International Conference on Open and Online Learning. ..... are concerned with their own level of proficiency in information technology, access to .... MSc Thesis.
Learning and Perceptual Styles Consideration in the Design of Hypermedia Courseware for Distance Learning Mohammad Issack Santally Virtual Center for Innovative Learning Technologies University Of Mauritius Abstract: In this study, we carry out a survey of the preferred learning and perceptual styles of students at the University of Mauritius to find out about their learning preferences. This study forms part of a larger research project (HyperLearn) of the development and evaluation of a webbased adaptive hypermedia system for e-learning which keeps an evolving student model with a learning style component that presents the learner with learning material and tutorial activities based on the evolution of his individual learning style(s). We find that current distance learning environments including e-learning are not mainly designed for consideration of students’ individual learning and perceptual styles. We also find that some intelligent tutoring and adaptive systems also do not cater for these aspects in their student and tutoring model. Moreover, TRANSLAB, a system developed in-house shows no significant difference in student performance when compared with those who followed instructor-led courses. We therefore propose to develop an adaptive system that caters for individual learning and perceptual styles to decide for the tutoring strategies.
Introduction With the recent technological advances of the Internet, acquiring tertiary education is a dream that is becoming true for many students who previously would have needed enormous amounts of money or first class results to pave the way towards their desired career. The Internet has brought about, according to me a revolution in the educational field, more precisely in distance education. Traditional distance education helped remove many barriers to education due to its relatively low price and high flexibility in the study modes. Students were given the opportunity to study at their own pace while doing a full-time job. This also motivated mature students to resume their studies without getting back to the school bench again. Nowadays, in this technology driven world, a new concept of distance education is emerging. Some people call it e-learning, others call it online learning while some will term it as technology-enhanced or web enhanced learning. Using the web as only a new kind of delivery medium for educational materials does not add significant value to the teaching and learning process. The integration of technology in learning, needs to address the very important issue of enhancing the teaching and learning process, rather than just being seen as a new delivery medium. E-learning, if used effectively can help address the many shortcomings of the traditional distance education methods as well as the inherent problems the classroom teacher is faced on a daily basis with a classroom of learners with different learning and perceptual styles and competencies. In this study, we carry out a survey of the preferred learning and perceptual styles of students at the University of Mauritius to find out about their learning preferences. We carry out a user-centered evaluation of current e-learning and web-enhanced modules with the students and investigate how the present situation and infrastructure can be improved to enhance the teaching and learning process through flexible and adaptive tutoring. This study forms part of a larger research project of the development and evaluation of a web-based adaptive hypermedia system for e-learning which keeps an evolving student model with a learning style component that presents the learner with learning material and tutorial activities based on the evolution of his individual learning style(s).
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Distance Education and the learning process: A critical review The main defining feature of distance education is the separation of teacher and learner, usually in both time and space (Holmberg, 1989). This separation fosters noncontiguous communication (communication that occurs between the learner and teacher from a distance), which has to be mediated. Consequently, mediated communication becomes the second defining feature of distance education (Rumble, 1989). Mediated communication is an important feature in both e-learning and traditional distance education. Distance education is flexible and adaptable in that learners can study anywhere and anytime. The notion of flexibility and autonomy has been seen to denote independence among distance learners. Garison and Shale (1990) however, postulate that the notion of independence in the educational transaction in distance education seems to overshadow two-way communication between the teacher and the student. They state that the educational process is dependent upon sustained dialogue and negotiation between teacher and student. Traditional distance education methods, in fact, becomes quite close to buying a book and studying the contents to take an exam at the end of some time period. Students have a manual and just study the contents. The question we ask as extension to the concerns treated above is - How much do distance learning environments fit-in the students preferred learning styles? If it is so, then how can these learning environments adapt themselves to the learners to maximize learning and at the same time help them to become more competent learners? In a traditional classroom setting a teacher can use questionnaires to get the statistics and then tries to adapt his teaching to the students learning preferences and find new activities to initiate the students to other styles of learning. Again this is a very difficult task since in such situations, most of the time individual attention to students may be needed. This is exactly where we believe e-learning has the answers. Elearning, providing a framework for networked and technology-enhanced learning must not be looked upon as only a new medium of delivery but one that presents us with a high potential to enhance the overall learning process. The ability of e-learning systems to support hypermedia contributes in efficient delivery and better quality of education. Hypermedia is defined as the integration of a computer and multimedia, to produce interactive, nonlinear hyper environments with the flexibility and interactivity that contribute to active learning by the students (Lockard, Abrams, Many, 1997). Another feature that many researchers in the field are interested is about adaptability and adaptivity in webbased learning systems. It is well known that one of the characteristics a web-based educational system should have is adaptivity (Patel & Kinshuk, 1997) that is the ability to be aware of user's behavior so that it can take into account the level of knowledge and provide the user with the right kind of documents. This is achieved using a student model, which keeps track of the students’ actions and kinds of interactions with the system. However, from different research projects (Kinshuk, 1996; Kinshuk, Patel & Russell, 2000; Petsangsri, 2002; Cristea & Okamoto, 2002; Boojawon, 2003), we observe that either the adaptive intelligent tutoring systems did not help to improve students’ performance, or many authors only focused on the achievements in the building of the system itself without evaluating from a user-centered point of view. However, the common finding was that student responded well with the innovative approach of using computers in education.
