Yassine Safsouf
email: yassine.safsouf@univ-ubs.fr

Khalifa Mansouri
email: khmansouri@hotmail.com

Franck Poirier
email: franck.poir-ier@univ-ubs.fr

Enhanced online academic success and self-regulation through learning analytics dashboards

Keywords: Learning experience, Learning analytics, Self-regulated learning, Learning analytics dashboards, Learner success

In the wake of the COVID-19 health crisis, governments around the world made educational continuity during school and university closure a priority. Many countries adopted online education as an alternative to face-to-face courses. This situation has led to an awareness of the importance of analyzing learning traces and data left by students to measure, evaluate and improve the learning process. This paper presents an interoperable online learning analytics dashboard that allows teachers to easily track the progress of their learners as well as to predict and remedy dropouts. For learners, the dashboard offers the possibility to visualize their learning process, analyze it and develop better selfregulation skills. The results of the study conducted on a blended learning course, showed that the dashboard led learners to spend more time on their online training, to perform the proposed activities much better and to respect the deadlines better, and finally to improve their academic success.

Introduction

In many countries, the COVID-19 pandemic has dramatically accelerated the shift, partially or fully, to online learning in higher education. It is a significant change in the way the student learns and, for the teacher, in the way the educational activities are organized and the student's work is monitored and evaluated. Current learning management systems (LMS) have little or no ability to motivate students and facilitate their work, nor to effectively monitor their work by the teacher. It is therefore necessary to design tools and integrate them into LMS to enable both students and teachers to be effective in online learning, in a general perspective to ensure a good learning experience (LX).

For students, one of the most important factors for success in an online learning environment is the ability to self-regulate their activities. Zimmerman who developed the theory of self-regulated learning defines self-regulation as "processes whereby students activate and sustain cognitions, affects, and behaviours that are systematically oriented toward the attainment of personal goals" [START_REF] Zimmerman | Handbook of Self-Regulation of Learning and Performance[END_REF].

In theory, learning analytics (LA) and teaching analytics (TA) helps measure and support students' self-regulated learning (SRL) in online learning environments by harnessing the hidden potential of interaction data generated by the use of learning management systems (LMS) [START_REF] Ferguson | Learning analytics: Drivers, developments and challenges[END_REF]. According to Sclater, the goal of learning analytics is to analyze the digital traces left by learners in order to better understand them and optimize learning [START_REF] Sclater | Learning Analytics Explained[END_REF].

However, in a review paper analyzing 54 articles on self-regulated learning and learning analytics [START_REF] Viberg | Self-Regulated Learning and Learning Analytics in Online Learning Environments: A Review of Empirical Research[END_REF], Viberg and al. show that there is little evidence of contribution of LA for SRL in these articles. LA performs mainly to measure rather than to support SRL among learners in online environments. Hence, there is a critical need to design tools such as learning analytics dashboard that leverage learner interaction traces to help learner self-regulation.

So, the research challenge is to collect learner interaction traces to analyze them in order to propose an effective visualization of the analysis results to the different users (students and teachers) [START_REF] Labarthe | L'analytique des apprentissages numériques[END_REF].

In this paper, we propose a learning analytics dashboard which is a visual communication tool, designed as a dashboard for teachers and learners that provides an analysis of learning data to facilitate the monitoring and control of the learning process, with the aim of improving the engagement, enjoyment of learning and success rate of online learners.

This paper begins with a literature review on the influence of self-regulated learning theory and learning analytics on online learners' success. Then, we share the reports of our tool created in the form of learning trace analysis dashboards. The next section is dedicated to the methodology our experiment, and finally the discussions of the results. Finally, we present a conclusion with some perspectives.

2

Related work

Self-regulated learning theory

Self-regulated learning theory (SRL) defines learning as a dynamic process in which the learner plans, monitors and evaluates his or her learning, applying appropriate strategies to achieve the goals [START_REF] Zimmerman | Self-Regulation, Where Metacognition and Motivation Intersect[END_REF]. It is a set of activities that individuals do by themselves in a proactive way [START_REF] Zimmerman | From Cognitive Modeling to Self-Regulation: A Social Cognitive Career Path[END_REF].

Panadero published an article that presents a review of the six most popular selfregulated learning models [START_REF] Panadero | A Review of Self-regulated Learning: Six Models and Four Directions for Research[END_REF], the article concludes that, most of these models are composed of three essential phases, namely, (1) the preparation phase, (2) the performance phase, and (3) the reflection phase. As presented in the model of phases and subprocesses of self-regulation by Zimmerman and Campillo [START_REF] Zimmerman | Motivating Self-Regulated Problem Solvers[END_REF], the preparation phase includes task analysis, planning, goal detection, and goal achievement; the performance phase involves the performance of the actual task completed while monitoring and controlling progress; and the final reflection phase, where the learner self-assesses, reacts, and adapts for future performance.

Winne and Hadwin [START_REF] Winne | Studying as self-regulated engagement in learning[END_REF] proposed another model of self-regulation composed of four linked phases that are open and recursive and controlled by a feedback loop: (1) task definition (understanding of the task), (2) goal setting and planning (goals and plan to achieve the task), (3) enacting tactics and strategies for learning (actions needed to reach those goals), and (4) adaptations (metacognitive processes for long-term modification of motivations, beliefs and strategies). Each task can be modeled by five facets called COPES model: Conditions, Operations, Products, Evaluations and Standards. The learner's performance depends in part on evaluation (Evaluations facet), i.e. internal and external feedback.

Many studies agree on the relevance of self-regulated learning as a predictor of academic success in online learning systems. Liaw and Huang investigated learner selfregulation to better understand learners' attitudes toward online learning [START_REF] Liaw | Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments[END_REF]. The results show that the factors perceived satisfaction, perceived usefulness, and interactive learning environments were identified as predictors of perceived self-regulation in the online learning context. In a study on formal and informal learning using social media, the authors showed that the use of social media as pedagogical means, to encourage students to control their autonomies [START_REF] Matzat | Self-regulated learning and social media -a 'natural alliance'? Evidence on students' self-regulation of learning, social media use, and student-teacher relationship[END_REF].

Learning analytics dashboards

One of the most common applications of learning analytics is the production of dashboards to provide stakeholders (primarily teachers and learners) with visual interpretations of the overall learning process [START_REF] Verbert | Learning Analytics Dashboard Applications[END_REF]. Schwendimann defined these tools as a set of single displays that aggregate many indicators about the learning process and/or context into one or more visualizations [START_REF] Schwendimann | Understanding learning at a glance: An overview of learning dashboard studies[END_REF]. In general, these dashboards are steering tools that summarize the company's activities and results by process; thus, allowing to supervise the achievement of any set objective [START_REF] Fernandez | Les tableaux de bord du manager innovant: Une démarche en 7 étapes pour faciliter la prise de décision en équipe (Management)[END_REF]. Jivet [START_REF] Jivet | Adaptive learning analytics dashboard for self-regulated learning[END_REF] proposed a literature review to better understand and describe the theoretical underpinnings behind the use of dashboards in educational settings. The study revealed that the most common foundation for the design of analytic dashboards is SRL. This theory is primarily used in the awareness and triggering of reflection, providing some support for the performance and self-reflection phases of the SRL cycle. A research conducted by Nicholas and colleagues to see how dashboards would be able to predict student outcomes at different points in a course shows that learner outcomes can be predicted with a supervised machine learning algorithm. These predictions were integrated into an instructor dashboard that facilitates decision making for learners classified as needing assistance [START_REF] Nicholas | An instructor dashboard for real-time analytics in interactive programming assignments[END_REF].

In a review paper analyzing 29 learning analytics dashboards (LAD), Matcha and al. [START_REF] Matcha | A Systematic Review of Empirical Studies on Learning Analytics Dashboards: A Self-Regulated Learning Perspective[END_REF] find that the information presented in the dashboards is difficult to interpret. They criticize the lack of theoretical grounding of the dashboards (SRL theories is not explicitly considered in the design of LADs), they also note the weaknesses of dashboard evaluations in the experiences described and ultimately their relatively low impact on learner behavior. The critical analysis of the 29 LADs leads to the proposal of the Model of User-centered Learning Analytics Systems (MULAS) which identifies four dimensions that should be considered by dashboard designers: theory, design, feedback and evaluation.

From this state of the art, we identify two research objectives. First, it is to draw on critical analyses and proposed models in the literature to design a learning analytics dashboard that provides quality and effective feedback to learners and teachers on their learning activity. Second, to conduct an experiment in order to evaluate the contribution of the dashboard to learners' self-regulation and success.

In the following, we present our dashboard called TaBAT for Tableau de Bord d'Analyse des Traces d'apprentissage in French.

Design of the learning analytics dashboard TaBAT

LMS platforms provide a variety of integrated reports based on journal data but they are primarily descriptive. They tell participants what happened but not why and they do not predict outcomes or advise students on how to improve their academic performance. These tools are mostly programmed to work with a single platform.

Created to work with different online platforms, TaBAT is designed as a dashboard accessible online via the following link https://safsouf.net/tabat. It allows to see what happened during the online course (descriptive aspect), to see which students will or will not succeed in the online course (predictive aspect), to know why students were declared as dropouts (diagnostic aspect) and finally to get information on the actions to be taken to improve students' progress and success in the online course (proactive aspect).

