1. Introduction
The penetration of information and communications technologies (ICTs) has resulted in major social changes, marking the onset of an era that has been characterized by globalization, the information society and knowledge. Likewise, it has meant a challenge in different spheres of activity, one of which is education, in which they are incorporated into teaching and learning processes. The incorporation of ICTs has entailed a growth in Internet services, which in turn has been reflected in a major increase both in the amount and in complexity of the information available online. A problem for users has emerged as a consequence of the information overload in terms of the time they spend on their search and the amount of information retrieved by it. Being able to suitably and reliably ensure relevant, useful information is a determining factor when taking decisions.
Recommendation systems (RSs) have emerged in order to deal with this problem, with the purpose of hel** users find what is genuinely relevant to their needs. According to previous study [
1], the RSs are software tools to help users in the decision-making process by applying information filtering, data mining, and prediction algorithms. This offers each user a variety of choices and options according to his or her interests and preferences [
2].
A classic way of categorizing the different types of RS was provided by Burke [
3], who distinguished between six different classes of recommendation approaches: Collaborative Filtering (CF), Content-Based Filtering (CBF), knowledge-based filtering, context-based filtering, demographic filtering, and hybrid filtering.
For Herlocker [
4], collaborative filtering systems are the simplest, they calculate the similarity between users, and they predict product ratings for the active user according to ratings provided by other users who have very similar preferences to the current users. Content-based RSs are based on features provided previously by the user, which will then be used to filter all the elements in the system. Articles that have the highest similarity score will be recommended to users [
5]. According to [
6], the knowledge-based approach suggests elements to the user according to the knowledge it has about them and their relations, so as to meet the user’s specific needs. The context-aware recommendation systems are able to recognize the user’s contexts and daily activities in real time such as location, environment information, etc., for suggesting contextually relevant items [
7]. In demographic filtering, recommendations are established on a demographic profile of the user [
8], and the recommendations are suggested from similarities in terms of demographic data in user profiles such as nationality, age and gender, etc. [
9].
Hybrid recommendation systems that combine two or more techniques from among the approaches described previously to improve recommendation performance have emerged as a means to overcome any problems that may emerge via the use of the different techniques, such as the cold-star problem [
3]. The cold-start problem refers to situations where there are only a few ratings on which to base recommendations [
4], which usually happens when new items are registered in the system and normally have no rate from the users [
5].
In recent years, artificial intelligence techniques have been introduced for RSs. The study [
6] includes the computational intelligence-cased recommendation in the classification, which include Bayesian techniques, artificial neural networks, Machine Learning (ML) techniques, genetic algorithms, and fuzzy set techniques. According to [
1], the use of these techniques has been a promising solution when designing RSs in the era of Big Data.
RSs have become a very commonly used tool in different domains such as e-commerce, social networks, digital media, and books [
10] and in the field of education, as well as in teaching and academic advisory services [
11], the latter being the subject of interest in this systematic review.
Recommendation systems depend to a great extent on the domain in order to operate, and taking the recommendation given by a system and transferring it to another system is no easy task. Therefore, the challenge facing educational recommendation systems is how to better understand the user’s interest and the purpose of the domain [
12].
The purpose of the domain is conditioned by the different levels and type of education, which may belong to one of three major groups: Formal education, non-formal education, and informal education, the latter being what is acquired in day-to-day life through interaction with individuals and their relationship with the environment.
Formal education is understood to refer to what is included within the education system, which follows an established school curriculum and includes nursery, primary, compulsory secondary and baccalaureate stages, as well as vocational training and university teaching. As for non-formal education, this does not follow any type of regulation, and is what enables individuals to pursue lifelong learning. It is devised as a means for achieving stable, evolutionary training in competences, knowledge, and skills [
13]. For Belando-Montoro [
14], the purpose of any learning activity pursued throughout one’s life is to improve knowledge, competences, and skills from a personal, civic, social, or work-related standpoint.
The number of educational resources is growing nowadays, making it increasingly difficult for traditional search engines to meet requirements related to online searches for information about educational products and services by students during the learning process [
15].
A significant number of recommendation systems have been proposed in the field of education, as well as in teaching and academic advisory services. Within the domain of education, target users are students, teachers, and academic advisors, and the recommended elements are educational materials, learning objects, papers, universities, and information such as that about courses, student performance, and the field of study [
11].
Applying RSs to the field of education requires taking into account a broad set of variables that may include, among others, level of knowledge, competences, and learning styles on the part of students. Given the rapid evolution of these systems, it is necessary to be aware of the trend in the techniques used for development.
The aim of this systematic review is to obtain an overview of RSs in education, its fields of work, recommendation elements, and the techniques used to identify any gaps, while at the same time providing a suitable framework guideline for future research activities. The search for articles was conducted between the years 2015 and 2020, in the course of which 98 works were analyzed after setting out relevant search criteria.
The article is structured as follows:
Section 2 explains how the method used in the systematic review was developed;
Section 3 provides an analysis of records; and lastly,
Section 4 contains the conclusions and discussion about future work.
