Emerging Web 2.0 for Adaptive Learning Management System Based on Student’s Learning Styles Samar Alkhuraiji13, Barry Cheetham1, Mohammed Abdel Razek2, Omaima Bamasak3 1
University of Manchester, School of Computer Science Oxford Rd, Manchester M13 9PL, UK 2 King Abdulaziz University, Deanship of E-learning and Distance Education P.O.Box 3614, Jeddah, 21481, SA 3 King Abdulaziz University, College of Computing and Information Technology P.O.Box 3614, Jeddah, 21481, SA
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Abstract. With the emerging of web2.0, the Internet and network based technologies have developed. E-learning platforms are now able to support more advanced collaborative environments and educational material by using web2.0 technology that emphasize online collaboration and sharing among users. While learning management systems are widely and successfully used to manage the delivery of self-paced online courses, they do not tailor content delivery mechanisms to learner's preferred learning styles and needs. In this paper, we implemented a way of enhancing the capability of existing e-learning management systems Moodle, by introducing adaptivity with respect to the way the information is presented to the learner based on learner’s learning styles. Adaptivity means that the presentation style is personalized to the preferred learning style of each student. The modeling of learner’s learning styles is based on learning theories that have been proven by much experience, and provide appropriate assessment procedures. Keywords: e-learning, Moodle, adaptive learning management system, learning styles, web2.0
1
Introduction
The term Web 2.0 is associated with web applications that facilitate participation platforms by using web 2.0 basic characteristics such as rich, interactive, user-friendly interface or access information by applications used through a web browser. These characteristics impact on the web application used. Today Internet is used as participation platform to access information generated by institutions or by other users, to share information such as knowledge or opinions with others, to control information such as delete, correct or complete, and to manage information. However, most learning management systems present mainly pre-designed fixed course material that is taken by learners whose concepts of learning, expectations, culture, background and
learning styles may be very different [1, 2]. This “one style fits all” approach means that all learners are expected to adopt the same learning style as dictated by the elearning environment which does not cater for the individual differences of learners [3-6]. Personalization in e-learning can modify the material and its presentation according to individual needs and characteristic behavior, as assessed by questionnaires and on-line monitoring of study patterns [7, 8]. The term ‘adaptive’ means that different ways of learning, e.g. learning styles, can be accommodated by the same elearning system. According to Brusilvosky [9], "Adaptive hypermedia (AH) systems build a model of the goals, preferences, and knowledge of each individual user, and use this model throughout the interaction with the user, in order to adapt to the needs of that user". It is already well known that such adaptivity can improve the progress and success of learners [6, 10]. More investigations about this matter have been published in previous research work [11] Learning theories state that there are characteristics within the personalities of most people that predispose them to particular ways of learning. Among these learning theories are Kolb’s learning style theory [12], the Dunn and Dunn learning style model [13], and the Fielder-Silverman theory [10]. The Fielder-Silverman theory is associated with a ‘learning style assessment instrument’ that was originally designed for traditional classroom learning in the context of engineering education [10]. The instrument is now available on the Internet, as an on-line questionnaire [14] that may be completed by any user to obtain a standardized assessment of his/her learning style preference. It has been widely used in published research on adaptive e-learning systems to obtain a ‘static’ assessment of learning style that does not change during the learning process. It is also used to provide an initial assessment of learning style in ‘dynamically’ adaptive e-learning systems that subsequently modify the assessment according to the user’s progress and responses to the current presentation style as monitored by the system. [15] Ccommercial learning management systems (LMSs) such as, BlackBoard [16], and Moodle [17], are now widely used for managing the delivery of self-paced online (elearning) courses, and support more advanced collaborative environments. They provide better authoring tools, sophisticated course management facilities and much more convenient access to course material and assignments. Administrative functions are provided for managing and allocating learning resources, dealing with student registrations and monitoring student progress and performance. However, most current LMSs contain fixed pre-designed courses. [18]. They mainly focus on teaching from a general point of view, without catering for individual learning styles [19, 20]. The provision of personalized learning mechanisms for individual learners in elearning environment is still an unsolved research problem [21]. Several adaptive web-based educational systems exist in the research literature but few of them incorporate adaptive learning systems [4]. This work aims to introduce multiple delivery mechanisms, with different formats such as text, video, images and audio. The concept of adaptivity will be extended Moodle platform capability to choosing between different delivery mechanisms. The assessment of learning style and behavior made according to Felder and Silverman learning styles model (FSLSM). The model will be initialized according to the results
obtained by the student in the Index of Learning Styles Questionnaire (ILS) to obtain the student learning style according to FSLSM, and then store in the student model to fine-tune the course and presented according to student’s learning styles.
