Towards Serendipitous Learning Resource Recommendation

Sahar Sayahi

, Leila Ghorbel

, Corinne Amel Zayani

and Ronan Champagnat

MIRACL Laboratory, Sfax University, Tunis Road Km 10 BP.242, Sfax, 3021, Tunisia

L3i Laboratory, La Rochelle University, Avenue Michel Cr

epeau, La Rochelle, 17042, France

Keywords:

Online Learning, Recommender System, Filter Bubble, Educational Dataset, Data Mining, Serendipity

Dimensions.

Abstract:

Since the outbreak of the pandemic, online learning has become widely applied. Indeed, learners follow

Learning Resources (LR) available on different platforms. Therefore, it’s very difﬁcult for learners to choose

LR that matches their needs. They may face disorientation and cognitive overload problems. In fact, multiple

studies have been conducted on Recommender Systems (RS) in order to provide learners with the best LR

that correspond to their needs and complete their training. Unfortunately, these basic RS can lead to an

overly restricted set of suggestions and inadvertently place learners in a so-called “ﬁlter bubble”. The latter is

resolved through serendipitous RS, which suggest to learners surprising LR based on serendipity dimensions

such as unexpectedness, novelty and usefulness. In this research paper, we ﬁrst present our serendipity-

oriented recommendation architecture. Then, we enrich our collected educational dataset with the dimensions

of serendipity. Finally, we evaluate the real learner’s satisfaction on serendipitous LR’s recommendation.

1 INTRODUCTION

Over the past few years, the need for online learning

has increased, especially after the Coronavirus pan-

demic. During the lockdown period, online learn-

ing became a necessity in numerous countries to

continue maintaining the educational process (Her-

mawan, 2021). This learning mode reinforces the im-

portance of enhancing and developing online learn-

ing platforms in several forms: LMS (Learning Man-

agement System), LCMS (Learning Content Man-

agement System) or MOOC (Massive Open Online

Courses) (Vora et al., 2020). These platforms provide

an important number of available LR which can gen-

erate for learners certain problems of disorientation

and cognitive overload (Dien et al., 2022).

RS have emerged as a solution to overcome these

problems. They aim to satisfy the learner through

suggesting the best LR to complete his/her training.

In literature, there are three basic recommendation ap-

proaches (Kundu et al., 2021), namely content-based,

collaborative ﬁltering, and hybrid. These approaches

suffer from the problems of cold start, sparsity and

scalability. In order to overcome the above mentioned

problems, advanced RS have emerged in a wide range

of ﬁelds. These systems take into consideration real

users’ social relationships (Troudi et al., 2021) and

use matrix factorisation and deep learning (Guo et al.,

2019; Dien et al., 2022; Zhang et al., 2022). De-

parting from this review, we notiﬁed that the major-

ity of basic and advanced RS focus on recommenda-

tions that are very close to the learner’s proﬁle. They

always provide him/her with the same type and con-

tent of resources on the same subject and idea. This

problem is called the ﬁlter bubble (Nguyen et al.,

2014), which is resolved by serendipity-oriented RS.

The latter suggest to learners surprising LR based on

serendipity dimensions.

The most important problem of serendipitous RS

is the lack of public available datasets for evaluation.

In order to overcome this problem, we mainly tackle

the basic principle and specify the steps of the con-

struction of our dataset. The latter must contain di-

mensions explaining the serendipity of LR from the

learner’s perspective.

The rest of this paper is organized as follows. In

section 2, some existing studies about recommender

systems, serendipity, and educational datasets are pre-

sented and discussed. Then, in section 3.2, we give an

overview of our approach. In section 4, we demon-

strate the data processing. In section 5, we identify

the applied algorithms to determine the serendipity

dimensions. In section 6, we describe the evaluation

results. Finally, section 7 concludes the paper and of-

454

Sayahi, S., Ghorbel, L., Zayani, C. and Champagnat, R.

Towards Serendipitous Learning Resource Recommendation.