As shown in Figure 1, the operating process of TaBAT consists of extracting learner data from data sources (student learning tracks), selecting and calculating assessment indicators, presents reports in various diagrams (based on the learning traces generated from the LMS platform in JSON files to ensure interoperability). Two independent views are presented: the student report and the teacher report.

Data collection phase

The first phase is to determine the source of the data, choose the LMS, prepare and retrieve the data we use for our reports. The data can be located either in a database in logstore tables (in Moodle for example), log files or both.

Analysis phase

In this second phase we create analysis algorithms based on the data collected from the previous phase. The goal of these algorithms is to specify and create indicators as well as to analyze student activity traces. The indicators we use are classified into 6 different categories, as follows: § Course category: Gives general information about the course. The three chosen indicators are: the number of students enrolled in the course, the number of sections planned and the number of activities/resources created. § Participation category: This category is more focused on the actions that can be done which consider students active. We distinguish two types of possible actions: consultation actions and contribution actions. § Section category: Here, the two chosen indicators are: the activities/resources consulted by the student within each section (Lessons, Quizzes, Assignments, etc.) and the number of activities/resources contained in each section. These two indicators are used to calculate the student's level of progress in each section of the course. § Progression category: A student's progress represents his or her status within a course. The three chosen indicators for the calculation of progress are: the number of activities the student has already completed, the number of activities not completed in respect of a deadline and the number of activities defined by the teacher at the beginning of the year. The level of progress will also be calculated on the basis of these indicators. § Social category: This category focuses on the social interactions that can take place during the course which considers the students socially active on the LMS. § Success Category: This category is specific to our approach. It is based on our previous research work that models a learner's success in an online course. This category is intended to provide an estimate of a learner's success. In our previous research, we proposed and statistically validated a causal model for evaluating online learner success (e-LSAM) [START_REF] Safsouf | An analysis to understand the online learners' success in public higher education in Morocco[END_REF]. This model allowed to identify the success factors associated with e-learning and to examine which factors explain a learner's success in an LMS. The result of our study shows that success is explained, with a prediction rate of 80.7%, by 24.1% of self-regulation (represented in our case by the level of progression with success) and by 75.7% of continuity in using the system. The latter is explained by 38.5% of the level of social interaction and 61.5% of the level of course participation. The indicators presented above give us a numerical value representing the data corresponding to a specific student. We have decided to represent the significance of the numerical data in the form of color indicators.

Data preparation phase

The third phase plays a main role in the process of our tool, it is the relay between the analysis phase and the results presentation phase. It is also an essential phase to ensure the interoperability of our tool. The goal is to allow, as well as to gather transform and prepare the essential data for our tool in order to generate data in JSON files with a standardized structure. Thus hiding their main source (we are talking here about platforms or data sources) and on the other hand to give the possibility to other developers to extend the use of our tool to other LMS platforms, by using any programming language which allows the generation of these same files (for example: PHP or Python).

Results reporting phase

In this phase, the reports in form of LADs are presented. These reports communicate directly with JSON files to get the necessary data back. Two aspects are presented independently: the report for the student and for the teacher.

Report for the teacher

The report for the teacher (showed in Figure 2) presents statistical data during the online course. The first page (1) includes the number of students enrolled, the number of sections, activities and resources in the course, the number of students who actively participate in the course, statistics on monthly connections for the current year, as well as statistics on the number of times students consult the activities and resources. The quiz analysis page (2) provides a table that shows, for each student, the list of quizzes taken or not taken, the number of questions answered, the total number of questions, the final score obtained as a percentage, and the time recorded for taking the test.

The assignment analysis page (3) provides a summary of the assignments that may (on time or late) or may not be returned by students.

The dropout page (4) presents a table that displays the list of students with an estimation of the overall time spent on the course, an indicator representing the level of success (based on the results of our theoretical model called e-LSAM (for e-learner Success Assessment Model) [START_REF] Safsouf | An analysis to understand the online learners' success in public higher education in Morocco[END_REF], [START_REF] Safsouf | A new model of learner experience in online learning environments[END_REF]) and finally a prediction status. This status indicates the result of the prediction either: risk of dropping out, minimal risk or success. A color coding allows to visually differentiate if the assignment is submitted or not, if the quizzes are done or not and the risk of dropping out or not.

Proactive phase

This last phase allows the teacher to contact the students manually or to schedule automatic notifications. The goal is to have alerts on the student's side about a variety of available actions. The last three pages of the report for the teacher (2,3 and 4), gives him the opportunity to select the learner(s) who will receive automatically suggestions (or notifications) regarding their achievements, assignments to submit or quizzes to do, resources to consult or even lessons to view. Each page also includes a contact button to send the student an email.

Methodology and date analysis

Context of the study and participants

This study aims to see the impact of the TaBAT dashboard, on the self-regulation and prediction of success of students in a higher institute in Morocco (ISGA of Marrakech). The target population is composed of 46 students who have participated in a course organized in a mixed modality. The class is divided into two groups of 23 students each (15 female and 21 male), aged between 18 and 35 years (39 between 18 and 25 years and 7 between 26 and 35 years). In terms of time of use of the Internet and computer devices per day, 5 students reported their time of use to be between two and five hours, 31 between five and ten hours, and another 10 more than ten hours per day.

Study Methodology

The course was available in a blended learning format, which combines face-to-face and online training. Students in both groups, all took a face-to-face course entitled "Object Oriented Programming", with some chapters online on the Moodle 3.8 platform, over an eight-week period, finalized by a supervised face-to-face exam.

To evaluate the impact of using the tool on self-regulation and prediction of student success online, one of the two groups was given the experiment to use the TaBAT tool (exposed group), while the second group did not have access to the dashboard (control group).

Study results

The part of the online course followed by all students consists of 7 sections (parts), with 7 lessons, 3 files to download, 7 URL links to visit, and 2 assignments due on dates planned at the beginning of the course. The analysis of the individual student traces in each group was done using the TaBAT dashboards via the teacher report. Table 1 describes the result of the experiment conducted on the two groups.

Discussion

We note at first that the 23 students in the exposure group all logged into the online course, while for the control group, 2 students did not take the online part of the course. This is because for the control group, the only way to communicate with the teacher was face-to-face. Whereas for the exposed group, the teacher had the possibility through the TaBAT dashboard to contact each student via e-mail, which allowed for individual monitoring.

The second observation concerns the total time spent doing the online course activities. This time is represented in Table 1, cumulated for each group. Students in the exposure group spent significantly more time (65% more) than those in the control group following and completing the online course activities and resources. This increase thus reflects independent functioning and resistance to distractions, thus making work at home a particular form of self-regulated learning.

The third remark concerns the performance of each group, this performance is represented in Table 1 by three score values, the maximum, minimum and average score of progression in the online course. The progress of each student represents the number of activities or resources consulted or accomplished over the number of activities or resources defined by the teacher at the beginning of the course. We notice a significant improvement in the performance of the exposed group mainly by an increase in the minimum value (3.25 times more) and the average progress (28% more) of the participants. This progression is mainly explained by the proactive actions made manually by the teacher or sent automatically by the TaBAT tool (proactive phase), in order to remind the students (with the help of notifications) if they have not yet accessed certain resources (file to download or url to visit) or unaccomplished activities (lesson, homework, quiz to do, etc.). Not to mention the important role of the student report which allows students to self-assess and follow metacognitive strategies to improve their online performance.

The fourth remark concerns the analysis of the return of homework. Indeed, the exposed group had a rate of 92.84% of assignments (planned at the beginning of the course) returned on time (assignments returned on time with those returned late), while for the control group, the same rate was 73.66%. This significant improvement is particularly due to the notifications received if there is an assignment due or not handed in on time (late). The notification includes the date and the number of days to hand in the assignment.

The final point concerns student success. In this study, the level of success calculated by the TaBAT tool based on our online learner success assessment model (e-LSAM) is compared to that obtained after the final exam. Table 1 shows that the TaBAT tool was able to demonstrate a high ability to predict the success of students for both groups in our experiment.

Conclusion

The development, implementation and experimentation of the learning analytics dashboard TaBAT represents the completion of our modeling work, which was design to identify factors that reduce the dropout rate of learners, and at the same time improve their success in online courses. We proposed in this paper a study to test the effectiveness of the learning analytics dashboard TaBAT in the analysis of learning traces in an online course planned by an engineering school in Morocco. The results of this study confirmed that the use of TaBAT increased the learners' performance, improved their autonomy, and finally improved their academic success.

In our future work we would like to extend the use of TaBAT to other online courses (of different natures and specialties), in order to generalize our experience and to see the impact of the tool on the performance and the real success of learners.

Fig. 1 .

 1 Fig. 1. The phases of the operating process.

Fig. 2 .

 2 Fig. 2. General view of report of the teacher dashboards.

Fig. 3 .

 3 Fig. 3. General view of report of the student dashboards.

Table 1 .

 1 Usage statistics of the TaBAT tool.

	Exposed group	Control group

Report for the student

The report for the student gives an overall view of each student's progress in the course. The three available interfaces are shown in Figure 3.

The first interface (1) gives a positioning of the student's progression level for each section of the course with two other levels: the level of progression of the best student and the level of the average student in the class. It also displays a ranking table of all learners in the class. This interface aims to motivate and support students' metacognition and self-regulation processes.