2
Learning Styles Model
Messick [22] defined learning styles as "characteristic modes of perceiving, remembering, thinking, problem solving and decision making". Coffield et al [23] identified 71 models of learning style and classified thirteen of them as the most important because of their common use, theoretical significance in the research field, and their influence on other learning style models. Among the most important and influential models is that by Kolb [12] ,Honey and Mumford [24] and the Felder-Silverman Learning Style Model (FSLSM) [25]. Our research will concentrate mainly on the FSLSM which classifies an individual’s preferred learning style according to the ways he/she processes, receives, perceives and understands information, which define the four dimensions of the model. For each dimension, there is a sliding scale which quantifies the extent to which a) processing is active as opposed to reflective, b) reception is visual as opposed to verbal, c) perception is sensing as opposed to intuitive, and d) understanding is sequential as opposed to global. The online Index Learning Style (ILS) instrument (questionnaire) developed by Felder and Silverman characterizes a person's learning style by quantifying each of the four dimensions by a ‘score’ in the range -11 to +11.
3
Moodle Learning Management System
In this work we used Moodle to develop a personalized LMS based on student’s preference learning styles. We selected Moodle because it is open source learning management system and very popular among educators as a tool for creating online dynamic web sites for their students. Despite Moodle being one of the most popular used learning management systems, but it has limited or no support for personalization [26] . Support is provided in terms of user interface and the ability to personalize the environment by selecting different themes. In terms of features, Moodle users can add a number of ‘plug-in’ software modules available on its website. In Moodle’s new version 2.2, teachers can design the course sequences path based on ‘Condition Activities’. In other words, the students can jump between the course pages based on condition settings that are specified by the teachers. The student answers certain questions correctly then he/she will jump to certain course pages. It is totally teacher’s responsibility to define the learning path.
4
Planning of the Experiments and User Trials
The experiments will not be based on whole courses. We assume that a course will be presented as a series of ‘units’ or ‘learning objects’, each of which is devised to con-
vey a single lesson or multiple lessons. Each ‘lesson’ is broken down into concepts and each ‘concept’ is broken down into sub-concept we called ‘Atom' where an Atom deals with one aspect of the concept in one particular way. An Atom could be a statement that has to be clearly understood, an example illustrating the statement, an exercise that must be completed satisfactorily, a requirement to discover a fact from references, and there are many more possibilities. Each Atom can be represented in four media type (text, audio, image, and video). For each Atom, there will be some assessment. A numerical assessment will be recorded for each Atom. Once all the Atoms have been taken, it is considered that the lesson has been completed. The way learning objects and its Atoms components are constructed will be illustrated by the following diagram Fig 1. The experiment aims to explore the value of dynamic adaptivity where this means that the learning assessment is updated as the student progresses through a ‘lesson’, from one Atom to the next.
