Yassine Safsouf
email: yassine.safsouf@isga.ma

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

Experimental design of learning analysis dashboards for teachers and learners

Keywords: Information visualization, Learning analytics, Learning analytical dashboard, Self-regulated learning, Higher education

Since learning in higher education is increasingly taking place online, the multiplication of web-based educational content, learning management systems (LMSs) and collaborative communication platforms have generated a large volume of data on learners and their learning activities. In recent years, interest has been growing in analyzing this data to support real-time decision making and improve the learning experience. This paper presents the results of a study conducted in higher education in Morocco, which evaluates a learning analysis dashboards (LADs) for both teachers and learners. The study shows that the dashboard, called TABAT, allowed a synthetic visualization of learning progress in courses and led to improved student engagement and success rates.

INTRODUCTION

A dashboard is not only a simple display of indicators, but most importantly is a decision-making tool. Yigitbasioglu presents dashboards as a "solution that should improve decision making by amplifying perception and capitalizing on human perceptual capacities" [START_REF] Ogan | A review of dashboards in performance management: Implications for design and research[END_REF]. The two management experts Robert S. Kaplan and David Norton see the dashboard as a management tool. They concluded by observing companies in the United States. In 1996, they created a strategy management system in the form of dashboards, named Balanced Scorecard [START_REF] David | The Balanced Scorecard: Translating Strategy into Action[END_REF], whose purpose is to build and steer the implementation of strategy.

Learning Analytics (LA) aims to exploit the hidden potential of interaction data generated by the use of online learning environments. This is a relatively new field created in 2011 and defined as the detection, collection, analysis and exploitation of digital traces left by learners in their daily activities. In order to understand and optimize learning and the different environments in which it occurs [START_REF] Siemens | Learning analytics special issue[END_REF]. However, the diversity of existing learning management systems (LMS) complicates the task of analyzing all these data, which is further amplified by the need to combine data from a variety of sources. The main challenge would be to have a system to analyze learning data in such a way that the precise meaning of the data is easily accessible and could be translated into an understandable format regardless of the LMS or technological environment used. This paper proposes a presentation of a multi-platform visual communication tool designed in the form of dashboards for teachers and learners. Our goal is to conduct a learning analysis to facilitate the monitoring and control of the learning process in order to improve the success rate of learners. In the rest of the paper, we will present related work in the field of learning analysis in section 2, we will also present our tool created in the form of learning trace analysis dashboards in section 3. Section 4 will be dedicated to the methodology, context and data analysis of the study. A discussion of the results obtained will be detailed in section 5, followed by a conclusion.

RELATED WORK

LA is an emerging trend in Morocco, particularly in higher education. The development of big data technologies and the widespread use of digital tools allow us to build up important data collections on student behavior. We can now measure, collect, analyze and process this data in order to better understand learners and improve their learning levels [START_REF] Siemens | Learning analytics special issue[END_REF]. Reports of LA approach are usually communicated to teachers and students through dashboards or personal emails. The value of these computerized systems is to promote academic success as well as to improve students' selfregulated learning (SRL) [START_REF] Durall | Learning analytics as a metacognitive tool[END_REF], but also to facilitate and improve the monitoring of students by their teachers.

Learning Analytical Dashboards (LADs) can give students a single display that combines multiple visualizations of different indicators on their learning processes [START_REF] Beat | Understanding learning at a glance: An overview of learning dashboard studies[END_REF]. LADs primarily aim to improve students' self-awareness which leads to improved academic performance as well as enhanced SRL. Several tools have been created in this sense, for example the NoteMyProgress plugin for Moodle, which aims to support students' self-regulation capacity in online and hybrid learning environments [START_REF] Pérez-Álvarez | Design of a tool to support self-regulated learning strategies in MOOCs[END_REF].

LADs can also provide teachers with important information about their students, including time spent, resource use and social interactions. The goal is to help teachers to understand learning processes and create predictive models to improve student outcomes. In research conducted by Nicholas et al. [START_REF] Nicholas | An instructor dashboard for real-time analytics in interactive programming assignments[END_REF], to see how LADs can predict student outcomes at different points in a course. The results show that student outcomes can be predicted with a supervised machine learning algorithm. Most of the commercial solutions are dedicated to a single LMS. Unfortunately, the diversity of existing LMSs complicates the analysis of this data. A situation that is further amplified by the need to combine data from various sources. The contribution of our work would be to have a system that can analyze learning data in a way that provides accurate and meaningful dashboards for both teachers and students, regardless of the platform or technological environment used.

TABAT TOOL DESCRIPTION

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. In this paper, we propose a presentation of an interpretable tool (functional with different LMSs) that we have created as LADs. With our dashboard, we can consult what happened during the online course follow-up (descriptive aspect), see who are the students who will or will not succeed in the course (predictive aspect), find out why students have been declared as dropouts (diagnostic aspect) and finally get information on what actions to take to improve the progress of students in the course (proactive aspect). As shown in Figure 1, the operating process of our proposed LAD consists of extracting learner data from data sources (Student Learning Tracks), selecting and calculating assessment indicators, generating JSON files that hide the source of the collected data and finally visualizing the results of the reports in the form of a LADs.

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: The last category is intended to provide an estimate of a learner's success level in an online course. According to our previous research, where we proposed and statistically validated in online learner success assessment model (e-LSAM) [START_REF] Safsouf | An analysis to understand the online learners' success in public higher education in morocco[END_REF]. This model allowed us 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.

The report for the student presents a clean and efficient synthetic vision of a student's progress in an online course.

The three available interfaces are shown in Figure 2. The 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 or her progress in the course. The progress detail is displayed in a table below the graph. 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. A script is programmed to send notifications automatically twice a day; at 08.00 in the morning and again at 20.00 in the evening. If the same notification has already been sent and hasn't been read yet, the sending is not done.

The report for the teacher (showed in Figure 3) presents statistical data (from the indicators in the course category) during the course as well as the summary graphs on the consultation of students in the class for each section of the online course as shown in the first interface [START_REF] Durall | Learning analytics as a metacognitive tool[END_REF]. The quiz analysis page (2) provides a table that shows, the list of the quizzes taken and validate or not by students. The assignment analysis page (3) provides a summary of the assignments that may 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 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 differentiate visually 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.

STUDY CONTEXT AND RESULTS

The main objective of this study is to test the TaBAT tool and assess its impact on self-regulation and prediction of success for 26 students enrolled in the second year of the engineering cycle at the Higher Institute of Engineering and Business (ISGA of Marrakech) in Morocco. Based on the decisions made to minimize the hygiene and sanitary risks caused by COVID-19, the institute has reduced the number of students to groups of 10 to 14 students, with respect for physical distance between students in different groups. The students all completed the classroom course entitled "Object-Oriented Database", with some online sections on the Moodle 3.8 platform, over an eight-week period, completed by a supervised exam.

The target class is divided into two groups of 13 students (8 female and 18 male), aged between 18 and 35 years old (24 between 18 and 25 and 2 between 26 and 35). In terms of time spent using the Internet and computer per day, 6 students reported using the Internet between two and five hours, 18 between five and ten hours, and 2 more than ten hours per day. One of the two groups had access to the experiment to use the TaBAT tool (exposed group), while the second group did not (control group). The analysis of student tracks in each group was done using the dashboards of the TaBAT dashboard via the report of the teacher. Table 1 describes the results of the experience.