DOI: 10.5220/0012059800003470

In Proceedings of the 15th International Conference on Computer Supported Education (CSEDU 2023) - Volume 1, pages 454-462

ISBN: 978-989-758-641-5; ISSN: 2184-5026

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

fers certain prospects for future works.

2 STATE OF THE ART

In this section, an overview about RS, serendipity and

the faced challenges is displayed followed by a de-

tailed synthesis.

2.1 Recommender Systems, Serendipity

and Challenges

RS have emerged in online learning to enhance

the quality of learning process and make it easier.

They both help and motivate learners to more ac-

curately learn and improve their academic perfor-

mance through suggesting items (Learning resources:

courses activities, videos, ...) that correspond to their

needs (Fraihat and Shambour, 2015).

Based on an in-depth study, we inferred that RS (Dien

et al., 2022; Guo et al., 2019; Troudi et al., 2021),

in different ﬁelds including online learning, focus on

items (courses, movies, jobs, etc.) that are very close

to the user’s (learner’s) proﬁle. In (Zhang et al., 2022;

Guo et al., 2022), authors developed a session-based

recommendation approach which takes into account

the dependency between items and user’s behaviour.

These works aim to capture the sequential dependen-

cies between items within the current session.

The above mentioned approaches always provide

users with the same type and content of items having

the same subject and idea. This problem is called the

ﬁlter bubble (Nguyen et al., 2014; Pardos and Jiang,

2020) which is resolved by a serendipity-oriented RS.

The latter helps the user ﬁnd a surprisingly interest-

ing item that he/she might not have otherwise dis-

covered (or it would have been really hard to dis-

cover) (Kaminskas and Bridge, 2016; Pardos and

Jiang, 2020).

Serendipity is a complicated and interesting con-

cept for research. The major source of complexity and

ambiguity of this concept resides in the fact that it is

in association with emotion (Ziarani and Ravanmehr,

2021b). As a result, deﬁning serendipity in RS is a

challenging problem.

In order to properly interpret user’s emotion to-

wards items, there are several serendipity dimen-

sions that are determined in different research studies

(Kotkov et al., 2020; Ziarani and Ravanmehr, 2021b;

Zhang et al., 2021). These dimensions can be mea-

sured in terms of item usefulness, novelty and un-

expectedness. Items with these dimensions are very

rare, making it hard to present serendipitous rec-

ommendations (Ziarani and Ravanmehr, 2021a; Li

et al., 2019). For this reason, the majority of the re-

search works that tackle the serendipity in recommen-

dation have faced numerous challenges. Authors in

(Ziarani and Ravanmehr, 2021b) stated different chal-

lenges, among which we can mention ambiguity in

serendipity deﬁnition, methods for serendipity evalu-

ation, emotional aspect and the lack of public datasets

for serendipity.

In this paper, we address the two last challenges.

The ﬁrst concerns emotional aspects of serendipitous

recommendations, which is always very subjective

and imprecise, contributing to the difﬁculty of ﬁnd-

ing them. The second challenge relates to lack of

public datasets for serendipity, and specially educa-

tional public datasets. In fact, the majority of avail-

able datasets that contain especially serendidpty label

are related to movie recommendation

. Additionally,

the avalability of general RS datasets such as Movie-

Lens

, OpenStreetMap

and Jester

help researchers

perform works that deal with serendipitous recom-

mendations .

In the current work, we are basically interested

in online learning domain where research works

are based on RS for Technology Enhanced Learn-

ing (TEL). From this perspective, before presenting

the serendipity recommendation approaches, we have

studied the most prominent datasets used in this do-

main for different purposes (Educational Data Min-

ing, Process Mining, etc.).

2.2 Educational Datasets

In literature, there are three types of data sources that

represent the public available datasets in educational

domain (Mihaescu and Popescu, 2021). The ﬁrst

type consists of general-purpose repositories where

educational datasets are uploaded such as UCI ML

Mendeley

and Harvard Dataverse

data repositories.

