Artificial Intelligence as a Tool to Support Students’ Bachelor’s

Degree and Vocational Training Choices

Aitor Moreno-Fernandez-de-Leceta

, Nagore Ipiña

, Koldo Diaz

2 c

, Ane Zubizarreta

2 d

Victor Gonzalez

and Leire Ezquerro

Artificial Intelligence Department (Ibermatica Innovation Institute i3b). Avenida de los Huetos,

75, Edificio Azucarera, 01010 Vitoria-Gasteiz, Spain

Mondragon University (Faculty of Humanities and Education), Spain

Department of Education of the Basque Government, Vitoria-Gasteiz, Spain

Keywords: Artificial Intelligence, Education, Segmentation, Hierarchical Clustering, BigData.

Abstract: Artificial Intelligence (AI) has great potential for supporting students in their Bachelor’s degree choices.

Studies have found that AI and big data in education may provide more effective monitoring and support in

real time. In this paper, we present the findings of a study carried out to create a model to support students in

their choices after compulsory education and match them with business needs. The study was conducted in

two phases. First, in an experimental study, 528 participants from secondary education in Spain filled in a

159-item questionnaire that identified their main interests and matched them with business needs. Second, an

algorithmic supportive model based on AI was created in order to offer schools and students the opportunity

to obtain extra data to help them choose their Bachelor’s degree or vocational training program with reference

to business needs. This paper presents the results from each of these phases, which show that AI and big data

may be useful to provide students, parents and teachers with extra data to justify the students’ choices.

However, it is necessary to empower educational agents to understand both the potential and and the risks of

AI and big data.

1 INTRODUCTION

Interest has been increasing every year in the

application of Artificial Intelligence (AI) in

education, which has undergone significant

development over the last twenty-five years (Roll,

2016). Along these lines, educators and researchers

are working on how to apply AI techniques such as

deep learning and data mining to complex educational

issues and the personalization of individual learning

processes (Chen, 2020). (Berendt, 2020) present AI

based on the definition given by the Encyclopedia

Britannica as “the ability of a digital computer or

computer-controlled robot to perform tasks

commonly associated with intelligent beings.”

(Chassignol, 2018) adds the idea that AI is dedicated

to solving cognitive problems commonly associated

with human intelligence, such as learning, problem-

https://orcid.org/0000-0003-0556-4457

https://orcid.org/0000-0002-9080-0540

https://orcid.org/0000-0003-3490-2320

https://orcid.org/0000-0001-9880-6050

solving, and pattern recognition. Regarding

education, (Berendt, 2020) note that AI is considered

to be a “way to improve education in ways that offer

more personalized, flexible, inclusive and engaging

learning.”

Through machine learning and data mining

techniques, AI is able to structure and analyze large

data sets and reveal patterns and trends to derive

predictions (Berendt, 2020), and this can also be done

in educational settings. Recently, AI has been used in

education to develop predictive models of student

dropout (Lee, 2019), predict students’ grades

(Adekitan, 2019), create dashboards and visual

dashboards to show learner progression on learning

paths (Rienties, 2018), establish recommendation

systems to help students (Ipiña, 2016), provide instant

feedback to students (Cope, 2020), create adaptive

systems to personalize learning paths (Chen, 2020),

558

Moreno-Fernandez-de-Leceta, A., IpiÃ

sa, N., Diaz, K., Zubizarreta, A., Gonzalez, V. and Ezquerro, L.

Artiﬁcial Intelligence as a Tool to Support Studentsâ

Z Bachelorâ

Zs Degree and Vocational Training Choices.

DOI: 10.5220/0011960900003612

In Proceedings of the 3rd International Symposium on Automation, Information and Computing (ISAIC 2022), pages 558-565

ISBN: 978-989-758-622-4; ISSN: 2975-9463

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

and link performance with university programs or job

applications (Berendt, 2017), among other things.

This article examines the potential of AI in

educational systems as a tool to support students’

future Bachelor’s degree or vocational training

choices and match them with business needs. The

article is structured as follows: in Section 2, we

discuss the potential of the use of AI in education. In

Section 3, we describe an experimental study carried

out to create an algorithmic supportive model. In

Section 4, we discuss the creation of a prescriptive

model. In Section 5, we offer some recommendations

on how schools and educational communities might

use the model created, and explore issues associated

with the use of AI in education.

