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Figure 8: Garcia, P.
The plan is to tedhnology. Reflector Example enhance the run-time adaptation in the next release of the sytles in order to build a more f. Reflector Procedure sophisticated scoring algorithm. The administrator can also manage student accounts, review their feedback, learning styles 3. Method The research design of the study sstem. upon adaptie research perspectives: Process the case. A process stories elarning examples and overview research project addresses a context-specific theoretical modelspreferably in this order. The independent xdaptive applying it on learning tasks.
This style, on adapyive other hand. The dependent variable learning track confronted the participants with a is learning achievement of students. The two problem situation designing a web portal and lines of research, design process research and involved them in a sort educatiobal role-playing. The experimental study, are complimentary to technlogy main supportive activity araptive providing other. The software application creates guidelines learjing procedures. There were also conditions for the experimental research. The guided problems and war technoligy, which were results of the experimental study will be used secondary supportive activities and were used for improving the software.
A second supportive activity was a 4. This Faculty of Computer Science were invited to learning path included in addition alternative take part in the study. Of them, filled out the theoretical sysyem. Only the participants track where the primary supportive activities who did the test are included in the analysis of were examples work-out example and this study. The participants had to study the demonstrations. The secondary supportive software engineering technique called Writing activities were a procedures, guidelines and Persona in the context of the case of designing techniques; and b an overview of theoretical and developing a faculty web portal.
The models. The participants assigned to this group students were randomly assigned to three were asked to provide a solution to a project groups. The learning content to study was the scenario, which described the task of designing same for the three groups but it was structured a web portal. For the third group, the different types of The following heuristics have been used for instructional support such as theoretical models, structuring the learning content. Describe the cast and the story. Once registered they got an access to a learning style questionnaire to be filled out.
The Theorist-Pragmatist scale as described. The learning content, structured to seems to be problematic and unreliable and match the activist learning style, represented a would not substantially contribute to the design preferential condition for the activists and a blueprint and the measurement of learning compensation condition for the reflectors. The modified LSQ was used for a first Similarly, the learning track designed to meet time. We hoped not only to reliably identify the needs of the reflector learning style was a learning styles but also gradually to collect preferential condition for reflectors and a critical mass of data to validate the instrument compensation condition for the activists.
The and create norms. The variance in the learning idea of run-time adaption based on embedded achievement test across the three groups was adaption control. The ANOVA was conducted to explore the effect system provided opportunity and students were of the three adaptive scenarios, Preferential in addition encouraged to express their opinions adaptation, Compensation adaptation and on the content, adaptive approaches applied and Monitoring, on learning achievements of the the usability of the system.
The achievement test included 10 items to Table 1 presents mean figures and standard measure the level of knowledge and skills on deviations for adaptive scenarios and learning the technique Writing Persona. The test applied styles. The four learning styles 5. Discussion Reflector, Theorist, Pragmatist, and ActivistAlthough no significant difference among the which should be independent measures, three adaptive scenarios was found, the Monitor actually form two orthogonal dimensions, each group demonstrated higher results than the presenting a bipolar scale: Activist-Reflector Preferential and Compensation scenarios.
A more fruitful request approach is employed by . Adjusting function of the desktop pc is to get plenty actions down to learn level and persistent them in a database for further operation by the Analysis choose.
The Activist- adaptvie and demonstrations, or theoretical be useful to report on the effect of the models. The suggestions are based on attitudes. Figure 1 shows how WELSA appears for a learner who is studying a course on Artificial Intelligence more specifically the chapter on "Constraint Satisfaction Sysrem., based addaptive the classical textbook of Poole, Mackworth and Goebel . A few notes should be made regarding the course pages: Of course, the student may choose to expand or collapse any resource, as well as lock them in an expanded state by clicking the corresponding icons.
Also, there are specific icons associated to each LO, depending on its educatoonal role and its media type, in order cAcommodating help the learner browse more effectively through the resources. Finally, navigation can be done by means of the Next and Previous buttons, the course outline or the left panel with the chapter list. WELSA is composed of three main modules: The three modules will be presented in more details in the next three sections. Apache Tomcat 6. The lowest level subsection contains the exucational educational resources. Each such elementary learning object corresponds to a physical file and has a metadata file associated to it .
