The field of distance education has faced much negative criticism

By Gary Allen,2014-11-25 19:15
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The field of distance education has faced much negative criticism



    Christy Keeler, Ph.D.

    University of Nevada, Las Vegas

    Abstract: This study describes learning style elements of secondary level

    online courses. The basis for the review is the “Instrument of Instructional

    Design Elements of U.S. High School Online Courses” which rated 22

    courses, 66 lessons, and 183 assessments representing 5 online high

    schools. Ratings occurred for 23 data points. Elements address Gagné’s

    human capabilities, Dunn and Dunn’s learner modalities, levels of peer

    interaction, and Gardner’s multiple intelligences. Findings suggest

    contemporary designs focus on intellectual development, verbal recall,

    visual modalities, whole class communication systems, and linguistic and

    logical intelligences. They infrequently require students to develop

    cognitive strategies, participate in kinesthetic activities, participate in

    small groups, and engage in use of their musical intelligence.

    Keywords: Learning Styles, Online Education, Distance Education,

    Instructional Design, Learning Environments, Instructional Technology,

    Differentiated Instruction

    The online education movement is growing rapidly (Meyen et al., 2002). Production of courses is occurring on massive scales (Thompson, Ganzglass, & Simon, 2000), and online course delivery to students is on the rise (National Center for Educational Statistics, 2005). Furthermore, students taking these courses come from diverse backgrounds urban and rural geographical locales, high and low ability levels, and introverted and extroverted personality types (Jones, 1997; Lorenzo, 2001; San Diego Regional Chamber of Commerce Foundation, 2001; Thomerson & Smith, 1996; Vail, 2001). Though there is much to support the equivalency of quality between

    Learning Styles in Online Education

    traditional and online courses (Cavanaugh, 2005; Russell, 2001), available research does not provide a broad picture of what exists within online courses (Keeler & Anderson-Inman, 2004). It does not inform instructional designers about whether contemporary designs ensure students have opportunities to learn via their preferred learning modalities.

    To meet the highly diverse needs of students enrolling in online courses, it is critical that researchers examine how to plan instruction to meet students where they are providing opportunities for them to maximize their learning potential (Institute for Higher Education Policy, 1999; Russo, 2001; Sanders, 2001). Online course designers must ensure students have opportunities to experience instruction delivered via their preferred learning modes. This study, focused on high school level online courses, uses a cross-school description of design elements to identify the prevalence of learning style factors within online course lessons. It helps understand current practice in high school online instructional delivery for guiding future course design.


    There are three primary information processing facets in educational environments: information perception, interaction with the information or content, and response to the new information or stimulus (Ally, 2004). These facets guide the learner through the process of obtaining, maintaining, and internalizing academic content. “Effective online lessons must use techniques to allow learners to sense and perceive the information, and must include strategies to facilitate high-level processing for transfer of information to long-term memory” (Ally, 2004).

    While instructional designers do not have control over the method of learner response to instructional stimuli, they have some control over learners’ perceptions of


    Learning Styles in Online Education

    and interaction with stimuli (Mupinga, Nora, & Yaw, 2006). These controls occur when designers elect to deliver content using a methodology or stimulus that appeals to the learner. The choice of stimulus that will most effectively lead the learner to deep perception, interaction, and response will vary based on individual learner preferences (Jonassen & Grabowski, 1993; Orlich et al., 1990). These preferences constitute learning styles, and tend to be predictable and stable for individual learners (Fahy & Ally, 2005). Therefore, by teaching online students via their preferred learning styles, designers will increase student opportunities to deeply comprehend the material. Furthermore, personal style affects individual motivation, task engagement, and processing habits (Akdemir & Koszalka, 2004; Aragon, Johnson, & Shaik, 2002). Appealing to students’ individual

    learning styles “may be what elevates a mundane segment of instruction into compelling, imaginative, and memorable instruction” (Smith & Ragan, 2005).

    Given these arguments, it behooves the instructional designer, collectively with the course instructor, to be sensitive to students’ individual characteristics and learning styles (Mupinga, Nora, & Yaw, 2006). This sensitivity begins during the course design phase with a focus on ensuring lessons provide students opportunities to learn via a variety of preferred styles. Presently, online courses tend toward a universal design perspective, attempting to meet the needs of all end users. This method appears appropriate given current delivery models because course designs occur before the first students enroll in the course. A future model may involve designing courses for students with specific learning preferences to address personal students’ needs more precisely (Layton, 1998). For instance, a future model may mean designing a geometry course focused on bodily-kinesthetic learners who prefer to use their motor skills. Course


    Learning Styles in Online Education

    assignments might involve sports related laboratory experiments such as determining angles need to adequately perform a visually-appealing ballet move or the most efficient means of reaching one base from another on a baseball diamond. In traditional environments, Dick and Carey (1996) and Orlich et al. (1990) believe instruction should be individualized to learners and learning contexts. Good teaching requires a variety of teaching methods and skills (Orlich et al., 1990; Rosenshine & Furst, 1973), perhaps for the purpose of appealing to students with varying learning style preferences. O’Brien

    (2001) argues that learning style theory should greatly influence online course design, development, and delivery because learning styles are what most influence how learners learn. She also highlights the importance of presenting material using all the senses, even though learners have certain preferred learning styles.