For the second interface (2), the student can see the details of his/her progress in the course. A chart presented in the form of a vertical progress bar summarizes the student's progress for each section of the course. This interface displays the details of the student's progress in each section.

The last interface (3) is the notification interface. Here the student can view the list of notifications (marked as unread) sent automatically by the system. Notifications are displayed by type, with a message indicating the actions to be taken. A script is scheduled to send notifications automatically twice a day; at 08:00 in the morning and again at 08:00 in the evening. If the same notification has already been sent and has not been read yet, the sending is not done.


Le juge de l'impôt peut-il être au centre des interactions sociales ? Par Philippe LUPPI* ((Sous la direction de Xavier LATOUR), Les juridictions au coeur des interactions sociales : approche comparée franco-russe) Le juge de l'impôt ne se rattache à aucun ordre juridique spécifique. Pour autant, il entend assurer son indépendance et le respect des principes généraux du droit privé ou du droit public. Il cherche à trouver un équilibre dans la fragile relation qui opposent l'administration fiscale au citoyen, ayant la qualité de contribuable, qui subit la charge de l'impôt, qui la perçoit davantage comme un châtiment que comme un honneur et qui, parfois, cherche à contourner le respect des règles en vigueur. Il peut contribuer, au travers de sa jurisprudence, à un renouvellement de la « confiance » du contribuable à l'égard de l'administration fiscale en préservant les droits et garanties de ce dernier. Si le juge de l'impôt se veut protecteur, le civisme fiscal est loin d'être un comportement, voire une règle (comportementale) absolue, à défaut de règles rigides, même s'il s'inscrit dans un système de déclaration contrôlée. La lutte contre la fraude fiscale reste un objectif de valeur constitutionnelle à géométrie variable mais si notre système d'imposition reste marqué par la présomption de sincérité, la revendication d'une plus grande équité fiscale est de plus en plus sous-jacente auprès d'une bonne partie de l'opinion publique (imposable ou non). Le juge de l'impôt ne peut pas ne pas s'imprégner de ce contexte économique et social pour forger sa jurisprudence. L'impôt ne remplit plus seulement une fonction financière. C'est un fait social. Il doit tenir compte des facultés contributives des assujettis mais il peut provoquer des réactions, voire des révoltes sociales (à l'image des gilets jaunes) et parfois même déboucher, sur fond d'exaspération sociale, sur un mouvement insurrectionnel. L'impôt peut être un baromètre de l'état d'un contexte social. Il s'inscrit dans une logique catégorielle, socio-professionnelle mais la sensibilité de l'impôt (la répartition de la charge fiscale) varie selon la nature de l'impôt (impôt réel/ personnel ; impôt progressif/proportionnel) et selon la base sur laquelle il est établi (revenu, patrimoine, chiffre d'affaires).

Le juge de l'impôt doit il s'autolimiter dans son rôle à un strict respect de la norme qui lui est soumise ou avoir un rôle prétorien qui ne sera pas sans incidence sur les relations sociales et sur l'évolution sociétale ? La réponse à cette interrogation nous conduit à établir un double constat ambivalent mais complémentaire : le juge de l'impôt forge sa jurisprudence indépendamment de toute pression sociale (I) ; il ne peut être hermétique ou se soustraire au contexte social (II).

I.-Le juge de l'impôt forge sa jurisprudence indépendamment de toute pression sociale

Seule la loi peut créer ou supprimer un impôt. L'article 34 de la Constitution et l'article 14 de la Déclaration des droits de l'homme et du citoyen de 1789 sont à la base du consentement de l'impôt et du principe de légalité fiscale. Pas plus l'administration fiscale qui l'applique que le juge qui l'interprète ne peuvent renoncer à l'application de la loi fiscale1 . La loi peut laisser le soin au pouvoir réglementaire de préciser ses modalités d'application en matière fiscale. Mais elle reste la norme qui encadre tout le dispositif fiscal. La loi fiscale n'est pas toujours d'une grande qualité et peut se montrer plus ou moins « bavarde » au gré du contexte économique et social.

Le juge de l'impôt, qu'il soit administratif ou judiciaire, reste le gardien de l'ordonnancement juridique au même titre que n'importe quel autre juge, du respect de la hiérarchie des normes, et plus particulièrement du principe de légalité fiscale. Compte tenu de la nécessité de l'impôt, de la nécessité de protéger le contribuable et de trouver un équilibre dans la relation inégalitaire entre ce dernier et l'administration fiscale, le juge de l'impôt s'en tient à une interprétation littérale des textes fiscaux 2 .Le contexte social ne peut lui permettre de transgresser le strict respect des normes.

Chaque citoyen, tout comme chaque contribuable, est en droit d'être imposé de la façon qui lui vaut une application « correcte » de la loi fiscale.

Lorsque le texte est clair, le juge s'en tient à cette interprétation stricte. Il refuse de se référer aux travaux préparatoires en présence d'un texte clair. Cette interprétation littérale (et donc stricte) de la loi fiscale est exclusive de toute recherche de l'intention de l'auteur. La liberté que peut s'octroyer le juge dans l'interprétation de la loi fiscale peut être perçue, comme certains auteurs 3 ont pu le souligner, comme « une forme d'atteinte au principe de légalité ». « La question de l'interprétation comporte un enjeu politique considérable puisqu'il touche aux fondements mêmes de la souveraineté, de l'état de droit, de la démocratie » 4 . Pour autant, on peut rejoindre ces mêmes auteurs, et notamment le Professeur COLLET 5 , lorsqu'il affirme que « le juge, au moins le juge placé au sommet de son ordre juridictionnel, est libre d'interpréter les textes comme il l'entend…..que cette liberté n'implique pas pour autant que le juge décide arbitrairement, en fonction de ses caprices du moment : sa décision est contrainte. Cette contrainte tient à la fois à des facteurs juridiques, comme par exemple l'obligation de motiver sa décision, et à des facteurs d'ordre sociologique et politique, tel que le souci de rendre une décision acceptable par l'opinion publique en tentant de convaincre de sa pertinence ». L'interprétation stricte des textes fiscaux est sans doute une particularité du juge de l'impôt, est admise aussi bien par la Haute juridiction administrative que par la Cour de cassation depuis fort longtemps mais il s'agit plus d'une règle de conduite que d'un principe de valeur juridique pour le juge. Le juge de l'impôt s'octroie moins de liberté que le juge civil dans son interprétation des textes qui lui sont soumis.

Le Doyen TROTABAS6 , ardent défenseur de l'autonomie du droit fiscal, avait mis en évidence « qu'aucune disposition de la loi fiscale ne prescrit en droit français une méthode d'interprétation particulière pour les lois fiscales ».

Le souci de protéger le contribuable, et parfois le législateur, voire l'administration fiscale, de ses turpitudes, conduit le juge de l'impôt à veiller à ce que soit respectée l'égalité de tous devant la loi fiscale, et plus particulièrement l'égalité devant les charges publiques. D'autres juges, comme la Cour de justice de l'Union européenne, veillent à ce que soient respectées les dispositions des Traités, et notamment les grandes libertés de circulation des personnes ou des travailleurs, en mettant en avant dans la motivation de ses arrêts, le respect de traitements non discriminatoires, la cohérence des systèmes et des contrôles fiscaux ou une répartition équilibrée du pouvoir d'imposition entre les Etats. Des divergences de jurisprudence peuvent parfois naître entre le juge administratif et le juge judiciaire sur certaines notions de droit fiscal, inscrites ou non dans la loi, tel que l'impôt ou le revenu imposable. Mais tant le juge administratif que le juge judiciaire de l'impôt prennent en compte dans leur jurisprudence, tant la jurisprudence du Conseil constitutionnel, que celle de la Cour de justice de l'Union européenne, voire celle de la Cour européenne des droits de l'homme.

Même si chaque juge garde, sous le chef de sa juridiction, sa compétence, le juge de l'impôt ne peut se soustraire au contexte juridictionnel et social, et ne pas mesurer la portée de ses décisions.

II.-Le juge de l'impôt ne peut-être hermétique au contexte social

Sans revenir ici sur une question qui a longtemps été débattue, celle de l'autonomie du droit fiscal, le juge de l'impôt s'est toujours adapté au particularisme du droit fiscal. Cette spécificité tient davantage du législateur que du juge lui-même. Comme avait pu le relever le Professeur Philippe MARCHESSOU, le droit fiscal est un droit comme les autres, soumis aux mêmes contraintes dans son utilisation et dans son évolution, même si les finalités conjoncturelles qui lui sont imparties et sa technicité intrinsèque obligent fréquemment à modifier ses dispositions. Il n'est pas rare que cette branche du droit emprunte ses concepts au droit privé ou au droit public.

Certains ont pu douter de la réalité des normes jurisprudentielles. Ainsi, le Professeur Maurice Christian BERGERES7 , avance deux raisons : d'une part, « l'existence d'un corpus de textes particulièrement dense semble devoir laisser peu de marge de manoeuvre au juge de l'impôt » ; d'autre part, « les normes jurisprudentielles sont trop souvent confondues avec les principes généraux du droit ».

La première raison est cependant mise en porte à faux par l'auteur lui-même au vu du paradoxe qu'il met en avant : « la densité révèle souvent la vacuité ou l'incohérence des textes. Dès lors, rien ne s'oppose à l'existence de normes prétoriennes en droit fiscal ».

