International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923 – Vol 17 No 05 (2023) Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? How Learning Styles Can Withstand the Demands of Mobile Learning Environments? https://doi.org/10.3991/ijim.v17i05.36403 Yassine Zaoui Seghroucheni1(), Mohammed Chekour2 1 Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco 2 Ibn Tofail University, Kenitra, Morocco y.zaoui@um5r.ac.ma Abstract—The contribution of learning styles is undeniable in the educa- tional field. Several learning styles exist in the literature. Their use in the field of e-learning remains limited to certain learning styles only. With rapid techno- logical development, their use in e-learning systems has proven its worth, par- ticularly in terms of personalizing learning paths. But with the transition to mo- bile technologies, it has become necessary to study how much use can be made of these learning styles in a mobile environment. In this paper we spread out the most relevant learning styles, while putting the focus mainly on the Felder- Silverman learning style models, since they remain the most used ones in adap- tive learning systems, and then we will answer an essential question concerning to what extent the adaptation according to the learning styles of Felder- Silverman remain possible in mobile environments. Keywords—learning styles, mobile learning, Felder and Silverman learning styles 1 Introduction Learning styles (LS) are a key component of the learning process according to re- searchers and educational theorists, and their incorporation into the classroom has the potential to facilitate learning. Felder [1] for example, contends that students who strongly favor one particular LS may experience learning challenges if the classroom setting does not support their desired LS [1], [2]. Theoretically we can state with confidence that the inclusion of LS makes learning considerably simpler and more effective. As a result, students that don't adhere to this paradigm may struggle with their academic progress. With the appearance of adaptive learning systems, much attention has been paid to LS, since they are largely based on personalizing the learning process. Certainly these systems have been able to achieve the goal of personalizing learning paths, however we have recently witnessed a rapid evolution in mobile technologies, hence the need to ensure learning in these environments too. Thus the question that arises is to what extent is it possible to achieve personalization of learning paths based on LS in mo- 84 http://www.i-jim.org https://doi.org/10.3991/ijim.v17i05.36403 Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? bile environments, knowing that the technical characteristics of mobile platforms may not be adequate with most of the LS dimensions. The learning objects format and the didactic resources types are two factors that learning styles place a strong emphasis on. The format of the learning objects is where the issue is present. Since connectivity and battery life are the two factors that make mobile settings unique, we will inevitably encounter a problem where favoring bat- tery life and connectivity will prevail over the recommendation of learning object according to the learning. This paper is structured as follow: Section 2 discusses the most significant and used LS. Section 3 presents the adaptive learning systems which use LS as an element of adaptation. Later in section 4 we will be putting the focus on the context awareness learning, mainly in mobile environments. Then in section 5, we will analyze one spe- cific LS which is the Felder-Silverman model. We'll investigate its applicability in a mobile setting as well. In section 6 we will discuss the finding of the study. Finally, we will make some judgments about the application of LS in mobile environments. 2 Learning styles The LS area remains complicated since it is influenced by so many aspects, leading consequently to different concepts. Several LS exist in the literature; each one is of- fering multiple classifications of learning. The authors in [3] have identified 71 learn- ing style models and classified 13 of them as the most relevant ones according to their impact and influence on the other models. We mentioned earlier that a large number of models of LS exist in the literature. In an attempt to reflect the point of view of LS theorists, [3] has classified those models in five categories: A first category is based on the fact that LS based on the four modalities: tactile, auditory, visual and kinesthetic. A second category which details the properties of cognitive structure. A third category refers to LS as a relatively stable element of a personality. In a fourth category, LS are described as being flexible and stable. A final category ex- tends LS to approaches and strategies of learning. Here in the following table, is a classification of the different LS. Table 1. A summary of learning styles, adapted from Cofield [3] Classes C1 C2 C3 C4 C5 Learning styles characteristics Type of personality relatively steady Linked to approaches and to strategies Complies with constitution Flexible and stable Structure cognitive Examples Myers-Briggs Entwistle, Grasha- Riechmann Dunn et Dunn, Gregorc