2 POTENTIAL OF AI IN

EDUCATION: USING DATA TO

SUPPORT CHOICES

This section explores the advantages and

potentialities of using big data analysis in the process

of aligning students’ interests with their Bachelor’s

degree or vocational training choices. In particular,

the present section analyses how big data can help

students to address existent mismatches and hence,

support them in their choices. Indeed, (Pérez, 2019)

state that 20.4% of students drop out or change their

bachelor’s degree program in their first university

year. This highlights the need to conduct studies in

this area.

As is well known, information and

communication technology (ICT)-related innovations

have fostered the creation of new types of data. Along

these lines, big data can offer new information on

students’ learning processes and therefore can help

increase students’ performance in their academic

development. Moreover, those data can be interpreted

for the purpose of aligning educational programs to

better prepare students for their future, making it

possible to personalize each student’s learning path.

Furthermore, as (Berendt, 2020), point out, a fine-

grained analysis of big data can support students,

families and educators in their decision-making

processes.

An important goal of education is to prepare

students for the labor market. Thus, several studies

have been carried out in higher education with an eye

to aligning students’ skills with trends driven by the

labor market. In a report prepared for the European

Union, (Berendt, 2017) conclude that big data

analytics can reveal training needs more accurately

and therefore, fix the gap between higher education

training and labor market needs. However, as the

authors point out, a new approach toward partnership

will be required to better understand and continuously

monitor the respective contributions of the labor

market and educational institutions. Other studies

conducted in the field (Chen, 2020) have also shown

that AI learning systems can improve learning

capabilities that could fit labor market needs. The

analysis concludes that AI systems can always be

adapted to offer aid to students. Therefore, AI

systems can offer extra help to students in their

processes.

Nevertheless, to the best of our knowledge, very

few studies have analyzed secondary students’

interests by means of big data to support their future

choices and match them with business needs. Big data

analysis in secondary education has been limited to

assessing students’ performance in relation to the

established educational program or to offering

learning personalization options. Indeed, as

(Kurilovas, 2018) states, the personalization of

learning objects and activities has become very

popular in recent years and the use of learners’

profiles (including prior knowledge, intellectual

level, interests, goals, cognitive traits, learning

behavior, learning styles models) is recognized to be

effective. Thus, as (Berendt, 2017) claim, it is

necessary to inform students about their interests and

competences so that they may frame their choices

better. In fact, examining how students make these

decisions has important consequences. (Baker, 2017)

assert that students often receive little guidance on

how to make such decisions, and most schools do not

offer the necessary structure to help in the decision-

making process.

Thus, building students’ profiles and grouping

and clustering them according to their learning

characteristics through algorithms can identify

different types of learners, and educational

opportunities could be adapted to their needs (Li,

2018). However, the development of a big data

infrastructure and analytics solutions to connect

Bachelor’s degree and vocational training options

with educational programs requires complex and

costly big data techniques and analysis tools. The aim

of the present study was to develop a supportive

model based on big data that can guide students in

their Bachelor’s degree and vocational training

choices and match their decisions with business

needs.

Artiﬁcial Intelligence as a Tool to Support Studentsâ

Z Bachelorâ

Zs Degree and Vocational Training Choices

559

3 CREATION OF THE

UNSUPERVISED

ALGORITHMIC MODEL

An experimental study was carried out to create an

algorithmic supportive model. This study included

528 participants from secondary education in Spain,

who first completed a 159-item questionnaire

designed to identify students’ interests based on the

Bachelor’s degrees and vocational training programs

offered in the Spanish province of Gipuzkoa. The

questionnaire was created by a group of experts and

corroborated by means of semi-structured interviews

carried out with 16 professionals in five knowledge

areas (arts and humanities, science, health science,

technical engineering, and legal science). The

questionnaire was delivered online and was

completed by 528 students, of whom 421 gave their

consent to use their information. Data quality was

analyzed statistically before defining the model; all

participants answered all the questions, the 159

variables were consistent, and no outliers were found.

