# Recommender Systems for Students in Flexible Education: An Exploration of Benefits and Risks

Suzanne Van Rossen, Erik Kluijtmans, Suzan Van Brussel, Milou Van Harsel, Johan Smarius, Esther van Der Stappen

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Suzanne Van Rossen, Erik Kluijtmans, Suzan Van Brussel, Milou Van Harsel, Johan Smarius, et al. Recommender Systems for Students in Flexible Education: An Exploration of Benefits and Risks. 19th IFIP Conference on Human-Computer Interaction (INTERACT), Aug 2023, York, United Kingdom. pp.196-204, 10.1007/978-3-031-61698-3_18. hal-05305359

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# Recommender Systems for Students in Flexible Education: An Exploration of Benefits and Risks

Suzanne van Rossen ID, Erik Kluijtmans ID, Suzan van Brussel ID, Milou van Harsel ID, Johan Smarius ID, and Esther van der Stappen (*) ID

Avans University of Applied Sciences, Breda, The Netherlands
{s.vanrossen,ej.vanderstappen}@avans.nl

**Abstract.** Since flexibilisation of education is stimulated by governments' strategic agendas, many higher education institutions are making their programmes more flexible by offering students more opportunities to choose elective courses within the curriculum. Digital tooling has the potential to support students in their decision-making processes imposed upon them by the flexibilisation of education. Recommender systems are increasingly suggested as a promising possibility to support students' choice processes. In this position paper, we explore the envisioned benefits and the looming risks of deploying recommender systems towards this end. Through a reflection on these benefits and risks, we list some important dilemmas that this technology evokes for stakeholders in educational practice, most importantly students. Given this non-trivial list of dilemmas, we believe the time is not ripe just yet to deploy recommender systems to students in real-life contexts. We posit that the design and implementation of these systems should first be rigorously studied in transparent, small-scale, research-based pilots to explore these dilemmas and gain insights into the impact that such technology has on students.

**Keywords:** Flexible Education · Recommender Systems · Ethical Dilemmas

## 1 Introduction

Flexibilisation of education is high on the EU Strategic Agenda for Higher Education [1] with the main goals being 1) better accommodating an increasingly diverse student population (accessibility and increased motivation) and 2) better preparation for future jobs (adaptability) [2]. Many higher education institutions therefore offer some kind of flexibility to their students. Curricula can be made more flexible in several different ways [2, 3]. To increase accessibility, we can offer students choices as to *where* and *when* they learn, and to increase adaptability, we can offer students choices as to *what* and *how* they learn [2].

Many institutions opt to make their programmes more flexible by offering students more opportunities to choose elective courses within the curriculum [8], since it is often easiest to implement: it requires less effort from teaching staff than enabling learning
independent of time and place or flexibility in learning activities or assessment forms [2]. Increasing the number of elective courses in a curriculum, puts the onus on students to compose their personalised learning trajectory, requiring many, possibly difficult, choices along the way. Giving students more opportunities to choose the contents of their study programme based on individual needs or interests is seen as an important factor to increase autonomous motivation, which has a strong positive relation with student's engagement, learning effort, and performance [4]. However, with this greater autonomy comes the responsibility to make difficult choices, which can lead to stress, procrastination, and less satisfaction with the final choice [5, 6], especially if the need for autonomy is not balanced with the need for competence and relatedness [4, 7].

Well-informed choices ensure identification with the choice [8], which, in turn, results in higher retention and student satisfaction [5]. So, it is crucial we support the choice-making processes of students in higher education, especially now the number of options to choose from is increasing rapidly, given that students can choose electives beyond their field of study, beyond their own institute, and even beyond country borders. And we know that choosing from more options is inherently more difficult than choosing from less options [6].

Digital tooling has the potential to support students in the process of composing their personalised study trajectory in higher education. In this position paper, we explore the envisioned benefits and the looming risks of deploying recommender systems towards this end.

# 2 Theoretical Background

## 2.1 Students' Decision-Making in Flexible Education

Most literature available on higher education students' decision-making on the content of their study programme considers the process of choosing a major to pursue. These processes are notoriously long and complex [9, 10], and are generally perceived as interest-based and dynamic [11].

There is research available on elective modules in higher education, but it is scarce. What we do know suggests that although students' choices are generally based on intrinsically motivated factors (e.g., goals, interests, value), they are more often based on extrinsically motivated factors (e.g., friends, lecturer, class schedule) [12]. Furthermore, students might not be able to select (the most) suitable elective courses if they do not manage to incorporate information adequately in their choice process [12]. And finally, when the number of options to choose from is overwhelming, students run the risk of becoming frustrated, using suboptimal decision-making strategies [7], and eventually being less satisfied with, and committed to, their choice [13].