Kolb, Honey et Mumford, Herrmann Felder and Silverman Pask iJIM ‒ Vol. 17, No. 05, 2023 85 Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? In the Table 2, we will describe the dimensions related to the 9 most commonly used LS models. These models were chosen based on the work of [3], with an empha- sis on their influence on other LS models. A crucial criterion has also been thought to be the usefulness of LS models in technology-assisted learning. Table 2. The dimensions related to the different learning styles Learning Style Dimensions Personality type according to Myer- Briggs [4],[5] Sensing/intuitive, thinking/feeling, judging/perceiving, extro- vert/introvert Pask Model [6],[7],[8],[9],[10],[11] Serialist learners - Holistic learners - Versatile learners. The deep approach and the surface approach from Entwistle [12],[13],[14],[15] Deep learning and learning surface The Grasha-learning style model Riechmann [16] Competitive – Cooperative - Avoidance-oriented Participative – Dependent - Independent Dunn and Dunn's learning style [17],[18],[19],[20] Environmental variable - The sociological variable The emotional variable - The physical variable The psychological variable Gregorc's Mind learning style [21],[22] The concrete-sequential style - The concrete-random style - The abstract-sequential style - The random abstract style The learning style model of Honey and Mumford [24],[25],[26] The pragmatic style - The reflective style - The theoretical style - The active style Kolb's learning style model [23] The diverger - The Assimilator - The Converger The accommodator The FSLSM [27] Sequential/Global - Visual/Verbal Sensory/Intuitive - Active/Reflective 2.1 Personality type according to Myer-Briggs The MBTI indicator [4], is a psychological assessment tool determining the type of a subject psychology wise, following a method proposed in 1962 by Isabelle Briggs Myers and Katherine Cook Briggs. Other LS models are based on certain reflections from MBTI because it is not directly about learning but rather how the learner's per- sonality influences his method of learning. As a result, the MBTI provides key ele- ments for learning. According to Jung [5], the MBTI differentiates a person's type based on 4 dichot- omies: Extrovert / Introvert, Sensing / Intuitive, Thinking / Feeling and Judging / Perceiving. All combinations that lead to a total of 16 types are conceivable. 86 http://www.i-jim.org Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? Fig. 1. The Myers-Briggs Type Indicator 4x4 Grid Structure Extrovert / Introvert: it refers to the orientation of a person. An extroverted per- son's point of focus is things and people, whereas an introverted person prefers focus on ideas. Sensing / Intuitive: it is concerned with how people perceive information. Sensing people detect and perceive data, using their 5 senses, but intuitive people prefer to perceive data from their unconsciousness. Thinking / Feeling: The difference between thinking and feeling is based on how data is perceived. Judging/Perceiving: People with the judgment dimension prefer step-by-step structures and approaches, while people with a perception dimension tend to be more flexible and spontaneous, while keeping all the options possible. 2.2 Pask model In order to determine the various learning and thinking styles, Pask [6] looked at conversation patterns amongst individuals while establishing the text-conversation cycle theory. This theory states that a crucial procedure known as the teach-back technique, in which students teach their pairs, resulted in the classification of learners into three groups. [7]. Serialist learners: who promote the conversational progression of sequentially, through a precise structure? Prior to conceiving of a broad image, they have a tenden- cy to concentrate more intently on specifics and procedures. Holistic learners use a holistic learning approach, prefer to develop wide descrip- tions, concentrate on multiple facets of the subject at once, and employ intricate con- nections to link the data. iJIM ‒ Vol. 17, No. 05, 2023 87 Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? The majority of learning styles, if not all, can be learned by versatile learners. The Spy Ring History Test [8] and the Clobbits Test [9] are two tests that Pask has devel- oped. A few years later, [10]-[11] created a self-report survey to help people choose between the serialist, holistic, or adaptable learning styles. 2.3 The deep approach and the surface approach from Entwistle Research by Entwistle [12]-[13] distinguish between deep learning and learning surface. Deep learning corresponds to behaviors, where learners actively process information and use strategies elaboration and organization, rather than memorization strategies. Information processing support strategies such as strategies metacognitive, affective or managerial could favor or counter one or the other approach. The work of [14]-[15] also authorize this interpretation. In an in-depth approach, learners develop and organize their knowledge. They are strongly emotionally involved and use more resources to learn. It is the opposite when they opt for a surface learning approach. Learners use coping strategies memorization and reproduction of knowledge, they