MACHINE_LEARNING_EM_SISTEMAS_DE_RECOMEND

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MACHINE LEARNING EM SISTEMAS DE RECOMENDAÇÃO 
EDUCACIONAIS: QUESTÕES ÉTICAS A SEREM 
ENFRENTADAS 
 
Fábio Josende Paz 1,2 , Francielle Marques do Nascimento 3, 
Raphael Leite Campos 1, 4, Priscila Piccolo 3 
1 Pós Graduação em Informática na Educação - Universidade 
Federal do Rio 
Grande do Sul (UFRGS) 
2 Universidade da Região da Campanha (URCAMP) 
3 Pós Graduação em Ciência da Computação - Universidade 
Federal do Rio Grande do Sul (UFRGS) 
4 Universidade do Vale do Rio dos Sinos (UNISINOS) 
 
fabiopaz@urcamp.edu.br, francielle.nascimento@inf.ufrgs.br, 
prof.raphaellc@gmail.com, priscila.piccolo@ufrgs.br 
 
Abstract: 
Educational Recommendation Systems (ERS) involves a degree of complexity when it comes to 
ethical and social aspects, in this sense the objective of this study is to analyze and present some 
Ethical Issues involved in machine learning in educational recommendation systems. This study is 
characterized as a bibliographical review of the theme. The specific contribution deals mainly with 
critical issues derived from the bibliography found on the integration of subjects, such as reflections 
on the ethical and technical implications of artificial intelligence such as Machine Learning (ML) 
applied in these systems. The present study intends to arouse interest and help to guide the first 
steps in ethical issues to be analyzed in initiatives in the field of Recommendation Systems focused 
on education. 
1. Introduction 
There is a growing demand to deal with an ever-increasing volume of data in order to 
promote its processing to make it useful as information. Therefore, the information goes 
on to subsidize decision-making systems, increasing the potential of computational tools 
that offer different possible solutions, with an increasing impact on people's lives and 
consequently on education. 
Computational Intelligence (CI) is a field whose main purpose is to study adaptive 
mechanisms so that intelligent behavior can solve complex problems that traditional 
approaches do not have as much effectiveness (ENGELBRECHT, 2007). Among the 
techniques of CI, Machine Learning (ML) uses concepts of human learning to develop 
software skills, from experience. 
According to Brownlee (2014), ML is the training of a model from data that generalizes 
a decision in relation to a measure of performance. Models can be generated from 
supervised and unsupervised approaches. The supervised approach requires data sets 
with defined classes, where training is done based on examples. While in the 
unsupervised approach, pattern recognition comes from groupings performed on a data 
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set. Recommendation Systems use this approach in an adaptive process and 
classifications from large databases. 
Recommendation Systems are considered to be very useful tools, especially for users of 
online systems, because they can handle properly with large databases. (RICCI et al., 
2011), but the Educational Recommendation Systems (ERS) aims to produce predictions 
and recommendations for its users, usually students and teachers (LIU et al., 2013; 
IMIRAN et al., 2014; FULANTELLI et al. al., 2015). They can still be used to provide 
teaching materials and useful information that take students' preferences into account 
and help them to achieve the learning objectives of the course (LIU et al., 2013, COSTA 
et al., 2013). However, Xing et al. (2015) and Siemens & Baker (2012) show that many 
do not present explanations or justifications for these recommendations, leading users to 
have no confidence in them. Johnson (2014) affirms that the benefits of such techniques 
are significant, however they present a number of ethical and social challenges. 
Concerns about ethical issues are increasingly evident in Artificial Intelligence (AI) 
(BOSTROM and YUDKOWSKY, 2011), Machine Learning and Recommendation 
Systems are subareas of AI. In this context, some ethical issues arise regarding 
application of Machine Learning in ERS, but little has been studied on this subject 
which demonstrates the bibliometric study that is presented in this article. Therefore, the 
purpose of this study is to analyze and present the ethical issues involved in learning 
machines in educational recommendation systems. This paper is structured in six 
sections, including the introduction and the theoretical studies for research background. 
In section 3, the materials and methods are presented, and in section 4, the analysis of 
results and discussion. Finally, the final considerations and the references of this study 
are presented. 
2. Theoretical reference 
In this section we will briefly present the Machine Learning issues in Educational 
Recommendations Systems, the Moral Dilemmas in Educational Recommendation 
Systems, in addition to present some Educational Recommendations Systems. 
2.1 Machine Learning in Educational Recommendations Systems 