Theory of Learning Styles The subject of how to help people to learn how to learn effectively has been an active research area over last decade (Mumford, 1982). Individuals have different learning styles, which indicate preference for particular learning experiences. Training programmes for learning strategies require a reliable and valid means for measuring students' deficits and progress (Kinshuk, 1996). A number of researchers (Schmeck, Ribich & Ramaniah, 1977; Entwistle, 1981; Weinstein, Schulte & Cascallar, 1985) have provided measures for successful implementation and evaluation of these programmes. Their inventories - Inventory of Learning Processes; Approaches to studying Inventory; and Learning and Study Strategies Inventory respectively - were designed largely to gain insight into the varied styles and strategies employed by students in their internalisation of cognitive material. However, the model which has stimulated most debate and research is that of Kolb (1976). More recently, Honey & Mumford (1986) developed an instrument, the Learning Style Questionnaire (LSQ), which they claim, is based on Kolb's model.
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The Kolb Theory of Learning Kolb's (1976, 1984) model combines the two bipolar dimensions of cognitive growth distinguished by many psychologists: the active-reflective dimension and the abstract-concrete dimension. The first ranges from direct participation to detached observation. The second ranges from dealing with tangible objects to dealing with theoretical concepts. Kolb used these polar extremes to define a four-stage cycle of learning. It begins with the acquisition of concrete experience. This gives way to reflective observation on that experience. On this basis, theory building or abstract conceptualisation occurs. The theory is then put to the test through active experimentation (Kinshuk, 1996). The cycle thus recommences since the experimentation itself yields new concrete experiences (Fig 1.0).
Figure 1: The Kolb model The Honey & Mumford Approach The Kolb model is the theoretical background to Honey & Mumford's (1986) Learning Style Questionnaire, which has four styles- Theorist, Activist, Reflector and Pragmatist (figure 2). The Kolb model describes learning as a continuous process which can be described in an endless loop. Each style is associated with a stage on the continuous learning cycle. People with Activist preferences, are well equipped for experiencing. People with Reflector approach, with their predilection for mulling over data, are well equipped for reviewing and reflection. People with Theorist preferences, with their need to tidy up and have 'answers', are well equipped for concluding. Finally, people with Pragmatist preferences, with their liking for things practical, are well equipped for planning (Honey & Mumford, 1986).
Figure 2: The kinds of learning activities to which people with each learning style are likely to respond well or poorly reflectors (I. Stanchev, E. Niemi, N. Mileva).
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Methods The research we present in this paper consists of a preliminary study for the HyperLearn Project designed to investigate effects of student learning and perceptual styles in an adaptive web-based hypermedia system for distance and technology-enhanced learning. We carry out two surveys in this phase: Learning Styles and Perceptual Styles. The learning style survey is based on the Honey and Mumford (1986) questionnaire that classifies learners in four types of categories: reflector, activist, pragmatist and theorist. The Perceptual Styles Survey is carried out to find whether learners prefer information to be presented orally (auditory), visually or in a kinesthetic form to them. We identify the relationship between learners’ preferred learning style with their perceptual style and these information form part of our criteria in evaluating the way our online courses are developed and delivered. We finally administer a questionnaire survey with the students following online modules to get their feeling of the system. We evaluate through the questionnaire the ease of use of the system, the user inferface and pedagogical aspects relating to the learning process from the learner’s point of view. We finally compare outcomes for students using our online courses which are mostly static and animated web pages with TRANSLAB, a computer-aided mathematics learning tool for teaching translation topics (Boojawon, 2003) developed in-house that simulates adaptive tutoring to find any differences in learner performances or preferences for the two systems. TRANSLAB uses a simple performance measuring approach to evolve its student model. The research questions we want to address with HyperLearn are as follows: 1.
Are the performances of student learning significantly different when using a computeraided tutoring package significantly different when compared to a human-led classroom?