La seconde raison est également écartée par ce même auteur car l'assimilation lui paraît inexacte.

Lorsque le juge de l'impôt fait état du respect de la légalité, il ne fait pas référence au principe de légalité de l'action administrative (et par là-même à la soumission de l'administration au droit) mais à la légalité fiscale, c'est-à-dire au principe selon lequel la décision d'imposer ou la fixation du régime d'imposition relèvent de la compétence du législateur8 . Il reste attaché au critère formel de la loi.

Lorsque le juge de l'impôt apprécie le respect du principe d'égalité devant la loi fiscale, il lui est arrivé de se référer au principe général du droit développé par le juge administratif dans sa jurisprudence administrative. Mais en matière fiscale, le principe d'égalité implique que la loi fiscale doit prévoir l'application d'un même régime aux contribuables se trouvant dans des situations identiques. Elle n'interdit pas pour autant qu'à des situations différentes soient appliquées des règles différentes. Le juge de l'impôt admet que le législateur puisse instaurer des différences de régimes fiscaux selon la situation personnelle du contribuable (notamment familiale), la catégorie socio-professionnelle ou son comportement économique. Il veille à ce que le législateur soumette tous les contribuables placés dans une situation économique ou sociale bien définie au même régime mais ne cherche pas à imposer au législateur qu'il soumette tous les contribuables au même régime d'imposition.

Le pouvoir créateur du juge de l'impôt est sans doute très largement sous-estimé. Selon le Professeur Maurice Christian BERGERES 9 , en droit fiscal, la création et l'affirmation des normes jurisprudentielles ont débouché sur deux séries de phénomènes parallèles : d'une part, il y a eu émergence de normes fondamentales (au travers de la promotion d'un principe général du droit en principe fondamental, éventuellement par le biais de la notion de principes fondamentaux reconnus par les lois de la République ou au travers de la justiciabilité des droits fondamentaux) : d'autre part, il y a consécration de normes « fonctionnelles » (principe de la non-immixtion de l'administration fiscale, théorie du bilan, auxquelles on peut rajouter la théorie de l'abus de droit fiscal). Ces théories jurisprudentielles ont trouvé une consécration législative. Il n'est pas rare que le juge constitutionnel ou le juge européen (CJUE ou CEDH) invite le législateur à revoir ses dispositions fiscales pour l'adapter soit aux principes de valeur constitutionnelle, soit au droit de l'Union européenne. La portée de ces censures est essentiellement économique, mais ces censures ne sont pas sans incidence sociale.

Le principe de liberté est sans doute l'un des vecteurs principaux de la recherche constante de la protection du contribuable. Le juge de l'impôt veille au respect des droits fondamentaux, et notamment au respect du principe du contradictoire. Cependant, ce principe doit pouvoir se concilier avec la nécessité de l'impôt et les autres principes fondamentaux dégagés par la jurisprudence. La lutte contre la fraude fiscale et la recherche de l'égalité en matière fiscale, peuvent parfaitement conduire à une différenciation de régimes entre différentes catégories de contribuables. « L'exercice des libertés et des droits individuels ne saurait en rien excuser la fraude fiscale, ni en entraver la légitime répression 10 ». Ainsi que l'ont constaté les Professeurs Jacques GROSCLAUDE et Philippe MARCHESSOU [START_REF] Jacques | Droit fiscal général[END_REF] « le juge a longtemps considéré que le respect des libertés publiques l'emporte sur la préservation des intérêts du Trésor public, et lors même que le contribuable a fraudé, le redressement qu'il a mérité sera annulé parce qu'une des formalités essentielles qui devait l'accompagner n'a pas été accomplie : le respect d'un délai suffisant pour préparer une vérification de comptabilité, l'annonce du droit à l'assistance d'un conseil ou le dépassement de la durée autorisée d'une vérification de comptabilité. Une évolution est perceptible qui se caractérise par une recherche plus attentive de l'ampleur du préjudice réellement infligé au contribuable par les errements de l'administration ».

Le plus souvent, le juge de l'impôt, lorsqu'il emprunte ses concepts dans une branche du droit, ne cherche pas à en contourner le sens, ni à s'en approprier la portée à d'autres fins que celle que lui a « assigné » le législateur. Pour autant, le juge de l'impôt ne s'estime pas lié par une loi fiscale étrangère, pas plus qu'il ne se sent lié par la solution adoptée par un autre juge dans une autre branche du droit. C'est le juge qui a forgé le principe d'indépendance des législations et des procédures [START_REF] Patrick | Procédures fiscales et garanties du contribuable[END_REF] . Même si une récente évolution de la jurisprudence de la Cour de cassation est perceptible 13 , l'action du juge pénal reste indépendante de l'action fiscale. Le juge de l'impôt n'est lié que par la matérialité des faits retenue par le juge répressif14 .

Le juge de l'impôt s'en tient à une interprétation littérale des textes qui lui sont soumis (loi, convention fiscale, doctrine administrative). Il lui arrive cependant de recourir aux travaux préparatoires dans le silence de la loi ou en présence d'une disposition très imprécise ou équivoque. Il a ainsi pu le faire pour établir les modalités de la mise en jeu de la solidarité de paiement entre un donneur d'ordre et un sous-traitant ayant eu recours à du travail dissimulé 15 . Mais indépendamment des lois, tous les textes fiscaux n'offrent pas au juge de l'impôt cette possibilité. Les travaux préparatoires ne sont pas toujours exploitables.

Une étude récente16 semble montrer un « dévoiement » du principe d'interprétation stricte de la loi fiscale, même si ses auteurs reconnaissent que « la position de la jurisprudence manque de netteté et tend de surcroît à différer selon l'ordre de juridiction considéré. Dans la jurisprudence judiciaire, la règle d'interprétation stricte est généralement mais non systématiquement employée en faveur du contribuable…..Dans la plupart des cas, le juge invoque ce principe simplement pour faire respecter la lettre de la loi ».La jurisprudence administrative, de son côté, a tendance à interpréter strictement moins la loi que « les exceptions à celles-ci, et plus précisément les exceptions favorables au contribuable ».

Le juge administratif de l'impôt statue parfois ultra legem. Il se refuse à donner à la loi une portée maximale qui paraitrait inique. Il l'a fait par le passé pour l'imposition des profits de construction. Il lui arrive de statuer praeter legem, par exemple pour la reconstitution fiscale d'un prix d'acquisition17 ou pour un rehaussement lié au respect de l'obligation de motivation dans le cadre de l'article L 55 du LPF [START_REF] Jean | Etablissement de la taxe professionnele-procédure de redressement-TA Marseille[END_REF] . Mais cette interprétation peut aussi parfois être contra legem [START_REF]L 56 du LPF ; voir également à propos de la notion d'opérations ayant pour « effet » de permettre la localisation de bénéfice[END_REF] .

Il lui arrive également d'adopter une interprétation extensive de certains textes, comme il a pu le faire dans le cadre de la théorie de l'abus de droit fiscal 20 ou en matière d'activité illicite. Il lui est par ailleurs toujours loisible de requalifier un acte et d'écarter la qualification de certaines opérations imposables, voire de substituer une base légale à la demande de l'administration dès lors que les droits et garanties des contribuables sont préservés.

Toutefois, si le juge de l'impôt a su infléchir la lettre des textes (et notamment la loi fiscale) dans le sens le plus opportun du point de vue économique ou social et du point de vue de l'équité, il n'est pas toujours parvenu à effacer les incidences et les iniquités liées à l'application littérale de la loi fiscale.

Ainsi que l'avait souligné un juge américain, le juge HOLMES, « les impôts sont le prix à payer pour une société civilisée ». Cette moralisation de la nécessité de l'impôt et ce rappel au civisme fiscal sont repris au fronton de l'administration fiscale américaine. Si les Etats-Unis sont parvenus à lutter contre la fraude fiscale, ainsi que l'ont montré deux auteurs [START_REF] Emmanuel | Le triomphe de l'injustice : richesse, évasion fiscale et démocratie[END_REF] , pendant plusieurs décennies, c'est avant tout parce que l'administration fiscale s'est vue dotée d'importantes ressources juridiques et budgétaires pour faire appliquer l'esprit de la loi fiscale. Mais c'est aussi parce que des normes sociales visant à renforcer le consentement à l'impôt ont été créées. Force est cependant de constater que de nombreux montages fiscaux à la limite de la légalité échappent au contrôle du juge, soit par manque d'informations, soit par manque de moyens [START_REF]Que ce soit en France ou dans d'autres Etats comme les Etats-Unis, les effectifs des administrations financières se sont, ces dernières années[END_REF] . Ce dernier exploite désormais davantage les clauses anti-abus que les textes, notamment de droit dérivé européen, ont multiplié. Mais en limitant la liberté de gestion des entreprises, en se plaçant sur cet autre terrain que celui de l'acte anormal de gestion [START_REF] Jérôme | La liberté de gestion des entreprises entre enfer et paradis (et plus près de l'enfer)[END_REF] , il n'est pas sûr que la moralisation et l'équité fiscale y aient gagné. Le juge a, par le passé, défini des critères de définition de l'abus de droit fiscal que le législateur a repris mais il n'est jamais parvenu à définir une frontière intangible entre l'ingénierie fiscale (relevant de l'optimisation fiscale, parfaitement légale) et le montage fiscal (relevant de l'évasion fiscale moralement condamnable). La mondialisation des économies offrira nécessairement de nouvelles possibilités d'optimisation fiscale. Le projet BEPS de lutte contre l'érosion des bases d'imposition (né de la crise financière, bancaire, économique et dans une certaine mesure, sociale), relayé en 2019 par le projet GLOBE, défendus par l'OCDE et soutenus par les chefs d'Etats et de gouvernement, au nom d'une plus grande transparence et équité fiscale, n'a pas permis de ralentir la course « au moins disant fiscal [START_REF] Emmanuel | Le triomphe de l'injustice : richesse, évasion fiscale et démocratie[END_REF] ». Au travers de sa jurisprudence, le juge de l'impôt peut contribuer à renouer avec une certaine forme d'équité fiscale mais il n'est pas nécessairement en mesure de faire émerger une norme sociale visant à rétablir un renforcement du consentement de l'impôt. Il peut s'efforcer de veiller à réhabiliter le pouvoir du législateur là où ce dernier ferait défaut mais il ne peut se substituer au pouvoir politique dans sa volonté de promouvoir ou non le civisme fiscal comme fondement du contrat social.