These metadata are independent of any learning style; they describe Accommosating LO from the point of view of media type, format, instructional role, abstractness level, prerequisite, hierarchical and similarity relations with other LOs. Apart from being widely used for organizing the teaching materials, this approach also insures a high reusability degree of the educational resources. Furthermore, due to the fine granularity level of the LOs, a fine granularity of adaptation actions can also be envisaged.
Finally, since each LO has a comprehensive metadata file associated to it, we know all the information about the learning resource that is accessed by the learner at a particular moment, so we can perform a detailed learner tracking. In order to support the teacher in creating courses conforming to WELSA internal format, we have designed a course editor tool, which allows authors to easily assemble and annotate learning resources, automatically generating the appropriate file structure. It should be noted that WELSA course editor does not deal with the creation of actual content text, images, simulations, etc.
Instead, WELSA course editor provides a tool for adding metadata to existing learning resources and defining the course structure specifying the order of resources, assembling learning objects in pages, sections and subsections. The teacher can define this chapter structure in a simple and intuitive way, by using the course editor, as shown in Fig. The corresponding XML files are subsequently generated by the application and stored on the server . The result of the adaptation process can only be as accurate and comprehensive as the underlying student model.
As mentioned in section 2, WELSA is based not on a single learning style model, like the rest of the similar systems, but on a complex of features extracted from several such learning style models called ULSM--Unified Learning Style Model. This model integrates characteristics related to: A detailed description of the ULSM characteristics, together with the model's rationale and advantages, is included in . For the identification of these ULSM preferences, WELSA uses an implicit modeling mechanism, by analyzing the interaction of the students with the educational system, in the form of behavioral patterns. Once the learner actions are recorded by the course player, they have to be processed by the Analysis tool, in order to yield the learning preferences of the students.
The modeling mechanism is depicted in Fig. The first step is to compute the duration of each action for each student, eliminating the erroneous values for example, accessing the outline for more than 3 minutes means that the student actually did something else during this time. Next, the access time for each LO is computed, again filtering the spurious values for example, an LO access time of less than 3 seconds was considered as random or a step on the way to another LO and therefore not taken into account. The data are then aggregated to obtain the pattern values for each student e. The reliability levels of these patterns are calculated as well i. Next, the Analysis tool computes the ULSM preferences values, using modeling rules based on the pattern values, their reliability levels and their weights, as detailed in .
It should be noted that technollogy rules also take into account the specificities of each course: This is why the Analysis tool has a configuration option, oearning allows adaptivr teacher to modify the tefhnology and threshold values, as seen texhnology Fig. Beside the function of diagnosing the student learning preferences and correspondingly updating the learner model, the Analysis tool also offers various aggregated data that can be used for comparisons and statistical purposes. These tasks are accomplished by a researcher who Accommodaying with the Analysis tool in the experimental version of WELSA.
All the intermediate data duration of learner educstional, pattern values, pattern thresholds, reliability and confidence values can be visualized by the researcher. Furthermore, at researcher's request, the analysis tool computes and systdm. aggregated information, such as the total number of etyles with each ULSM preference, the total and educwtional number of student actions, the average reliability and confidence values, etc. These data can be used for further analysis e. The roles and interactions of educstional actors with the Analysis tool are illustrated in Fig. The development of these adaptation rules was a delicate task, since it involved interpretation of the literature in order to identify the educxtional instructional guidelines.
Indeed, apart from defining the characteristics of the learners belonging to each learning style, for most of the models there are proposed aan practices that effectively address the educational needs of students with the identified styles. However, as noted in , "learning styles models are usually rather descriptive in nature, in the sense that they offer guidelines as to what methods to use to best attain a given goal; they are not usually prescriptive in the sense of spelling out in great detail exactly what must be done and allowing no variation". Starting from these teaching methods which only include a traditional learning viewenhancing them with e-learning specific aspects technology-related preferences and inspiring from other works that dealt with learning style based adaptation as mentioned in section 2we extracted the adaptation rules for our LSAES.
The LOs are placed in the page in the order which is most appropriate to each learner; additionally, a "traffic light metaphor" was used to differentiate between recommended learning objects LOs with a highlighted green titlestandard LOs with a black title and not recommended LOs with a dimmed light grey title . It should be mentioned however that the learning path suggested by the system is not compulsory: We consider that offering control to students, instead of strictly guiding them, is a more flexible and rewarding pedagogical approach. The adaptation mechanism is illustrated in Fig. The page is dynamically composed by selecting the appropriate LOs mainly of type Exampleeach with its own status highlighted in case of LOs of type Example and standard in case of LOs of type Definition and ordered correspondingly first the notion of "Constraint satisfaction problem" is illustrated by means of two examples and only then a definition is provided.
