads to a big diversity amongst the students

who choose to be educated through this system. So, it

was crucial to be able to gather and analyze

successfully all this information that comes from the

students’ admissions.

For example, data coming from HOU students’

admission in the last four years analyzed in BAT Lab

showed that there is a greater number of female

applicants, although there is a progressing decline in

the total number of applicants throughout the years

(figure 1). School of Humanities is much more popular

among the female applicants, whereas in the School of

Science and Technology the male applicants far

Figure 1: Number of applications per gender during 2003-

2013 in HOU (Stavropoulos et al., 2017).

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459

outnumber the female. According to figure 2, the

highest percentage of the applicants comes from the

regions of Attiki, Achaia, and Thessaloniki although,

for the rest of the country, there is not a particular

pattern that can be detected.

Figure 2: Applications per region (Verykios &

Stavropoulos, 2018).

3.2 Assisting Tutors to Catch up with

Students’ Progress

The percentage of the students who submitted

assignments and quizzes, or the level of the students’

and the tutors’ engagement, or other relevant

information is very significant for online courses

where the transactional distance is longer than it is in

a face to face course (Moore, 2013). For example,

data on how long does a student stay connected to the

platform or on how many connections does she/he

make to the platform on average per day, provides

information on student’s dedication time. The same

information is also available about tutors (figure 3). It

is notable that even though some tutors seem to

dedicate the least amount of time, in comparison to

their colleagues, they still make an adequate number

of connections. This sort of information allows the

module coordinator to create some guidelines, in a

sense of best practices and benchmarks for the tutors

in order for the latter to become more efficient in their

work and for the coordinator to access more

objectively tutors’ work.

Another piece of information that could help the

tutors become more effective in their roles, is the

students’ engagement in relation to the time of the

day. It appears that students are more active in the

evenings (figure 4) which is something that makes

sense since the majority of the students in HOU are

mature students who have family and job obligations

during the rest of the day. Thus, it would be more

Figure 3: Tutors' connections per day.

Figure 4: The distribution of active students and students’

activities to daily hours (Gkontzis et al., 2017a).

productive for tutors to be also connected to the

university platform in the evening rather than in the

morning when students’ engagement appears to be

very low. As a result, every student communicates

directly with the tutor, asking for advice and help and

the whole educational process becomes more

productive for both of them. The student progress bar

(figure 5) shows how many of the allocated activities

a student completes. From the overview page, the

tutor can compare every student’s performance to the

performance of the whole class.

Figure 5: Students' progress bar (Gkontzis et al., 2017a).

All these important facts allow tutors to be supportive

to students, provided that these data are easily

accessible. Thus, visualization is a major concern.

Learning Analytics dashboards had been successfully

used, enabling tutors with no previous LA training to

detect students’ trends in order to provide

personalized assistance (Gkontzis, et al., 2017).

Furthermore, students’ logins, replies, and

quizzes were blended with the average grade of the

main written assignments throughout an academic

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460

year. Useful observations from the students’

educational online activities were made that can be

used as predictive factors for their academic

performance (Gkontzis, et al., 2018a). In a related

work Gkontzis, et al. (2018b) presented an analytical

framework that provides a comparison between

students’ actual predicted grade, identifying students

who have included third-party services in their

assignments.

3.3 Evaluating the Efficiency of

Methods and Educational Material

While it is true that big data is a field of immense

interest, unfortunately, the spotlight is on how to

store, index, retrieve and interrogate the data rather

than how to analyze and utilize it in an efficient and

user-friendly way which is a shame as the answers to

most problems are hidden within this ocean of data

(Verykios & Stavropoulos, 2018).

In a project run by Paxinou, et al., (2017) the

authors presented that the Classical Test Theory

(CTT) method is not able to prove successfully the

lead of a virtual reality laboratory in preparing

Science students in HOU for the experiments in the

wet lab, as this method doesn’t separate the difficulty

of the questions in the written tests from the students’

ability. Contrariwise, the applications of more

advanced theories and methods, as the Item Response

Theory (IRT) allows highlighting the positive impact

of a teaching method to the students’ learning

outcomes. This result poses a significant issue of the

importance of choosing a suitable analysis framework

in order to achieve a result that best describe reality.

There is a great value in distinguishing the skill level

of a student from the difficulty level of the questions

of each evaluation test. This significant result that

takes advantage of the IRT model, was proven from a

massive data analysis of a several million recorded

online games of chess held by Anderson from

Microsoft, Kleinberg from Cornell University and

Mullainathan from Harvard University (2016). Their

research brought to light unquestionable proof for the

players’ behavior concerning decision making. The

same way Guo et al., (2014) changed the way that

educational videos are made by analyzing a vast

number of MOOC data concerning students’

engagement.