The recommendation systems are intended to use algorithms to provide 
recommendations according to the user's interest. Recommendations are made to help 
the user to find new items based on user information or the recommended item. 
Techniques such as Machine Learning, Deep Learning, and algorithms in general seek 
trends in user access, either through (Collaborative Methods), or (Content-Based 
Methods), or a combination of the two strategies (Hybrid Methods) (PORTUGAL, 
ALENCAR, COWAN, 2017). 
These three methods use strategies to make the recommendation to the user in different 
perspectives of interests. Content-Based uses similar items / products previously 
accessed by the user. In Collaborative Methods the items are presented to the user based 
on the preferences and similar tastes of other users. And the Hybrid method can present 
several models with different combinations, as an example, they produce lists of 
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recommendation of each approach and join the results, or use weights for the filtrations 
(enhance items that had more accesses) (ADOMAVICIUS, TUZHILIN, 2005). 
In this context, some Machine Learning techniques that are recurrently applied in 
Recommendation Systems are Decision Trees, Naive Bayes Classification, Support 
Vector Machine, Clustering Algorithms, Neural Networks, Deep Learning, Neighbor-
based, among others. Most of these techniques are able to classify user profiles, 
collecting data related to user preferences and behavior, such as income, age, gender, 
access history, and information that may be useful in the discovery of RS knowledge and 
application. 
Likewise, the Educational Recommendation Systems seek through these technological 
devices to identify and suggest ideal contents for students, based on the characteristics of 
each group or individual. In this perspective, the teaching and learning process is the 
central object of the system, since, from a modeling, the system can filter educational 
resources, make suggestions, personalize content, with the objective of providing the 
student a better experience of learning process. So, the educational process becomes 
more interactive, dynamic and innovative. 
Based on the information and data collected, the ERSs allow a more in-depth assessment 
of the methodology applied and the resources used in the learning systems. In addition, 
the technique allows students to focus their studies based on their difficulties, either 
through reinforcement learning (reinforcing subjects already studied) or to acquire new 
knowledge (COSTA, AGUIAR, MAGALHÃES, 2013). 
2.2 Moral Dilemmas in Educational Recommendation Systems 
Information and data collected for an ERS application may violate privacy, as they 
collect characteristics of an individual, either explicitly or implicitly 
(BERDICHEVSKY, NEUENSCHWANDER, 1999). ERSs use this data to infer a 
group's profile or an individual's profile, which may present or incorrectly generalize the 
interests of a class. Although, the purpose of the educational system is to utilize this 
knowledge to provide differentiated learning and experiencefor the student, the 
recommendations made by the system can persuade the student to follow a line of 
reasoning, rather than providing resources for student to form their own opinions. 
Knowing this, in the development of ERSs, must take into account the information 
security, privacy, and the impacts that will be generated in the formation and 
development of the student. In addition, educational systems available on the Internet 
must have “clear and complete information about the collection, use, storage, processing 
and protection of its personal data” (BRASIL, 2014), the use of this information is only 
allowed when specified in the services or terms of use and privacy policy (BRASIL, 
2014). 
In relation to moral dilemmas some Educational Recommendation Systems, among them 
MELOD, presents a dashboard with indicators that should be used by teachers to analyze 
students' behavior in relation to their interactions during the learning experience and thus 
to carry out educational interventions (FULATELLI et al., 2015). 
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Imiran et al. (2014, 2016) in their studies present an ERS tool, the PLORS that connects 
with Moodle to obtain information from undergraduate students, the focus of the tool is 
to present which learning objects within the course are most useful to them, considering 
the learning object they are visiting, as well as the learning objects visited by other 
students with the same profile. 
Another ESR found is the ELAT (Learning Analytics Toolkit), which allows teachers to 
explore and relate the use of learning objects, user behavior (student), as well as 
evaluation results based on dashboards. I t is intended to use technology to enhance 
teaching and learning scenarios and to identify opportunities for interventions and 
improvement (DYCKHOFF et al., 2012). 
 
