1 - Computer Science & Software Engineering



Utilizing Learning Styles for Interactive Tutorials

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Abstract: Web-based learning environments, such as eLearning and distance learning systems, have become more common and popular in commercial and educational settings. They constitute a growing business today, and the concept of learning ‘anywhere, anytime’ has received much attention. These systems are still in their infancy, however, and are faced with various challenges to become fully effective tools. Their lack of individual preferred ways of instruction is a challenge we address in this paper.

We introduce a Web-based learning environment that teaches concepts from Artificial Intelligence to college students. This environment is intended to be used as a complementary tool for the standard lectures. By adapting the instruction and learning material to the individual student’s learning style, the tutorial gives the student a personal learning experience. The system was developed in collaboration with HCI students to combine the use of learning styles with the principles of user-centric design.

Keywords : learning styles, web-based learning system, personal instruction, HCI

1 Introduction

When the Internet boomed in the mid-nineties, Web-based instruction started to become high in demand from both corporations using it for employee training and educational institutions interested in meeting the students’ needs. This learning environment provides more flexibility with time, pace and place, and is often characterized as ‘anywhere, anytime.’

Other benefits of Web-based learning are support for classroom instruction and platform independence. An application installed and supported in one place can be used by learners equipped with any kind of computer connected to the Internet. This kind of learning environment is often referred to as eLearning, Web-based training, distance-learning, or online education system.

Developing successful Web-based learning environments, however, has shown to be a challenging and difficult endeavor that requires knowledge from multiple domains like pedagogy, psychology, knowledge engineering, software engineering, and Web technologies.

There are several Web-based learning systems available today that we will look at later, but research shows that there are many challenges to overcome before these systems are efficient enough. These systems usually assume the users to be a homogenous learning group and are therefore presenting interfaces, functionalities and the same course content uniformly to every user. They expect that all the different users learn the same way, and do not accommodate for the rich diversity of learning styles nor the user’s preferred

ways of learning. This can lead to dropping interests by the students, and failure in achieving the expected academic results.

2 Motivation

In this experiment we wanted to explore potential ways for improving such Web-based learning systems by utilizing learning styles, and develop a prototype, the Interactive Teaching Tutorial (ITT). We wanted to investigate the hypothesis that implementing a personalized learning environment would increase the student’s performance and motivation for learning.

The ITT was developed as a tool for the students enrolled in an Artificial Intelligence class at California Polytechnic State University, San Luis Obispo, as a complement to the standard classroom instruction. We wanted to discover how learning style theories could be utilized developing a Web-based learning system, and discover the tradeoffs of applying these theories.

3 Existing Systems

In the 1960’s the US military started developing training systems referred to as drill and practice and simulation activities, e.g. flight simulators. This form for training could be used instead of teacher-directed instruction, as a supplement to the traditional training, and as partial replacement of costly training in the real environment. In situations where testing of skill and knowledge is required, Computer Assisted Instruction (CAI) is used for management skills, industrial factory floor training, information technology products, health care, government services, and many other domains. CAI has been in use for several decades in various forms and applications.

Another area of development is Intelligent Tutoring Systems (ITS). ITS allow the emulation of a human tutor in the sense that an ITS can know what to teach (domain content), how to teach it (instructional strategies), and to acquire information about the student being taught (Capuano, 2000). These systems originated from the Artificial Intelligence (AI) movement of the late 1950's and early 1960's. It seemed reasonable to assume that, once we created machines that could think, they could perform any task we associate with human thought, such as instruction. This has proven to be harder than expected, and even today’s ongoing large ITS projects are not close to their forefathers’ high visions. They are especially struggling with high complexity and development costs.

Web-based learning (WBL) systems, however, are less ambitious and less complex than the Intelligent Tutoring Systems. WBL is one of the tools used by academic institutions and corporations (Berkeley, 2002). In traditional academic institutions, WBL systems are generally housed administratively in a "distance education" department alongside other at-distance delivery methods such as correspondence, satellite broadcast, two-way videoconferencing, videotape and CD-ROM/DVD delivery systems. All such systems seek to serve learners at some distance from their learning facilitator.

WebCT (WebCT, 2002) is a commercial course management system for higher education. According to (Diaz, 1999), WebCT is used at over 1700 institutions with University of Georgia as the largest user with 1,150 courses used by 32,117 students. WebCT provides utilities, components and tools to develop and maintain a virtual university. Some examples are: course builder, course appearance, student manager, file manager, course homepage, assignments, quiz and surveys.