4. Discussion and Conclusions
The incorporation of ICTs into education has marked changes in its processes, whether in distance learning or in support for the different processes through educational resources available online. These are growing rapidly, whereby an increase in RSs has been noted in this sphere of activity, especially as support in formal education.
From the analysis, it can then be observed that RSs take into account user preferences when making suggestions based on recommendations from similar users, while [
21,
30,
35,
45,
53] make recommendations based on learning style, and [
24,
31,
46,
50,
61,
64] based on diagnosis/student progress and the knowledge group. Likewise, another element they take into account are user skills and/or competences [
40,
42,
56,
69] and competences related to work associated with their profile within Internet job search portals.
RSs also base their suggestions on learning style [
25,
35,
96]. The proposal of [
30] use a survey based on the Felder and Silverman Learning Styles Model (FSLSM) to determine this, and these learning styles are classified according to the following categories: Sensory, intuitive, visual, verbal, reflexive, sequential, and global.
In terms of RSs that use the collaborative approach, they are thought to evidence the cold-start problem, with works [
36,
41,
56] suggesting a greater volume of data to improve performance, and [
22,
48] adding more parameters to the user profile, such as learning styles or reading tastes—in general, it is suggested that this be combined with other approaches in order to improve performance. Ref. [
84] indicates that the use of deep learning techniques with collaborative filtering deals with the cold-start problem in recommender systems and [
115] solves the cold-start problem using collaborative filtering system by adding classification information. Furthermore, [
69,
86,
110,
113] indicates that the use of ontology for the representation of user information helps to solve the cold-start problem.
Of the articles that use the hybrid approach, we can draw attention to [
69], which states that the context should be incorporated in the user profile in order to improve performance, while [
27,
64] suggests incorporating social networks in the future. As for those that use the knowledge-based approach, the systems work with ontologies and semantics, recommending that information be gathered from a range of sources and be represented via ontologies.
In the systematic review, we have analyzed 98 articles related to the use of SRs in education, most of them in a formal education context, so we can suggest further study in non-formal education.
Table 10 shows the trend in the use of ML over the last two years. These techniques are combined with different filtering approaches to improve recommendations and cold-start-related problems.
A feature found in the analysis of the articles is the heterogeneity of the data in the domain of education, where [
69] indicates that the integration of data from multiple heterogeneous sources helps the system to improve recommendations. There is also a need to study algorithms based on a semantic approach in more detail, the idea of which would be to use ontological knowledge to describe the elements in order to obtain a detailed representation of their content. This may in turn contribute towards improving the results obtained from the recommendation in terms of relevance of the educational material suggested and, therefore, would improve the student’s learning process.
In the validation of RSs, the source and size of the dataset must be considered. Different strategies are used to determine the quality of the recommendation, such as offline and online validation, with possibilities for expert assessment up to the use of multiple metrics.
The RSs subject to study mainly sought elements to be recommended in a single place, although the search for information should also be explored in a range of sources so as to be able to ensure a wide variety of elements and offer a better recommendation service. Social networks also constitute a potential means for searching for information via recommendation systems. Ref. [
27] indicates that the use of social media could improve the problem of data sparsity. According to the report on prominent indicators from the information society issued by the Spanish National Observatory for Telecommunications and Information Society, the percentage use of social networks in Spain is 67.9% and in the European Union 64.9% [
118].
To conclude, out of the 98 articles included in the systematic review, the questions posed could be answered to a large extent, where Q1, Q2, and Q3 were covered in their entirety. From the analysis of the articles, we can highlight the following:
According to the type of education, the SRs cover mostly formal education, especially oriented towards students.
As for the elements subject to recommendation, they are very varied, highlighting educational resources and courses.
The most commonly used development techniques are collaborative filtering, followed by RSs that combine different techniques. Similar systematic reviews, such as the one presented by [
119], agree with this result, finding a gap in the use of intelligent techniques. It can be seen in our review how this potential area has been covered since 2019, where proposals for RSs with machine learning are presented.
The incipient use of ontology for the representation of information and the construction of user profiles can be observed.
In the analysis of question Q4, in which we asked about the type of platform used by the RSs, not much information was obtained, as 76% of the articles analyzed did not specify the type of platform on which they were to be implemented. However, from the work [
119], we can observe 50% of RSs using a web platform.
In addition to the limitation found in question Q4, we can list the following:
When selecting the articles for analysis, we selected those where the focus was on higher education, because the interest of the researchers is adult education. In order to obtain a broader view, it is suggested to take into account all levels of education.
The evaluation metrics of the SRs were only analyzed in the selected studies according to the quality metrics. For further study, it is suggested to extend this analysis to all articles.
Some gaps were identified in the systematic review, which allows us to suggest that future work should focus on the following:
The development of hybrid systems, in particular, the use of intelligent techniques and the use of ontology and evaluate the performance of RSs.
User information should be explored with information from different sources, such as social media, to build a more complete profile beyond user preference and similar users.