CE,

Juillet 1936, Ministre des finances c/ X, Rec. Lebon, p. 871. 2 Voir Philippe MARCHESSOU, « L'interprétation des textes fiscaux « , Paris Economica, 1980 ; Julien GUEZ, « L'interprétation en droit fiscal », Paris LGDJ, Collection Bibliothèque de l'Institut André TUNC, 2007 ; Philippe MARCHESSOU, « Réflexions contemporaines sur l'interprétation des textes fiscaux », in Mélanges Paul AMSELEK, BRUYLANT, 2005, p. 345 et suiv. ; Julien GUEZ, « L'interprétation jurisprudentielle en droit fiscal doitelle être spécifique ?-Retour sur le principe de l'interprétation stricte en droit fiscal », L'Année fiscale, 2004, p. 139 à 169. 3 Michel BOUVIER, « Introduction au droit fiscal général et à la théorie de l'impôt », Paris, 14 ième éd. LGDJ, 2018. 4 Martin COLLET, « Droit fiscal », Paris, 8 ième éd PUF Thémis, 2020 p.97. 5 Martin COLLET, « Droit fiscal », Paris, 8 ième éd. PUF Thémis, 2020 p.94, 95.

Louis TROTABAS, « Le principe de l'interprétation littérale des lois fiscales », Mélanges en l'honneur deGENY, 1934.

Maurice Christian BERGERES, « Les normes jurisprudentielles en droit fiscal » : Dr.fisc. 2000, n° 13, Etude 100109.

Voir Loïc PHILIP, « Droit fiscal constitutionnel : évolution d'une jurisprudence », Paris, Economica,2014 ; Bastien LIGNEREUX, « Droit constitutionnel fiscal », Préface Guillaume GOULARD, Paris, LexisNexis, 2020 ; Voir Martin COLLET, « Droit fiscal », Paris, 8 ième éd. PUF Thémis, 2020 p.20.

Maurice Christian BERGERES, « Les normes jurisprudentielles en droit fiscal » : Dr.fisc. 2000, n° 13, Etude

CE, Ass., 21 Juillet 1972, n° 72508, « Sieur X. », RJ, n° IV, p. 51 : Dr. fisc. 1973, n° 18-19, comm. 726, concl. G. Schmeltz.

CAA Versailles, 27 Février 2020, n° 18VE01544, « SAS BRUNET » : Dr. fisc. 2020, n° 26, Comm. 290.

Colin HALARD, Clémence VU TRAN, « L'interprétation des lois fiscales ambigües : in dubio centro fiscum » : Dr.fisc. 2020, n° 18, Etude 231.

Voir CE, 3 e et 8


Mohammed Baidada
email: mohammed.baidada@univ-ubs.fr

Khalifa Mansouri
email: khmansouri@hotmail.com

Franck Poirier
email: franck.poirier@univ-ubs.fr

Hybrid Filtering Recommendation System in an Educational Context

Keywords: E-learning, Recommender system, Content-based filtering, Collaborative filtering, Hybrid filtering, Experiment

In education, the needs of learners are different in the majority of the time, as each has specificities in terms of preferences, skills and goals. Recommendation systems have proven to be an effective way to ensure this learning personalization. Already used and tested in other areas such as ecommerce, their adaptation to the educational context has led to several research studies that have tried to find the best approaches with the best expected results. Believing that a hybridization of recommendation systems filtering methods can improve the quality of recommendations, we have conducted an experiment to test an approach that combines content-based filtering and collaborative filtering. The results proved to be convincing.

Introduction

The use of new technologies in education systems has become an unavoidable reality. Learners find themselves in front of systems able to help them in their learning scenarios, or even guide them by presenting them the pathways to follow as well as the pedagogical contents best suited to their needs and their profiles [START_REF] Garrido | On the Use of Case-Based Planning for E-learning Personalization[END_REF][START_REF] Segal | A difficulty ranking approach to personalization in E-learning[END_REF]. Teachers, for their part, find within their reach tools that can help them in their missions to make the most appropriate pedagogical decisions.

Personalization is an essential element that differentiates online education from classical face-to-face teaching which offers common content to a group of learner, without taking into account their specificities [START_REF] Clément | Online Optimization and Personalizationof TeachingSequences[END_REF].

Recommendation systems (RSs) have been used and adapted in the field of education as a means of offering each learner the educational resources best suited to his or her profile or needs, as is done in the field of e-commerce [START_REF] Burke | Recommender Systems: An Overview[END_REF]. According to the filtering methods, the RSs are based on two essential techniques: the first focused on the specificities of the user [START_REF] Herath | Intelligent Recommendations for e-Learning Personalization Based on Learner's Learning Activities and Performances[END_REF][START_REF] Leblay | Aide à la navigation dans les parcours d'apprentissage par reconnaissance de procédés et recommandations à base de traces[END_REF], and the second focused on the preferences and tendencies of the group to which the user belongs [START_REF] Salihouna | The exploitation of traces serving tutors for the reconstruction of groups within aCBLE[END_REF][START_REF] Tadlaoui | Approche pour recommandation de ressources pédagogiques basée sur les liens sociaux[END_REF].

In order to improve the quality of recommendations in the field of education, we have experimented a hybrid approach that combines the two filtering approaches previously mentioned [START_REF] Baidada | Hybrid Recommendation Approach in Online Learning Environments[END_REF]. To do this, we developed a recommendation module that was integrated into the Moodle platform, and then we experimented our approach on a real case in online test. The results of this experiment are the subject of this article. Before spreading our experimentation and its results, and doing an analysis and interpretation, we summarize in the next section, the general context of our work and a state of the art on the personalization in the online learning environments (OLEs), and RS and their use in the field of education.

Theoretical background and state of the art

Personalization in OLEs

On the one hand, in a classical teaching approach the teacher is obliged to give the same educational content to a group of student, without being able to take into account the differences between them in terms of levels and preferences. On the other hand, this becomes possible in OLEs, by personalizing activities and content to learners, by adapting the pace of teaching to them, and by considering their motivations [START_REF] Clément | Online Optimization and Personalizationof TeachingSequences[END_REF]. Personalization also consists of proposing content and learning paths adapted to learners, by taking into account their preferences, skills and also their objectives [START_REF] Bejaoui | Cadre d'analyse de la personnalisation de l'apprentissage dans les cours en ligne ouverts et massifs (CLOM)[END_REF][START_REF] Garrido | On the Use of Case-Based Planning for E-learning Personalization[END_REF][START_REF] Herath | Intelligent Recommendations for e-Learning Personalization Based on Learner's Learning Activities and Performances[END_REF].

Several research studies have examined the proposal of personalization approaches in OLEs. It is often the personalization of content and learning pathways that are discussed [START_REF] Bejaoui | Cadre d'analyse de la personnalisation de l'apprentissage dans les cours en ligne ouverts et massifs (CLOM)[END_REF][START_REF] Garrido | On the Use of Case-Based Planning for E-learning Personalization[END_REF][START_REF] Herath | Intelligent Recommendations for e-Learning Personalization Based on Learner's Learning Activities and Performances[END_REF][START_REF] Klasnja-Milicevic | E-Learning personalization based on hybrid recommendation strategy and learning style identification[END_REF]. Works in this area have tried to rely on different concepts each time to try to propose efficient systems that are well adapted to specific contexts. Some researchers have suggested hypermedia to provide learners with the opportunity to navigate in the e-learning system according to their needs [START_REF] Tsortanidou | Adaptive Educational Hypermedia Systems based on Learning Styles: The Case of Adaptation Rules[END_REF]. Other researchers have relied on intelligent tutors to provide personalized learning environments [START_REF] Clément | Online Optimization and Personalizationof TeachingSequences[END_REF][START_REF] Segal | A difficulty ranking approach to personalization in E-learning[END_REF]. [START_REF] Mandin | Modèle de personnalisation de l'apprentissage pour un EIAH fondé sur un référentiel de compétences[END_REF] proposed a skillsbased model of personalization of learning. Cakula et al. (2013) used the principles of Knowledge Management to present a model of personalization based on ontologies.