Fig. 1. Course Structure
5
Methodology to be Adopted for Experiments
The volunteer participants will be divided randomly into two groups ‘static’ and ‘dynamic’. Each member of the ‘static’ group will be presented with material matched to his/her learning styles and the assessment of learning style will not change. Each member of the ‘dynamic’ group will be presented with material initially in a style matching his/her preliminary learning style assessment. However, during the learning process the assessment for each member of the dynamic group will be allowed to change depending on the measured response to each Atom (or some of them) a right or wrong answer will be given an appropriate mark. If the student assessment shows
that he/she didn’t understand, the adaptive system will be present the student with the sequence path, according to machine learning algorithm that diagnostic the student behaviour and predict the student's learning style. If the student assessment shows that he/she understood, the adaptive system will go on to the next Atom and present it with the same learning style as used for the previous Atom. We will apply the FSLSM in two dimensions (verbal-visual and sequential- global), just to simplify the experiments. The content will be present to the student in 4 presentations according to student learning style's scores got it form index learning style questionnaire (ILS) Visual – sequential presentation The atoms content will be presented in visual image and in linear steps, with each step following logically from the previous one Visual – global presentation The atoms content will be presented in visual image and organised through hyperlinks. The topic will be presented as a big picture and the students can explored the atoms through hyperlinks. Verbal – sequential presentation The atoms content will be presented in text format and in linear steps, with each step following logically from the previous one Verbal – global presentation The atoms content will be presented in text format and organised through hyperlinks. The topic will be presented as a big picture and the students can explored the atoms through hyperlinks.
6
Static Adaptive Mechanism
The ‘static adaptive’ system aims to organise the course content in a sequence of steps that match to student preferred learning style. Each student of the static group will be presented with material matched to his/her learning styles. The assessment of learning style will not change during the whole course. Several different approaches have been proposed in the literature for adaptive learning systems and more common approaches start the initial states randomly or based on the idea from the instructional design designer. The published Arthur system [27] assumes four leaning styles: auditory, visual, tactile or a combination of these styles, and there is suitable course content sequences for each style. When student first time enters the system, the system selects the student’s learning style randomly. Then, the course content is delivered to student randomly. After that, the system monitors student’s learning process and base on student’s evaluation, the system updates student’s learning styles with another random style. The Arthur system presents next course content based on student’s latest learning styles. The learning styles model that supported by Arthur system are not based on any educational learning style models, is just preferences and selected randomly by the system. In our static adaptive system, the course’s sequences organised base on a group of teachers from the same field collaborate to create a course sequences, which is similar to a syllabus, for the course content. In our static adaptive system, we ex-
tend teachers’ and students’ features in Moodle to generate course content sequence automatically based on experiences of many teachers to accommodated to students’ learning styles. Our adaptive Moodle used teachers’ experiences and their practical skills to arrange student’s learning path sequences in the way that accommodate to student’s learning style. By this way, we are not let the system selects randomly the student learning path. The selection will be based on the teachers’ practical experiences. The first step in developing our static system is to find the best way to determine the course content sequences that suit to each student is preferred learning style. For this aim we firstly asked several teachers from the Computer Science department in KAU University to fill in ILS questioner to determine their learning style. Secondly, we have integrated a new engine to the adaptive Moodle system called ‘teacher personalization engine’. This engine allows teachers to arrange the course content Atoms’ in the sequences that fits to their learning styles and associate each Atom with suitable media type. All the teachers’ selection stores in teachers’ database. Then, the 'generate engine’ takes teachers' selection to generate the best common sequence’s path that has been chosen by most of the teachers. Thirdly, students are asked to fill in the ILS questionnaire when they log into the system for the first time and the student model is initialized correspondingly. After obtaining the student learning style, the adaptive Moodle system uses that information to adapt the sequence learning path that match to student's learning styles. The student’s learning style stores in the student model and the personalised course presents to student. When the student has finished all the learning, some questions are offered. These final questions are based on all of the prior learning content to assess students’ knowledge. Fig 2 explains the idea.