Blackboard (Blackboard, 2002) began as collaboration among a team of students and faculty at Cornell University in 1996. Blackboard is a similar web-based product also used worldwide. Among 1600 institutions, Boston University is among Blackboard’s largest users with 896 courses used by 18,881 students.

LearningSpace (LeaLotus, 2002) is another commercial system developed by Lotus Development and currently marketed by IBM. This is a similar system to WebCT and Blackboard. However, this system has been built on top of the groupware product Lotus Notes and the Domino Web Server software, and takes advantage of the features that Lotus Notes offers for conferencing, e-mail and meeting-scheduling.

[pic]

Figure 1: Course entrance for Lotus LearningSpace

(LeaLotus, 2002)

Taken to some extreme as so-called virtual universities, academic institutions have started offering more and more courses through their distance learning programs, or as conventional courses with substantial WBL capabilities.

4 Learning Styles

The idea that people learn differently is venerable and probably had its origin with the ancient Greeks (Wratcher, 1997). Educators have, for many years, noticed that some students prefer certain methods of learning to others. These dispositions, referred to as learning styles, form a student's unique learning preference and aid teachers in the planning of small-group and individualized instruction (Kemp, 1998).

According to (Felder, 2000), learning styles are “characteristic strengths and preferences in the ways we take in and process information”. There are a lot of discussions about what learning styles are and at which level of cognitive abstraction to describe these. Curry’s Onion Model (Curry, 1983), illustrates these different levels; see Figure 2 for an overview.

Figure 2: Curry’s Onion Model

The outer layer of Curry’s model examines instructional preference. Learning style models in this layer measure interactions with the environment. The main theory of instructional preference was proposed by Dunn & Dunn (Dunn, 1978). The middle layer of Curry’s model concerns an individual’s intellectual approach to assimilate information and encompasses many of the currently popular learning style theories. This layer is considered to be more stable than the outer layer because it does not directly interact with the environment. Kolb's experiential learning theory (Kolb, 1984) postulates the existence of four learning modes that combine to form two learning dimensions, concrete/abstract and active/reflective. The inner layer of Curry’s model examines cognitive personality style, addressing an individual’s approach to adapting and assimilating information. This layer is considered to be an underlying and relatively permanent personality dimension.

The Felder and Silverman Learning Style Model (Felder, 2000) overlaps the middle, information processing layer, and inner, cognitive personality layer. It classifies students along five spectra: sensing/intuitive, visual/verbal, inductive/ deductive, active/reflective, and sequential/global. Although students are classified on the five spectra, the assessment tool is a forty-four-question inventory developed by Felder and Silverman. It is available as a questionnaire that is submitted and automatically scored on the Web. The score is set of numbers that indicate preferences for each learning style.

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Figure 3: Felder-Silverman Learning Style Spectra

(Felder, 2000)

|Definitions |Dimensions |Definitions |

|Do it |Active |Reflective |Think about it |

|Learn Facts |Sensing |Intuitive |Learn Concepts |

|Require Pictures |Visual |Verbal |Require reading |

| | | |or lecture |

|Step by Step |Sequential |Global |Big Picture |

Table 1: Preferences in the Felder-Silverman Model

(Felder, 2000)

Most of the research in learning styles theories in relation to educational technology are referred to as an aid in instructional improvement (Jarc, 1999, Ross, 1999, Howell, 2001). According to the literature, suitable educational software has the potential to benefit students with specific styles of learning. The reasons for this belief are the differences from learning with educational software to traditional classroom education. Unlike the classroom environment, learning with educational software is a self-paced type of learning, which enables the student to spend as much time as is needed to master the material. Educational software has the potential to provide an exploratory environment, in which students can learn through experimentation. Although scientific laboratory settings provide a similar environment, because of the lower cost of creating virtual environments, the range of possibilities can be much greater. Finally, with the development of the new multimedia technologies, educational software can become a highly visual learning environment, one that might also include pictures, animations, movies, and other visual modes of presenting information.

There has been some research on educational software as an instructional medium for meeting the different learning styles, but the experiences for improved learning have been mixed (Diaz, 1999) (Howell, 2001) (Grasha, 2000).