Recommendation systems in OLEs

With the growth in the use of the web and the important data generated by interactions between users and systems, several global companies operating mainly in e-commerce (Amazon, Netflix, Ebay, Facebook, Twitter, LinkedIn, among others) have introduced more and more sophisticated RS [START_REF] Burke | Recommender Systems: An Overview[END_REF][START_REF] Klasnja-Milicevic | E-Learning personalization based on hybrid recommendation strategy and learning style identification[END_REF]Linden et al., 2007), by offering their users content that could potentially interest them to retain them or to encourage them to consume.

There are several definitions of RS, according to [START_REF] Sharma | A Survey of Recommender Systems:Approaches and Limitations[END_REF]: "Recommendation System is an intelligent system that makes suggestion about items to users that might interest them". [START_REF] Isinkaye | Recommendation systems: Principles, methods and evaluation[END_REF] give a similar definition evoking the filtering aspect: "Recommender systems are information filtering systems that deal with the problem of information overload by filtering vital information fragment out of large amount of dynamically generated information according to user's preferences, interest, or observed behavior about item".

There are several filtering methods, of which several classifications have been given in the literature [START_REF] Haydar | Les systèmes de recommandation à base de confiance[END_REF][START_REF] Lemdani | Système hybride d'adaptation dans les systèmes de recommandation[END_REF][START_REF] Tadlaoui | Approche pour recommandation de ressources pédagogiques basée sur les liens sociaux[END_REF]. They essentially meet in the classification given by [START_REF] Isinkaye | Recommendation systems: Principles, methods and evaluation[END_REF], whose description is as follows:

Content-based filtering:

It is a technique that proposes to a user, taking into account his or her profile, the items having the same values corresponding to a set of attributes that describe them. A limitation of this technique is that it cannot present to the user the categories of items outside of those that he or she accustomed to consult. The problem can be solved with the following method. Collaborative filtering: this technique considers the user in a group setting, and proposes items that he or she has not evaluated before, but that other members of his or her group have already evaluated. The objective of this filtering method can be either to propose to the user items that he or she has never evaluated, or also to deduce what would be the evaluation of a user for an item if it has already been evaluated by his or her group. This distinction leads us to define two subcategories: memory-based filtering for the first case, and model-based filtering for the second one.

Note that in the case of the model-based filtering, we can consider the similarity between the users: user-based, or between the items evaluated by these users: item-based. Hybrid filtering: the previous methods have some limitation, such as cold start or sparsity of data. These problems are often mitigated by the hybridization of two or more approaches. This hybridization also makes it possible to increase the quality of the recommendations, which is the objective of our work.

Works on the recommendations in the OLEs

In addition to e-commerce, the recommendation RSs have been integrated in other areas, notably in e-learning, with the aim of recommending resources and pedagogical activities that will be best adapted to the preferences and learner needs. RSs have emerged as an indispensable tool in the field of personalization in OLEs [START_REF] Anaya | Recommender system in collaborative learning environment using an influence diagram[END_REF][START_REF] Berkani | Recommandation personnalisée des ressources dans une communauté de pratique de e-learning[END_REF][START_REF] Herath | Intelligent Recommendations for e-Learning Personalization Based on Learner's Learning Activities and Performances[END_REF][START_REF] Klasnja-Milicevic | E-Learning personalization based on hybrid recommendation strategy and learning style identification[END_REF][START_REF] Tadlaoui | Approche pour recommandation de ressources pédagogiques basée sur les liens sociaux[END_REF][START_REF] Tadlaoui | Système de recommandation de ressources pédagogiques fondé sur les liens sociaux formalisation et évaluation -Thesis[END_REF].

Several research studies have examined the use of RS to propose the best approaches. Often it is the algorithms based on artificial intelligence and machine learning that have been used in RS. [START_REF] Portugal | The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review, Expert Systems With Applications[END_REF] conducted a study of the algorithms used in RS, and claimed that those based on Bayesian networks and decision trees are the most used. Several research studies have examined the implementation of RS in an educational context. [START_REF] Berkani | Recommandation personnalisée des ressources dans une communauté de pratique de e-learning[END_REF] experimented with a combination of several filtering approaches to provide learners with pedagogical resources based on their goals and needs. [START_REF] Tadlaoui | Approche pour recommandation de ressources pédagogiques basée sur les liens sociaux[END_REF] proposed a recommendation system that uses social links to provide the most appropriate learning resources for a learner; they considered the most popular, most useful and most recently accessed resources.

3 Description of our approach

Background

Our research work fits in with the context of personalization in OLEs. For a better personalization, we were interested in the use of the RS in order to approach the learning resources proposed by the learning system to the profiles of the learners and their preferences. We assume that if the characteristics of an educational resource are close to the learner's preferences, they will be encouraged in their learning process by being more comfortable.

According to the literature review, the combination of several filtering approaches makes it possible to go beyond the limits of the filtering methods used separately, but also to improve the quality of the recommendations [START_REF] Berkani | Recommandation personnalisée des ressources dans une communauté de pratique de e-learning[END_REF][START_REF] Burke | Hybrid Web Recommender Systems[END_REF][START_REF] Isinkaye | Recommendation systems: Principles, methods and evaluation[END_REF][START_REF] Lemdani | Système hybride d'adaptation dans les systèmes de recommandation[END_REF][START_REF] Paradarami | A hybrid recommender system using artificial neural networks[END_REF]. [START_REF] Burke | Hybrid Web Recommender Systems[END_REF] identifies seven types of hybrid filtering in the literature:

• Weighted: The scores of the different recommendation approaches are weighted (combined numerically).

• Switching: The system chooses among the recommendation approaches and applies the selected one.

• Mixed: The recommendations of the different approaches are presented together.

• Feature Combination: The characteristics derived from the different approaches are combined and given to a unique recommendation algorithm.

• Feature Augmentation: One recommendation technique is used to compute a feature or set of features, which is the input to the next technique.

• Cascade: this method consists of applying successive recommendation approaches. Each approach proposes a set of candidate items for the next one.

• Meta-level: One recommendation technique is applied and produces results that enrich the input of the following technique.

As we have already presented, hybridization makes it possible to overcome the limitations of the different approaches, but also to improve the quality of the recommendations, which is our goal, we started from the two approaches of filtering associated with RSs, namely content-based filtering and collaborative filtering; their definitions were presented previously, and we considered a weighted hybridization.

Our approach is also based on the notion of the learner profile and the standards of learning objects description presented below.

The learner's preference component is one of the eight main components given by the most important models defining the learner profile which are: IEEE PAPI (Public and Private Information for Learners) and IMS LIP (Learner Information Package) [START_REF] Pavlov | Personalized and Adaptive Elearning -Approaches And Solutions, Chiron Open Workshop[END_REF][START_REF] Wei | Learner Profile Design for Personalized E-learning Systems[END_REF]. They define the notion of the profile through the following elements: Personal information, Preference, Performance, Information session, Portfolio, Goal, Security and Relation information.

It was also necessary to make a choice of metadata that could describe the pedagogical resources that were to be the core of the exchanges between the learners in our system. The study of the standards for the Learning Object Metadata (LOM) revealed the three most important ones which are IEEE LOM, CanCORE LOM and Dublin CoreMetadata [START_REF] Roy | A Comparative Study of Learning Object Metadata, Learning Material Repositories, Metadata Annotation & an Automatic Metadata Annotation Tool[END_REF]. They described a learning object by the following elements: General, Life cycle, Meta-metadata, Technical, Educational, Rights, Relationship and Annotation.

We have selected three elements that are related to learner's preferences, namely General, Technical and Educational. The following table gives more details on our choices of these elements by specifying their possible values: To evaluate our approach, we carried out an experimentation for which it was necessary to develop a platform that integrates and implements the three filtering methods namely collaborative filtering, content-based filtering and hybrid filtering. This platform should integrate the elements that are usually found in any e-learning platform, in terms of course management and pedagogical activities as well as a space for exchange between learners so that we can consider the social component of our approach to consider the learner in the context of the group to which he belongs.

The following sections will be dedicated to a technical description of the platform and to a presentation of the experimental protocol.

Technical description

The basic platform is Moodle version 3.2. It has been enriched by the installation of the SocialWall plug-in which allows changing the format of the courses by adopting "social network" view integrating the notions of post, comment and like. It was also necessary to introduce the modules of recommendations which were developed separately, then integrated in the source scripts of Moodle. It was necessary to develop three modules relating to each selected filtering method:

• Individual recommendation module: relating to the content-based filtering method;

• Social recommendation module: relating to the collaborative filtering method;

• Hybrid recommendation module: relating to the hybridization of the two methods: content-based and collaborative filtering.

Figure 2 shows the architecture of the platform in the hybrid case: The learning environment keeps the traces of the learners with the system, and also the traces of the exchanges between learners. This is guaranteed by the social exchange module integrated into the platform. Thereafter, the recommendation modules rank the resources according to the implemented algorithm described below, and an arithmetic mean of the two rankings will result in the final recommendations proposed to the learner.

Experimental protocol

Objective and type of experiment

The objective of this experiment is to evaluate a hybrid recommendation approach of pedagogical resources in a context of face-to-face and distance learning. This approach considers both the individual characteristics of the learner and the links that connect him to his or her group, in order to propose the most appropriate resources.

