«IMPACTS OF LEARNING STYLES AND COMPUTER SKILLS ON ADULT STUDENTS’ LEARNING ONLINE Salih RAKAP Department of Special Education, College of ...»
TOJET: The Turkish Online Journal of Educational Technology – April 2010, volume 9 Issue 2
IMPACTS OF LEARNING STYLES AND COMPUTER SKILLS ON ADULT
STUDENTS’ LEARNING ONLINE
Department of Special Education, College of Education, University of Florida, 1403 Norman Hall PO Box
117050, Gainesville, FL, 32611-7050, USA
This study investigated the influences of learning styles/preferences, prior computer skills and experience with online courses on adult learners’ knowledge acquisition in a web-based special education course. Forty-six adult learners who enrolled in a web-based special education course participated in the study. The results of the study showed that (a) learning styles/preferences had significant effects on adult students’ knowledge acquisition, and (b) there is a moderate positive correlation between computer skills and students’ success. Data analysis also showed that there is no relationship between prior experiences with online courses and success in a web-based course.
Keywords: Web-based learning, learning styles, computer skills, adult learners
Many teachers and other related service providers often find that their knowledge, skills and practices are not compatible with the current research-supported, evidence based practices and in need of professional development (Ludlow, Foshay, Brannan, Duff, & Dennison, 2002). However, many professionals, especially the ones living in rural areas, have little opportunity to travel to higher education institutions to take courses to upgrade their knowledge, skills and practices. One answer to this challenge is the design and implementation of web-based education and training programs related to special education (Blackhurst et al., 1998). Web based instruction is convenient and flexible, and as such, ideally suited for in-service teachers without access to higher education institutions. It also makes the teaching and learning possible any time and in any place (Steinweg, Davis, & Thomson, 2005; O’Neal, Jones, Miller, Campbell, & Pierce, 2007). Although web-based courses and programs have opened new avenues for many professionals to update their knowledge, in order to be effective, this type of course delivery requires a careful planning in terms of instructional design, learning activities and materials.
Much of the research in special education investigating the effectiveness of web-based courses has focused on either learners’ satisfaction and perceptions (Beard & Harper, 2002; Ludlow et al., 2002; Spooner, Jordan, Algozzine, & Spooner, 1999) or the comparison of web-based courses with traditional face-to-face courses based on grade, satisfaction, and instructor evaluation (Caywood & Duckett, 2003; Pindiprolu, Paterson, Rule, & Lignugaris/Kraft, 2003; Steinweg et al., 2005; O’Neal et al., 2007). The findings of the research investigating the outcomes of web-based instruction in the area of special education are positive and suggest that participants of web-based courses like the course format. The studies comparing on-campus and distance education found no difference between two methods in terms of student achievement, satisfaction and instructor evaluation.
Although many researchers and educators have long acknowledged and supported the concept that personal differences play an important role in learning and academic achievement (Kim & Michael, 1995; Moallem, 2007; Zhang, 2002), the research addressing web-based courses and their effectiveness in special education has not paid much attention to these differences. These individual differences, in the case of online learning, include but not limited to differences on learning styles/preferences, prior experience with online courses, selfregulation, and computer literacy (Miller & Miller, 2000).
Purpose of the Study The main purpose of the current research, therefore, was to investigate the influences of individual learning styles/preferences and prior computer skills on adult learners’ knowledge acquisition in an online text-based
special education course. The research questions for this study were as follows:
1. How do individual learning styles/preferences influence adult learners’ knowledge acquisition in a webbased special education course?
2. What is the relationship between adult learners’ computer skills and learning in a web-based special education course?
3. Is there any difference on student success based on prior experience with web-based courses?
Copyright The Turkish Online Journal of Educational Technology 108 TOJET: The Turkish Online Journal of Educational Technology – April 2010, volume 9 Issue 2 Learning Styles There has been a growing body of research investigating web-based instruction and its various aspects. However, the characteristics of learners who enroll in online programs have not been investigated extensively (Kelly & Schorger, 2002; Liu, 2007; Saba, 2000). Moreover, the relationship between web-based learning and learning styles of individuals who enroll in online courses has received little attention in the literature (Harris, Dwyer, & Leeming, 2003). However, many researchers stated that in order to provide appropriate learning opportunities to students, improve their motivation and maximize their learning in web-based courses, it is important to identify their learning styles and adapt teaching methods that meet the diverse needs of learners (Drennan, Kennedy, & Pisarki, 2005; Hawk & Shah, 2007; Johnson, 2004; Wehrwein, Lujan, & DiCarlo, 2007). Research investigating the learning styles has mainly focused on comparing learning preferences of students who enrolled to online and traditional courses and their academic achievement (Aragon, Johnson, & Shaik, 2000; Buerk, Malmstrom, & Peppers, 2003; Downing & Chim, 2004; Garland, 2003; Halsne & Gatta, 2002; Liu, 2007; Manochehri & Young, 2006). The result of these studies is non-conclusive. Only in two studies, the researchers examined the relationship between learning styles and academic performance. Bozionelos (1997) reported that students with a particular learning style (i.e., active experimentation model) performed better than their classmates with other learning preferences in an online course. Harris et al. (2003) found that individual learning styles did not influence students’ mean test scores. However, participants of these studies were either undergraduate or graduate students and there is no study found in the literature that focused on examining the relationship between adult students’ learning preferences and academic achievement in online courses.