The dual-cycle model of Zimmermann et al. [14] describes the phases young adults go through during their identity development, which involves making many choices that contribute to the development of their identity [9]. The model describes an iterative process of exploration and commitment making, exploring choices and re-evaluating them until identification with commitment occurs. Not only might students perceive the amount of options as overwhelming; these choices are also inherently hard because none of the available alternatives is clearly 'better' than the other [15]. We argue that
therefore especially the exploration in breadth phase and the exploration in depth phase are becoming more difficult for students. In a recent study, tentative design propositions were developed for course catalogues (without recommendation functionalities) supporting these exploration phases [16].

## 2.2 Recommender Systems for Elective Course Selection

Besides efforts from educational science to study how we can best support students in their decision-making in flexible curricula, it is not surprising that IT departments in higher education institutes are also thinking about what they can do to support students by technology. From our experience, recommender systems are increasingly considered by both IT staff and (IT) researchers as a potential solution. Recommender systems are a type of Decision Support System (DSS), which is “*a computer-based system that represents and processes knowledge in ways that allow decision making to be more productive, agile, innovative, and/or reputable.*” [17, p. 22]. In recommender systems, the software provides “*suggestions for items that are most likely of interest to a particular user*” [18], [19, p. 1]. Recommender systems are generally developed in contexts where individuals have to choose from a potentially overwhelming number of options, but lack the experience, competence or time to do so [18].

Recently, research on recommender systems in education has grown. They encompass models and algorithms trained with student, course and cohort data in order to provide students with recommendations on which courses to choose [20–24]. These recommender systems most often apply content-based filtering or collaborative filtering [25], while a hybrid approach is gaining popularity [21–23, 25]. The papers we could find on this topic all discussed the recommender system in question from a technical perspective, such as the development of the recommendation algorithm or the software architecture. We are not aware of any literature evaluating these systems from either the educational science perspective or the ethical perspective. We consider these perspectives crucial for this theme since, for example, deploying a recommender system to students inherently implies that instead of the full course catalogue, the set of elective courses is filtered, leaving out specific options for specific students. The arrival of such recommender systems raises many questions we aim to explore in this paper.

# 3 Exploration of the Theme

## 3.1 Benefits

Recommender systems can be a useful tool for students in flexible curricula given the complex nature of the choices students are facing. Below, we list several envisioned benefits we identified of using this technology. It is good to be aware that these benefits are listed with the most ideal situation in mind and may not be realized if not implemented correctly.

1. **Personalisation.** Recommender systems can provide students with recommendations for courses or learning materials. These recommendations can be personalised if the system 'knows' the user, i.e., student data is available on for example goals,
interests, previous academic performance, and learning preferences. Students can thus be enabled to make more ‘focused’ decisions about what to study next, and how to study it, since the recommended courses are tailored to their personal characteristics, based on the personal data the system has.

2. **Efficiency.** When using recommender systems, students can save time and effort, since they no longer have to search through a large number of options themselves; the system selects for them those courses or learning materials most relevant to their interests and goals.

3. **Motivation and engagement.** By suggesting courses or studies that are interesting and relevant to students, recommender systems can help increase student motivation and engagement. For example, by suggesting courses that align well with students' interests and values, educational recommender systems can positively impact the motivation of learners. It could also change the dynamic of the extrinsically motivated factors (e.g., friends, lecturer, class schedule) and the intrinsic factors that are highlighted by the system. This can lead to a more intrinsically motivated choice. Moreover, recommendation systems which are based on collaborative filtering promote interaction and social navigation. This helps to find learners with similar ideas and preferences and to enhance the experiences within the existing communities. In conclusion, these systems can improve both social and cognitive presence in the learning environment [26].

4. **Performance.** We know that motivation and engagement positively influence academic performance [4]. Moreover, learning recommendation systems can identify students with particular difficulties how can, based on the recommendations, adjust the content of their studies to better fit their needs.

## 3.2 Risks

As we have seen in the previous section, recommender systems can be a useful tool for students in flexible curricula. However, there are also some potential drawbacks to consider. These risks can be mitigated, but often times the full scope of risks can be assessed after the creation and implementation of a recommender system.

1. **Bias.** Recommender systems may be biased in their recommendations, potentially perpetuating existing inequalities in access to education or limiting students' exposure to new ideas and perspectives. Different biases play a role here [27]. For instance, in the popularity bias [28], popular items are recommended more often, making them even more popular, while other items that were chosen less at the start, are less likely to be discovered. A common bias in humans is the confirmation bias [29]. This bias is amplified by recommender systems through the 'filter bubble' [30, 31], where the student will continuously get exposed to pre-existing beliefs, interests and preferences while being shielded from alternative perspectives and recommendations. Many different biases can influence the impact a recommender system has in all its lifecycle phases: design, development, implementation and usage.

2. **Lack of transparency & the need for explanation.** Students may not understand why certain courses or learning materials are recommended to them, which could lead to confusion or frustration. Additionally, if the algorithm used to generate recommendations is not transparent, it could be difficult for students to trust the system; students
may perceive the recommendation system as a ‘black box’ [32]. Students might need
explanations on how the recommender system works and recommendations came to
be.