have an instrumental interest for knowledge; they are not emotionally committed and use minimally the resources they have. 2.4 The Grasha- Riechmann LSM The Grasha-Riechmann model developed by psychologists Grasha and Riechmann [16], classifies learners into six categories: competitive, cooperative, avoidance- oriented, participative, dependent and independent. Competitive: This group includes learners who acquire knowledge, to perform better than other learners in the class. They believe they should compete with others to be rewarded. They like to be the center of attention and to be recognized for their accomplishments in class. Cooperative: a typical category of learners who feel that they can learn, by sharing ideas and talents. They cooperate with teachers and enjoy working with others. Avoidance-oriented: Learners show little enthusiasm for learn and attend classes. They do not participate with the teachers and other learners in classroom activities. They are not interested because they are overwhelmed by what is happening in the classroom. Participative: good citizens in the classroom, these learners like to attend courses and take part in as many activities as possible organized in the framework of the course. In general, they are eager to get the maximum benefit from the course re- quirements, whether compulsory or optional. Dependent: Students learn only subjects that are required of them and exhibit min- imal intellectual curiosity. They look to the teacher and their peers for structure and support, and they anticipate that those in positions of power will establish the rules for how things should be done. Independent: specific to learners who demonstrate autonomy of thinking and have confidence in their learning abilities. They prefer to acquire the knowledge they deem 88 http://www.i-jim.org Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? important, they tend to work in an autonomous on course projects, rather than work- ing with other learners. 2.5 Dunn and Dunn's learning style The learning style model by Dunn and Dunn [17]–[18] was initially put forth in 1974, then it was improved upon and increased over time. Five variables, each de- pending on a number of variables, make up the model, which separates adults from children. Environmental variable contains temperature, sound, design and light. Factors linked to the choice for learning on one's alone, in a small group, or as a member of a team are included in the sociological variable. Parental and teacher motivation is a further consideration for kids. The emotional variable involves the following factors: compliance / responsibil- ity motivation, persistence and the need for structure. The physical variable is composed of factors concerning the preferred mode per- ception of information (visual, auditory, tactile/external kinesthetic, internal kinesthet- ic). Right or left hemisphericity, global/analytical preferences, and impulsive/reflective preferences are all included in the psychological variable. To detect the preferences based on the style model Dunn and Dunn learning, dif- ferent versions of questionnaires were elaborated. The Learning Styles Questionnaire [19] was established for children and exists in 3 versions. 2.6 Gregorc's Mind learning style The model of Gregorc [21]-[22] is grounded around 2 dimensions bearing on pref- erences for perception and organization. When it comes to perception, individuals may prefer an abstract or concrete way to perceive. Perception refers to the aptitude to procedure information using intuition. Physical, On the other hand, refers to the ca- pacity for sensory information processing. The dimension organization is interested of how a learner organizes, prioritizes and uses information in sequential, random or combination of the two. The interviews conducted with students revealed two dimensions that characterize the behavior of learners and that they consider complementary: concrete-abstract and sequential-random. So the tool says the Gregorc Learning Style Delineator, assesses four learning styles defined from the relative position on these two dimensions: the style concrete-sequential, the concrete-random style, the abstract-sequential style and the abstract-random style. The concrete-sequential style is characterized by a preference for what is practi- cal, orderly and stable. It favors the gathering of information, in concrete and practical experiences. The concrete-random style is characterized by a preference for an environment rich in stimuli and free from all restrictions. It is characterized also by a need to ex- periment with concepts and ideas by favoring a trial-and-error approach. iJIM ‒ Vol. 17, No. 05, 2023 89 Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? The abstract-sequential style is characterized by a preference for mentally stimu- lating, content-rich and organized presentations, as well as a force at the level of sym- bolic decoding, whether the latter is written, verbal or imaged. The random abstract style is characterized by a preference for an unstructured learning atmosphere, leaving room for freedom of expression, by a strong awareness of human behavior and an ability to interpret it. 2.7 Kolb's learning style model According to David A. Kolb published [23], there are four learning styles: The diverger prefers the phases of concrete experience and reflection on this expe- rience. He is interested in people and emotions. He has a sense of observation and he is adept at perceiving an object or problem from different angles. He enjoys innova- tive activities and has a fertile imagination and interests varied. He is interested in people and values feelings. He enjoys learning by experience. The assimilator: he prefers phases of reflection and conceptualization, abstract and theory of an experiment. The Assimilator likes to create patterns theoretical and is less interested than oth- ers in people and applications knowledge practices. They logically reorganize infor- mation and juggle ideas and theories. They appreciate the theoretical courses. The Converger prefers the phases of abstract conceptualization and theory of the experience and application of the idea/action. Convergers like to be practical and tend to be unemotional. Moreover, they prefer to deal with things rather than people. They prefer to solve problems whose solution is unique. They have ease in tasks techniques and decision-making. They enjoy projects and activities self-managed. The accommodator prefers the phases of concrete experience and implementation application of the idea based on this experience. Accommodators fit easily to new experiences and tend to find solutions. He learns manipulatively by performing tasks. He likes to be involved in the planning and carrying out activities, it works by trial and error rather only by logic; he tends to follow the thoughts of others rather than his own analysis. He easily accepts to take risks and he appreciates the small group exer- cises. 90 http://www.i-jim.org Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? Fig. 2. David Kolb's Learning Styles 2.8 The Honey and Mumford LSM Honey and Mumford [24] retain from Kolb [23] the idea of a four-phase experien- tial learning model that they name: the experience, the feedback on the experience, the formulation of conclusions and planning. According to them, each of the phases includes behaviors and own attitudes and is important to successfully complete the process of learning. But most people, through the successes and failures of behaviors in their attempts to learn, develop preferences which make them particularly "like" certain phases of the process. To the extent that these phases are favored by individuals, they define four learning styles, each of which corresponds to "one of the descriptions of attitudes and behav- iors that determine a preferred way of learning for an individual" [25]. In doing so, Bokoros [26] postulate the existence of four unipolar dimensions, rather than two bipolar dimensions as does Kolb [23]. This important difference between Honey [24] et Bokoros [26] in their ways of conceiving learning styles, could partly explain the weakness of correlations obtained by Bokoros [26] between learning scores Kolb's Style Inventory (LSI), first and second version and those in the Learning Styles Ques- tionnaire (LSQ) of Bokoros [26]. The four learning styles according to Honey [24] are: the active learning style that describes the behavior of the person who privileges, the attitudes and behavior specif- ic to the experience phase. The reflective style describes those of the return on experi- ence phase; the theorist style describes those of the phase of formulation of conclu- sions and the pragmatic style that describes those of the phase of planning. The active style is characterized by the taste for concrete involvement in an expe- rience, to dive into activity here and now. This taste is particularly stimulated when the experience has an element of novelty or challenge and that there is an opportunity to play an active role in interaction with other people. The active style is also marked by the desire to engage with people, to confront their ideas with theirs and to take up iJIM ‒ Vol. 17, No. 05, 2023 91 Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? challenges or solve problems as a team. It is also characterized by the presence of invention of ideas in the absence structural constraints or standards. The reflective style is characterized by the importance of hindsight and distance taken in relation to people and things. It is marked by prudence and careful thought before making decisions and acting. observation, listening, the exhaustive accumula- tion of data before issuing an opinion appear essentials, look back on events and re- view what happened are important behaviors. It is also characterized by the desire to take decisions without time constraints. The theoretical style is characterized by the search for logic and coherence in the organization of the accumulated information. It is also characterized by the a taste for analysis and synthesis, an interest in basic presuppositions and the underlying princi- ples, a valuation of the rational and objectivity. This taste is stimulated when it comes to understanding and explaining, exploring methodically, the links between ideas or by being confronted with systems, models or theories. The pragmatic style is characterized by an interest in the application ideas, theo- ries and techniques, with the explicit aim of validating operation. It is also character- ized by a marked preference for realistic and practical solutions, by the desire to make useful decisions and to solve concrete problems. Respond to a well-identified imme- diate need, find concrete benefits and see practical advantages are considered as im- portant dimensions of learning. 