The second type of Datasets used in competitions

stands for a category that became very popular in the

last years. These datasets are invested to speed up

the comparative analysis of proposed solutions com-

pared to the solution of other competitors. The third

type corresponds to standalone datasets. The dataset

maintenance, the proposed solution and the results are

https://grouplens.org/datasets/serendipity-2018/

https://grouplens.org/datasets/movielens/

https://www.openstreetmap.org/map=6/33.971/9.562

https://eigentaste.berkeley.edu/dataset/

UCI Machine Learning Repository,

https://archive.ics.uci.edu/ml/index.php

Mendeley Data Repository,

https://data.mendeley.com/

Harvard Dataverse, https://dataverse.harvard.edu/

Towards Serendipitous Learning Resource Recommendation

455

at the disposal of the author.

Data represented in these datasets are collected af-

ter the analysis of resources and learners’ data in order

to improve their learning experience and skills.

After checking the content of these datasets, we

realized that they differ a lot in their structure, their

recorded features which are useful uniquely for a spe-

ciﬁc purpose and their usefulness in terms of citations,

practical uses and approaches. Furthermore, we no-

tice that most of data are encrypted.

Although these datasets have been used for a long

time and are among the most referenced ones, we ﬁnd

it difﬁcult to ﬁnd some of them as they are cited but

not publicly available. Consequently, we are unmoti-

vated to use them in our work and even in our future

academic research.

For this reason, we opted for elaborating our

dataset.

Table 1 exhibits some educational datasets and

their main features.

2.3 Synthesis

Owing to the importance of RS, various works have

been developed in this context. However, in view of

the complexity and variety of challenges, RS for on-

line learning have not been yet well elaborated, where

most researchers use basic RS. As far as our research

is concerned, we seek to make serendipitous recom-

mendations. Therefore, we discuss approaches that

invest serendipity for RS. These approaches display

various basic shortcomings.

The ﬁrst shortcoming is related to the dimensions

of serendipity in the recommendation (unexpected-

ness, novelty, usefulness, etc.) to achieve serendip-

ity. We notice that some works are confused in terms

of using one or two features (Ziarani and Ravanmehr,

2021a; Zhang et al., 2021; Li et al., 2019; Kaminskas

and Bridge, 2016; Pardos and Jiang, 2020).

The second shortcoming resides in the fact that the

majority of works that are concerned with serendipity

do not take into account user’s behavior and the emo-

tional aspect (Ziarani and Ravanmehr, 2021a; Zhang

et al., 2021; Kaminskas and Bridge, 2016).

Finally, it is noteworthy that almost all RS re-

search studies are about movies or music (Ziarani and

Ravanmehr, 2021a; Zhang et al., 2021; Li et al., 2019;

Kaminskas and Bridge, 2016).

From this perspective, in order to overcome these

limitations, we set forward our approach for LR rec-

ommendation based on serendipity dimensions. The

basic merit of this approach lies in extracting a spe-

ciﬁc educational dataset and determining the dimen-

sions of serendipity.

Figure 1: Motivated scenario.

3 OVERVIEW OF THE

PROPOSED APPROACH

In order to introduce our proposed approach, we

demonstrate the basic objectives to achieve serendipi-

tous LR recommendation by a scenario in section 3.1.

Then, we identify the proposed architecture in section

3.2.

3.1 Motivating Scenario

In this part, we exhibit a motivating scenario to clar-

ify the basic purpose of the proposed approach that

relates to the importance of serendipity-oriented rec-

ommender systems for online learning. In ﬁgure 1,

We consider a student “Ali” who wants to accumu-

late knowledge and skills associated with his interests.