RESULTS

DISCUSSION

We note at first that all students in the exposed group logged on to the online course, while for the control group, 2 students did not take the online part of the course. The second remark concerns the total time spent doing the activities of the online course. Students in the exposed group spent almost triple the amount of time that students in the control group took to complete the online course. This increase therefore reflects self-directed 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. We notice a significant increase in the average progression score of the exposed group, explained by the proactive actions done manually by the teacher or sent automatically by the TaBAT tool (proactive phase), in order to remind students if there are still resources not consulted (file to download or URL to visit) or activities not completed (lessons, homework to return, quizzes to do, etc.).

The fourth remark concerns the analysis of the assignments.

In fact, the exposed group had a 71.15% return rate for assignments, while for the control group, the same rate was 26.92%. This result is due in particular to the notifications received. The last remark concerns the success of the students. In this study, the level of success calculated by the TaBAT tool is compared to that obtained after the final exam.

The TaBAT was able to predict 100% success for students in the exposed group and 80% success for students in the control group. This result shows that students' performance and success is partly due to their ability to provide effective work at home, better preparing them for the classroom tests.

CONCLUSION

This paper presents a new learning analysis dashboard, based on a theoretically validated model. Our dashboard allows to visualize the learners' progression as well as the prediction of student dropout rates. After a first study, we can conclude that the tool has provided useful and clear information to motivate and regulate the students' effort, as well as helping the teacher to monitor the students and detect dropouts. We will conclude by mentioning that this first study has two limitations, first of all, this study is limited to one class, the integration of several classes into the study would require more resources. Finally, due to this sample size limitation, more statistical analyses were not conducted. These initial results are very encouraging and will need to be confirmed in a more extensive study.

Figure 1 :

 1 Figure 1: The phases of the operating process of our tool.

Figure 2 .

 2 Figure 2. Dashboards summarizing the report for the student.

Figure 3 .

 3 Figure 3. Dashboards summarizing the report for the teacher.

Table 1 . Study results obtained from the TaBAT tool.

 1

		Exposed	Control
		group	group
	Number of active users	13	11
	Cumulative time to complete the course	45 h 57 min 15 h 57 min
	Average percentage progress score	85,54 %	37,61 %
	% of assignments returned on time	44,23 %	11,54 %
	% of assignments returned late	26,92 %	15,38 %
	% of assignments not returned	28,85 %	73,08 %
	Prediction of success (online success)	13/13	8/13
	Effective success (validation of the face-to-face exam)	13/13	9/13


Wijdane Kaiss
email: wijdanekaiss@gmail.com

Khalifa Mansouri
email: khmansouri@hotmail.com

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

Towards a model of self-regulated e-learning and personalization of resources

Keywords: E-learning, Personalization, Self-regulated learning, Learner learning experience, Learner success

E-learning systems, also called virtual learning environments, are systems that promote education and training activities using modern information and communication technologies. Therefore, e-learning is the application of modern multimedia technologies and the Internet to improve the quality of learning by making resources and services more accessible, as well as exchanges and collaboration at a distance that can help learners in their studies and that can also help teachers to predict the weaknesses, strengths and level of understanding of learners. Preparing and providing the quality e-learning system and rich learning experience are major challenges. The lack of interaction, feedback, helping the learner to self-regulate, assessing the degree of knowledge and adapting teaching methods and resources to the real needs of the learners which imply a lack of motivation and a high dropout rate, diminish the richness of the learning experience. Hence, we propose in this article a robust model of adaptive e-learning environments to optimize learning for each learner, taking into account the heterogeneity of profiles so that students succeed in their learning experiences.

Introduction

Today, online learning has outgrown the traditional classroom environment. Due to the spread of the coronavirus pandemic, LMS (Learning Management System) has experienced unprecedented growth should continue to develop and evolve in the coming years. Most students feel less stress as a result of online learning and feel more comfortable emotionally [START_REF] Baudoin | Le bien-être et la motivation des élèves en période de ( dé ) confinement Note de synthèse[END_REF]. Unfortunately, distance education poses pedagogical issues that reduce the richness of the learning experience. Prepare and provide a quality system and a rich learning experience are major challenges. The separation of the physical and temporal of the tutor and the learner, and between the learners themselves, can lead to a feeling of isolation and a lack of discussion and interaction and feedback that generally concerns the learners' communication mechanisms with their teachers and peers. These gaps can reduce the richness of the learning experience. Whereas, peer teaching contributes to improving student success [START_REF] Bowman-Perrott | Academic benefits of peer tutoring: A meta-analytic review of single-case research[END_REF]. For the learner, distance education requires good organization and pursuit of goals so that he/she succeeds in his/her training. Dropout rates from online courses are high due to the inability to self-regulate [START_REF] Lee | A review of online course dropout research: Implications for practice and future research[END_REF], this may be due in part to the fact that students underestimate the effort involved and fail to properly plan their work to be successful in online courses. Distance education poses another issue, which consists of evaluating the student's degree of assimilation of the material taught. This evaluation is essential if we want to accompany the learner and ensure concrete progress, it allows students to determine which parts of the course they need to study and it allows instructors to know what course content needs to be adjusted.

In addition, e-learning environments do not always present the resources most suited to the real needs of learners such as the language of teaching, what they want to learn, the need to work with peers or individually, the type of questions, and type of learning objects (e.g. video, image, text, etc.). Not all of these needs are automatically detected, which implies a lack of motivation among learners and a high dropout rate that can reduce their success rate in these e-learning systems. Thus, all students learn best when teachers adapt the teaching methods, resources, and learning environment that suit their interests, needs, strengths, and level of knowledge. But even for the best teachers, it is difficult to fully understand each learner's profile and adapt the program accordingly and help them to self-regulate. This is why technology could be of great use; therefore using it adds new possibilities for tracking learners' activity and providing them with more immediate feedback on their progress. Hence, the conclusion that one of the main axes is the personalization of learning that must be taken into account if we want to see student outcomes improve.

To resolve these issues, we judged that it is essential to include personalization, evaluation, feedback, help the learner to self-regulate, etc. while adapting them to the learner's profile (knowledge, preferences, abilities, objectives, etc.) due to their importance. In this article, we propose a model of e-learning environments, personalizing the pedagogical resources so that students succeed in their learning experiences. Rather than imposing a unique program on all learners independently of their respective abilities and needs, personalized learning allows the development of individualized learning that they will motivate them to follow.

The article is structured as follows: The following section reviews the literature on key theories and models presenting several factors that can contribute to the use, engagement, satisfaction, and success of learners in the e-learning system. Our proposed model is presented in Section 3. Subsequently, the proposed approach is described in detail in section 4. Then, we will end our article with a conclusion and a presentation of our future research work.

Related Work

A large number of studies over the recent decades have attempted to identify the success factors of e-learning that need to be managed to increase the effectiveness of e-learning systems.

According to [START_REF] Eom | A System ' s View of E-Learning Success Model[END_REF], the e-learning success model adequately explains and predicts the interdependence of six critical success factors for e-learning systems including course design quality, instructor involvement, student motivation, student-student dialogue, student-instructor dialogue, and self-regulated learning. Several researchers have been interested in aspects of the learner and how they can influence the learning process. Some have looked at personal characteristics such as learning styles [START_REF] Jahanbakhsh | Learning Styles and Academic Achievement: a Case Study of Iranian High School Girl's Students[END_REF] or motivation such [START_REF] Kyndt | The direct and indirect effect of motivation for learning on students' approaches to learning through the perceptions of workload and task complexity[END_REF] and claimed that these have an important influence on the success or failure of learners. According to [START_REF] Sorgenfrei | The Effectiveness of E-Learning Systems : A Review of the Empirical Literature on Learner Control[END_REF] the effectiveness of learner-controlled online learning depends on individual cognitive characteristics and related to motivation such as learning styles, cognitive abilities, attribution of success, and self-regulation abilities.