3. **Overreliance & limited agency.** Students may become overly reliant on the recommender system and fail to explore other options or take responsibility for their own learning decisions. This could limit their ability to develop skills like critical thinking and decision-making. The agency of a learner determines how they engage in the process of learning and what is being learnt [33]. When a system restrains agency and perceived autonomy by recommending learning paths, this in turn may negatively impact learning performance [4]. In addition, students may attribute unsuccessful performance to the recommender system instead of themselves, possibly leading them filing official complaints at their institute.

4. **Humanity.** Both students and teachers might not appreciate when their human judgment is overridden by technology. Students might perceive these data-driven systems as ‘being treated as a datapoint’ – even if it was for their own good. Study coaches and academic advisers to students might argue that their training, experience, intuitions and pedagogical differentiations are being overridden by the recommender system. The call for the safe-keeping of human interaction, autonomy and judgement in education is getting louder the last few years [34].

3.3 Dilemmas

We identified the benefits and risks of using recommender systems. When we analyse
these benefits and risks, combined with the EU guidelines for the ethical use of AI and
data in education [35], we see multiple dilemmas arising.

1. **Personalisation versus Privacy.** To train an effective, personalised recommender system, one that gives recommendations that for each individual student matches their goals, interests, previous academic performance and learning preferences, you need a lot of student data. So the collection and processing of large volumes of personal data raises some crucial questions that need to be considered before implementation. How much, and which, data do we actually need to train an effective recommender system? Do we need, next to academic performance data, data on personal values, ideas or passions? If so, to what extent and in what way will that effect the recommendation? Who is in control of the data? Does a student have full transparency (also see below) and control to see the data that has been collected and used for recommendation? [32] What happens if students wish to opt-out? Will they still be able to use the recommender system? And if not, what alternative support is available for such students to make sure they have equal chances to the students that are using the system? If data is anonymised, how do we make sure that students cannot be re-identified [36]?

2. **Efficiency versus Autonomy.** Students will use these systems to make decisions that will impact their next semester, year(s), or even life. Efficiency, i.e., not having to consider all available options, can be important for students struggling with making these choices, but it remains to be seen if the decision process and its potential impact in the student's life should be based on an efficient choice. An inefficient choice, made by themselves, resulting in more commitment, satisfaction and motivation, might turn out to be better [8, 13, 14]. Moreover, students will probably feel the need to be treated
as humans rather than datapoints, even if the system suggests relevant items to the students. In recommender system research, we are still studying how to respect the value of human autonomy in the design of this technology [37]. Hence, we believe we collectively need more knowledge and insights from both research fields (educational sciences and recommender systems) to assess how these systems impact and support students' decision-making processes and their outcomes.

3. **Fairness, Explainability and Trustworthiness.** Students can benefit from recommender systems if they are committed to the recommendations and especially to the resulting decision [8, 14]. However, algorithmically generated recommendations can be unfair when the system - and the developers and users of the system - are biased. The biases at play differ per recommendation system and context, hence it is important to start identifying the possible biases before the recommendation system is built [38]. Fortunately, research into the debiasing of recommender systems is gaining attraction [27]. Explainable recommendation [39] can help to increase both fairness and trustworthiness of the system, since clear explanations of the recommendation and how it was established, can help the student to interpret the recommendation, which in turn can lead to a more convinced decision. On the other hand, when users do not know why specific recommendations are provided, students are less inclined to accept the results because they perceive the system as non-trustworthy. It is important to note that increasing explainability might imply decreasing accuracy of the recommendation system [32]. A recent literature review shows that none of the papers describing recommender systems for learning specifically discussed questions about transparency or trustworthiness in recommendations [25]. Finally, these explanations are ethically important in terms of respecting the users (students) as well as enabling them to assess whether the rules of the recommendation system are justifiable [32].

## 4 Conclusion: Our Current Stance

In this position paper, we explore the envisioned benefits and the looming risks of deploying recommender systems aiming to support higher education students in their decision-making processes imposed upon them by the flexibilization of education. Given the list of non-trivial dilemmas in the previous paragraph, we believe the time is not ripe just yet to deploy recommender systems in real-life situation to students. We posit that the design and implementation of these systems should first be rigorously studied in transparent, small-scale, research-based pilots to develop the answers to the questions above. The risks and dilemmas that come into play when creating, implementing and using a recommender system need to be discussed in light of the context and the goals of the recommender system in question. Even the benefits need to be checked and studied, and the system might possibly be adapted accordingly, before they truly become beneficial to all the students that are using the recommender system. Therefore, the list of benefits, risks and dilemma's mentioned above can shift depending on the context, data and recommender system. We hope our contribution will serve to facilitate discussions on this topic in both educational practice and research.
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