Therefore, at the ﬁrst step, he subscribes to the Moo-

dle Learning Management System (LMS) provided

by his school. Then, he follows the existing courses

in Moodle delivered by his teachers (1). In order to

enhance the education level, researchers include a ba-

sic recommender system based to the learner pref-

erence (2). Unfortunately, using this type of rec-

ommender systems makes the provided courses very

similar to Ali’s proﬁle. In fact, Ali’s proﬁle con-

tains the school’s courses, which will make him feel

so bored and less motivated to learn more. For this

reason, there is a real need to add “Serendipitous

Courses” in the Moodle platform to make Ali more

motivated and enable him to accumulate further new

information. Therefore, the idea is to enhance the rec-

ommender system in Moodle by adding the serendip-

ity aspect. In this case, Ali will receive serendipitous

courses (3), which will offer him the possibility to im-

prove his skills and capacities with useful, unexpected

and novel courses. Thus, based on our solution, Ali

would feel very satisﬁed.

EKM 2023 - 6th Special Session on Educational Knowledge Management

456

Table 1: Educational Datasets.

Datasets Repository Nb of cita-

tions

(nb of in-

stances, nb of

features)

Purposes

Student Performance

Dataset

2014

UCI ML 394 (649,30) Prediction of students’ grade

Educational Process

Mining Dataset

(EPM)

2015

UCI ML 42 (230318,13) Predicting learning difﬁculties, ana-

lyzing structured learning behavior

or discovering student behavior pat-

terns

Video Game Learn-

ing Analytics

2020

Harvard

Dataverse

(331,25) Early Reading and Writing Assess-

ment in Preschool

RecSysTEL Datatel

Challenge

2010

Competition not mentioned not mentioned Technology Enhanced Learning

EdNet: A Large-

Scale Hierarchical

Dataset in Educatio

2020

Competition not mentioned 780 K users Collect real students’ question-

solving logs

Learn Moodle

August

2016

Standalone not mentioned 6119 students Inspire better teaching practices ev-

erywhere

Student Performance Data Set, http://archive.ics.uci.edu/ml/datasets/Student+Performance

Educational Process Mining (EPM): A Learning Analytics Data Set, https://tinyurl.com/y27yduo3

Early Reading and Writing Assessment in Preschool, https://doi.org/10.7910/DVN/V7E9XD

RecSysTEL Datatel Challenge 2010, http://adenu.ia.uned.es/workshops/recsystel2010/datatel.htm

EdNet competition site, http://ednet-leaderboard.s3-website-ap-northeast-1.amazonaws.com/

Learn Moodle August 2016, https://research.moodle.org/158/

3.2 Proposed Architecture to Attend

Serendipitous Recommendations

In order to elaborate a serendipity-oriented RS, we

build up an architecture that can be summarized in

four main layers: Social media, Data extraction,

Serendipity dimension extraction and Recommenda-

tion. Figure 2 illustrates the proposed architecture.

The second and the third layers aim to erect educa-

tional serendipity-oriented data. The Data extraction

layer will be detailed in section 4, and the Serendipity

dimension extraction layer will be detailed in section

The recommendation layer of our architecture will

be addressed in the subsequent work. It aims to build

a RS oriented serendipity using the extracted data.

The algorithm of recommendation takes place at two

stages. The ﬁrst stage predicts a sequence of k courses

(as LR). The second stage applies the re-ranking of k

predicted courses according to the dimensions of the

serendipity and recommends the course that best ver-

iﬁes the usefulness, unexpectedness and novelty ac-

cording to the learner’s interests and behavior.

4 DATA EXTRACTION

As mentioned earlier in section 2.2, the available ed-

ucational dataset has several limitations and cannot

cover our requirements to achieve serendipity. For

this reason, we collected our own dataset correspond-

ing to our needs. Our chief target is to gather data in

a legal way via API (Application Program Interface)

provided by diverse social media platforms such as

YouTube and Linked-In. This choice of source di-

versity is enacted basically to have data variety with

different types of LR such as courses represented by

texts, videos, etc.

In our experiment we are conﬁned to extract the data

from YouTube using its available API by “beautiful-

soupe”.

