Learning Styles and Online Interactives

David T. Schaller and Steven Allison-Bunnell, Educational Web Adventures, and Minda Borun, Museum Solutions, USA

Abstract

Although virtual exhibits consisting of pictures and text are still common, educational Web developers increasingly employ techniques borrowed from interactive exhibit developers, video game producers, and museum educators to create compelling activities that fully exploit the strengths of the Web medium. However, such transfers from other learning environments to the Web pose unique challenges. For example, the effective teacher in a face-to-face learning environment responds to various cues about the learner's knowledge, interest, and ability. We do not have that ability on-line. Instead we must attempt to formalize within the programming of the activity much of the tacit feedback to which the teacher can react. This task becomes even more difficult when we take into consideration the diverse ways that people perceive and process information.

This paper reports on current research into the impact of learning style on preference for on-line informal learning experiences. Building on our pilot study of user preferences for Web-based activity types (Schaller et al., 2002), we are currently developing ways to measure children's learning styles and testing hypotheses about learning style, activity preferences, engagement, and satisfaction.  An understanding of individual differences in learning styles will provide valuable insights into the specific requirements of computer-based learning media and guide developers to design more engaging and effective experiences for a wide variety of learners.

Keywords: Web, on-line, computer, computer-based, interactive, learning style, Kolb

Introduction

Although virtual exhibits consisting entirely of pictures and text are still common, educational Web developers increasingly employ techniques borrowed from interactive exhibit developers, video game producers, and museum educators to create compelling activities that fully exploit the strengths of the Web medium. However, such transfers from other learning environments to the Web pose unique challenges. For example, the effective teacher in a face-to-face learning environment responds to various cues about the learner's knowledge, interest, and ability. We do not have that ability on-line. Instead we must attempt to formalize within the programming of the activity the tacit understanding teachers have of their students. This task becomes even more difficult when we take into consideration the diverse ways that people perceive and process information. An understanding of differences in users' learning styles can provide valuable insights into these differences and guide developers to design more engaging and effective experiences for a wide variety of learners (rather than just developing materials that suit the intuitively-felt learning styles of the developers themselves). The present turn toward outcome-based evaluation of informal learning programs creates a need for best practices in the design and development of informal on-line learning activities. We need a solid framework for guiding development so that we can go beyond a cookie-cutter formula for the replication of successful products. Since engagement and satisfaction are correlated to  with positive learning outcomes, this research project will help identify some of the ingredients for successful informal on-line learning.

This research project, a collaboration among two educational Web developers (Schaller and Allison-Bunnell) and two museum researchers (Borun and Chambers), is exploring the role of learning style on children’s'preferences of Web-based informal learning experiences.  We hypothesize that when the shape of the learning experience fits an individual's preferred learning style, the experience will be more engaging and more satisfying, and thus more successful as an informal learning experience. We are pursuing the following research questions:

1. Do children's learning styles predict their preferences for on-line activities?
2. When children engage in their preferred activity type, do they show greater engagement, satisfaction, and learning?
3. How can our research findings inform the development and design of more successful online learning activities?

Since this research is in progress, this paper reports on the theoretical framework, methodology and early phases  of our current study.

Literature Review

Learning Styles

Learning styles are commonly applied in public education and corporate settings as an effective framework for recognizing and accommodating individual differences. Serrell (1990) advocates consideration of learning styles in exhibit development:

Thinking about the visitors' experiences in terms of preferences and styles leads us to very different conclusions about skills, cognitive ability, or prior knowledge. The wide range and diversity of our audiences' styles can be addressed without being too insulting to some or obscure to many.

For similar reasons, learning style typologies are particularly useful in our proposed research. The field of computer-based informal education is such new territory that typologies and classifications are a necessary early step on the way toward more sophisticated theoretical understanding and coherent best practices. 