5 The Interactive Teaching Tutorial

The rationale for this experiment was to explore and find ways for improvements in educational software, especially teaching tutorials and distance learning frameworks. We therefore wanted to examine the potential of Learning Styles, and an Interactive Teaching Tutorial (ITT) was developed as a framework. Demonstrating learning styles in a distance-learning environment is a larger undertaking and went beyond the scope of this experiment. The results, however, are intended to be applicable to distance learning environments and might also give some ideas for next generation distance learning systems

Except for Intelligent Tutoring Systems (Burger, 2001), (Shang, 2001), there has been little research on systems that accommodate the needs and learning preferences of individuals separately. D. Howell (Howell, 2001) discusses that to increase the quality of the learning experience, the current “one-size fits all” systems need to be more personalized, and should try to accommodate the different learning styles of users.

Prior to designing a learning system that utilizes learning styles, however, a thorough domain analysis could be significant and save development time. Maybe the system would not benefit from learning styles, e.g. if the user group is homogeneous and the variation of learning styles is small. A system could then be developed concentrating on that particular user group without being concerned about a larger set of different types of learners.

The second objective of the ITT was to develop a system for the students in a computer science class at California Polytechnic State University, CSC 480 Artificial Intelligence.

The learning material contains concepts of artificial intelligence, in particular search algorithms. However, the framework of the ITT is intended to be flexible, so that it can be used for other majors and classes as well. The interactive part of the tutorial is also an interesting aspect, and is intended for teaching active learners that prefer “learning by doing” (which happens to be Cal Poly’s motto).

5.1 Feature Driven Development and User Centered Design

Developing the ITT fell into two major categories: establishing the feature set of the framework, and developing the learning material. The process for developing the framework is based on an agile development process, the Feature Driven Development (FDD) (Coad, 1999) for determining goals, analysis, design, and then implementing each feature in iterations. For developing the learning material, principles from user-centered design were used (Truchard, 1998). For both parts, Felder and Silverman’s Learning Style Model (Felder, 2000) was adopted.

An agile process like FDD is a team-oriented process that delivers working software early and welcomes changing requirements (Beck, 1999). Every iteration of the development was a new version of the ITT implemented with features that were discovered or stated. An iteration is a 1 to 2 week period of work done by the development team. A feature set is a group of related features ("evaluating a student" or "enrolling a new student"). Feature sets can further be grouped into major features ("student management"). This grouping of features aids with reporting on the progress of a project. Features are then assigned to iterations based on the user and development priorities and on the number of hours available. Features are similar to requirements, but focus also on how the software should perform, not only what the software should accomplish.

This FDD process is divided into five different steps by (Coad, 1999): 1.) Develop an overall model, 2.) Build a Feature list, 3.) Plan by Feature, 4.) Design by Feature, 5.) Build by Feature. The FDD followed the steps in the order requirements, system description, analysis, overall architecture and {design, implementation and testing} within each iteration.

5.2 Case Study – “ Artificial Intelligence Search Algorithms”

Since Artificial Intelligence is a large subject, the subtopic search algorithms was chosen for the case study. This included problem solving by search strategies and informed search algorithms (Russell, 95). Among the algorithms that were studied: Depth-First Search, Breadth-First Search, Uniform Cost Search, Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative Improvement Algorithms.

The class instructor maintains and updates the system. He provides learning material and quizzes, and also manages the quizzes. Since the instructor is normally very busy lecturing and grading, time and effort put into maintaining such a system, should be as effective and effortless as possible, or supported by administrative personnel.

5.3 Criteria

The primary goal for the ITT is to accommodate functionality and learning material for different learners and their specific learning styles. This is the first criterion in Table 2 for both the framework and the learning material. The ITT also should be an online system and easy to maintain by the instructor. The speed of the framework needs to be reasonably fast, not requiring more than a few seconds of waiting for the different pages in the tutorial. The learning material should be up to date and related to the concept discussed. Both the ITT Framework and learning material were also designed using principles from Human-Computer Interaction (HCI) and User-Centered Design. The web pages should have a consistent layout, be easy to navigate, and pleasant in appearance. These criteria are listed in the table below with a priority number.

|ITT Framework |Learning Material |

|Accommodate Different |Accommodate Different Learning |

|Learning Styles |Styles |

|Online 24/7 |Online 24/7 |

|Easy to Maintain |Easy to Maintain |

| Satisfactory Speed |Up to Date and Related to the |

| |Concepts |

|Collaboration Possibilities | Clear and Understandable |

|User Centered Design |User Centered Design Principles |

|Principles | |

Table 2: The priority list of criteria for the ITT

5.4 The Process of Accommodating Different Learning Styles

In the literature, no guidelines could be identified for the application of learning style theory to the development of a computer-based tutorial. Thus we decided to explore possibilities offered by the different learning style models, starting with Felder and Silverman’s learning style model (Felder, 2000). The next step was to outline an approach that accommodated the different learning styles within this model in an online learning environment. The online learning environment, the ITT, has two major components, the ITT framework, and the learning material based on the domain knowledge.