An interesting study highlighted the importance

of data analysis for profiling new coming university

students. The study was conducted through four

successive years in freshmen students of the School

of Education at Patras University in a collaborative

project between BAT Lab and University of Patras

(Panagiotakopoulos et al., 2017). Data analysis

revealed the difference between students’ opinion and

their actual attitudes. Although they stated their

acknowledgment for the significance of technology

use for educational purposes, data showed that they

barely take advantage of technology in their studies.

Concerning HOU students, several sets of data have

been analyzed in order to find out the level of

engagement with the educational material. The

graphs in figure 6 illustrate the engagement students

have with the content provided for them on their

course. A large number of students who do not use

the resources indicates that perhaps the content is

problematic and needs to be rethought by the tutors

and coordinator.

Figure 6: Students' access to content material.

3.4 Gaining Insight into Students’

Behavior

In an effort to see beyond logins and submission

dates, network analysis was conducted. These graphs

(figure 7) show the interaction between students and

tutors, and between the students themselves. This

gives an overview of the communication that is taking

place and it allows us to see how a tutor is interacting

with his/her group.

Sentiment analysis of students’ fora and emotion

classification (figure 8) was held by Gkontzis et al.,

(2017a) in postgraduate students of the School of

Science and Technology in HOU. The study revealed

that positive polarity was dominant in students’ posts.

This fact was more obvious in the most active

students, indicating an emotion of satisfaction.

Additionally, this work pointed out some issues for

future research: possible correlation of sentiment with

From Analytics to Cognition: Expanding the Reach of Data in Learning

461

Figure 7: An integrated view of the groups’ forum graph.

academic and demographic factors, more active

students’ sentiment compared with less active ones

and the effect of tutors’ methods in students’

sentiment expressed in fora.

Figure 8: Emotion classification in forum posts (Gkontzis,

et al., 2017b).

Text mining in forum data whereby individual terms

are picked out of texts sent by students has been used

in an attempt to better understand their educational

needs. A high frequency indicates that students are

having difficulties due to misconceptions or require

further clarification. The tutors can intervene by

providing more information on the subject or

improving their teaching of the subject.

3.5 The Prime-EDU Software

Recently our team has designed and developed a new

educational application named PRIME-EDU. Its

purpose is to transfer educational data to a Cloud

Database. These data are analyzed using Learning

Analytics techniques so that the application users can

easily make better educational decisions. The above-

mentioned Data Base is designed in the form of Cloud

Figure 9: Data visualisation in PRIME-EDU software.

Warehouse Database and its data provide real-time

Visualization, Correlation, and Prediction.

The PRIME-EDU application is designed to receive

data from “MySchool”, “Moodle”, the “DIARROI”

(STUDENTS DROP OUT) application and the

“Classter” of Vertitech. “My School” is the web-

based application of the Ministry of Education, that

all schools of Secondary Education in Greece use

compulsory. Moodle is one of the most established

asynchronous e-learning systems that offers many

opportunities for analyzing training data. It was also

used by HOU. Finally, the DIARROΙ application is a

training software that our team has developed which

offers telematics services to reduce early school drop-

out (ESL) and educational leakage (Samaras et al.,

2018).

The PRIME-EDU application is designed with C# in

Microsoft Visual Studio 2017. It makes use of the

Microsoft Virtual Machine libraries. NET

Framework 3.5 works on various versions of

Windows.

Figure 10: PRIME-EDU Interface.

The application automatically sends learning data to

the Warehouse Database from Excel, Access, SQL

SERVER. It is also texting information to parents,

such as updating for their children's attendance. The

basic requirement of the application is that the data

sent to the Cloud Warehouse Database must be

anonymous in order to comply with the European

Data Protection Regulation. Besides that, personal

data don’t help us make educational decisions.

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The PRIME-EDU application transfers data to the

Cloud Warehouse Database, that has been developed

on SQL Server 2017. The Warehouse Database is

designed with a table of events and dimensional

tables in a star model. The event table has each

student's daily attendance and the dimensional tables

contain the most features that affect the student's

attendance such as environment, school teacher,

family etc.