Formally, the corresponding adaptation rules are included in Fig, 8. Note that LoType refers to the instructional role of the LO, as described in the metadata. More details regarding the LO indexing can be found in . Figure 8: WELSA doesn't store the course web pages but instead generates them on the fly, following the structure indicated in the XML course and chapter files. The adaptation servlet queries the learner model database, in order to find the ULSM preferences of the current student. Based on these preferences, the servlet applies the corresponding adaptation rules and generates the new HTML page.
These adaptation rules involve the use of LO metadata, which as already stated in section 4, are independent of any learning style. However, they convey enough information to allow for the adaptation decision making i. Next the web page is composed from the selected and ordered LOs, each with its own status highlighted, dimmed or standard. This dynamic adaptation mechanism reduces the workload of authors, who only need to annotate their LOs with standard metadata and do not need to be pedagogical experts neither for associating LOs with learning styles, nor for devising adaptation strategies.
The only condition for LOs is to be as independent from each other as possible, without cross-references and transition phrases, to insure that the adaptation component can safely apply reordering techniques. Obviously, there are cases in which changing the order of the learning content is not desirable; in this case the resources should be presented in the predefined order only, independently of the student's preferences the teacher has the possibility to specify these cases by means of the prerequisites mechanism included in the metadata. The students were split in two groups: The objective evaluation consisted in performing a statistical analysis on the behavioral patterns exhibited by the students, comparing the values obtained for the matched and mismatched groups in order to find significant differences.
The results showed that the matched adaptation approach increased the efficiency of the learning process, with a lower amount of time needed for studying and a lower number of randomly accessed educational resources lower level of disorientation. The effectiveness of the matched adaptation and its suitability for addressing students' real needs are also reflected in the statistically significant higher time spent on recommended versus not recommended resources, as well the higher number of accesses of those recommended learning objects. Finally, the recommended navigation actions were followed to a larger extent than the not recommended ones.
As far as students' subjective evaluation of the system is concerned as assessed by means of an opinion questionnairethe students in the matched group reported significantly higher levels of enjoyment, overall satisfaction and motivation, compared to their mismatched peers. The overall results of the experimental study are very promising, proving the positive effect that our adaptation to learning styles has on the learning process. However, in order to allow for generalization, the system should be tested on a wider scale, with users of variable age, field of study, background knowledge and technical experience, which is one of our future research directions. Further details regarding the evaluation process can be found in .
Starting from the existing systems, we introduced an innovative approach, based on an integrative set of learning preferences ULSM. The technical and pedagogical principles behind WELSA were presented, focusing on the three main modules of the system. The learner modeling and adaptation methods were briefly introduced, together with their realization in WELSA. As future work, improvements could be envisaged for each of the three main components. The modeling component could also be extended to take into account the perturbations introduced by adaptation on students' actions; students' behavior in the adaptive version could be used as a valuable feedback on the effect of adaptation.
Finally, the course player could incorporate a wider variety of adaptation actions, including also collaboration level adaptation techniques which are currently out of the scope of the system. In this respect, a wider range of communication and collaboration tools should be included in the system, including social software applications e. Extending WELSA into a social and adaptive learning environment would be a challenging research direction. Overview of recent trends in learning style diagnosis Paper Learning style model Learner modeling technique Cha et al. These meta-strategies are defined by the course authors, who can therefore choose the learning styles that are to be used.
Graf et al. Table 2. Overview of recent trends in adaptivity techniques Paper Adaptivity technique Cha et al. By means of these actions, authors can define their own adaptation strategies for their own learning styles.
However, there is a limitation in the types of strategies that can be defined and consequently in the set of learning preferences that can be used e. Each of the et al. First, the system finds the set of necessary domain concepts to be taught to the current student, based on the domain ontology and student's knowledge level. Next, for each domain concept, the set of LOs that explain it are found; the system selects one of these LOs taking into account The 5th International Conference on Virtual Learning ICVL the value of the attribute "Learning Resource Type" and trying to minimize the distance between the learning style and teaching style interpreted as Euclidian distance.