Motivation is essential to learning and what really makes students learn is their conscious engagement in these learning activities, because "engagement leads to outcomes such as success" and "motivation underlies engagement", engagement and motivation are not the same [START_REF] Martin | Motivation and engagement: Conceptual, operational and empirical clarity[END_REF]. Motivation can be transformed into engagement with an appropriate design of support. According to [START_REF] Kim | From Motivation to Engagement : The Role of Effort Regulation of Virtual High School Students in Mathematics Courses[END_REF] intrinsic value and self-efficacy are motivation variables, whereas effort and metacognitive regulations are regulation variables, and cognitive and emotional engagements are engagement variables. Supporting students' effort regulation is a unique way to improve the motivation and engagement that influences their success and it is the metacognitive and effort regulation that learners put into the process of their learning that transforms motivation into engagement [START_REF] Kim | From Motivation to Engagement : The Role of Effort Regulation of Virtual High School Students in Mathematics Courses[END_REF].

The word "self-regulation" refers to a student's ability to regulate his or her thoughts and actions [START_REF] Zimmerman | Self-regulated learning and performance: An introduction and an overview[END_REF]. For example, students can reflect more about how they learn and what strategies they will need to succeed in their university studies. To achieve specific learning and performance goals, a self-regulated learner employs motivational, metacognitive, and behavioral processes (for example, help-seeking, goal setting, self-evaluation, and metacognitive monitoring) [START_REF] Zimmerman | Motivational Sources and Outcomes of Self-Regulated Learning and Performance[END_REF], [START_REF] Zimmerman | Investigating Self-Regulation and Motivation: Historical Background, Methodological Developments, and Future Prospects[END_REF]. Self-regulation processes considered motivating allow a learner to initiate and maintain targeted activities focused on objectives while ignoring distractions and setbacks [START_REF] Dale | Motivation in Education: Theory, Research, and Applications[END_REF].

A self-regulated learner actively engages these processes by using strategies in what [START_REF] Zimmerman | Self-regulated learning and performance: An introduction and an overview[END_REF] have called Self-Regulated Learning Strategies (SRLS) [START_REF] Zimmerman | Self-regulated learning and performance: An introduction and an overview[END_REF], [START_REF] Nancy | Studying self-regulated learning in classrooms[END_REF]. Examples of SRLS include tutoring, keeping a study journal, and emailing the instructor. Additionally, not all self-regulated learners use the same strategies. Thus, empirical research has shown that implementing various strategies in online courses can promote SRLS in students and that structuring the e-learning environment to promote self-regulated learning is a central element to fostering the successful use of SRLS by students [START_REF] Barnard-Brak | Self-regulation across time of firstgeneration online learners[END_REF], [START_REF] Ferla | Judgments of self-perceived academic competence and their differential impact on students' achievement motivation, learning approach, and academic performance[END_REF]. Researchers have suggested that SRLS are of greater importance in e-learning environments due to their more autonomous nature [START_REF] Dabbagh | Supporting Self-Regulation in Student-Centered Web-Based Learning Environments[END_REF]. Thus, providing self-regulated learning strategies to online instructors to promote the use by students of these strategies in online courses aim to improve the academic performance of their students [START_REF] Wandler | Promoting Undergraduate Student Self-Regulation in Online Learning Environments[END_REF].

According to [START_REF] Safsouf | An Analysis to Understand the Online Learners' Success in Public Higher Education in Morocco[END_REF] learner success in an e-learning system can be explained by self-regulation and learners' intention to continue using educational platforms, they also show that learners' intention to continue using these educational platforms can be explained by learner satisfaction, and self-regulation can be explained by personal effort and course flexibility, and although the following factors: perceived usefulness, social interactions, system quality, course and information quality, course flexibility, and diversity of assessments can improve learner satisfaction with educational platforms.

According to [START_REF] Diaz | Personal, family, and academic factors affecting low achievement in secondary school[END_REF], parents (family causal factors), teachers (school causal factors), and students (personal causal factors) can influence final performance. The combination of these factors influencing academic performance, however, varies from one academic environment to another, from one group of students to another, and even from one cultural setting to another.

Other factors play a role in online learning satisfaction and success that were all related to learner characteristics and course design characteristics of e-learning environments [START_REF] Kauffman | A review of predictive factors of student success in and satisfaction with online learning[END_REF]. On the one hand, learner characteristics including self-regulation skills, time management, online learning style, knowledge, one that demonstrates better emotional intelligence (i.e. a better knowledge of his/her needs, self-regulation of feelings), self-motivation, self-discipline, organization, planning, and self-assessment. On the other hand, professors/instructors need to know the course design characteristics of online learning environments that promote student success and satisfaction, including time management, course alignment, and organization, integration of content with technology to facilitate engagement, instructor facilitation, instructor's comments, prompt feedback which is a primary factor in online satisfaction, and the size of the course which should not be large. Successful online learning also requires interaction between the learner, the instructor, the learners, and the technology.

The virtual learning environment effectiveness model of [START_REF] Piccoli | WEB-BASED VIRTUAL LEARNING AND A RESEARCH FRAMEWORK ENVIRONMENTS[END_REF] posits that two antecedents (design dimension and human dimension) determine the effectiveness of elearning systems. The human dimension concerns two human entities (the students and the instructor) and their different attributes; and the design dimension includes learning management systems (LMS), self-regulated learning (SRL) and learner control, the quality of course design, and the interaction between human entities.

Learning analytics provides opportunities for students to reflect on learning and develop metacognitive skills. So in order to be successful in self-regulation, it is necessary for students to understand their own cognitive process. To this end, linking visualizations to learning goals can help learners and instructors assess whether the goal has been achieved [START_REF] Durall | Learning Analytics as a Metacognitive Tool[END_REF].

Interactions with the learning community members that includes learners and instructors, social presence, positive support, engagement, critical thinking, and overall assessment results have a positive impact on the concept of knowledge and skills acquired during the online learner's learning path that represents the backbone of the online learner experience [START_REF] Hammad | eLEM : A Novel e-Learner Experience Model[END_REF]. In addition, forums play an important role in collaborative student learning, also allow students to interact. Students who participate most actively in the discussion forum are those who succeed in the course [START_REF] Romero | Predicting students ' fi nal performance from participation in on-line discussion forums[END_REF]. Course structure, class size, feedback, prior digital communication knowledge, interface characteristics, content area experience, student roles and pedagogical tasks, differences in student demographics and abilities, etc. are factors that can influence student participation in these online discussion forums [START_REF] Yukselturk | An investigation of factors affecting student participation level in an online discussion forum[END_REF]. Other research results affirm that all students learn best when the instruction, resources, and learning environment are wellmatched with their strengths, interests, needs, and their level of knowledge [START_REF] Prud | Différencier d'abord auprès de tous les élèves : un exemple en lecture[END_REF], [START_REF] Przesmycki | La pédagogie différenciée[END_REF]. Hence, the conclusion that personalization of learning is one of the main axes that must be taken into account if we want to see student outcomes improve.