2.9 The Felder-Silverman LSM Felder-Silverman [27] characterizes learners according to four dimensions. They show how learners prefer to process (Active/Reflective), perceive (Sensory / Intui- tive), receive (Visual / Verbal) and comprehend (Sequential /Global) information. In terms of learning styles, these aspects are not particularly novel, but the way they describe a learner's learning style may be. While the majority of learning style models with two or more dimensions, such as those by Briggs and Myers [4], Gregorc [21]–[22], Kolb [23], and Honey [24], statistically determine the types of learners represented by these dimensions. Felder and Silverman [27] use measures from 11 to -11 for each dimension to describe learning styles (Only odd values). The accommodator dimension in Kolb's learning style model is comparable to the Active/Reflective dimension. Active learning involves actively engaging with, ex- perimenting with, and applying the subjects being studied. Reflective learners love to ponder and reflect on their learning resources. They prefer to operate alone or in a small group with pals when it comes to communication. The Sensory/Intuitive dimension is based on Briggs and Myers [4] and has simi- larities also with the accommodating style of the model Kolb [23]. Learners with a sensory learning style enjoy learning facts, using their sensory experiences as an es- sential source. They enjoy using conventional methods to solve issues and are fre- quently more detail-oriented. The visual/verbal dimension relates to the preferred form of input and distin- guishes between students who retain more textual representations, whether they are 92 http://www.i-jim.org Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? written or spoken, and students who remember better what they have seen (such as drawings, diagrams, flow charts, etc.). The fourth dimension, Global/Sequential, categorizes learners based on their modes of comprehension, either global or sequential. This dimension is based on the learning style model put forth by Pask [6], in which learners who study sequentially are referred to as serialist learners, while learners who learn globally are referred to as holistic learners. Sequential learners build their knowledge in discrete, logical phases. Fig. 3. Dimensions of Felder and Silverman Learning Style Model 3 Adaptive learning systems Adaptive learning systems belong to a very important class of systems, namely adaptive hypermedia. They have the vocation of personalizing the learning process according to the learners’ profiles. In order to achieve this personalization, it is neces- sary to collect information on the characteristics and preferences learning wise. Gen- erally, there are two methods of collecting information, a first so-called implicit method [28]-[29]-[30]-[31]-[32] which consist in observing the learner's behavior during the learning phase and then building an adequate profile. The second method is said to be explicit [33]-[34]-[32]-[35]-[36] and consists of offering learners forms in which they are invited to express their learning preferences. In this work we are only interested in systems that make use of explicit methods. In fact in the following table we summarize the most known adaptive systems which are based on the learning styles [37]. Most learning systems use Felder-Silverman learning styles. The reasons for the popularity of Felder's learning styles-Silverman are highlighted by Brown [38]-[39]. They justify this choice by the fact that the model fulfills most Criteria required by hypermedia systems, namely: ─ Learning styles and model computations should be quantified by the model, as in the case of Felder-Silverman and the index (ILS). ─ The model must have a high level of validity, reliability, and internal consistency in order to accurately measure the learning style. ─ The model must be compatible with a web-based adaptive teaching system. • Sequent ial / Global • Visual /Verbal • Active / Reflecti ve • Sensing /Intuiti ve Perc eptio n Proc ess Unde rstan ding Inpu t iJIM ‒ Vol. 17, No. 05, 2023 93 Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? ─ The model needs to be simple for college students to use. As Sangineto [39] has pointed out, FSLSM has undergone thorough testing and validation on a population of engineering students. Additionally, while other models may have theoretically sound foundations, FSLSM offers practical suggestions helpful for tailoring train- ing [39]. Table 3. Overview of ALS System Learning style CS383 FSLSM Manic Combination of Learning styles IDEAL Determined by the tutor Masplang FSLSM LSAS FSLSM (Sequential /Global) Iweaver Dunn and Dunn Inspire Honey and Mumford Tangow FSLSM AHA! Determined by the tutor ALS_CORR[LP] FSLSM 4 Context awareness learning In interactive applications, taking context parameters into consideration has be- come crucial, especially for applications whose