We developed a dataset consisting of three data

tables. The ﬁrst collected data are named “Course-

Data”, where every course is identiﬁed with an ID and

other features as depicted in table 2. CourseData in-

volves information about 2362 courses represented by

9 features: identiﬁer, title, description, type, course

link, number of likes , views and comments as well as

the duration.

The second dataset “LearnerDtata” involves in-

Towards Serendipitous Learning Resource Recommendation

457

Figure 2: Serendipity-oriented recommendation architecture.

formation about 99394 learners represented by 7 at-

tributes, as plotted in table 3. The learner is identi-

ﬁed with his/her name and linked with the informa-

tion about his/her interactions, some of which charac-

terize his/her behavior.

The extracted data on the learner will be added to

his/her proﬁle.

Once the courses data and learner’s data are

extracted from the Data extraction layer, we

switch to the serendipity dimension extraction layer

(section3.2).

5 SERENDIPITY DIMENSION

EXTRACTION

Notably, the serendipity dimensions that are mostly

used in previous works are novelty, unexpectedness

and usefulness. However, these dimensions are iden-

tiﬁed speciﬁcally in domains other than online learn-

ing. From this perspective, we determined them

through emotions analysis algorithms and other al-

gorithms to identify attributes to enrich the extracted

data. These dimensions are used in order to provide

serendipitous recommendations.

5.1 Usefulness Dimension

Authors in (Kotkov et al., 2016) deﬁned a useful item

that a user likes, consumes or is interested in. How-

ever, in the educational domain, the usefulness affects

equally the quality of the course. As far as our work

is concerned, we consider that a useful course should

be represented in a pedagogical way and has a pos-

itive impact. Additionally, it should be close to the

learner’s interest. Consequently, we assert that the

usefulness of course can be achieved using the equa-

tion below.

Usefulness = (CoursePedagogy = True)

& (CourseMark = Positive)

& (BERT sim > threshold)

(1)

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458

Table 2: CourseData.

Features Description

CourseId Represents the unique ID of

the course

CourseTitle Represents to the title of the

course

CourseDescription Represents the description

of the educational course

content

CourseUrl link of the course.

PublishedDate Represents the date on

which the course is pub-

lished in the form of

time-day-month-year.

ViewsNB Represents the number of

views on the course

LikesNB Represents the number of

learners who likes a course

CommentNB Represents the number of

comments related to the

course

CourseDuration Represents the duration of

the course in terms of hours,

minutes and seconds.

Table 3: LearnerData.

Features Description

LearnerName Name of the learner.

CourseId Id of course followed by a given

learner.

CommentId Id of the comment done by the

user.

CommentURL Comment link, if does not exist,

it will take the value “nan”.

CommentDate Date time-day-month-years,

when the comment is added.

CommentLikes Number of the likes on the com-

ment.

RepliesCount Number of replies on the com-

ment.

where the pedagogy of course is veriﬁed by this equa-

tion:

CoursePedagogy = (CourseSeniarization = True)

&(CourseMark = Positive

∥CourseMark = Neutral)

(2)

The learner’s comment describes his/her emotion fac-

ing the given course. For this reason, in our analysis

we used comments on courses as input. We invested

Lexicon-based emotion analysis to classify courses

according to their notices if positive, neutral or nega-

tive. In fact, we used three lexicon-based approaches

(Aljedaani et al., 2022): the TextBlob, VADER and

AFINN. Notably, these approaches provide output

polarity scores to determine learner’s emotion on a

course. Since each model has its own advantages, we

chose to use all the three together so as to increase

the performance of the labeling. Therefore, we ob-

tained three course’s marks outputs, then we applied a

majority algorithm to determine the ﬁnal course mark

(CourseMark), in order to use it as a feature in the

data set.