At the moment, however, research into the examination of the role of individual preferences or learning styles in computer-based informal education is rare. The task is confounded by the many different models of learning styles that have been proposed. These models are not all describing the same aspects of this complex and not easily reduced phenomenon. Guild and Garger (1998) suggest that we are in a pre-paradigmatic phase of learning style research: the blind researchers have each described a different part of the elephant, but have not yet synthesized their findings into a unified picture of the multi-dimensional beast.

Our first task, then, is selecting the learning style theory most relevant to computer-based media. Howard Gardner's theory of Multiple Intelligences (MI) draws on research in cognitive psychology to expand our notion of intelligence beyond the traditional emphasis on verbal, logical, and mathematical aptitudes. By emphasizing multiple modes of thinking, MI theory is useful in the development of Web-based learning experiences. As Gardner himself has noted, it leads beyond static text and imagery to more diverse approaches (Veenema & Gardner 1996). Gardner's seminal work has focused the attention of the entire educational community on the importance of individual differences in learning. However, because Gardner focuses on the types of information people prefer to process, it is less useful to our research than other models that examine differences in the ways that people perceive and process information. That is, while the theory of Multiple Intelligences may remind developers to include a wide range of information types, producers of online activities need greater assistance in structuring and organizing the content.

David Kolb's Experiential Learning Theory (ELT) (1999) draws on research by Dewey and Piaget, among others, to identify two major dimensions of learning: perception and processing. Each dimension has two extremes: perception ranges from concrete experience to abstract conceptualization, and processing ranges from reflective observation to active experimentation. These two dimensions form a four-quadrant field for mapping an individual's learning style. (See Appendix 1 for a schematic representation of Kolb's model).

Bernice McCarthy combined Kolb's four-style typology with brain hemisphere research, resulting in her 4MAT System, an eight-part model, with a left and right brain component within each of Kolb's quadrants (McCarthy 2000). With this model, McCarthy describes a complex template for curriculum development, strongly emphasizing following a clockwise cyclical path, starting in the upper-right quadrant, through the eight steps, as the key to successful learning. Designed for classroom teaching, McCarthy's approach is not well suited to online learning since it assumes a highly structured formal learning environment for group discussion and interaction.

Research on Computer-based Informal Education

Many researchers have examined computer-based learning over the past twenty years, but the vast majority of studies focus on formal education: K-12, post-secondary, and adult training. Research into computer-based informal learning is rare, particularly for studies focusing on Web-based informal education. As a recent review of museum Web sites notes:

What we found in relation to museum Web sites and their visitors was fairly sparse. Although the field of distance education is rich in studies, little of it was transferable to museum Web sites. Most of the research and any assessments of learning were directly dependent on traditional classroom dynamics, such as lectures and testing. As physical and virtual museums move away from a transmission-absorption model of learning towards a more constructivist model, distance education research is less pertinent to virtual museum assessments. (Haley Goldman and Waldman, 2002)

A few studies do explore online learning in an informal context, but as Haley Goldman and Waldman note, most of these are based on quantitative analysis of Web site server logs and are unable to provide data on visitors demographics, social context, motivation, or learning (Ibid.).   There have been only a few qualitative studies.

Sumption devised a typology of cognitive strategies that museums have used for Web-based outreach learning and calls for evaluation to better understand real outcomes, against those intended, to guide the development of learning products (Sumption 2001). Squire's review of literature on learning from computer games such as SimCity and Civilization notes that the "instructional context that envelops gaming is a more important predictor of learning than the game itself" (Squire 2002). And while he believes that computer games hold promise for both formal and informal education, all of his recommendations apply to a school environment. Miller et al.'s (2002) evaluation of a Web-based neuroscience game sees much promise for the use of Internet gaming in informal education, but his own study relies on classroom students to measure the game's effectiveness.

There are almost no studies examining the role of individual preferences or learning styles in computer-based informal education. A user-centered design project conducted by IBM found that users preferred a "guided tour" approach to informal Web-based learning (Vergo et al. 2001).  But, IBM's  narrow sample (adult staff and interns at an IBM facility) casts doubt on the broader applicability of their results.