context is dynamic. Similar to porta- ble and widespread computing. We need a better understanding of what an application is and what context is in order to be able to leverage context more effectively and construct apps more quickly. There are a number of definitions for the term "context," but the one adopted by [40] stands out as the most pertinent. It emphasizes that a context is any data that can be used to describe the circumstances of an entity, which can be a person, place, or thing, that is thought to be relevant to the interaction between a user and an applica- tion, including the user and applications themselves. The following Context Perception Model (UCPM), which is based on Albrecht Schmidt's writings, emphasizes the concurrent perception processes occurring in the user and the system. If they disagree, we develop systems with an awareness mis- match, in which the system behaves differently than what the users would anticipate. The context highlights four crucial components for learning systems, which are shown in Figure 4: 94 http://www.i-jim.org Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? Fig. 4. The context elements The context is built around four main components, as seen in the above diagram: the user, the device, the environment, and the activity. The primary characteristics of each element are illustrated in the following Table 4. Table 4. The context element attributes Context User Device Environment Activity Attributes Personal characteristics Connectivity Battery Localization Mobility Learning objects Different versions of the learning objects Objective In section 3, we have stated that most of the adaptive learning systems use the Felder-Silverman Learning styles. How adaptable are the LS of Felder and Silverman in a setting where the qualities are dynamically changing is the question that now has to be answered. 5 Felder-Silverman LS vs. Mobile environment We are interested in this section at studying the relationship between the device at- tributes (Battery and Connectivity) and the FSLSM. Table 5. Learning Object vs. Device requirement FSLSM dimension Corresponding Learning object Device Requirement Battery Connectivity Active Assessment, Exercises Low Low Reflective Examples, outlines, summaries, result, pages High High Sensing Examples, explanation, facts, practical material High Low Intuitive Definitions, algorithms Low High Visual Images, graphics, charts, videos High High Verbal Text, audio High High Sequential Step by step exercises, link pages High Low Global Outlines, summaries Low High Con text Device User Environ ment Activity iJIM ‒ Vol. 17, No. 05, 2023 95 Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? Finally, the learning objects in the previous table bring us to the most pressing is- sues: to what extent can we follow the offered recommendations regarding learning style in a mobile environment? [42] Should we be forced to abide by the recommen- dation even when the battery is low, and the connectivity is not fully assured? Fig. 5. Learning Object vs. Mobile attributes 6 Discussion It is clear that the learning object recommendation based on learning styles in mo- bile environment [42], [43], [44], [45] has some limitations. Going back to the Felder- Silverman learning styles, we have found that the visual/verbal dimension presents a crucial criterion with regard to the recommendation of learning objects. There is a problem with projecting this dimension onto mobile environments: Both aspects of the visual/verbal dimension need an instantly available connection and a fully charged battery. That brings us to the conclusion that the adaptation of the Felder-Silverman learning style can only occur in particular circumstances. It is necessary to have a very good connection as well as a relatively full battery, other than that, we risk com- promising the functioning of the device. If this is likely to happen, this will cause a problem with the recommendation. So in this case, the adaptation obviously will not take place and the system will switch to normal learning mode, namely the one size fits all paradigm, which considers learners as a single entity with the same learning preferences and obviously the same learning styles. 7 Conclusion Although the learning styles have been proven in online learning environments, es- pecially the Felder-Silverman learning styles because they satisfy several criteria 96 http://www.i-jim.org Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? related to online learning. Their effectiveness has recently come up against the tech- nical characteristics of mobile environments. We have seen how some dimensions of the Felder-Silverman learning styles, namely the visual / verbal dimensions, are not compatible with the technical characteristics, in particular those concerning the bat- tery and connectivity. This allowed us to answer the question of how conflicting it is to adopt Felder-Silverman's learning styles in mobile environments. Which lead us to conclude that the adaptation of content according to FSLSM is only possible under optimal conditions. 