Fig. 2. Adaptive Moodle System
7
Experiment Pilot Study for Adaptive Moodle System
The experiment was organised at King Abdul-Aziz University/College of Computing and Information Technology in April of 2012. The participants of the pilot experiment were 9 senior students who had taken statistics and probability. From those 9 students participating in the pilot study, the results of 1 student were recognized to be unreliable because the student had very low scores in the tests and the time that was taken in learning the case study’ Bayes Theorem’ was very short. The pilot study designed to test the feasibility and flexibility of using our adaptive Moodle system. The first objective of this pilot study was to test the validity of the system and to see if the system is able to present an adoptive course content base on student’s preferred learning styles. Second, to check the flexibility of our topic that has been presented by the adaptive Moodle system, in particular, their use of the media components. Third, measure the experiment timeline and to see if students could accomplish the topic content in a reasonable time. In this experiment three performance parameters were evaluated: the score of the student on the test, the time taken for completing the test and the amount of time spent on learning the Atoms content. All these parameters can be obtained from the system and students log files, either directly or after some preprocessing. The experiment results show that five students are having visual-global learning style, one student has visual-global style, one student has verbal-sequential, and one student has verbal-global. The average time the students spent in taken the test is 43 minutes, in learning the content is 36 minutes, and in test score is 66/100. The obtained results showed that the average time in learning the content is relatively short to fully understand the presented content. The results showed that 2 students test score is 80/100 and the time they spent in learning the content is 45 minutes and in the test is more than 50 minutes. For the student who scored is 40/100, she spent 15 minutes in the test which is not enough to answer 10 numerical questions. For the rest four students, two got 70/100 and two got 60/100. The overall pilot study results are acceptable because the topic itself was new for all the participants and purely scientific. The students’ opinion on the design and the implementation of the adaptive Moodle system is clear and understandable.
8
Dynamic Adaptive Mechanism
According to Felder-Silverman Model, each learning style dimension score will be in one of three interval scales (1-3, 5-7, and 9-11).Each learning style such as visualsequential, the visual dimension scores could be in one of the three intervals scales and sequential dimension score could be in one of the three interval scales. In this case, we will have 36 learning styles from for the two dimensions: verbal/visual and sequential/global. Fig 3 below shows the ILS scores’ intervals. The variable ve1 represents to verbal learning style in interval 1-3, and ve2 represents to verbal learning style in interval 7-5, G2 represent to global learning style in interval 5-7 and so on.
Student learning styles can be represented by any pair such as (ve1, s1). The state space for student learning styles can be represented by the following set: Learning styles state space = { (ve1, s1), (ve1, s2), (ve1, s3), ( ve1, G1), ( ve1, G2), (ve1, G3), ………..(vi1, s1), (vi1, s2),……, (vi3 ,s1),…….,….., ( vi3, G3) } So, the total learning styles = 36 learning styles In our case study ‘Bayes Theorem’, we have 3 concepts and each concept has 4 to 2 sub-concepts, which we named by Atom. Totally, we have 10 sub-concepts or Atoms. We need to produce 360 Atoms to define all the learning styles. For example, the style (ve1, s1) can be represented it by (Audio, in sequential way). The aim of the dynamic adaptive system is to present the right learning sequences path to the right student. The question here is “How to select the best learning path?” We will use Machine Learning algorithms (ML) to answer this question. In sequential style, we want the ML finds the optimal learning paths among the entire successful learning paths. In global style, we want the ML to find the best items or Atoms that have been chosen by most students and succeed
Fig. 3. ILS Scores' Intervals
9
Future work
We have presented the analysis of the first results of the pilot study of adaptive Moodle system based on students’ learning styles. The obtained results demonstrated the potential and the necessity of further experimental on a larger number of participants in order to come up with truly convincing results. In this work, we proposed the idea of static adaptation system. We designed and implemented the proposed static adaptive system by extended Moodle version 2.2 features. Our ongoing work devoted to complete the design and the implementation of the dynamic adaptive algorithm using machine learning algorithms. In future, we are planning to analyze the results of the two experiments ‘static’ and ‘dynamic’ adaptive systems to show the effectiveness of dynamic and static adaptation.
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