 
3. Materials and methods 
This study is characterized as a bibliographical review on the ethical theme in the 
application of Machine learning in Educational Recommendation Systems, for which a 
quantitative bibliometric study was carried out in the Google Scholar database, where 
articles were searched with the keywords “Machine Learning”; “Educational 
Recommendation Systems”, “Machine Learning and Ethics”, and “Educational 
Recommendation Systems and Ethics”. The period for the search for results was 
between 2012 and March 2018, using articles in the English language published in this 
period. In general terms, the methodological procedures used were the following: 
 
 In the bibliometric research the following keywords were used: "Machine 
Learning"; "Educational Recommendation Systems", "Machine Learning and 
Ethics" and "Educational Recommendation Systems and Ethics" in the Google 
Scholar database; 
 It was carried out the crossing of keywords searched in the database; 
 An analysis was carried out by the number of articles published; 
 
Additionally, to complement the study, international articles about ethical issues 
involved in ML and ERS were analyzed. The following are the results and analyzes of 
what could be observed during this research. 
4. Results and discussions 
The purpose of the quantitative bibliometric review was to analyze the number of 
publications on the topic of this research, as well as to search for a publication 
perspective in the stipulated period. 
Table 1: Quantitative analysis of terms 
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Tabl
e 1 
sho
ws 
the 
resul
ts of 
the 
bibli
ome
tric study, which shows the importance of Ethics in Machine Learning with 16700 
studies in recent years, however, the low number of studies of the union of the keywords 
“Educational Recommendation Systems” and “Ethics” demonstrates a field which still to 
be explored, which was chosen as the focus of this study. 
We make it clear that this article is not intended to bring answers, but rather to bring up 
some ethical discussions about the application of Machine Learning in Educational 
Recommendation Systems. Following are some discussions raised in the readings done. 
Johnson (2014) raises many questions that should be well evaluated in the 
implementation of Educational Recommendation Systems, including issues related to 
students' privacy and individuality. The privacy may be violated if personal information 
is used in an incompatible manner with the context, i.e. information that would not allow 
some people comfortable with disclosure. Willis et al. (2013) raises an interesting 
question “Is it worth exchanging privacy for other benefits within the new social 
economy?” Another risk can be electronic reputation by presenting student performance 
to teachers and staff through electronic dashboards (FULANTELLI et al., 2015). 
In the studies by Imiran et al. (2014, 2016) groupings of students are carried out through 
their profiles. It is noted that the grouping of similar students can be a risk by not taking 
into account the individuality of the student. In ERS the tendency to treat the individual 
as a collection of attributes rather than an entire individual. The autonomy of the student 
can also be withdrawn by implanting suggestions to the students of both contents, and 
even in the choice of subjects to be taken, always leading them to the ways defined by 
the system, that is, trying to make the ideal student for the institution and not let him 
follow his own ways. 
In the study by Willis et al. (2013) it is presented a list of questions that educational 
institutions should make when implementing Educational Recommendation Systems, 
they are: Does college administration allow students to know that their academic 
behaviors are being monitored? What and how much information should be provided to 
the student? How should the teacher react to the data? Should the teacher contact the 
student? In what way? Should the data influence student perceptions and classification 
tasks? What amount of resources should the institution invest in students who are 
unlikely to succeed in a course? What is the student's obligation to ask for help? They 
are perceived as interesting ethical questions and should be analyzed with some care. 
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How can the use of artificial intelligence, based on machine learning from patterns found 
in historical data series, impact the learning process of human beings? Standardization 
may come with cultural prejudices and political-ideological biases. In addition to not 
contemplating the emergence of the learning process. (FERREIRA, 2017). In addition, 
we must also consider a potential risk that is to use great students to carry out training in 
the computational models and send these models to not so good students, generating 
frustration and consequently dropping out of school. 
In the same line as the AI impacts questionnaire presented by the above authors, the 
Ethics and Artificial Intelligence Committee of the IEEE (IEEE 2018) adds that moral 
values are included in intelligent systems, but are included by programmers, engineers 
who design systems which reflect reality, or the context and should be evaluated. 
 “[...] Computers and robots already reflect values in their 
choices and actions, but these values are programmed or 
designed in by the engineers that build the systems. Many 
of the existing experimental approaches to building moral 
machines are top-down, in the sense that norms, rules, 
principles, or procedures are used by the system to evaluate 
the acceptability of differing courses of action, or as moral 
standards or goals to be realized.” (IEEE, 2018, p. 43). 
According to IEEE (2018) the inclusion of rules, principles, norms and ethical 
procedures in the systems to evaluate the different courses of action are made top-down. 
However, AI techniques already exist that allow the construction of bottom-up 
standards, in other words, these systems would learn, just as a child learns to recognize 
ethical and moral standards in a society. 
Also in (Ferreira, 2017) the situation is presented in which these systems identify courses 
of action thatwere not predicted. It is evident how this issue dialogues with the non-
consideration of the emergence in the data and updating of the members of the groups. In 
addition, imperfect datasets impact AI learning for decision making, as punctuated in: 
“However, biases may still emerge from imperfections in 
the norm identification process itself, from 
unrepresentative training sets for machine learning 
systems, and from programmers’ and designers’ unconscious 
Embedding Values into Autonomous Intelligent Systems 
assumptions.” (IEEE 2018). 
The question of how to assess the impact of decision-making on such cases is pertinent. 
Considering imperfect data, machine learning techniques such as classification, grouping 
and association will generate imperfect models. 
Consequently, supporting Manouselis et al. (2011) affirms that in the case of systems of 
recommendations aimed at the teaching and learning process, the theme is quite 
complex, because there are several dimensions of evaluation, such as the pedagogical 
dimensions that should be taken into account in its construction. 
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5. Final Considerations 
There is no doubt that Educational Recommendation Systems are useful in teaching, 
many students, teachers and institutions enjoy their benefits, however ethical issues 
cannot be left aside when creating and using ERS. We believe that this work will assist 
institutional researchers who must understand the risks in this deployment and ensure the 
contextual integrity of the information flow to protect the actors involved. From this 
study, the authors hope to arouse interest and help guide the first steps in ethical issues to 
be analyzed in initiatives in the field of Recommendation Systems focused on education. 
The limitations of this work are in the bibliographical research carried out which some 
important articles may have been excluded from this study and for future work it is 
desired to deepen the studies on the ethical issues raised in this article as for example the 
privacy and individuality of the students, besides the search of the maintenance of its 
autonomy. 
 
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	1. Introduction