8 References [1] Richard M. Felder, North Carolina State University Linda K. Silverman, Institute for the Study of Advanced Development [Engr. Education, 78(7), 674–681 (1988)]. [2] Barbara A Soloman and Richard M Felder. Index of learning styles questionnaire. North Carolina State University, 2015 [3] Frank Coffeld. Should we be using learning styles? What research has to say to practice 2004? [4] Katharine Cook Briggs and Isabel Briggs Myers, 1977. New global versions of MBTI Step I and Step II assessments published, New version of MBTI online launched, 2019 [5] Jung, C. G. (1923). Psychological Types: Or the Psychology of Individuation. [6] Pask, G. A fresh look at cognition and the individual. International Journal of Man Ma- chine Studies, 1972, 4, 211–216. https://doi.org/10.1016/S0020-7373(72)80002-6 [7] STYLES AND STRATEGIES OF LEARNING By G. PASK (System Research Ltd., Richmond, Surrey), B,. J. educ. Psychol. 46, 128-148, 1976 Gordon Pask and BCE Scott. Cast: A system for exhibiting learning strategies and regulating uncertainties. International Journal of man-machine studies, 5(1) :17{52, 1973. https://doi.org/10.1016/S0020-7373 (73)80008-2 [8] Gordon Pask and BCE Scott. Caste: A system for exhibiting learning strategies and regu- lating uncertainties. International Journal of man-machine studies, 5(1) :17{52, 1973. https://doi.org/10.1016/S0020-7373(73)80008-2 [9] Noel J Entwistle. Styles of teaching and learning. Chistester: Wiley, 1981. [10] Nigel Ford. Learning styles and strategies of postgraduate students. British Journal of Edu- cational Technology, 16(1) :65{77, 1985. https://doi.org/10.1111/j.1467-8535.1985.tb00– 483.x [11] Ference Marton and N Entwistle. What does it take to learn? Some implications of an al- ternative view of learning. Strategies for research and development in higher education, pages 32{42, 1976. [12] Noel J Entwistle. Improving teaching through research on student learning. University teaching: International perspectives, pages 73{112, 1998. [13] Paul Ramsden and NJ Entwistle. Effects of academic departments on students ‘approach to studying. British Journal of Educational Psychology, 51(3) :368{383, 1981a. https://doi. org/10.1111/j.2044-8279.1981.tb02493.x [14] M Frenay, B Novell, Ph Armentieres, and M Romeoville. L'étudiant apprenant. Perspec- tives en éducation, de Boeck, Bruxelles, 1998. [15] Anthony F Grasha and SW Riechmann. Student learning styles questionnaire. Cincinnati, OH: University of Cincinnati Faculty Resource Center, 1975. iJIM ‒ Vol. 17, No. 05, 2023 97 https://doi.org/10.1016/S0020-7373(72)80002-6 https://doi.org/10.1016/S0020-7373(73)80008-2 https://doi.org/10.1016/S0020-7373(73)80008-2 https://doi.org/10.1016/S0020-7373(73)80008-2 https://doi.org/10.1111/j.1467-8535.1985.tb00%E2%80%93483.x https://doi.org/10.1111/j.1467-8535.1985.tb00%E2%80%93483.x https://doi.org/10.1111/j.2044-8279.1981.tb02493.x https://doi.org/10.1111/j.2044-8279.1981.tb02493.x Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? [16] Rita Dunn and Kenneth Dunn. Learning style as a criterion for placement in alternative programs. The Phi Delta Kappan, 56(4): 275{278, 1974. [17] Rita Dunn and Shirley Griggs. Synthesis of the dunn and dunn learning styles model re- search: who, what, when, where and so what {the dunn and dunn learning styles model and its theoretical cornerstone. New York: St John's University, 2003. [18] Rita Stanord Dunn, Kenneth J Dunn, and Gary E Price. Learning style inventory (LSI). Price Systems, Incorporated (PO Box 1818, Lawrence 66044), 1989. [19] SM Rundle and R Dunn. The guide to individual excellence: A self-directed guide to learning and performance solutions. New York, Performance Concepts International, 2000. [20] Anthony F Gregorc. An adult's guide to style: Gabriel systems, 1982. [21] Anthony F Gregorc. Gregorc on Style. Gabriel Systems, Incorporated, 1982. [22] David Kolb. Experiential learning as the science of learning and development. Englewood NPH, editor1984, 1984. [23] Peter Honey and Alan Mumford. Using your learning styles. Peter Honey Maidenhead, 1986. [24] Peter Honey, Alan Mumford, et al. The manual of learning styles, 1992. [25] Michael A Bokoros, Marc B Goldstein, and Mary M Sweeney. Common factors in _ve measures of cognitive style. Current Psychology, 11 (2) :99{109, 1992. https://doi.org/ 10.1007/BF02686832 [26] Richard M Felder and Linda K Silverman. Learning and teaching styles in engineering ed- ucation. Engineering education, 78(7) :674{681, 1988. [27] Vincenza Carchiolo, Alessandro Longheu, Michele Malgeri, and Giuseppe Mangioni. Ar- chitecture to support adaptive e-learning. International Journal of Computer Science and Network Security, 7 (1) :166{178, 2007. [28] Nabila Bousbia, Issam Reba Jean-Marc Labat, and Amar Balla. Analysing the relationship between learning styles and navigation behavior in web-based educational system. Knowledge Management & BIBLIOGRAPHIE E-Learning: An International Journal (KM&EL), 2(4) :400{421,2010. https://doi.org/10.34105/j.kmel.2010.02.029 [29] Silvia Schiano, Patricio Garcia, and Analia Amandi. E-teacher: Providing personalized as- sistance to e-learning students. Computers & Education, 51(4) :1744{1754, 2008. https://doi.org/10.1016/j.compedu.2008.05.008 [30] Sabine Graf and K Kinshuk. Providing adaptive courses in learning management systems with respect to learning styles. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, volume 2007, pages 2576{2583, 2007. [31] Paraskevi Tzouveli, Phivos Mylonas, and Stefanos Kollias. An intelligent e-learning sys- tem based on learner profiling and learning resources adaptation. Computers & Education, 51(1) :224{238, 2008. https://doi.org/10.1016/j.compedu.2007.05.005 [32] Zourmpakis, A. I., Papadakis, S., & Kalogiannakis, M. (2022). Education of preschool and elementary teachers on the use of adaptive gamification in science education. International Journal of Technology Enhanced Learning, 14(1), 1-16. https://doi.org/10.1504/IJTEL. 2022.120556 [33] Boyan Bontchev and Dessislava Vassileva. Courseware adaptation to learning styles and knowledge level. Edited by Anderson Silva, Elvis Pontes, page 1, 2012. https://doi.org/ 10.5772/29340 [34] Stephanie Jean-Daubias. Thi-thu-hong phan. Different levels of modeling for learner pro- files, 2009. [35] John Seely Brown. New learning environments for the 21st century: Exploring the edge. Change: The magazine of higher learning, 38 (5) :18{24, 2006. https://doi.org/10.3200/ CHNG.38.5.18-24 98 http://www.i-jim.org https://doi.org/10.1007/BF02686832 https://doi.org/10.1007/BF02686832 https://doi.org/10.34105/j.kmel.2010.02.029 https://doi.org/10.1016/j.compedu.2008.05.008 https://doi.org/10.1016/j.compedu.2007.05.005 https://doi.org/10.1504/IJTEL.2022.120556 https://doi.org/10.1504/IJTEL.2022.120556 https://doi.org/10.5772/29340 https://doi.org/10.5772/29340 https://doi.org/10.3200/CHNG.38.5.18-24 https://doi.org/10.3200/CHNG.38.5.18-24 Paper—How Learning Styles Can Withstand the Demands of Mobile Learning Environments? [36] Katsaris, I., & Vidakis, N. (2021). Adaptive e-learning systems through learning styles: A review of the literature. Advances in Mobile Learning Educational Research, 1(2), 124- 145. https://doi.org/10.25082/AMLER.2021.02.007 [37] Katsaris, I., & Vidakis, N. (2021). Adaptive e-learning systems through learning styles: A review of the literature. Advances in Mobile Learning Educational Research, 1(2), 124- 145. https://doi.org/10.25082/AMLER.2021.02.007 [38] Elvira Popescu, Costin Badica, and Philippe Trigano. Description and organization of in- structional resources in an adaptive educational system focused on learning styles. In Ad- vances in Intelligent and Distributed Computing, pages 177{186. Springer, 2008. https://doi.org/10.1007/978-3-540-74930-1_18 [39] Enver Sangineto, Nicola Capuano, Matteo Gaeta, and Alessandro Micarelli. Adaptive course generation through learning styles representation. Universal Access in the Infor- mation Society, 7(1-2) :1{23, 2008. https://doi.org/10.1007/s10209-007-0101-0 [40] Dey, A. Understanding and Using Context. Personal Ubi Comp 5, 4–7 (2001). https://doi.org/10.1007/s007790170019 [41] Qureshi, A., & Qureshi, N. (2021). Challenges and issues of STEM education. Advances in Mobile Learning Educational Research, 1(2), 146-161. https://doi.org/10.25082/ AMLER.2021.02.009 [42] Barianos, A. K., Papadakis, A., & Vidakis, N. (2022). Content manager for serious games: Theoretical framework and digital platform. Advances in Mobile Learning Educational Research, 2(1), 251-262. https://doi.org/10.25082/AMLER.2022.01.009 [43] Kalogiannakis, M., & Papadakis, S. (2020). The use of developmentally mobile applica- tions for preparing pre-service teachers to promote STEM activities in preschool class- rooms. In Mobile Learning Applications in Early Childhood Education (pp. 82-100). IGI Global. https://doi.org/10.4018/978-1-7998-1486-3.ch005 [44] Papadakis, S. (2020). Apps to Promote Computational Thinking Concepts and Coding Skills in Children of Preschool and Pre-Primary School Age. In Mobile Learning Applica- tions in Early Childhood Education (pp. 101-121). IGI Global. https://doi.org/10.4018/ 978-1-7998-1486-3.ch006 [45] Barianos, A. K., Papadakis, A., & Vidakis, N. (2022). Content manager for serious games: Theoretical framework and digital platform. Advances in Mobile Learning Educational Research, 2(1), 251-262. https://doi.org/10.25082/AMLER.2022.01.009 9 Authors Yassine Zaoui Seghroucheni is a professor of computer science at the faculty of Sciences, Mohammed V University in Rabat since 2020. He has several publications namely in the field of e-learning, adaptive learning systems and mobile learning (Email: y.zaoui@um5r.ac.ma). Mohammed Chekour is a professor of Information Technology at the College of Education and Training (ESEF) at the University of Ibn Tofail, in Kenitra, Morocco. He is the author of multiple publications in the field of education and pedagogy (Email: Mohamed.chekour@uit.ac.ma). Article submitted 2022-10-26. Resubmitted 2022-12-17. Final acceptance 2022-12-17. Final version published as submitted by the authors. iJIM ‒ Vol. 17, No. 05, 2023 99 https://doi.org/10.25082/AMLER.2021.02.007 https://doi.org/10.25082/AMLER.2021.02.007 https://doi.org/10.1007/978-3-540-74930-1_18 https://doi.org/10.1007/s10209-007-0101-0 https://doi.org/10.1007/s007790170019 https://doi.org/10.25082/AMLER.2021.02.009 https://doi.org/10.25082/AMLER.2021.02.009 https://doi.org/10.25082/AMLER.2022.01.009 https://doi.org/10.4018/978-1-7998-1486-3.ch005 https://doi.org/10.4018/978-1-7998-1486-3.ch006 https://doi.org/10.4018/978-1-7998-1486-3.ch006 https://doi.org/10.25082/AMLER.2022.01.009 mailto:mohamed%20chekour@uit.ac.ma