In their thesis Lamdani (2016) and [START_REF] Haydar | Les systèmes de recommandation à base de confiance[END_REF] present that there are essentially three methods of experimentation:

• Offline test: simulating algorithms on datasets, it has the advantage of being inexpensive in time, but it requires a dataset that must be well adapted to the experiment objectives;

• User study: with real users generally recruited and paid for the test, which can mitigate the credibility of the results;

• Online test: with users who use the system in real life situations, this gives better results but is more time consuming.

With the difficulty of finding datasets adapted to our context, we opted for an online test type; this had the disadvantage of being more expensive in time, but it guaranteed us better results.

Target groups

The experiment has targeted two groups of students in two different institutions of higher education in Morocco, the first is a 2nd year common core group of engineering degree in a private institution, and the second is a 1st year group of engineering cycle specialty in a public institution. We started with an initial enrollment of 87 students, but stayed at 82 after eliminating 5 students who did not participate in the test. The average age of the group was 21.07 with a standard deviation of 1.32. There were 32% female and 68% male.

Each group was divided into three subgroups:

• Subgroup 1: recommendations based on content-based filtering;

• Subgroup 2: recommendations based on collaborative filtering;

• Subgroup 3: recommendations based on hybrid filtering.

The following table gives an idea of the final distribution in terms of numbers:

Choice of course

The students were enrolled in two different courses, relative to their level: an advanced programming techniques course for the private institution group, and an object-oriented programming course for the public institution group. Course materials were shared on the platform to force students to enter the platform, and to be present in the social module. It is through this module that they must exchange educational resources with each other.

Duration of the experiment

The experiment was conducted in the second half of 2018/2019 between April 1 and May 15, 2019.

Functional description

Each student could login to the platform with his or her account to access the course he or she was enrolled in. Let us reiterate that the courses were presented under a "social network" view. Afterwards he or she could carry out the following actions (figure 3):

• Add a post

• Add a URL: In a post, add a URL link to an external resource that he or she found interesting

• add the description of resource characteristics after each link added. Table 3 presents the different possible values of the characteristics

• comment a post

• make a "like" or "dislike" on a post For each student appear links to recommended resources (figure 4). These links may change depending on the evolution of the exchanges. Constructing an Item/Attribute Matrix (it will be called I): Each line of the matrix corresponds to the description of an item with respect to the various attributes selected; these attributes are video, document, web article, courses, exercises and tests, forum, English, French and Arabic. For example to a video type element, of course, we will associate the vector/line (1,0,0,1,0,0,1,0,0) which is a 9-dimensional vector with respect to the 9 characteristics mentioned above.

1 0 0 1 0 0 1 0 item 2 1 0 0 0 1 0 0 1 0 item 3 0 1 0 1 0 0 1 0 0 item 4 1 0 0 1 0 0 1 0 0 item 5 0 0 1 1 0 0 0 1 0
2. Construction of a user vector (we will call it U): For a given user (the one who is connected) we will assign 1 to an item that he prefers (like), and 0 otherwise. For example, for a user who has evaluated the five items in the example in table 4, and has made "like" for items 1, 3 and 5, his or her vector U will be (1,0,1,0,1).

3. Construction of a user profile vector (we will call it P): P = U * I, by multiplying the vector U by the matrix I. This vector profile, also a 9-dimentional vector, represents a projection of the user with respect to the criteria. For the example in table 4, and after calculation, P will be equal to (0,2,0,1,1,0,1,1,0).

4. Then we proceed to the calculation of the Euclidean distances between P and each of the lines of the matrix I.

5. Using the k nearest neighbors method (k-NN) [START_REF] Adeniyi | Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method[END_REF], the recommendation will correspond to the items with the lowest distance and other than those that the user prefers, i.e. the item vectors that are most closely related to the vector P.

For the collaborative filtering recommendation

1. Constructing a User/Item matrix: it represents for each user in relation to each item a score. In general, this score can be assigned by the user; it can also be the number of accesses, the duration of access, etc. In our experiment, we chose the number of accesses to the items [START_REF] Shi | Collaborative Filtering beyond the User-Item Matrix A Survey of the State of the Art and Future Challenges[END_REF]. 2. We calculate the Euclidean distances between the users (the rows of the matrix).

3. Minimum distances are considered.

4. The recommendation will correspond to scores calculated by the following formula:

Score(Ui,item)=

For the hybrid filtering recommendation

The distances relative to the items according to hybrid filtering is the average of the distances generated by the two previous filtering methods.

5 Evaluation of the experience

Evaluation method

Through the exchange of resources on the platform, four classes of data have been generated relative to the educational resources:

• The recommended resources (generated by the algorithm);

• The relevant recommended resources (generated by the algorithm and appreciated by the learner);

• Resources not recommended (not generated by the algorithm);

• Relevant resources not recommended (not generated by the algorithm and appreciated by the learner).

To evaluate our referral system, we have based ourselves on the three main indicators used in these contexts [START_REF] Burke | Hybrid Web Recommender Systems[END_REF][START_REF] Isinkaye | Recommendation systems: Principles, methods and evaluation[END_REF][START_REF] Portugal | The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review, Expert Systems With Applications[END_REF][START_REF] Roy | A Comparative Study of Learning Object Metadata, Learning Material Repositories, Metadata Annotation & an Automatic Metadata Annotation Tool[END_REF], namely:

• Precision: the number of recommended resources (like) found relative to the total number of resources recommended:

• Recall: the number of recommended resources (like) found relative to the total number of relevant resources:

• F-measure: it combines the two indicators into a harmonic mean:

Precision represents accuracy and quality, while Recall represents completeness and quantity. The F-measure represents a harmonic mean because precision and recall are ratios.

The data collected were presented in a similar table:

Analysis Methods

To evaluate our experiment, we used the SPSS software and selected some of the methods of analysis presented below:

Analysis of variance (ANOVA)

The analysis of variance, called ANOVA, aims to test the significant differences between the averages. This method is used when we want to measure the effect and influence of one or more qualitative variables on a continuous variable to explain.

In variance analysis, we try to explain the variations of a metric variable by one or more nominal explanatory factors. Its principle is to test the equality of averages of several normal populations In the case of the one-way analysis of variance, called ANOVA1, we try to explain the variations of a single dependent metric variable by a single explanatory factor.

As part of our empirical study, we try to test the effect of the group variable (qualitative variable) on each of the quantitative variables namely Precision, Recall and F-measure. In other words, we explain the variations of each indicator according to the group factor. It is therefore a question of comparing the three groups each time compared to an indicator. This justifies the use of one-way analysis of variance (ANOVA1).

We used ANOVA1 to compare the 3 subgroups of the experiment, considering the qualitative variable "Group".An initial analysis was made for all students in both institutions. Then, the same analysis is done for each establishment separately.

Student T-test

Student T-test is a statistical test that makes it possible to compare the averages of only two groups of samples. It is to know if the averages of the two groups are significantly different from the statistical point of view.

The purpose of this test is to compare the means of two populations using two samples. For our study, it is a question of comparing the two public and private institutes each time relatively to an indicator by considering the qualitative variable "Institute".

Results and interpretations

We will present the results obtained and their interpretations. Firstly, we conducted an ANOVA1 analysis to compare the three groups across the two institutes, and then we did it separately for each institute. At the end, by using the Student T-test, we make a comparison between the two institutes.

We based ourselves on the formulation of the null hypothesis. It is a question of globally testing the equality of the averages of the three groups.

• H0: there is no significant difference between the three groups.

• H1: at least one group is different from the others.

In an ANOVA1 analysis, and to compare between groups, the Tukey test is used. This proposes a significance threshold (Tukey significant difference called sig) of 5% (0.05). If the significance level obtained is less than 5%, then the null hypothesis H0 is rejected and the hypothesis H1 is retained, ie at least one group is different from the others.

6.1 ANOVA1 for the two institutes (public and private)

Analysis of the Precision variable

Using the SPSS software, the intergroup analysis of the Precision variable for the two grouped institutes gave a significance level (sig) of 0.000, which leads us to conclude that the Precision indicator is on average not identical in all three groups. The null hypothesis H0 is therefore rejected. The hypothesis H1 is then retained, and it was necessary to know the group or groups that is or are different, making a multiple comparison of means.

Multiple comparisons of means

When the analysis of variance test is significant, we must conclude that there are significant differences between some of the averages of these normal populations. In this case, the multiple comparisons of means seek to determine a ranking of the means by indicating the significant differences and those that are not. According to the table of multiple comparisons and the Tukey method, a significance below the threshold of 0.05 makes it possible to identify the groups that give different results; so the groups G1 and G3 allow different average of Precision (sig = 0.000). It is the same for groups G2 and G3 (sig = 0.001).

The following analysis classifies groups into subsets, to find those with similarities and those that stand out. The figure 5 shows that the group 3 has a harmonic average much greater than the others groups 1 and 2. A classification in homogeneous subsets was subsequently made. The analysis give a result that distinguishes 2 subsets, the first one composed of groups G1 and G2 for which there is no significant difference, and the second one composed of group G3 which has an average precision of 0.57 (Table 8). We then find that for the private institute, there is no difference between the groups. This may be due to the reduced size of the three groups.