From the Cloud Warehouse Database, we isolate the

data needed to make educational decisions. The

technique we use is the business intelligence that

companies with a lot of data have been using for many

years in order to monitor the behavior of their

customers and their organization. Thus, with CUDE

and ROLLUS techniques, we are able to isolate an

educational phenomenon and handle it per period, per

region, per students’ age etc. With the data that we

isolate with CUBE techniques and the abilities

(potentials) of the R Tool Visual Studio (RTVS) tool,

we are able more easily visualize an educational

phenomenon, to apply Correlation and look for the

forecast.

3.6 Privacy and Anonymization

A significant field of concern is privacy protection

and data anonymization. This concern also applies to

the educational field. In order to protect students that

provide data, it is necessary to omit those

characteristics that permit identification. Relevant

methods and techniques are presented in a series of

successive works as the followings: Sakkopoulos et

al., (2013) presented a framework that allows

conducting research on anonymity techniques in a

real-life environment using smartphones. The

framework also includes logging mechanisms that

facilitate positioning research dataset development in

open format. Additionally, Karapiperis, & Verykios,

(2016) proposed an efficient scheme for privacy-

preserving record linkage by using the Hamming

locality-sensitive hashing technique as the blocking

mechanism and the Bloom filter-based encoding

method for anonymizing the data sets at hand. Highly

accurate results were achieved and simultaneously

reduced significantly the computational cost by

minimizing the number of distance computations

performed. Moreover, Bit Vectors (BV), an accurate

distance-preserving encoding scheme for

representing numerical data values in privacy-

preserving tasks, were used by Karapiperis, et al., in

their work (2017). Key components of this

embedding process were the employed hash functions

and the threshold that is required by the distance

computations, which they proved that can be

specified in a way that guarantees accurate results.

Finally, it is noted that as the level of anonymization

rises, the quality of data declines. Consequently, it is

important to achieve a balance between tanking

advance of data the best possible way and complying

with privacy regulations.

4 CONCLUSIONS AND FUTURE

WORK

4.1 A Holistic Approach

Overall, it is obvious that Learning Analytics and its

application to different contexts and environments

has the power to transform the educational system as

a whole, as well as to tailor the learning experience to

the specific needs of individual students which will

lead eventually to their success. Using the resources

available in ΒΑΤ lab, we have been able to conduct

research and experiment with the implementation of

various Learning Analytics case studies with some

promising outcomes. Even though we are at an initial

stage we hope to be able to discover ways to

revolutionize our learning and teaching environments

by offering our students the best possible services.

4.2 From Learning Analytics to

Cognitive Science

Since translating experience into words makes a

major difference in future actions (Pennebaker et al.,

2014), analyzing written text is meaningful in

education as it can reveal insights that go deeper than

grades or login counts. Therefore, the use of Natural

Language Processing methods is crucial in an

integrated educational data analytics approach.

Cognitive computing combines neuroscience,

supercomputing, and nanotechnology to develop a

coherent, integrated, universal mechanism inspired

by the mind's capabilities Modha et al., (2011).

Learning Analytics combined with State-of-the-art

Cognitive Computing can provide new effective ways

for content management, especially in Distance

Learning Universities where the educational material

has to fill the gap in the lack of tutor’s physical

presence. An example is the work of Dessì et al.,

(2018) where they used the aforementioned methods

to manage the content of micro-learning videos in

order to improve their retrieval from students. By

using sophisticated systems that provide assistance to

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463

students, at the same time a pool of data is created,

allowing the gaining of insight into their behavior and

their way of learning. Cognos Analytics provides a set

of resources for data analysis that can uncover hidden

patterns that could lead to significant conclusions

even in cases when the initial hypothesis is not clearly

stated. This kind of serendipity is what we consider

as the main benefit of the employment of

continuously integrated methods and tools.

4.3 The Future Scope

In the future, we hope to be able to create increasingly

sophisticated applications and gadgets and to create a

kind of augmented reality environment which can

communicate in real time how the student is

performing. By being able to visualize their progress

both the students and the faculty will be able to avoid

negative outcomes, such as poor grades or even worse

students’ dropouts. In a recent study, Brown et al.,

(2019) provided evidence that large scale data form

application users via smartphones can outweigh the

noise inherent in collecting data outside a controlled

laboratory setting and produce valid results. This

conclusion supports the idea of the creation of an

online educational application that would gather and

present to students selected data in an understandable

and useful way in order to enhance their self-

regulation and their awareness about their progress.

The goal is to make optimal use of all available data.

After all, as Schmidt (2011) stated: “Technology is

not really about hardware and software anymore. It’s

really about the mining and use of this enormous

volume of data in order to make the world a better

place…”.

ACKNOWLEDGMENTS

The co-authors would like to acknowledge the

support of New York University Abu Dhabi.

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