Consequently, the gathered data were considered

consistent and valid. In a subsequent step, the data

were enriched by sociodemographic variables such as

unemployment rates, and contextual variables.

Sociodemographic data obtained from different

OpenData sources were added to the questionnaire

dataset (Figure 1), specifically, according to the

postal code of the center, including data referring to

the business and industrial concentration of the

education center area, data referring to the job offer

rate, and data regarding the unemployment rate in

recent years in the area.

Figure 1. Internal and External data processing in the

project. CNAE, National Classification of Economic

Activities (Clasificación Nacional de Actividades

Económicas).

The main idea was to obtain in an unsupervised and

automatic way the different groups into which the

students were triangulated with respect to their

answers on the survey. Note that we have not

identified the students in any a priori way, and one of

the important objectives of the project was to

characterize sets of students within homogeneous

groups large enough to be able to generate general

recommendation rules per group, but specific enough

for those rules to be efficient at the level of

personalization of the recommendations.

Four segmentation algorithm methods were

executed in each profiling:

 Kmeans (KM): a centroid-based clustering

method that works when clusters have similar

sizes and are locally and isotropically

distributed around their centroid. Euclidean

distances are used in the similarity search

function.

 AffinityPropagation (AP): a relatively new

clustering algorithm that operates by

simultaneously considering all data points as

potential exemplars and exchanging messages

between data points until a good set of

exemplars and clusters emerges.

 AgglomerativeClustering (AC): the most

common type of hierarchical clustering used to

group objects in clusters based on their

similarity. It is also known as Agglomerative

Nesting (AGNES). The algorithm starts by

treating each object as a singleton cluster. Next,

pairs of clusters are successively merged until

all clusters have been merged into one big

cluster containing all objects. The result is a

tree-based representation of the objects, called

a dendrogram.

 GaussianMixture (GM): this algorithm can be

viewed as an extension of the ideas behind k-

means, but it can also be a powerful tool for

estimation beyond simple clustering.

When generating the models, all of these algorithms

were applied to the enriched data set. In order to

evaluate the results, the following statistics were

used: Gini, Silhouette, Calinski-Harabasz, and

Davies-Bouldin. Table 1 shows the results of the

different algorithms evaluated by different statistical

metrics.

Table 1. Clustering methods and evaluation.

Clustering

Metho

Statistical Criterion Statistical

Validation

KM Gini 0.85

Silhouette 0.09

Calinski-Harabasz 0.58

Davies-Bouldin 0.29

ISAIC 2022 - International Symposium on Automation, Information and Computing

560

AP Gini 0.93

Silhouette 0.03

Calinski-Harabasz 0.15

Davies-Bouldin 0.26

AC Gini 0.83

Silhouette 0.08

Calinski-Harabasz 0.47

Davies-Bouldin 0.30

GM Gini 0.97

Silhouette 0.09

Calinski-Harabasz 0.58

Davies-Bouldin 0.30

The GM algorithm, which had the highest Gini

validation, was selected. In the first stage, a cluster of

the GM type was generated from the data. The

extracted profiles are descriptive, based on an

analysis of their centroids. As shown in Figure 2, the

clustering algorithm models the original vector space

in very general profiles, which explains the

preferences based not on professional topics, but on

scientific disciplines. However, by applying a

profiling model to each of the obtained subsets

(Subclustering), we obtain, preferences at the

professional subject level at the second level of the

hierarchy (Figure 3). For this second subcluster, we

selected the AC, within which the Gini statistic

showed the highest median value, at 0.85 in the

subprofiles.

This demonstrates that a hierarchical analysis of

the same layers of original information is the most

efficient way to automatically divide and analyze

information related to student preferences. In this

way, it is possible to generate a graph of the

relationships between the general categories and the

obtained subcategories, and it is possible to locate

each student within one of the branches of the

Cluster-Subcluster graph, based on their surveys

responses and environment data. It is also possible to

analyze similarities between different students based

on their combined profiles.

Figure 2. Automatically extracted profiles and centroids.

Figure 3. Automatically extracted subprofiles and

centroids.