    Despite the inherent desire of designers to appeal to student characteristics that ultimately promote deep learning, little research on learning styles in online courses appears in the literature (Fahy & Ally, 2005). When studies do appear on the topic, they tend to focus on learning styles of the students (Aragon, Johnson, & Shaik, 2002; Curry, 1990; Mupinga, Nora, & Yaw, 2006; Neuhauser, 2002; Simpson & Du, 2004), as opposed to prescriptive suggestions for instructional design (Aragon, Johnson, & Shaik, 2002; Smith & Ragan, 2005). For instance, within distance environments, Aragon, Johnson, and Shaik (2002) found that students could learn equally well in traditional and distance courses, regardless of student dominant learning style.

    Amidst the research literature, there are also critics of learning styles (Denzine, 2006). Some recognize the inherent flaws in the field because of the self-reporting nature of style preferences (Jonassen & Grabowski, 1993), particularly in terms of the validity


    Learning Styles in Online Education

    of the measures (Curry, 1990; Jonassen & Grabowski, 1993; Smith & Ragan, 2005). Others, as a result of scientifically-based research, have concluded there was no learning increase when students were matched or mismatched with instruction in their preferred learning styles (Akdemir & Koszalka, 2004; Curry, 1990). Those who believe that learning styles do hold promise in terms of prescriptive instructional design suggest “online learners should be provided with a variety of learning activities to achieve the

    lesson learning outcome and to accommodate learners’ individual needs” (Ally, 2004).

    They further state that “online learning can cater for individual differences by

    determining the learner’s preference and providing appropriate learning activities based on the learner’s style” (Ally, 2004).

    Many learning style inventories exist to determine learning style preferences though some have earned greater credibility than others have. One of the most researched of these is the Kolb Learning Style Inventory (1993). Kolb bases his theory of learning styles on a cognitivist perspective centering on individual learning needs relating to perceiving and processing information (Ally, 2004; Jonassen, 2003). He separates learners into four adaptive learning modes: concrete experience, reflective observation, abstract conceptualization, and active experimentation.

    Another well-regarded learning style theorist is Gregorc. The Gregorc learning style inventory also includes four categories concrete sequential, concrete random,

    abstract sequential, and abstract random. Like with the Kolb inventory, specific learner characteristics and preferences are associated with each learning style type. For instance, concrete sequential learners prefer direct, hands-on experiences while concrete random learners prefer intuition and independence.


    Learning Styles in Online Education

    While some of the characteristics of learning style types may lead to possible prescriptive teaching strategies (e.g., the concrete sequential learner may prefer a lab-based science class), many of the characteristics of these learning style types do not easily align with prescriptive strategies (e.g., abstract sequential learners tend toward indecision). Furthermore, though all learners may be classified into one or more of the categories existing with each of these learning inventories, the focus on the Kolb study is on adult learners. The current study’s sample population involves adolescent learners.

    To identify leaning style factors that could have prescriptive design implications, it was critical to review learning style inventories that included easily manipulated elements that would also appeal to a secondary-level audience. Four categories of variables showed promise for this purpose and were therefore included in the “Instrument

    of Instructional Design Elements of High School Online Courses.” [For developmental procedures for this particular measure, see Keeler (2003a).] The four learning style categories included: human capabilities, modalities, peer interaction, and intelligences. While some of these categories face current debate in the field of educational research, each category does provide rich objectively-based data points that can translate into lesson design changes.

    The first section, human capabilities, is based on Gagné, Briggs, and Wager’s (1992) theory of learner capabilities. They saw these ideas as a result of synthesizing ideas of Bruner (1956), Rothkopf (1971), Skinner (1968), and Krathwohl (1964). The categories include intellectual skills, cognitive strategies, verbal information, motor skills, and attitudes. [Detailed objective rubrics for rating these and later data points appears in Appendix A.]


    Learning Styles in Online Education

    The second section of learning styles section of the “Instrument of Instructional Design of High School Online Courses” focuses on modalities, a construct originally

    recognized in traditional classrooms by Dunn and Dunn (1978; Russo, 2001). Recognizing that all features of the Dunn and Dunn model are not controllable within online contexts (e.g., room lighting), it was critical to identify which factors could be manipulated. This resulted in two elements: modalities and peer interaction. Lessons may be reviewed based on the modalities with which students must engage during lessons (i.e., visual, auditory, tactile, kinesthetic) and whether students work independently, in pairs, small groups, as a class, or in an optional configuration.