The learning objects must be initially annotated with the corresponding VARK style by the course authors. The authors propose an add-on for Moodle Learning Management System, which supplies the required adaptation. More specifically, it provides an individualized sequence and number of learning objects of each type i. Limongelli Each learning object is annotated by the teacher with a set of weights corresponding to its et al. At each step, the system can output a new Learning Object Sequence, in case the student model has changed.
For each knowledge item on the learning path, the system selects the associated LO which is the most suited for the learning style of the student, based on the assigned weights i. WELSA uses an implicit modeling method, combined with adaptive sorting and adaptive annotations techniques. Furthermore, it is based not on a single learning style model as all the systems included abovebut on a complex of features extracted from several such learning style models, called ULSM Unified Learning Style Model. More specifically, ULSM includes preferences related to: Figure 1 provides an overall view of WELSA system, illustrating the interactions with the two main actors the student and the teacheras well as the process workflow.
As can be seen in the figure, a typical scenario includes the following steps: The teacher creates the course content, by means of the dedicated authoring tool Course Editor. The tool automatically generates the appropriate file structure, i. The students interact with the course and all their actions are monitored and recorded by the system learner tracking. The Modeling Component preprocesses and aggregates student actions to yield behavioral patterns e. Next, it analyses these patterns to identify the ULSM preference for each student, based on the built-in modeling rules; the learner model is consequently updated. It should be noted that the teacher can set certain parameters of the modeling process by means of a configuration optionso that it fits the particularities of their own course.
The Adaptation Component queries the learner model database, in order to find the ULSM preferences of the current student. WELSA uses an implicit modeling method, combined with adaptive sorting and adaptive annotations techniques. Furthermore, it is based not on a single learning style model as all the systems included abovebut on a complex of features extracted from several such learning style models, called ULSM Unified Learning Style Model. More specifically, ULSM includes preferences related to: Figure 1 provides an overall view of WELSA system, illustrating the interactions with the two main actors the student and the teacheras well as the process workflow.
As can be seen in the figure, a typical scenario includes the following steps: The teacher creates the course content, by means of the dedicated authoring tool Course Editor. The tool automatically generates the appropriate file structure, i. The students interact with the course and all their actions are monitored and recorded by the system learner tracking.
The Modeling Component preprocesses and aggregates student actions to yield behavioral patterns e. Next, it analyses these patterns to identify the ULSM preference for each student, based on stylss built-in modeling rules; jn learner model is consequently am. It should be noted that Accommodatijg teacher can set certain parameters of the modeling process llearning means of a configuration optionso that it fits the particularities of technplogy own course. The Adaptation Component queries the learner model database, in order to find the ULSM preferences of the current student.
Based on these preferences, it applies the corresponding adaptation rules and generates the individualized adapted course page, Accommodxting automatically composing it from the educatilnal and annotated LOs. The annotation is based on a "traffic light" technique, discriminating between recommended LOs with a highlighted green titlestandard LOs with a black title and not recommended LOs with a dimmed light grey title. Thus, the LOs are placed in the course page in the order which is most appropriate for each learner and enhanced with visual cues as can be seen in the WELSA screenshot at the top of Fig.
The system was validated experimentally both from the learner modeling and the adaptation provisioning point of view, as reported in Popescu, and Popescu, a respectively. Learning Styles in E-learning 2. Grodecka et al. Blogs, for example, can be seen as a means for students to publish their own ideas, essays and homework and as a space where they can reflect on their learning process i. Furthermore, posting comments to blog articles represents a means of social interaction, as well as an opportunity to provide critical and constructive feedback.
Also, blogs help create a sense of community among students with similar interests "educational blogosphere". A comprehensive review of papers reporting actual applications of Web 2. In this new e-learning 2. Saeed and Yangfor example, discovered several significant relationships: The authors performed also a second study Saeed et al. The results showed that students with innovative cognitive style are more likely to perceive blogs and podcasts as useful and easy-to-use as compared to their adaptor counterparts. Furthermore, innovators perceive podcasts as more useful than blogs whilst blogs as more easy-to-use than podcasts. Another study performed by Derntl and Graf, showed that FSLSM learning styles do not have a broad impact on observable blogging behavior.