Another aspect to take into account is the evaluation of learning. Students' learning depends on how they are evaluated and with what instruments [START_REF] García Carreño | Hacía una evaluación integral con ePortafolio por evidencia y bPortafolio[END_REF]. Thus, the teacher assesses the student to identify the evolution through different levels of learning. On the one hand, depending on the level reached, the instructor proposes actions and material to improve the performance of the students [START_REF] Leyva | Reflexiones sobre la evaluación de la calidad del aprendizaje en la práctica pedagógica en la escuela primaria[END_REF] evaluations, videos, tests, conferences, among others. Each student, on the other hand, learns at his/her own pace. This requires personalizing the mechanisms used according to each student. Information and communication technologies can help the teacher discover who is learning and who is not motivated. They can suggest actions like when, who, and how to evaluate or what activities can be realized.

Proposed Model

In this section, we will describe our proposed robust model of e-learning environments that offer the personalization of learning to the specific skills and needs of each learner in order to improve the learning experience of the students, whose goal is to increase their success rate as well as reducing their dropout rate in these e-learning systems. Fig 1 shows our proposed model. It is then a matter of personalizing the learning objects, adapt teaching methods, helping the learner to use self-regulated learning strategies, as well as the feedback and evaluations provided by the teacher, that they will be personalized according to the learner's profile. In an e-learning system, they can be applied as components, also linking the visualizations to the learning goals in order to help learners and teachers to evaluate if the objective has been achieved. This is a useful practice for teachers and especially for learners since it allows the learner to be accompanied at any time and any place and tests his/her knowledge and skills. Hence the system requires a dynamic combination between the learner's demands (his/her interests, preferences, level of knowledge) and the possibilities of personalization. The Learner's need is to learn. Our objective is to help him/her learn, to become autonomous, to be satisfied, and to succeed in his/her learning. In the e-learning system, the learner visualizes the learning objects provided by the teacher which are personalized according to his/her profile by the e-learning system, he/she has to evaluate his/her knowledge and level of understanding. Also, the learner has to be able to define his/her objectives, to make decisions in his/her program, to apply the right learning strategies which are cognitive strategies where the learner can take notes, summarize, create mental associations, use images and audio documents, use the mother tongue; metacognitive strategies are there to reinforce memorization and reflect on the learning process by setting goals, reflecting on him/her way of working, having a personal learning journal, self-evaluation; socio-affective strategies bring the learner closer to his/her emotions and feelings by cooperating with peers, encouraging each other, opening up to others.

The role of the Teacher is crucial. He/she has to design educational sequences, evaluate the level of the learners, use the educational material provided... E-learning doesn't consist of simply letting learners navigate through a set of resources; it is a matter for the teacher accompanying them in their learning process through the design of educational activities integrated into a coherent scenario. The teacher's and the elearning system's roles are therefore also to guide the learners to identify the best strategies, to help them learn and understand also to helping them to self-regulate.

The sub-components of the "Learner profile" component are as follows: history of his/her navigation (his/her learning established on the basis of his/her last interactions with the learning environment); his/her performance (this sub-component updates information about the learner through the results of the suggested evaluations); his/her preferences (based on his/her interaction with the learning environment, we determine the language of teaching, types of learning objects and preferred types of questions). ful feedback to students. • Evaluation as learning: evaluation allows learners to become aware of how they learn, to adjust and progress their learning by assuming increased responsibility for it. • Evaluation of learning: the information gathered from the evaluation allows students, teachers, and the online learning environment to be informed about the learning outcomes achieved at a specific time in order to highlight successes, plan interventions, and keep promoting success. The evaluation should be planned according to these goals. Evaluation in the service of learning, evaluation as learning, and evaluation of learning each have a role to play in supporting and enhancing students learning. In addition, the effective evaluation does not apply the same evaluation strategy to all learners because their knowledge is different. Some students should be evaluated on the entire learning content to evaluate their overall knowledge; others only need to assess their current knowledge in a few learning steps, which allows them to access the parts of the course that are most appropriate for their level of knowledge. The aim is to orient the learner to the most appropriate resources for his or her needs through a personalized evaluation. Based on the results of the test, a personalized learning itinerary will be provided. The three most popular types of evaluation are as follows:

• Pre-evaluation: Before they start learning, it is essential to know what type of students it is addressed, situating their level of knowledge and preferences. A personalized program can then be created based on the data collected. • Formative evaluation: used during the first attempt at learning. By keeping track of students' progress and obtaining feedback to identify obstacles and individual difficulties to the diversity of students to provide individualized help and to adapt "Teaching methods". Basically, three teaching methods enhance the student learning experience: descriptive learning (reading, hearing), visual learning (pictures, videos, and demonstration), and collaborative learning (participatory). • Summative evaluation: aims to evaluate if the most important knowledge has been acquired when the training is completed, in order to adjust the rest of his/her instructing according to the level achieved. Also, providing learners with personalized "Feedback" on their performance is an effective strategy for promoting learning, with congratulatory messages (e.g., if the learner has successfully achieved his or her goal, a message appears saying "Well done, I'm proud of you") and advisory messages (e.g., if the learner receives a low grade, a message appears saying "Anyone can make mistakes, take extra time next time. "). We then focus on promoting students' use of "Self-Regulated Learning Strategies" in an e-learning environment and on personalizing them according to their profile. The model of [START_REF] Zimmerman | Motivational Sources and Outcomes of Self-Regulated Learning and Performance[END_REF] was the most frequently accepted and we will use it. As shown in Fig 2 of [START_REF] Zimmerman | Motivational Sources and Outcomes of Self-Regulated Learning and Performance[END_REF], who postulated that SRL occurs in three cyclical phases: forethought, performance, and self-reflection [START_REF] Zimmerman | Motivational Sources and Outcomes of Self-Regulated Learning and Performance[END_REF], [START_REF] Zimmerman | Attaining self-regulation: A social cognitive perspective[END_REF]. Each phase of self-regulated learning consists of processes that self-regulated learners engage in (Fig 3 describes the fishbone diagrams of each process), and each phase influences the processes of the next phase, and the third, in turn, influences the first again. The fishbone diagram is a diagram that graphically represents the causes leading to an effect, and can also be called a cause and effect diagram [START_REF] Watson | The Legacy Of Ishikawa[END_REF]. Since students do not necessarily develop SRLS when taking online courses, even though these SRLS are of greater importance in online learning environments due to their more autonomous nature, instructors should consider implementing these strategies to help learners promote positive SRLS [START_REF] Wandler | Promoting Undergraduate Student Self-Regulation in Online Learning Environments[END_REF], but for instructors, it is difficult to fully understand each learner's profile to help them use these strategies and to (a (a)

(a) (b) (c)
personalize these SRLS according to each learner's profile. Therefore, we propose to provide e-learning systems with specific strategies to encourage and help students to use these effective SRLS, which will be personalized to their profiles. Some students know how to launch plans, but some for example do not know how to manage their time. Also, for example, some students want help (Help-Seeking) while others want help to guide their learning. It is therefore the role of our proposition, to offer personalized self-regulation strategies to e-learning systems, to help them self-regulate. Therefore, to succeed in self-regulation, students must understand their cognitive process [START_REF] Durall | Learning Analytics as a Metacognitive Tool[END_REF]. Using goal-oriented visualizations of activity tracking is an interesting experience in analyzing student-centered learning through visualizations [START_REF] Santos | Goal-oriented visualizations of activity tracking : a case study with engineering students[END_REF]. In this case, we add a "Dashboard" component to the e-learning system to allow students to reflect on their activity and compare it with their peers through dashboards. For this aim, the collected information such as their learning performance will be displayed in visualization to help them in their future decisions on task or goal completion.