2.
Are the performances and gains in student learning in current e-learning/online courses significantly different from classroom-led courses (quantitative and qualitative)?
3.
Are the gains in students’ knowledge and learning, in a learning/perceptual style adaptive hypermedia based learning environment (HyperLearn) comparable (or even better than) to classroom led and current e-learning courses.
4.
What are the views of the learners of the system towards the way information is presented to them in the learning environment in terms of: o o o
5.
Media Selection Interactivity Tutoring Strategies, compared to the feeling of users of current e-learning courses and the frequency of human intervention required by the system.
The strengths and limitations of the adaptive courseware design methodology employed in HyperLearn for courses across different fields.
Data Collection Phase Data for more than a hundred students out of approximately a thousand who enrolled at the University for the academic year (2003-04) has been collected online through the VCILT Test Centre (http://vcampus.uom.ac.mu/testcentre), an online infrastructure to survey students learning styles and feedback on online courses at the University. The students have been chosen from different faculties and departments since they all do the module “Introduction to Information Technology –CSE 1010E ” online. To ensure privacy of students’ records, each student is given a unique code throughout the study and their names and student ids are not used at any moment. To facilitate analysis of data, students are divided in blocks based on departments and faculties and personal data such as sex, date of birth and secondary school subjects are recorded for interpretation to be made from multiple perspectives.
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Results & Discussion In this study, we address the first two research questions wanted to address in the HyperLearn project.
Activist Reflector Theorist Pragmatist Sum %
Vision 10 26 7 6 49 48.0
Auditory 8 20 3 6 37 36.3
Kinesthetic 5 7 2 2 16 15.7
Sum 23 53 12 14 102
% 22.5 52.0 11.8 13.7
Figure 3: Learning and Perceptual Style Preferences of Students From the Learning style survey (n=102) as shown in figure 3, we find that Mauritian Learners have a tendency to prefer the reflector learning styles while many students prefer more than one learning style. On average, 52 % of students are reflectors, while 22 % are activists; 11 % and 13 % are theorists and pragmatists respectively. We also find 48 % of learners preferring to process visual information, 36 % auditory and 16 % preferring kinesthetic and tactile instruction. However, we found no significant relationship with a student’s particular learning style with his perceptual styles (figure 4). These differed from student to student. For instance, a student may be a reflector but he prefers kinesthetic activities. This seems strange but our study shows that this is possible although this can also form part of experimental error. Both factors therefore seem to be as important as each other when considering the design of hypermedia learning systems. Source Learning style Perceptual style Interaction Error Total
SS 976.4 615.3 351.4 670.0 2613.1
df 3 2 6 24 35
MS 325.5 307.6 58.6 27.9
F 11.65786 11.01902 2.098095
F table 3.01 sign diff 3.4 sign diff 2.51 no sign diff
Figure 4 : Two-way ANOVA analyzing effects of learning & perceptual styles The second finding of this study involves a performance evaluation of students who used the TRANSLAB tool both as first-time users and those who failed the subject after having once followed a classroom based instruction. The results indicate no significant difference between students who used TRANSLAB and those who followed classroom instruction. TRANSLAB proved useful, however, in motivating those who failed the subject when they followed a classroom led instruction. The finding in TRANSLAB (figure 5) correlates well with other research projects carried out in the field (Kinshuk, 1996) showing that there is not necessarily an increase in performance (p=0.613).
Between Groups
Sum of Squares Df 2.934 1
Mean Square 2.934
Within Groups
669.394
59
11.346
Total
672.328
60
F .259
Sig. .613
Figure 5: ANOVA Table
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The third finding of this research study is about students’ views of the current e-learning modules that are being delivered on-campus. Students generally like the idea of having online modules. They feel this gives them much more flexibility to learn the module and at their own pace. There is a minority of students, however, who are concerned with their own level of proficiency in information technology, access to computers and the Internet. They also find that the courses are generally of static nature and they would prefer print the pages rather than read the whole lot of text online. Furthermore, they do not find that online module helps them in performing better or make the learning process seem easier to them. This has also been confirmed statistically. These three preliminary findings, form the basis of further investigation through the HyperLearn project about the need of adaptive Hypermedia as a means to enhance the overall teaching and learning process especially in a distance learning environment where learner support through scaffolding strategies are very important to help the learner achieve the learning outcome, and where learner profiles significantly differ in terms of learning/perceptual styles and low/average achievers and gifted learners. From the study and a description of the results, we may summarize our findings as follows: 1.
Students in general, prefer the reflector learning style. However, we also find students with other learning styles or with more than one learning style.
2.