In order to accommodate the “anywhere, anytime” requirement, a Web-based environment was used. Users of the online system register and create their own profile, containing their learning styles, to which the system would adapt. By adapting to the user’s learning style, it was our expectation that the tutorial would increase the user’s knowledge about the specific topic.

The learning material was developed according to the different learning styles described by the Felder & Silverman model and integrated in the tutorial (see Table 3 below for an illustration). The Visual/Verbal, Active/ Reflective and Sensing/Intuitive learning styles had to be accommodated. Visual learners get more learning material with diagrams and illustrations than the verbal learner, who gets more text-based material. The active learner will be given more opportunities for interaction than the reflective learner who will get more question-based and learning material containing critical analyses. The sensory learners receive facts, glossaries and previous work by other students, while the intuitive ones are presented with abstract material and hyperlinks leading to other sources.

The process for developing the framework and integrating the learning material is described in the following six stages:

1. Choose a Learning Style Model

2. Identify and Categorize the Learners in the Model

3. Identify and Categorize the Learning Material

4. Map Learning Material and Presentations to the Learners

5. Implement the Tutorial Framework and Learning Material

6. Evaluate and Obtain Feedback on the Tutorial

In more traditional software development processes the first four steps of the process can be considered as part of the design stage, while step five is the implementation stage, and step six testing. Choosing the model, identifying learners and identifying correlated learning material is a vital part of the design. These steps consist of studying the different models in more detail and trying to come up with ideas for how to implement them in an online teaching environment. After implementing the first version and having it evaluated by students, the feedback is used for future improvements, making the tutorial more useful and effective for learning.

In choosing a learning style model, step 1, we found that the second layer, information processing, was the best fit for our purpose of developing a computer-based teaching tutorial. This layer contains many different learning style models relevant for an individual’s approach to the processing of information. There are some tradeoffs, however, to be considered, especially the number of learner groups that needs to be accommodated versus the development time. The Gregorcan (Gregorc, 1982) learning style model accommodates only four discrete learner groups, while the Felder & Silverman model accommodates a combination of four different spectra. The Felder-Silverman model has a large combination of learners, and requires a potentially huge choice of presentation modes for the course contents. In applying such a model, it is possible to reduce the number of combinations in practice.

Steps 2, 3 and 4 can be summarized in Table 3. It shows the learning styles from the Felder-Silverman model and their relation to different types of learning material. The learning material is categorized and put together according to the different presentation modes, or learning material forms. Step 5 and 6 of the process will be described in the next two sections.

6 The ITT Framework

Since the learning material was stored as Web pages, a Web-based system was a natural choice. It would be accessible 24 hours, 7 days a week. This would be beneficial for the students since they can use the tutorial anywhere at anytime, when it is convenient and at their own pace.

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Table 3: Relations between Learning Material and

Learning Styles

To adapt to different learning styles, input from the user is needed to figure out that user’s preferred learning style. By letting every user complete an online questionnaire, a profile is created, subsequently stored in the user’s profile, and later used by the ITT.

Interfaces

There are two different interfaces for the system, one for the students and one for the instructor. Both interfaces are Web-based and the start page users is a login menu that requires a username and a password.

The first time students are using the system they register and complete a learning style questionnaire. The learning style the user prefers or belongs to is stored in the Profile Database. The user navigates through the Web pages to learn the class material maintained and updated by the instructor. The menu pages are dynamically generated, based on the available classes and modules.

The interface for the instructor can display quiz results from the users. The instructor logs on as an administrator and has access to quiz results retrieved from the quiz database. The purpose of this interface is to examine how the different types of learners perform after using the tutorial. Each quiz shows individual total score, score for each question, quiz averages, and performance from each different learning style group.

Repositories

There are two repositories in the framework. One repository contains user profiles (Profile Database), and the other stores quiz results and quiz keys (Quiz Database). Information is stored in the Profile Database when the user registers. To identify which learning style the user has, information is retrieved from this database when the user logs on the system. When the user is taking a quiz, the results are stored in the Quiz Database and the instructor can log on to examine the quiz results. The quizzes consist of multiple-choice questions and are found in the different Concept Modules. Each quiz can be taken only once.

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Figure 4: The ITT Framework

Learning Style Templates

The learning style template currently contains mostly information for presenting material to sequential learners. The template describes, for example, the sequence in which the material will be presented in the tutorial for a given learning style.