Student T-test between the two institutes

As we have already presented before, to consider the qualitative variable "Institute" and compare between the two private and public institutes, we used Student's T-test which is used to compare between two populations. The results are presented in the following. The purpose of this test is to verify whether the average of an indicator for the private institute is or is not equal to the average of the same indicator for the public institute.

The null hypothesis is H0, there is no difference between public and private institute. According to the results provided by the SPSS software, we note that for all three indicators the significance probability (sig) is below the 5% threshold (table 22), which leads us to reject the null hypothesis and conclude that for the three indicators there is a difference between the two institutes. 23), we can see that the values of the three indicators are higher, on average, among the students of the public institute. Especially the precision presents an interesting difference (0.49 compared to 0.39). Since the precision reflects the quality of the recommendations, we can explain this by the fact that the students of the public institute have often a better level and have a certain maturity, which has been reflected in their reaction to the resources exchanged on the platform, being more careful with regard to the choice and evaluation of shared resources.

Discussion

The experiment aimed to compare three methods of recommendation filtering: content-based filtering, collaborative filtering and hybrid filtering, in both a public and private institute. In each institute, the students were divided into three subgroups, each corresponding to a filtering approach. The analysis focused on three indicators: precision, which reflects the proportion of appreciated resources (like), recall, which reflects the proportion of relevant recommendations relative to relevant resources, and F-measure, which is a harmonic mean of the two previous indicators.

The results of the experiment showed that by considering the distribution of the three groups on both private and public institute, and by conducting an analysis of the impact of the qualitative variable "Group" on the three indicators, the group 3 clearly distinguishes itself from the other two groups 1 and 2. This confirms that the hybrid filtering approach conducted for group 3 gave better results for all three indicators.

The analysis was repeated on each institute separately. For public institute, we obtain the same result, that hybrid filtering provides more relevant recommendations. For private institute, the three approaches have produced almost equivalent results; which may be due to the small number of students considered for this institute, only seven students per group in the private institute compared to 21 in the public institute.

Regarding the analysis of the impact of the qualitative variable "Institute" on the three indicators, we find that the values of the three indicators are on average higher among students at the public institution. It is especially the precision that presents a significant difference. Knowing that public institute students have often a higher level, we can assume that they are more relevant both in terms of resource sharing and in their appreciation (like).

Conclusion

In this article, we have described our experience in determining whether hybridization of two content-based filtering methods on the one hand and collaborative on the other can improve the relevance of recommendations in an online educational context.

The results showed that the hybrid recommendation approach works best when considering the public institution alone and when considering the public and private institutions together.

Several perspectives are planned in extension to this work; we plan to study the effect of the weighting of the two filtering approaches on the relevance of the recommendations. We can also consider an evaluation of resources on a scale of 1 to 5 instead of a binary evaluation (like or dislike), to have a more detailed appreciation. The inclusion of persuasion devices will also be an advantage to encourage participating learners to become more involved in the experience, such as obtaining badges after obtaining good third-party evaluation. We also plan to use the enriched database of learner profiles to provide external resources that match their preferences.

Figure 1

 1 Figure 1 Classification of filtering methods

Figure 2

 2 Figure 2 Platform Architecture Used for Hybrid Recommendation Case

Figure 3 .

 3 Figure 3. Interface for adding posts and links to resources

Figure 4 .

 4 Figure 4. Interface of links to recommended resources

Figure 5 .

 5 Figure 5. Comparison of harmonic means-Precision variable, the two institutes combined

Tableau 22 .

 22 Student T-Test for Precision, Recall and F-measure indicators

Table 1

 1 Description of Metadata Retained for Educational Resources

	Section	Element retained	Values
	General	Language	English, French, Arabic
	Technical	Resource format	Video, Document (pdf, slides, …),
			Web article
	Educational Resource type	Course, Tests or exercises, Forum

Table 2 .

 2 Distribution of Groups

		Subgroup 1	Subgroup 2	Subgroup 3	Total
	Public	19	21	21	61
	Private	7	7	7	21
	Total	26	28	28	82

Table 3 .

 3 Description of the Characteristics of the Resources and their Possible Values

	Characteristic	Possible values
	Language	English, French, Arabic
	Resource type	video, document, web article
	Activity type	course, exercises, forum

Table 4 .

 4 Example of the item/attribute matrix

		Video Document Artweb Course Exercises Forum English French Arabic
	item 1	0

Table 5 .

 5 Example of the user/item matrix

	Item1 Item2 Item3 Item4 Item5 Item6 Item7
	User A

Table 6 .

 6 Table of Indicators Obtained

		Qualitative variables	Quantitative variables
	Student	Institution Group Precision Recall F-measure
	Student 1	Private	G1	0.25	0.16	0.19
	…	…	…	…	…	…
	Student i	Private	G1	0.29	0.18	0.22
	Student i+1	Private	G2	0.27	0.16	0.20
	…	…	…	…	…	…
	Student j	Private	G2	0.45	0.27	0.34
	Student j+1	Private	G3	0.43	0.24	0.31
	…	…	…	…	…	…
	Student k	Private	G3	0.52	0.29	0.37
	Student k+1	Public	G1	0.45	0.27	0.34
	…	…	…	…	…	…
	Student l	Public	G1	0.35	0.21	0.27
	Student l+1	Public	G2	0.62	0.32	0.42
	…	…	…	…	…	…
	Student m	Public	G2	0.62	0.32	0.42
	Student
	m+1	Public	G3	0.40	0.23	0.29
	…	…	…	…	…	…
	Student 82	Public	G3	0.56	0.32	0.41

Table 7 .

 7 ANOVA1-test on precision variable -the two institutes combined

	Group1 Group2 Group3
	Group1	0.127	0.000
	Group2 0.127		0.001
	Group3 0.000	0.001

Table 21 .

 21 Distribution into homogeneous subsets -F-measure variable, public institute

		Size	Subset 1
	Group1	7	0.24
	Group2	7	0.31
	Group3	7	0.33

Table 23 .

 23 Group statistics

	Institute		Size Average Standard	Mean
					deviation	standard error
	Precision Private institute	21	0.40	0.13	0.03
		Public institute	61	0.50	0.14	0.02
	Recall	Private institute	21	0.23	0.07	0.02
		Public institute	61	0.28	0.08	0.01
	F-measure Private institute	21	0.29	0.92	0.02
		Public institute	61	0.35	0.10	0.01
	According to the table of group statistics (table

It is therefore concluded that for the precision indicator, group G3 differs from the other two groups.

For the analysis of the other variables Recall and F-measure, we will see the same thing, so there is the same interpretation as for the case of the variable Precision.

Analysis of the Recall variable

The intergroup analysis of the Recall variable in the case of the two grouped institutes gave a significance level (sig) of 0.000 as well, which leads us to conclude that the Recall indicator is on average not identical in the three groups. The null hypothesis H0 is then rejected and the hypothesis H1 is retained. A multiple comparison must then be performed to determine the group or groups that stand out.

Group1 Group2 Group3

Group1 0,830 0.000 Group2 0,830 0.000 Group3 0.000 0.000

Table 9 shows a different average value of the Recall variable between group 1 and 3 (sig = 0.000), and also between groups 2 and 3 (sig = 0.000).

Comparison of harmonic means again gives a distinction for group 3 (figure 6) with an average of 0.32. The classification into homogeneous subsets gives a grouping of groups 1 and 2 in the same subset and group 3 in another (table 10). We therefore conclude that for the Recall indicator, group 3 is also different from the other two groups 1 and 2.

Analysis of the F-measure variable

For the F-measure variable, we have the same observation: the null hypothesis is rejected and the 3 groups therefore have a difference (sig = 0.000).

The multiple comparison of means again shows that the F-measure variable has a difference between group 1 and 3 (sig = 0.000) and also between group 2 and 3 (sig = 0.000) (table 11). The harmonic average comparison graph shows that group 3 always stands out with an average of 0.41 (figure 7). And finally, the homogeneous subset classification always puts groups 1 and 2 in a homogeneous subset and group 3 in another (table 12). We conclude that for variable F-measure, group 3 always stands out.

ANOVA1 for the public institute

As previously presented, we also conducted an ANOVA1 analysis for each institute separately.

In the case of the public institute, the inter-group analysis of the three Precision, Recall and F-measurement indicators gave a significance level equal to 0,000 each time, so the null hypothesis had to be rejected, and it was necessary to determine the group that stands out. For the 3 variables, group 3 has a different mean value with respect to the other two groups 1 and 2 (tables 13, 14, 15). The comparison of harmonic means also gives a distinction for group 3 with an average of 0.60 for the Precision variable, 0.34 for the Recall variable and 0.43 for the F-measure variable (figures 8, 9, 10). And with respect to the classification into homogeneous subsets, the analysis always gives the group 3 in one subset and the two groups 1 and 2 in another (tables 16, 17, 18). We conclude that for the three Precision, Recall and F-measure indicators, group 3 is distinguished each time from the other two groups 1 and 2.

ANOVA1 for the private institute

For the private establishment, the ANOVA1 analysis showed a significant difference greater than the 5% threshold (0.68, 0.246, 0.161 respectively for the Precision, Recall and F-measure variables). This means that the hypothesis H0 is retained and that the 3 groups do not show any difference. This is confirmed by the multiple comparisons. The homogeneous subset distribution also put the three groups in a same subset (tables 19, 20, 21).