Students prefer mostly the visual and auditory mode of perception than the kinesthetic mode.
3.
There is no significant relationship (interaction) between preferred learning styles and the perceptual styles. This is based mainly on an individual basis for each learner.
4.
Computer-based learning does not necessarily increase student performance in the exams. However, there is increase motivation and preference of the students to learn with computers.
5.
Students do not prefer to read online but they would rather print the materials, which are mostly static pages although graphics is present. Reproducing print based material online therefore is not seen to be adding value to the student learning experience and to the overall learning process.
From these observations, although our current e-learning environment helps to promote technologyenhanced and networked learning (through different computer-mediated tools), it seems that student preferences for learning are overlooked in the design of courseware materials. Traditional instructional design methods do not seem to suffice in such an environment. For e-learning environments to be able to address the many shortcomings of traditional distance education methods, re-engineering of such environments is important to be able to address individual needs. The environment needs to be able to offer personalized instructions to the learners based on their individual preferences to be able to achieve the learning outcomes. Kolb model for learning (1984) describes a framework where the different types of learner characteristics can be addressed. Application of Kolb model in the different fields of study is yet to be investigated so that a generic instructional design framework can be developed for such systems. Learning, Perceptual Styles & Hypermedia: Application to e Learning Research demonstrates that both low and average achievers earn higher scores on standardized achievement tests and aptitude tests when taught through their learning styles preferences (Dunn, Griggs, Olson, Gorman, Beasley, 1995). At the same time, we need to take into account the fact that no single learning preference is better than any other. Students become more competent learners if they can have preferences for more than one single learning style. This makes them more versatile learners. This reflection can be sustained by the fact that gifted learners prefer kinesthetic instruction but they also have the ability to learn auditorially and visually (Dunn, 1989). Furthermore, underachievers tend to have poor auditory memory. They learn better through graphics and animations rather than text (Dunn, 1998). Low achievers are also said to encounter difficulty to do well in school because of their inability to remember facts through lecture, discussion, or reading where teachers mostly talk and students mostly listen (Dunn, 1998). Hypermedia, providing a pool of interlinked multimedia objects for the web can be very useful in the design of courseware that addresses the learning difficulties of low and average achievers while gifted students
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would have no problems to adapt to the type of material presented to them. Hypermedia therefore can provide a good opportunity for average and low achievers to improve their performance by teaching through their learning and perceptual styles. On the other hand, the navigational freedom of hypermedia applications often leads to comprehension and orientation problems (Nielsen, 1990). Adaptive hypermedia, however, attempts to overcome these problems by adapting the presentation of information and the overall link structure based on a user model (De Bra, Brusilovsky & Houben, 1990), which is known as the student model in educational contexts. Our empirical findings and current research (Dunn, Griggs, Olson, Gorman, Beasley, 1995) suggest that learning and perceptual styles are important parameters to consider for the design of adaptive hypermedia systems for a better learning experience for the learner. The HyperLearn project, which aims at developing an adaptive web-based hypermedia system (figure 5) that will be incorporated in the existing e-learning infrastructure for the delivery of online courses will use a student model with these components and a tutoring module that will decide on effective teaching strategies based on the evolution of the student model and the learning styles. The expert model in HyperLearn will be a pedagogical expert rather than a content expert. The pedagogical expert will store a variety of teaching techniques that will be used based on individual learner characteristics.
Student
Relief Tutor
I N T E R F A C E
Student Model
Tutoring Strategies
Content in Multiple representations
Knowledge base
Inference Engine
Intelligent hypermedia tutor
Figure 6 : Web-Based Framework for Adaptive Hypermedia Tutoring We may therefore summarize that HyperLearn aims to provide an independent self-learning and adaptive tutoring environment for web-based courses. It also aims at providing a personalized learning framework for each learner based on his preferred ways of learning and information processing. The design of HyperLearn system’s infrastructure will hopefully provide useful insights for the development of Distance Learning courses especially for e-Learning as well as to provide a framework for the designing of such adaptive hypermedia systems.
Conclusion In this paper, we address the very important issue of hypermedia courseware design to enhance the teaching and learning process. We show from empirical and qualitative studies that current courseware and existing tutoring tools do not have significant effects on student performance and learning. We also investigate
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students’ individual learning and perceptual styles and identify these as possible important features that need to be taken in consideration as improvement of current practices. We review the importance and benefits of hypermedia and propose to extend our research to the design of adaptive courseware using learning styles and perceptual styles as components of the student model that will have a direct implication on the tutoring model and strategies. In further studies, we shall try to address the remaining research questions by the development and evaluation of an adaptive hypermedia tutoring system.
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