Among the few studies that have been conducted in formal settings, few researchers have examined learning styles as a tool in computer-based instructional design. Currie (1995) applied Kolb's learning style typology and found thatrole-playing exercises and other techniques which involve being thrust into the limelight were appropriate for 'activists' [Practical and Social]. ‘Reflectors' [Creative and Intellectual] preferred self-assessment exercises and paper-based material that they could take away and chew over. ‘Theorists' [Intellectual] preferred a lecture and discussion within the program. Practicals found specific techniques (such as force-field analysis for problem solving) useful, particularly where given the opportunity to try them out in a situation similar to the workplace."(Currie 1995, cited in Henke 1997).

Evaluations of engagement and satisfaction with online learning have been conducted in controlled settings such as museum computer labs, with online surveys, and through Web server statistical analysis (Vergo et al. 2001, Ockuly 2003, Camp et al. 2000).  Although none of these methods captures true in situ behaviours, they can reveal valuable insights into the user's experience. On the other hand, lab testing in an informal environment such as a museum can apply standard methods of measuring engagement and satisfaction in museum exhibits. These methods can focus on visitor/user behaviors ("asking and answering questions, talking about an exhibit, pointing to sections of an exhibit, reading label text, engaging in hands-on activities," (per Borun et al. 1996) and post-experience interviews (Falk and Dierking 2000).

Current Research Study

We are currently engaged in a research study, funded by the National Science Foundation, to explore the relationship between learning styles and preferences for computer-based interactives. We have chosen Kolb's Experiential Learning Theory as the model on which to base our research on Web-based learning activities. Kolb's clear delineation of learning styles highlights major differences in the way people perceive and process information, yet it is simple enough to allow us to test for correlations between learning styles and computer-based activity types. Also, as we discussed in a recent paper (Schaller and Allison-Bunnell 2003), Kolb's styles appear to be a good match for the variety of computer-based learning experiences, which vary from analytical models (e.g. simulations) to open-ended tools (e.g. story-telling or drawing). Researchers have applied various labels to each of Kolb's styles; we have devised our own which we believe are more meaningful to our target audience: Our labels are in parentheses alongside Kolb's labels as follows: Accommodating (Social), Diverging (Creative), Assimilating (Intellectual), Converging (Practical).

Figure 1. Experiential Learning Theory, from Kolb et al. 1999

Using a typology of computer-based activities developed for an earlier study (Schaller et al. 2002), we hypothesize that a computer-based learning activity that engages an individual's dominant learning style will result in a more engaging and satisfying experience than one that does not match the preferred learning style. Though we do not anticipate a one-to-one match between any style and computer activity type, we hypothesize some degree of correlation between certain activity types and Kolb's four learning styles as follows:

• Role-Play allows users to adopt a persona and interact with other characters.  We hypothesize that Role-Play may appeal to those with a Social learning style.     
• Simulation employs a model of the real world that users can manipulate to develop an understanding of a complex system. We expect that this activity type will appeal more to those with an Intellectual learning style.
• Puzzle/Mystery involves analysis and reasoning to reach a logical solution. The user relies on evidence from people, nature, or reference material.  This activity type may appeal more to those with a Practical learning style.
• Creative Play  emphasizes open-ended inquiry and experimentation, with a personal creation as the product of the experience.  We anticipate that this activity type will appeal more often than the other types to the Creative learning style.
• Interactive Reference  provides multimedia content in a topical or thematic structure, for self-directed browsing.  We anticipate that this activity type will appeal more often than the other types to the Intellectual learning style.
• Discussion/Forum  facilitates interpersonal communication among users, and with subject-area experts. Users can post questions or comments, and engage in asynchronous conversation with other people. We anticipate that this activity type will appeal more often than the other types to the Social learning style.