In the next section, a description of the proposed approach is shown in the form of a sequence diagram that shows the scenario behind the background of our model.

Scenario of the proposed model

In this section, we will describe the context of our research using the personalized learning environment scenario. Once identified, the teacher adds learning objects, evaluations, and feedback. One identified as well, the learner navigates and consults educational resources. These traces of the learner's interaction with the learning system are used by the adapter component to better define the learner's preferences (teaching language, types of learning objects, types of questions, etc.). The adapter's function is to personalize the learning resources according to the learner's profile and to consult the traces, results, and profiles of other learners in order to propose the best configuration to each new learner. The adapter will also help the learner to self-regulate.

The learner must define his/her goal, if he does not do so the system asks him/her to define it. For example, Yasmine is a student in computer engineering who would like to learn the C language, the system sends Yasmine's objective, the resources (learning objects, evaluations), and the feedback provided by the teacher to the adapter to form a pre-evaluation on the chosen objective. After Yasmine answers the corresponding questions and according to the results obtained (the questions that were not answered correctly), the adapter depends on it to generate personalized feedback to Yasmine, teaching methods adapted to her needs, and a program personalized to her profile (preferences, performance).

She starts learning and to achieve her goal she starts using self-regulation strategies, so the adapter will help her to use them aiming to improve her academic performance. For example, she decides to ask for help every week on the parts she did not understand well, and unfortunately at the end of the semester, Yasmine does not receive a good grade in the course. So she attributes the cause (i.e., causal attribution) to the strategy used (i.e., seeking help did not help her achieve her goal). In the course that follows, Yasmine will decide not to pursue this strategy. In order to successfully self-regulate, the system displays the collected information, e.g. her learning performance, in a goal-oriented visualization so that Yasmine can track her progress towards her goal. Thus, to ensure that Yasmine understands the important concepts of the C language, questions will be researched by the adapter and presented to constitute personalized formative evaluations on this lesson, estimating her level of knowledge at each stage of the learning. The adapter should save Yasmine's progress and tracks in order to use them for future evaluation activities and form personalized feedback according to her profile until she achieves her goal.

Finally, the adapter forms a personalized summative evaluation to Yasmine to affirm that she has succeeded in achieving her goal, that she has understood the important language C concepts, and give her personalized feedback, e.g. motivational messages, advice, etc. For example, if she succeeded with a good grade the adapter sends a message saying "Congratulations, it was hard but you did it", this is a motivational message. Figure 4 illustrates a sequence diagram, which shows this scenario behind the background of this personalized e-learning system.

Conclusion and Future Work

In this article we have proposed a new model of online learning environment, helping the learner to self-regulate and make his/her learning in the e-learning system more accessible, easier, more personalized, more credible, and more desirable which ultimately leads him/her to better success. Thus, we presented the main factors that influence learners' success in their learning experiences that we included them in the model in order to improve the learning experience and presented a description of the proposed approach in the form of a sequence diagram that shows the scenario behind the background of our model. For future studies, we will experimentally validate this proposed model. Additionally, we suggest further study regarding learner-controlled online learning, how, what, when, and where to learn, which may lead to selfregulation skills and may be a factor in dropout rates of e-learning courses.

Fig. 1 .

 1 Fig. 1. Our Proposed Model of E-Learning System for Successful Education.The "Evaluation" component aims to provide the learner with a personalized evaluation based on the information provided in his/her profile (e.g. preferences, type of questions, results obtained in the evaluations). It plays an essential role in how students learn, how they are motivated to study, and how teachers instruct. While teachers, students, and the online learning environment work to achieve the learning outcomes, evaluation plays a critical role in providing useful information to guide teaching, help students achieve the next steps, and verify progress and accomplishments. Evaluation serves a variety of goals: • Evaluation in the service of learning: evaluation informs teachers and the online learning environment about what students understand and allows them to know what learning objects to adjust and to plan and guide teaching while providing use-

Fig. 2 .

 2 Fig. 2. Phases and sub processes of self-regulation [33]. The Forethought Phase of self-regulated learning describes the processes by which learners start to launch plans to complete a task or attain a goal (see Fig 3 (a)). The Performance Phase describes the processes learners employ to accomplish a task or goal (see Fig 3 (b)). In the Self-reflection Phase, learners reflect on their progress toward the task or goal, or the results if they have completed the task (see Fig 3 (c)).

Fig. 3 .

 3 Fig. 3. Fishbone diagrams of the Forethought Phase (a), Performance Phase (b), and Self-Reflection Phase (c) processes [33].

Fig. 4 .

 4 Fig. 4. Personalized E-Learning system sequence diagram.


domain: scco sde shs

text:
Salah Bouregbi
email: bouregbi.salah@univ-annaba.orgsalihbourg@yahoo.fr

Translation of Metaphor: The Dilemma of Conceptualisation

Keywords: Conceptualisation, Metaphor, Translation, Language Transfer, Cognition

Any translation is subject to mental processing. In a literary text, this processing is very problematic. Words and expressions that vehicle such transfer do not (re)produce the source faithfully. The received image could be perceived and conceived differently because of language suggestiveness. Undoubtedly, Cognitive Process of thinking is helpful in understanding and actualizing thoughts into words and expressions. But how could it be with metaphor? Is it transferrable? Are we able to reproduce and transfer the same image from the source to the target? Metaphor is shaped and mapped within the culture that produces it, but differences in culture become the clue of the problem. The way to conceptualise and perceive it is dialogically related to the degree of differences and similarities of the SLT (Source Language Text) and TLT (Target Language Text).

Résumé:

Dans un texte littéraire, le transfert d'un sens d'une langue à une autre est très problématique. Les mots/expressions qui véhiculent un tel transfert ne (re)produisent pas fidèlement le texte. L'image reçue pourrait être perçue et conçue différemment en raison de la suggestivité de la langue. Il ne fait aucun doute que le processus cognitif de la pensée est utile pour comprendre et actualiser les pensées en mots et en expressions. Mais comment pourrait-il être avec la métaphore ? Est-elle transférable ? Pouvons-nous reproduire et transférer la même image de la source à la cible ? La métaphore est façonnée dans la culture qui la produit. Mais les différences de culture deviennent la clé du problème. La façon de la conceptualiser et de la percevoir est liée de manière dialogique au degré de différences et de similitudes du TS (Texte Source) et du TC (Texte Cible).

Mots clés : Conceptualisation, Métaphore, Traduction, transfert de Sens, Cognition

1-Introduction

Thoughts channelized through metaphor become a real dilemma for both reception in the mother tongue and perception in the target language. Specificities of languages and their suggestiveness make the transfer/translation of metaphor a real dilemma. Utterance, itself, is a kind of translation of thoughts into words, words into expressions and expressions into message(s). But whatever is done, no word ever gives the right thought and image of what we want to say: it gives more/less than what we want or expect. That is, language is suggestive, and its suggestiveness makes it indirect and open to many interpretations. The thinking process and memory operations have little efficiency because meaning processing is beyond mechanical transfer and thinking. Undoubtedly, the process of thinking is necessary in understanding, receiving, producing and actualizing thoughts into words and expressions. But how could it be with metaphor? Is it transferrable? Is it permissible in all languages? If we remove metaphor from its original context and cognitive environment, can we get the same semantic transfer, or have we to lose something of the original to make it fit the target? Reception and perception are two operations useful for meaning transmission and production. The fulcrum of all these is, no doubt, the mind: it perceives meaning in relation with the way it receives thoughts through words. But do these words reproduce exactly what is in the source language? Or do they transform the message into another production, which can deviate from its source due to the context or/and cognitive environment?