Poor LR structure is one of the main factors for

learner dropout. For this reason, we recommend

courses organized in a pedagogical way. Thus, we

set forward a method to determine whether a course

is pedagogical or not. We assumed that a course, in

addition to its mark (positive or negative), needs to be

well organized and divided into sections by time dis-

tribution to be scenarized (CourseSeniarization). In

fact, we used the description of course as the input

of the algorithm that veriﬁes the scenarization. We

have considered that the learner’s interest can be de-

ﬁned by the topic of the taken course. Indeed, the

title of the course generally describes its subject mat-

ter. In order to verify if the given courses are close to

the learner’s interests or not, we implemented a model

that veriﬁes the similarity between the course title and

the sequence of followed courses in the learner’s pro-

ﬁle. We used the high performance semantic simi-

larity BERT (Peinelt et al., 2020), and we supposed

that two courses are similar if the result of the model

BERTsim>threshold.

5.2 Unexpectedness Dimension

Unexpectedness is the core of the serendipity in the

recommendation. As deﬁned in (Kotkov et al., 2016),

an unexpected item differs from the proﬁle of the user

regardless of whether it is novel or useful. In our case,

we deﬁne that an unexpected course should have a

good mark and make the learner positively surprised.

Further more, it should be different from the learner’s

proﬁle. Therefore, we applied the equation below:

Unexpectedness = (CourseMark = Positive)

& (CourseSur prise = true)

& course ∄ in the Learner

′

s pro f ile

(3)

In order to verify if a course can surprise a learner,

we analyzed the learner’s emotion by implementing a

lexical approach based on an algorithm using a dic-

tionary containing surprising terms. For this analysis,

we took the learner’s comments on this course as an

input to our algorithms.

Towards Serendipitous Learning Resource Recommendation

459

5.3 Novelty Dimension

Novelty refers to the ability of recommending new

and unprecedented recommendations (Ziarani and

Ravanmehr, 2021b). Hence, in order to improve on-

line learning, we need to recommend courses that are

novel for the learner. The novelty of the course can be

expressed according to this equation:

Novelty = (PublishedDate < threshold)

& (LikesNB > avrgLikes)

(4)

A course is considered novel if it is recently published

and has a good feedback from a large number of learn-

ers. The feedback can be likes, comments or shares.

5.4 Data Enrichment with Dimensions

of Serendipity

After applying the previous algorithms based on

serendipity, we added new features that are necessary

for achieving serendipity dimensions. As a result, we

integrated 6 new features to the CourseData, as dis-

played in table 4:

Table 4: Data enrichment.

Features Description

CourseLeghth Refers to the fact whether

the length of the course is

short, medium or long.

CourseSeniarization Stands for the fact whether

the course is scenarized or

not.

CourseType Refers to the type of

course content: text,

video, image or audio.

CourseMark Represents the mark of

the learner on the course

which can be positive,

CoursePedagogy Refers to the educational

pathway of the course

construction, if it is peda-

gogical or not.

6 EVALUATION

Evaluating a recommender system makes it possible

to assess its performance against its objectives. To

evaluate a recommender system, two approaches are

possible, namely an online (user studies) and an of-

ﬂine evaluation.

Figure 3: Learner Satisfaction with recommendations.

6.1 Online Evaluation

In the online evaluation, the recommender system is

tested by real users investing in a real application. It

allows the system not only to generate very reliable

results but also to measure the performance of the ap-

plication in a real-use context. Since the serendipi-

tous recommendations are based on emotional anal-

ysis, it is important to conduct a test on real learners

to measure user satisfaction through explicit ratings.

Users receive the generated recommendations, then

rate them.

In our work, we are proposed that we can achieve

the relevance of recommendation only when it is char-

acterized by the three dimensions of serendipity (cf.

section 5). In order to evaluate our RS (more pre-

cisely the dimensions of serendipity), we conducted

a real test with 100 learners. Then, we test their sat-

isfaction on serendipity. For this reason, we asked

them if they are satisﬁed with the serendipity of rec-

ommendations or not. The result in ﬁgure 3 reveals

that 91,2% of courses are serendipitous. Therefore,

we conclude that the recommendations are relevant.