This advanced analysis was conducted and 4 different

profiles were defined: [profile 1] Arts and Humanities

(22.56%), [profile 2] Engineering and Mathematics

(31.35%), [profile 3] General Interest (26.12%), and

[profile 4] No Interest (19.85%). The four clusters

were analyzed and subdivided into subcategories, as

shown in Table 2:

Table 2. Interpretation of clusters and subclusters.

Clusterin

Subclusterin

[profile 1]

Arts and Humanities

Business-Oriented

Vocational Training

Services

History

Translation, Language

and Literature

[profile 2]

Engineering and Mathematics

Engineering, Natural

Science

Physics Teaching

Technical

Engineering

sics

[profile 3]

General Interest

Education

Natural Sciences

History, Language

and Literature

Chemistry, Education,

Engineering

[profile 4]

No Interest

No interest

Any vocational

training

Robotics, Lan

Finally, a relationship graph was generated to show

the relationships between the cluster-subcluster

profiles and career opportunities (Figure 4). This

graph illustrates the possible combinations, called

“feasible regions,” that we can assign as

recommendations once the student has been

triangulated into the appropriate group(s).

Artiﬁcial Intelligence as a Tool to Support Studentsâ

Z Bachelorâ

Zs Degree and Vocational Training Choices

561

Figure 4. Graph of the relationships between profiles and

career opportunities.

4 CREATION OF THE

PRESCRIPTIVE MODEL

Data were optimized for each category and

subcategory and the feasible region was defined by

means of the possible career paths available for that

profile. The feasible region was generated by taking

the interests of students with that profile into account.

A ranking was prepared for each subcategory taking

other sociodemographic variables into consideration,

such as variables related to the demands of the labor

market. Two optimal routes were designed for each

category and subcategory, one based on general

criteria for future careers (Figure 5) and a second one

considering local demand and genre (Figure 6).

Figure 5. Optimization criteria for recommendations using

Route 1.

As shown, Optimal Route 1 was developed within the

feasible region taking into account the occupation

rate, average salary, and percentage of unskilled

employment within the sector, while Optimal Route

2 was defined by the number of contracts made by the

municipality, gender and CNAE data.

Figure 6. Optimization criteria for recommendations using

Route 2.

In order to determine the optimal routes for each

student, linear regressions were generated based on

data pertaining to the student’s context, and based on

the student’s gender, given that there is a significant

gender bias in job opportunities (particularly in regard

to the temporality of contracts). The regression linear

equation is the following:

Rm,l = 2ol + 2sl - 1el - 1al + 1dp,l + 0.5ip,l + 0.25sp,l

- 0.5np,l + 2hg,l + 1tg,l

where:

R: ranking in the labor supply by feasible region by

student (m)

l: labor supply

p: profile/subprofile

ol: occupancy rate by labor supply

sl: average wave by labor supply

el: unskilled unemployment by labor supply

ap: number of students with the same

profile/subprofile

dp,l: direct relationship between academic demand

and labor supply by profile/subprofile

ip,l: indirect relationship between academic demand

and labor supply by profile/subprofile

sp,l: overqualification of academic demand with

respect to labor supply

np,l: no relationship between academic demand and

labor supply by profile/subprofile

hg, l: work with indefinite contract by gender, where

g is student gender, by labor supply

tg, l: work with temporal contract by gender, where g

is student gender, by labor supply.

Thus, a prescriptive model was generated for each

student, based on the student’s subjective data,

preferences, and contextual environment with respect

to sociodemographic and labor data, as well as the

competence of other similar students. The student has

only to answer a number of questions on a form, and

the system is able to recommend the best route in

terms of work projection, based on the optimization

of the previously described equation, 𝐿





𝑚𝑎𝑥𝑅

,





, which finds the labor supply of the

market that maximizes the R

m,l

function for each

student, that is, the best labor supply (𝐿



Each student is registered at one and only one

educational center, therefore, we will need to know

the demand for labor supply at each center. In

addition, the number of students at each center is

weighted with respect to the percentage that that

center represents in terms of the population of each

municipality, so we can calculate the total demand for

ISAIC 2022 - International Symposium on Automation, Information and Computing

562

labor supply for each profession in each municipality.

On the other hand, we also have the capacity of each

municipality to absorb said demand or not, depending

on the educational centers that offer training in these

professions, so finally, we have a photo of each

municipality’s capacity to respond to existing

demand, and the number of students who must travel

to other municipalities if the recommendations made

were executed.