Profiles

The Profiles contain information about individual users such as username, password, first name, last name, email address, user ID number, and the result from the Learning Style Questionnaire.

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Figure 5: Student navigation of the ITT

Tutorial Menus

The menus are used by the student to navigate in the tutorial. The menu system is illustrated in Figure 5. It shows the structure of the tutorial where the student can select between classes and concepts. Once a concept is chosen, the student is presented with a menu containing a toolkit, the tutorial, preferences and discussion group.

Modules

The learning module contains all the learning material for the tutorial and the menu module presents all available classes. The class modules contain all the learning material for a specific class, and its menu contains the different concepts available for that class. The concept contains all the learning material for the specific concept and its menu presents the available options for the specific concept. Each concept contains a toolkit, a tutorial, preferences and a discussion group. The learning material was put together in HTML using interactive Java applets, animations, diagrams and text.

The toolkit is a menu that contains hyperlinks to all the learning material available for all the different types of learners. The preference page is a menu for customizing the tutorial, where the user can set up his preferred tutorial by checking learning material from all learner types.

The discussion group is a menu with threaded hyperlinks to different messages. The student can either start a new thread or respond to an old thread.

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Figure 6: The Start Menu for both Global and Sequential learners.

Figure 6 shows the menu for both learners for each concept in the Concept Module (See Figure 5). The student can browse all the learning material in the Toolkit, start the Tutorial, set up his own Tutorial or participate in the discussion group.

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Figure 7: For the Sequential learners, the Tutorial

starts with a concrete example.

Figure 8 shows the Tutorial Menu for Global learners. These learners prefer to start out with an overview of the available learning material.

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Figure 8: Overview of Learning Material for Global

learners

Figure 9 shows learning material for the Global learner

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Figure 9: An Overview slide the to Global learners

6 Implementation

For implementing the ITT framework, Microsoft’s ASP was used for Web scripting and the presentation of learning material based on the learning style in the profile. By progressing through the folder structure on the web server, it presents the files and folders as hyperlinks for the student. Which material to present is decided by the student’s learning style described in the Profile. MySQL was used for storing profile and quiz information in the respective databases. Microsoft FrontPage 2000 was used for developing the Web pages, interfaces and menus, and Microsoft Internet Information Server IIS 4.0 was installed as the Web server. For implementing the ITT learning material, several media were utilized as described in Table 3: diagrams, video lectures, PowerPoint slides, Java applets, text, sample code, audio and animations.

7 Evaluation

As a first step, students in Artificial Intelligence II class registered as a new users on the system, logged on, explored the system and answered questions from a survey.

The students found the ITT useful, and seemed to enjoy using it. All the 25 test subjects agreed that if the tutorial was fully available, they would have used it in the CSC480 class to become more familiar with the new AI concepts. They also found the tutorial, even in its partially implemented form, useful as a reference tool and would like to have access to this kind of learning environments to accompany the standard classroom lectures. Students whose first language was not English found the video clips of the instructor explaining the search algorithm, accompanied by semi-animated PowerPoint slides, especially useful because the could view them repeatedly, and at their own speed. Most criticism was targeted at the obvious implementation deficiencies of a first prototype, and the relatively unsophisticated design of the Web pages. Some students also reported that the categorization of their learning style did not fully match their own observations about their learning preferences.

8 Conclusion

In summary, a process and a framework were developed to gain experience with learning styles and educational software development.

The many learning style models were originally developed for improving class instruction and some of them seemed to be harder to implement in a computer-based teaching tutorial than others. The Felderman model seemed reasonable to implement at first due to its clear and specific learning style descriptions. However, the ranges and combinations of potential types of different learners made it hard to implement. Hence, to overcome this problem we had to constrain them to eight different types of learners.

The largest challenge in this experiment, however, was encountered in step four of the suggested development process, mapping the learners to the different learning material and presentation forms. The challenge was not to so much to come up with good suggestions for mapping learning styles to different parts of the ITT, but to make sure these suggestions actually were useful and of high quality. In hindsight, repeated usability tests and surveys of the students are probably needed to overcome this challenge. The main goal of our process was to contribute our experience to help other developers developing similar systems.

For future work, more time should be spent trying to discover what each different type of learner prefers, especially for Web-based learning environments. This may require a modified learning style theory that is more suitable specifically for such systems. Short-term improvements consist of refinements in the implementation itself, revisions of the process, the integration of additional learning material, and further testing with larger user populations.

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