Afterward, we give a questionnaire from which

we test the satisfaction of learners for each dimen-

sion. We asked the main questions according to the

serendipity dimensions inspired from (Taramigkou

et al., 2013). These questions are outlined in table

Table 5: Real learners based evaluation.

Question Yes No

Do you think this recommendation

is useful for you?

Have you seen these courses be-

fore?

Are you expecting a suggestion

similar to this one?

As reported in ﬁgure 4, most of recommended LR

are considered “useful” by (85%), “novel” (70%) and

“unexpected” (67%), which implies that the learners

tried and appreciated the serendipity of the recom-

mendations.

EKM 2023 - 6th Special Session on Educational Knowledge Management

460

Table 6: Comparison of obtained results based on serendipity dimensions.

Approach/Serendipity measures Usefulness Novelty Unexpectedness Precision Recall F-measure

(Ziarani and Ravanmehr,

2021a)(Zhang et al., 2021)

X 0.67 0.73 0.69

(Li et al., 2019) X 0.85 0.93 0.88

(Kaminskas and Bridge,

2016)

X 0.7 0.76 0.72

(Pardos and Jiang,

2020)(Menk et al., 2017)

X X 0.68 0.75 0.71

Our Approach X X X 0.91 1 0.95

Figure 4: Serendipity dimension satisfaction.

6.2 Ofﬂine Evaluation

In order to make the evaluation of the recommender

system more accurate, the ofﬂine evaluation is based

on a well-deﬁned mathematical calculation, in which

the values of the precision, recall, and F-measure are

used.

The precision (Pre) determines the probability that

a recommended item is relevant, by dividing the

Number of Relevant Recommendations (NRR) by the

Total Number of Recommendations(TNR)

Pre = NRR/T NR

(5)

The recall (Rec) highlights the Number of Relevant

Recommendations that were returned to the user out

of the Total Number of Relevant Recommendations.

Rec = NRR/T NRR

(6)

The F-measure (F-me) considers both the last two

measures simultaneously and indicates the overall rel-

evance of the list of recommendations.

F-me = (2 ∗ Pre ∗ Rec)/(Pre + Rec)

(7)

In order to study the performance and reliability

of the proposed approach, we have taken into account

the different dimensions of serendipity. We com-

pared our approach with the following state-of-art ap-

proaches in table 6.

From the results of the table, we can observe that

relying only on one or two dimensions of serendip-

ity is somewhat less satisfying with F-measure values

between 0.69 and 0.88. We conclude that using all

three dimensions together gives better results with an

F-measure value of 0.95.

7 CONCLUSION

In this paper, we are basically interested in achieving

serendipitous recommendations of LR. For this rea-

son, we investigated RS as well as serendipity dimen-

sions and challenges. We notice that the most crucial

challenges in this context are the availability of edu-

cational datasets and the determination of serendip-

ity dimensions. First, in order to explain our goal

and method, we presented a motivational scenario and

the architecture of the proposed approach. The latter

consists of four layers: social media, Data extraction,

Serendipity dimension extraction, and Recommenda-

tion. In the data extraction layer, we deﬁned a dataset

describing the learner and the characteristics of the

taken courses from social media. Then, we applied

some algorithms to determine the three serendipity

dimensions: Usefulness, Novelty, and Unexpected-

ness. Indeed, these dimensions allow us to provide

”serendipitous” recommendations.

We undertook an online and ofﬂine evaluation for

learners who proved the relevance of serendipitous

recommendations.

In future research work, we will detail the fourth

layer. We aspire to adapt and test the recommenda-

tion layer of the proposed approach in such Learning

Management Systems as Moodle.

ACKNOWLEDGEMENTS

This work was ﬁnancially supported by the PHC

Utique program of the French Ministry of Foreign

Affairs and Ministry of higher education and re-

search and the Tunisian Ministry of higher education

and scientiﬁc research in the CMCU project number

22G1403.

Towards Serendipitous Learning Resource Recommendation

461

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