𝐶



 





𝐿



 

𝐶

𝑁



𝐶



is the number of students weighted by school based

on labor demand (by profession), where:

l: labor supply

m: students

𝐿



: best labor supply by student

C: number of students per center

N: number of centers by municipality

𝐷



 𝑂



 𝐶𝐶



𝐷



is the demand covered by educational centers with

respect to professions (l), by municipality, where 𝑂



the offer by the number of students and profession in

each municipality.

The prescriptive algorithm allows each student to

be assigned an optimal job offer route, and even

suggests the municipality in which to study, based on

the following algorithm:

Prescriptive Algorithm

1. The student fills out the subjective question

survey.

2. The sociodemographic data of the student

are added according to their study center.

3. Information is normalized (z-transformed).

4. Calculate centroid GM(m).

5. Calculate centroid AC(m).

6. Function R

m,l

is optimized to obtain the best

professional route 𝐿



7. The result is added to the data set of the

center 𝐶



and the capacity of the demand is

calculated 𝐷



8. If 𝐷



< 0) the student is assigned to the

center (𝐶′



) with the closest professional

offer (l) available 𝐷′



 0

The indicators above allow a descriptive analysis

of the information, including the following points:

 Level of labor demand by center.

 Level of labor demand by municipality (Figure

7).

 Level of global labor demand.

 Average distance of travel by municipality in

order to satisfy demand.

 Demand not covered by the global educational

offer (Figure 7).

These descriptive models can be filtered, analyzed

and visualized, both from an analytical point of view

and from a geospatial point of view, through a

dynamic visualization platform, either by the public

administration or by the centers themselves.

Furthermore, it is integrated with the students’

response forms so that the assignment of optimal

training routes and the selection of centers can be

done online.

Figure 7. Professional demand by municipality, and

demand not covered by the global educational offer.

5 RECOMMENDATIONS AND

CONCLUSIONS

This article analyzes AI issues in education,

emphasizing how data can be used to help students

with choices about their future. Moreover, we assert

that it is not sufficient to use big data and analytics

solely to evaluate what learners have done (Long,

2011), but that this technology can also be used to

Artiﬁcial Intelligence as a Tool to Support Studentsâ

Z Bachelorâ

Zs Degree and Vocational Training Choices

563

help them with decisions about their future. We are

aware that linking predictions made by AI systems

with students’ interests will likely have a serious

impact on the students’ future choices (Berendt,

2020), therefore, the model proposed in this paper

should be complemented with the students’

qualitative views, educators’ perspectives and the

opinions of the students’ families. That is to say, our

model should be used as extra input together with

other information provided to the students by their

group of teachers. Along these lines, in order to

prevent biased data-driven decision-making and

considering that big data skills are becoming

increasingly important in all areas, it is necessary to

invest in capacity building and training of both

students and teachers to further support the ICT

infrastructure (Berendt, 2020). In that vein, our model

was provided to schools with recommendations and

guidelines for using the questionnaire and

interpreting the results appropriately.

It is important to remember that models based on

prediction such as ours will need to be updated due to

the fact that skills and interests may change owing to

technological and social developments. Hence, both

more detailed and informative longitudinal studies of

skills requirements and more fine-grained analyses

will be needed. As mentioned above, an important

goal of education is to prepare students for the labor

market, where there may be increasingly dynamic

developments in skills demands.

Nonetheless, legal and ethical issues require

deeper discussion, particularly when taking into

account the fact that our model was designed and

piloted with secondary education students. In fact, as

most organizations are likely to implement AI

strategies and pilot AI solutions to enhance decision

making (Chassignol, 2018), ethical issues should also

be part of the discussion. Furthermore, it could help

students as future citizens to educate them on these

new perspectives. This work contributes to the

existing knowledge on AI in education and is

interesting not only for professionals who support and

teach students but also because of its potential to

empower students in their decision making.

ACKNOWLEDGEMENTS

This work was funded by the Department of

Economic Development, Rural Environment and

Territorial Balance of the Provincial Council of

Gipuzkoa (Talent and Learning 2019).

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