Variety in culture is richness, but this richness becomes problematic when it comes to translation and transfer. Metaphor is shaped and mapped within the culture that produces it; but differences in culture become the cause of such problem. Haidee Kruger and Jan-Louis Kruger (2017) maintain that: -Translation studies depend on the notion of translation effects-conceived either as effects on an (often idealized) individual reader or effects on a society more broadly‖(p.72). Cognitive environment contributes potentially to the translation of metaphor in the TLT(Target Language Text). Its modulation depends on the way the reader makes it on the basis of what he has as culture of the target language. In the words of Jaits Toshika Saeka (2009): -Intention can be formed through a translator's personal thought on the original text…. Intention can also reflect the social expectation of the translator's role in a certain communicative situation‖ (p.230).

Language is dressed up with metaphors. Its images encode thoughts and ideas that are tied up with/to the cognitive environment and linguistic bases of the language. And so, translating is a process of transferring to the target language what the source intends to communicate. Fang [START_REF] Wang | Cognitive mechanism for metaphor translation[END_REF] states that: -Given the cognitive quality of metaphor as well as the nature of translation, translation of metaphors is taken as a cognition-oriented process, in which the human brain unravels and delivers the cognitive information of the source metaphor‖ (p. 2327). The paper, thus, focuses essentially on the cognitive conceptualisation of metaphor and its reception and perception in the target language, through translation.

2-Cognition

Cognition is a process of acquiring knowledge and meaning via the use of mind, experiences of senses and linguistic environment. It is the mind that processes language data and takes -as input and on the basis of their linguistic properties assigns to them formulae that ‗mean' or ‗represent' something of the semantic representations‖ (Gutt,2014, p.25). But the meaning we get from senses and mental processes is not ever-fixed. Furthermore, what the linguistic expressions do give as meaning is not what the expression conveys or communicates. That is to say, what we utter and read is not necessarily what this linguistic composition/structure wants to say. In the words of Gutt: -The way in which audiences get from semantic representations to propositional forms crucially involves the use of context‖(p 26). In the same vein, Hanna [START_REF] Risku | Cognitive approaches to translation[END_REF] maintains that: -The repetition of situations creates relative (individual and cultural) stability, but meaning is nonetheless in a constant process of change‖ (p.3). Words could conceive many meanings due to intentions and contexts, the same as in painting. One palette could produce a variety of colours through intentional and contextual matching. Meaning in painting exceeds its colour design, and so is the text. Its meaning exceeds the words they produce it.

2-1-Cognition and Translation

Every word is identical to itself and does not, in most situations, hold the same meaning of any other word, except when it deviates and gets a new meaning suggested by the context. In the domain of translation, frequently, words that exist in two different languages do not hold the same meaning, except some common international words (probably coined for the sake of internationalisation), as ‗sugar,' ‗sucre,' ,'سكر‗ şeker, etc… This is what we call false friends: though words could have the same script and orthography, they do not share the same equivalence. But according to Moritz Schaefer (2017), translation could be possible through two ways: -Either via shared representations (horizontal translation) or via representations regarding propositional content (vertical translation). In order to arrive at the ‗sense of the message,' a certain amount of information is necessary, while establishing links between items in the two languages can happen at all levels and early in the process‖ (p.18).

Meanings are not ready made and stored within the brain of the individual and where the mind has only to generate them. Meaning reproduction is a mapping of thoughts and images within a cognitive conception suggested by the mental operation processing. Haidee and Jan-Louis [START_REF] Kruger | Cognition and reception[END_REF] state that:

Meaning is not stored in our brains in the form of static mental representations, but emerges dynamically by the interaction of brain, body, and environment, and consequently it does not focus on translators' isolated black boxes, but takes into account how translators' interactions with tools and artifacts and with other participants in the translation process shape their behavior and outcomes. (p. 83) So, meaning negotiation is dialogically related to the intellect and the cognitive environment of both the source and the target languages.

Translation process is not mechanical residing in the association of one word from the source to another in the target. This process is beyond words and expressions. It is a meaning creation through the use of the mind, which stores innumerable data for such aim and through negotiation. To partake of this negotiation, the translator is in need of some strategies and choices, besides the cultural and linguistic backgrounds. The latter influence his thinking, understanding and competence to transfer correctly the meaning from the source to the target. (Risku, 2013, p. 1) In other words, the translator is supposed to -look behind the observable processes and, when doing so, refers to certain cognitive scientific concepts or approaches as a framework (which can thus be confirmed or disapproved)‖ (p.1).

But how can we confirm the meaning or disapprove it? What parameters do we use and suggest for us the right meaning of the transferred words and expressions? How and where does meaning locate? The text holds contexts. Thus, our understanding is dialogically related to what we have as thoughts and what we get as interpretation of the text we read and try to apprehend. Reading strategy is the gateway for interpretation and the manner we want to understand the text. Thus, our mind is directed to map a meaning related to this strategy. So, translation is not specifically a linguistic matter; it is, rather, a conceptualization based on the mind, which tries to receive and perceive it through its cognitive environmental bases. This is what Mikokaj Deckert (2017) calls conventionalisation. Deckert writes: -Conventionalisation is then differentiated from entrenchment, with the former being a property of a community and the latter being individualised, i.e. a matter of particular language users.…The former being social and the later being psychological, they deal with different types of entities and are influenced by different forces‖(p.123). But is conventionalization the only parameter for meaning generation? Can individualization/entrenchment be really excluded? Is language suggestiveness social or individual, or both? Is Metaphor conventional, or does it exceed any convention and falls within individual conceptualization?

3-Metaphor

Metaphor, according to Oxford English Dictionary (2018), is -a figure of speech in which a word or phrase is applied to an object or action to which it is not literally applicable‖ (Metaphor). Conventionally speaking, metaphor is a figure of speech aesthetically used in the rhetoric structure of language. It is beyond common literary use and understood through cultural environment and intention of the utterance. In other words, it is the semantic deviation of the word/expression.

Metaphor is, for most people, a device of poetic imagination and ornamental aspect of speech that is used for some artistic and rhetorical purpose. Moreover, it is typically viewed as characteristic of language alone, a matter of words rather than thought or action. (Burmakovaa, 2014, p. 727) But regardless of this traditional definition, metaphor is very significant because it is related to the conceptualization of the world.