    Multiple intelligences, originally theorized by Gardner (1983), suggest a plethora of mechanisms for delivering instructional content to students. The “Instrument of Instructional Design Elements of U.S. High School Online Courses” addresses the following intelligences: verbal linguistic, logical mathematical, spatial, bodily-kinesthetic, musical, interpersonal, intrapersonal, and naturalist. It does not address Gardner’s other recently proposed intelligences such as existentialism, but it does include an optional intelligence selection (Gardner, 2003).

    Events of instruction (Gagné, Briggs, & Wager, 1992) allow students to perceive material and guide students through the process of transforming content into their own personal experience. Varieties of perceptive opportunities create prime learning grounds. By allowing students access to delivery modes that cross learning styles, all students should have the opportunity to benefit from learning within their preferred style at some point in a course.


    Learning Styles in Online Education


    This study reviewed 66 lessons appearing within 22 courses. The courses represent five different online high schools chosen based on reputation, time in existence, number of course offerings, breadth of student types, enrollment size, use of a Hannum’s (2001) “virtual classroom model,” and willingness to participate in the study. The sample

    schools annual enrollments vary from five hundred to tens of thousands of pupils, they span the United States, and have different accrediting bodies. Two are large commercial vendors and three are state-based schools.

    Biased randomization (see Keeler, 2003a) served as the basis for course selection. Of the 22 courses selected, three to five were from each sample school, at least three were from each major discipline (mathematics, science, English, and social studies), and five were from other disciplines (art, languages, and technology). Four of the courses had “advanced placement” designations, and two were “advanced” or “honors.” Selecting three lessons per course also involved biased randomization. In cases where the randomized sample lesson failed to be representative for the course (e.g., the lesson was either an introduction to a new unit or was solely an exam), a randomly-selected replacement lesson was used.

    Using the learning styles section of the “Instrument of Instructional Design

    Elements of U.S. High School Online Courses” (Keeler, 2003b), a single individual rated

    all sample lessons. Inter-rater reliability for elements in this section of the instrument is 94% with individual rater agreements ranging from 91% to 96%. To rate elements, the reviewer used the definitions and rubrics available within the instrument (see Appendix A). These definitions underwent an expert review validation process and provided


    Learning Styles in Online Education

    objective criteria for making reliable choices. Responses were electronically recorded.

    Then, raw data underwent frequency analyses for each data point.


    Table 1 provides a summary of results from this study. Appendix A includes a

    table describing each element and data point in detail.

    Table 1. Learning Styles Aggregate Results

    Element Data Point Raw Data Percent

    Intellectual Skill 61 92 Capabilities

    Cognitive Strategy 3 5

    Verbal Information 56 85

    Motor Skill 8 12

    Attitude 14 21

    Visual 66 100 Modalities

    Auditory 12 18

    Tactile 10 15

    Kinesthetic 6 9

    Pairs 1 2 Peer Interaction

    Small Groups 0 0

    Class 17 26

    Optional 1 2

    Alone 65 98

    Verbal Linguistic 66 100 Intelligences

    Logical Mathematical 28 42

    Spatial 16 24

    Bodily-Kinesthetic 15 23

    Musical 1 2

    Interpersonal 14 21

    Intrapersonal 11 17

    Naturalist 9 14

    Optional 0 0

    The most common learning capability in the sample lessons was intellectual skills

    (92%) though most lessons also taught verbal information (85%). The sample included

    lessons addressing each of Gagné’s human capabilities with the least commonly


    Learning Styles in Online Education

    addressed capability being cognitive strategies (5%). Of the lessons addressing motor skills, 50% occur in a single course titled “Drawing.”

    All sample lessons presented material using a visual mode. This occurs because the definition for “Visual” includes any visually processed material, including graphics and text. Because the World Wide Web is primarily a visual medium, this modality will probably always appear in all courses. Only a blank Web page would fail to meet the criterion. Even pages with audio files include progression bars that may act as a scaffold for keeping visual learners focused. Some lessons also presented material using the auditory mode (18%), tactile mode (15%), or kinesthetic mode (9%). Those courses integrating kinesthetic modalities were almost exclusively science courses in which students engaged in laboratory activities. The courses with tactile modalities varied widely across subject areas.

    1Most lessons required students to work independently (98%). In 26% of the

    lessons, they also work with their classmates as a group. “Class” interactions occurred exclusively in threaded discussion forums. Students worked in pairs or had a choice of their peer group configuration in only one course reviewed. None of the lessons required students to work in “small groups.”

    Instructional activities required students to use their “verbal linguistic” intelligence in 100% of the courses. The next most frequently used intelligence was “logical mathematical” (42%). Also integrated into some lessons were spatial (24%),

    bodily-kinesthetic (23%), interpersonal (21%), intrapersonal (17%), and naturalist (14%) intelligences. Only one course required students to engage their musical intelligence. There were no examples where students had the option to select the intelligence to use.

     1 This does not imply they work completely alone; they may interact several times with the teacher.


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