Determining Learning Style

At the time of this writing, we are in the process of developing an instrument for measuring learning style among children so we can then test our hypothesis about the relationship between learning style, engagement, and satisfaction. Kolb's 12-question Learning Style Inventory has been validated for high school-age youth (ages 14-18) and adults (Kolb et al., 1999), but not for younger children. Our current task is to simplify the inventory for use by middle school children.  To this end, we have developed a series of inventories, testing each one via online surveys. We post the survey on our Web site (www.eduweb.com) and direct Web users to it from three on-line interactives elsewhere on our site ("A. Pintura," "Inside Art," and "Amazon Interactive"). These interactives have high levels of youth traffic, enabling us to collect roughly 50 responses daily from this population. The survey appears automatically as a pop-up window when users arrive at the home page of each interactive. To reduce the impact of pop-up blocking software, we also put a prominent invitation on the page to take the survey. Survey responses are saved to a text file on the Web server, which is downloaded and analyzed using Microsoft Excel.

With this method, we can post a survey for several days and gather a sufficient number of responses for analysis within and between age groups, then revise the survey instrument and repost it to gather fresh responses. While consisting of convenience samples (i.e. a nonsystematic approach that allows a potential respondent to self-select into the sample) (Schonlau et al. 2001), we believe these surveys are valid tools for our purpose. All respondents belong to our target population: secondary school-age users of educational Web sites.  Once we have a valid inventory for use with middle school-aged children, we will be able to determine the distribution of learning styles within this population, and to look for correlations with preferred educational activities.  While the distribution of learning styles among this population is not known, it does not matter for our current purposes, which is comparing respondents' score on Kolb's inventory with their score on our inventory. To date, we have tested several versions of our inventory and looked for correspondence between each respondent's score on Kolb's and our inventories. Our focus here has been high school students, since Kolb's inventory has been validated for that age group.

Our first effort, consisting of one-paragraph descriptions of each style, showed poor correspondence with Kolb's instrument. Many respondents also found it difficult to place themselves into a single style, noting that they could see themselves reflected in aspects of multiple styles. Our second effort employed a learning style instrument validated for children, developed by researchers in India (Dangwal n.d.). Although based on Kolb, this instrument was validated with other psychological instruments and did not show sufficient correspondence with Kolb's inventory for our purposes. Currently we are testing simplified versions of Kolb's own inventory and believe that it will meet our needs. Once we have confidence in our inventory, we will proceed to the next step: looking for correspondence between learning style and preferences for online interactives. We will post results and analysis on our Web site, at http://www.eduweb.com/research.html

Conclusion

Museums and other organizations are investing substantial resources in the Web as an educational medium, yet little research exists to guide sound development of such resources. Our research will help fill this gap by examining the user's experience through the framework of learning styles. This framework can illuminate key aspects of computer-based learning and help us to understand how different types of activities can engage different types of learners. For example, knowing the frequency of each learning style among children will provide a new and valuable way for developers to think about their audiences.

While incorporating all activity types in a single online project might be ideal from the standpoint of meeting the needs of a diverse audience, it can be functionally difficult to do, or simply be cost-prohibitive. However, if our research indicates some learning styles to be predominant for particular age ranges or that some activity types are more engaging and satisfying across a broader range of learning styles, museums can use that information to make better use of limited resources. We anticipate that results of this research will offer much-needed insights into the user experience and valuable guidance to developers of Web-based informal education activities.

Acknowledgements

This paper is based on research partially supported by the National Science Foundation under Grant No. 0337116.

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Appendix I: Experiential Learning Theory

Cite as:

Schaller, D. T., S. Allison-Bunnell and M. Borun, Learning Styles and Online Interactives, in J. Trant and D. Bearman (eds.). Museums and the Web 2005: Proceedings, Toronto: Archives & Museum Informatics, published March 31, 2005 at http://www.archimuse.com/mw2005/papers/schaller/schaller.html