Metaphor has a cognitive function. It is, according to Annette [START_REF] Sjorup | Cognitive effort in metaphor translation: An eye-tracking and key-logging study[END_REF], -necessary for us to be able to grasp and relate to abstract concepts in the world around us. [Metaphor is regarded] as adding a new layer of meaning to the concepts they refer to and have been found to be more common than generally expected‖(p. 9). In the same context, Ruhan [START_REF] Guçlu | Translating the metaphor in poetry: A Cognitive approach to Le Bateau Ivre and its translation in Turkish[END_REF] maintains that: -Metaphors cast light upon the process of conceptualisation between two different domains: the source domain (the conceptual domain drawn upon to create metaphorical expressions) and the target domain (the conceptual domain to be described by the metaphor)‖(p. 222). That is to say, metaphor is not limited to its lexical structure, but, rather, it is bound to cultural environment and can be understood and translated through the mind. The latter tries to process it after its reception, and then produces it through (re)conceptualisation. In other words, metaphor extends its cultural environment and expresses the perception of the world through a conceptual system and mind processing. It shapes how we think and behave and re/construct our utterances. (Fan, et al., 2017(Fan, et al., , p.1720) ) Being cognitive, metaphor is related more to conceptualisation, which depends on the way language and thought are presented through utterances and, subsequently, to the reception and perception of messages. Goran [START_REF] Schmidt | A Cognitive-Linguistic approach to the translation of metaphor from English to Croatian[END_REF], when introducing the theory of conceptual metaphor, claims that scholars of cognitive linguistics have shown/proved that: -There is no objective or absolute truth, rather the human experience and understanding play the central role in our interpretation of meaning: primarily on the basis of linguistic evidence‖(p.31). In other words, Schmidt wants to tell us that the linguistic constituents or expressions reveal for us the metaphorical conception. They are a means for the conceptualisation, but not themselves conceptualization. They evoke metaphor, but not in themselves metaphors. That is, -The way we talk reveals the way we think‖ (p.33).

3-1-Metaphor and Translation

Metaphor is semantically bound to culture and the cognitive environment of language. Given such specificity, its transfer from one language to another, through equivalence seems to be problematic. The message of SLT should be transferred ‗faithfully' to TLT and understood by the target audience. That is, the foreign reader is supposed to understand and comprehend the intention of the message in the SLT-not a new message that distorts the original. But if the constituents of the source metaphor do not have any lexical as well as cultural equivalence, here lays the matter. How can the translator help it? Can he search for another metaphor in the target that looks similar but holds different ingredients of its constitution?

Frequently, we think of metaphor as linguistic as well as cultural differences of languages. For [START_REF] Schmidt | A Cognitive-Linguistic approach to the translation of metaphor from English to Croatian[END_REF]: -Most of the terminology used is that of semantic theories, going as far back as Richards (1936): 'image' or 'vehicle' for conventional referent, 'object' or 'topic' for actual referent, 'sense', 'ground' or 'tenor' for similarities, i.e., analogies between them‖(p11). But being subject to cognitive mechanism and processing, metaphor has been defined as a conceptual phenomenon, which is shaped by cultural knowledge and perceptional production more than linguistic variations and lexis from and to which we transfer. -Sometimes even small differences in the expression of a metaphor can point to much larger differences in culture‖(p.12). Having such differences in modes of expressions and cultural environment, this disparity brings into question the possibility of the translatability of metaphor. That is, there is a problem in its mental conception and perception in the target language. Kevin P. Guzzo and Monica Naranjo (2010) maintain that:

The translation of metaphor has been treated as a part of the more general problem of 'untranslatability.' This association has two principal causes, the metaphor is considered -indirect‖ and therefore more difficult to understand and interpret. Also, the relationship between the two concepts, or domains, from which the metaphor gets its meaning is rooted in the shared cultural values of a particular society. This quality proves of particular difficulty for the translator because the meaning of the lexical constituents cannot be predicted from its referential meaning. (p.13) So, the difficulty of translation of metaphor is not really in its equivalence: what is intriguing is that the lexical equivalence found in the target could not have the same signification, and the referent could not hold the same meaning and the same conceptualisation of the words or/and expressions. For example, ‗owl' in Arabic is a sign of pessimism and bad fate, whereas in Greek, and Christian world in general, it is the sign of wisdom. [START_REF] Guzzo | Translation and Cognition: Metaphors in specialized texts[END_REF] state that:

The translator should not only identify the metaphor in context and its meaning, but furthermore realize a contextualized interpretation of the intentionality and the conceptual cohesion behind the phenomenon to recreate the discourse in the target language and culture that has the same linguistic and extra-linguistic characteristics as in the source language and culture. (p.22)

4-Metaphor and Cognition

Metaphor is the conceptualisation of our world through words and expressions. It is mapped with a structure that produces what we perceive through the processing of our mind. But structure is, in no way the meaning of the locution or the utterance. Understanding or conceiving a message is dialogically related to the processing of the mind of the reader or the listener. Being different in mental procession, even though we have the same cognitive environments, understanding differs from one individual to another. [START_REF] Guzzo | Translation and Cognition: Metaphors in specialized texts[END_REF] point out that: Within this perspective metaphors are not only a stylistic resource used to embellish a discourse, they become essential elements in the process of understanding, creating, and categorizing reality. The cognitive theory states that the majority of meanings are metaphorical and the adequate interpretation is reached by conceptualization, instead of reinterpretation. (p 17)

In other words, metaphor does not signify the sum of terms and expressions that compose it; it, rather, evokes a meaning conceived through its mapping by the mind process and cognitive environment. In the words of Jasmina Hanic and Tanja Pavlovi ( 2016): -The cognitive view of metaphor takes it not as a rhetorical by-product of objective thinking, but as the basis of the human conceptual system. Metaphors may be expressed in language accurately, for human thought processes are fundamentally metaphorical‖ (p.59).

Suggestiveness of language evokes a myriad meanings related to the way we perceive it. Our perception of metaphor is fundamentally cultural and psychological.‖[It] is not a distorted literal thought, but is a basic scheme by which human experience and the outside world are conceptualized‖(p.59). In other words, conceptual metaphor is independent from the linguistic structure that composes it. The structure is a basis of a conception but not the conception itself. Conception is a sum of elements that make harmony and unity and form an image, a map.

5-Metaphor, Cognition and the Dilemma of Translation

Metaphor resists to translation due to its conceptualisation, which is beyond linguistic structure that composes it. That is, it is a product of ideas and thoughts-a mapped image. The cognitive process becomes, thus, a relevant tool to be used to suggest a solution and, subsequently, a translation. Mental processing of transfer of metaphor tries to establish a link between the linguistic aspects and the historical as well as cultural environments of both the source and the target languages. This cognitive processing attempts to find an adequate mapping, or at least an alternative, to the source language in the target. Hanic and Pavlovi (2016) point out that: -The awareness of conceptual metaphorical mapping systems used in the source and target languages (SL and TL respectively), when triggered by cognitive linguistics input, results in translator providing either the equivalent linguistic means to codify the mapping in the target text (TT) or finding alternative conceptual domains that are equivalent to those in the source text (ST)‖(p.91). In the same vein, Ahlam Ahmed Othman(2017) maintains that:

Since different cultures conceptualize the world in varying ways, metaphors are culture-specific. The cognitive approach defines metaphor as a means to understand one domain of experience (the target domain) in terms of another (the source domain). The translatability of metaphor is an issue of conceptual systems rather than individual linguistic expressions in source and target languages. (p 14)

In the translation of metaphor, linguistic structure should be distinguished from signification and meaning processing. Linguistic structure (grammar and linguistic components) contributes in the formation of the meaning, but they cannot determine it; it is the contextual information that generates it through assessment and suggestion. Freeda Catherine [START_REF] Wilson | A Model for translating metaphor in proverbs (French to English): A Cognitive descriptive approach[END_REF] states that: Linguistic meaning must be carefully distinguished from other types of meaning, for the linguistic signification of a form does not refer to anything outside of the language itself, as does referential, or emotive meaning, but rather to the meaningful relationships which exist within the language. On the other hand, linguistic meaning is similar to referential and emotive meanings, for all types of meaning are derived essentially from the signaling of a relationship.‖ (p.5) Wilson suggests that, in order to have a feasible production of metaphor, there are three premises that are of prime importance in translation: -1) Metaphors contain meanings, 2) Metaphors are a form of pragmatics, and 3) We think in metaphors. Each of these premises has a certain degree of merit, yet no single premise can be taken in complete isolation. A view of metaphor which will work best for the translator includes certain aspects of each of these premises‖(p. 8). She extends further claiming that: -Meaning differs from message in that meanings are a component of messages, while messages are generally comprised of an essence that surpasses meaning, including, but not limited to, the implied meanings, linguistic meaning, connotations and metaphor(s) derived from context‖ (p.9).

6-Illustrative Discussion

Language is affected by culture and the community that uses it. The conception of the world, in any community, is related to the attitudes, culture and the cognitive environment. Though disparity in culture and cognitive environment do exist in languages that have the same origins, the worst is to be found in distant languages, which have no direct link, or no link at all, in culture, attitudes and manners of thinking. Subsequently, such disparity becomes a major problem when translation comes into work. People do not think in the same manner due to differences in culture and tradition. Even in languages that have, nearly the same cultural environment, people do not conceive and think in the same way. Ali R. [START_REF] Al-Hasnawi | Cognitive approach to translating metaphors[END_REF] gives for us two plausible concrete examples that show such difficulty, which affects the problem of transfer. He says: if you say "a man has a 'big head' in English, it means 'he is arrogant,' whereas in Italian 'he is clever.‖ (Al-Hasnawi ). The only way here, according to Hasnawi, is to look for cognitive equivalence. Though we lose its real effect, mainly in its common contextual link, we, nonetheless, keep something of the meaning and what the author wants to communicate. Probably, the degree of loss could not affect the overall meaning. So, the role of culture is very crucial in signification and conceptualisation of metaphor in the target language.

The translator, who knows both cultures, should generate cognitively the transfer of metaphor from the SL to the TL. In other words, he should re-conceptualise and remap and render the metaphor, even though he does not find the equivalence. [START_REF] Gutt | Translation and Relevance: Cognition and context[END_REF] claims that: -If the information in question stems from the encyclopedic entry of a concept that appears in the semantic representation, then it is part of the context and so would fall outside the scope of a translation concerned with the preservation of communicative clues. It would be the responsibility of the audience to familiarize themselves with such information‖(p.143). In the words of Peter [START_REF] Gardenfors | Cognitive semantics and image schemas with embodied forces[END_REF]: -The meaning of an expression is something out there in the world. In technical terms, a semantics for a language is defined as a mapping from the syntactic structures to things in the world. [But there is no explanation of how] a person can grasp the meanings of different words‖(p. 57).

Another example, suggested by J Toshioka Seaka ( 2009), illustrates how linguistic expressions are metaphorical and the conceptualisation could be common in all languages. He gives us two sentences: ‗Life is a journey.' and ‗Death is sleep.' In these two examples, Saeka shows that there is a difference between conventional and unconventional metaphors. The former is related to daily reasoning of abstract concepts. The latter is constructed by the writer/speaker, who introduces new conceptualisation of a metaphor, which could be particular and specific. The reader could not understand it, unless he manages to decode and conceptualise it through the writer's intentionality. (p.232) In other words, the writer can use new conceptions or what is called self-metaphorisation. The interpretation of unconventional metaphorical expression needs the analysis of the concrete and the abstract altogether in order to understand their insinuation and, subsequently, the meaning they reveal.

Hasan Said [START_REF] Ghazala | Translating the Metaphor: A Cognitive stylistic conceptualization (English -Arabic)[END_REF] gives us another example that illustrates well the metaphorical conceptualisation of the world and the difficulty to find equivalence to it. *'This room is an oven'. This metaphor could be -conceptualized as ‗an enclosed heated compartment / an extremely hot place' which is the vehicle for the metaphorical transfer.‖ (p.60). But this metaphor suggests more than one meaning and understanding. Such multiplicity in meaning makes translation and transfer of metaphor into another language very problematic: which one do we to accept? He gives us five meaning variations of the same sentence:

1-This room is boiling. 2-It is an oven. It is really hell in here. 3-I mean it is burning here. 4-It is unbearable here.

5-It goes to blazes.

So, it is noticeable that there is no literality in metaphor translation and transfer. Furthermore, the problem does not reside in equivalence, but, rather, in culture and the manner people use language. In other words, the absence of cultural equivalence could force the translator either to modify or paraphrase the constituents of the metaphor of the source language. In the words of Yi-chen Chen (2017):

By comprehending and analyzing the semantic motivations underpinning metaphors, a metaphor can be transferred to another language in another culture. A metaphor which has a different cognitive domain from the target language equivalent expression results in more delay and greater uncertainty in the translation process, due to the difficulty in finding an appropriate conceptual mapping and cognitive domain in the target language. (p.32) Khalid [START_REF] Mansoor | The Concept of metaphor in the English-Arabic translation[END_REF] has pointed out other difficulties in the translation of metaphor between Arabic and English. He gives us the following examples:

1 -

يتبنى طريقا واضحا

The sentence literally means: -He is on the right way.‖ The English metaphorical meaning is -To keep a straight path.‖ 2 --الفتنت يزرع

The sentence means: -he gossips,‖ while in English is -To sow division between them.‖ -3

القرار بين يديه

The sentence literally means -They have right to decide now.‖ In English, it could be translated metaphorically as -The ball is in their court now‖ (p.159).

In'am Nadjim [START_REF] Jaber | Translating metaphoric expressions: Translating cultures[END_REF] has given some cases of metaphor that create ambiguity if the translator is not cognitively aware of the cultural environment and language use in the SL and TL. Her cases are from English and Arabic: 1--Cold shoulder‖: (p.9)

7-Conclusion

For any translation of metaphor, the translator is obliged to know the socio-cultural basis of the target language so that he could process cognitively the representation of this metaphor. But if the cultural bases of the source and of the target do not meet, and if absences are found and no possibility of conceptualising it, what can we do? Sometimes, in a literary text, metaphor is beyond any cultural manifestation: what seems to be culture of a society is no more a culture of society, since the artist transfers, through deviation, its common meaning into an uncommon one. And here, it is for the reader/translator to guess through interpretation in order to reach the aim of the artist. In other words, the more the cultures of the Source Language and the Target Language are similar, the more the translator job becomes easier and the transfer manageable. The more they converge, the more the transfer fails.

The translator should keep in mind that knowing more than one language is not the only good way to translate, but he should consider any language and its specificity and the context of metaphor within the literary text: one word could be different in meaning in different contexts in the same language because of the intentionality of the writer, who never means what he says and the meaning you get from your understanding could, in no way, be the only cognitive operation that could generate this meaning. That is, metaphor is not only restricted to rhetoric; it has become a means where the artist conceives his perception of the world: the macrocosmic in general and the microcosmic in particular. So, the translator should be trained and have theoretical basis of literary criticism so that he could ‗reach' the perception of the world and the way it is conceived in the text.

  الصفر نقطت من البدايت 3--Rule of thumb‖: االبها قاعذة م --عمليت قاعدة 4--Blood is thicker than water‖: الذم الماء من أثخن ---إسناد مصدر القرابت 5--Dog days‖: أ كلب يام الحرارة أيام أشذ :