Many different learning styles/preferences and definitions of learning styles exist in the literature. Keefe (1979) defines learning styles as typical psychological, cognitive and affective behaviors that serve as reasonably stable indicators of how individuals perceive, respond to and interact with learning environments. Reid (1995) characterizes learning style as favored ways of taking in, processing and maintaining new information and skills.
Fleming (2001) defines learning style as “an individual’s preferred ways of gathering, organizing, and thinking about information (p.1). As parallel with variation on its definition, there are many different methods for assessing learners learning styles. One of the commonly used learning style/preference inventory, the VARK Questionnaire (Fleming, 2001), is used in the current study. VARK stands for visual (V), aural (A), read/write (R) and kinesthetic (K) learning preferences. Learners with visual learning preference learn best by observing, watching and seeing. Aural learners learn through listening, discussing and talking. Read/Write type learners learn best by interacting with textual materials. Learners with kinesthetic learning preference learn best by doing.
Table 1 provides the learning activities offered by Fleming (2001) to support each learning style. Additional information about the VARK Questionnaire is provided in the Methods section.
Computer Skills Computer skills and comfort with different technological applications are considered essential components of student success when the courses are offered online (Erlich, Erlich-Philip, & Gal-Ezer, 2005; Jameson & McDonnell, 2007; Martz & Reddy, 2005, Shih, Munoz, & Sanchez, 2006; Summers, Waigandt, & Whittaker, 2005). However, several researchers have claimed that computer skills have little or no impact on student success and participation (McIsaac, Blocker, Mahes, & Vrasidas, 1999; Rumprapid, 1999). Since the results of these researchers is not convincing, there is a need for further investigation of this relationship. In addition, these studies have been conducted with either college or graduate student and no previous research examining the relationship between adult learners’ computer skills and knowledge acquisition is identified in the literature.
Copyright The Turkish Online Journal of Educational Technology 109 TOJET: The Turkish Online Journal of Educational Technology – April 2010, volume 9 Issue 2 Course Format The Department of Special Education in a southeastern university has been offering a series of courses that upon completion will allow special education teachers to obtain the Autism Endorsement through the state’s teacher certification office. The series consists of 4 web-based courses Teachers who participate in the project complete online program in one year.
The present study was conducted in their first semester in the program when students took a course focusing on intervention techniques to support communication and social development of students with autism in the Summer 2008 semester. This course was designed to prepare educators to understand the communication and social skills of individuals with autism. The focus of the course was on the classroom-based strategies for promoting effective communication through the use of assistive technology and augmentative and alternative communication. The course was delivered asynchronously and utilized a text-based format.
The course consisted of six modules. One topic in each module was introduced and covered each week. The objectives of the module, an introduction and a power point presentation were provided at the beginning of each module. Students were assigned readings, given a quiz and an assignment in each module. The assignments provided students with the opportunities to apply the knowledge they acquired through readings, class presentations and discussions. The students also participated in threaded discussions related to the module.
Threaded discussions were asynchronous and available 24 hours per day during the module periods. These discussions provided students with opportunities to interact with each other and the instructor. The instructor developed at least one activity or course material to address different needs of each learner. The course model and delivery of instruction is illustrated in Figure 1.
METHODSParticipants Following their acceptance into the program, registered students were asked to complete consent forms and a background information form if they agreed to participate in the current study. As a result, a total of 46 students agreed to participate in this study. Participants’ demographic information and information regarding their computer skills is presented in Table 2.
Instruments Instruments used in this study include a background information survey, the VARK questionnaire, and SelfEvaluation of Technology Use survey.
Evaluation of learning styles: The VARK learning preference questionnaire was selected to evaluate learning preference of adult students because it is very easy and quick to complete and available online. The VARK includes 13 multiple-choice questions to examine four different modalities (i.e., Visual, Aural, Read/Write, and Kinesthetic). In each question, respondents are placed in a real life learning situation, offered four options and asked to choose option(s) which best characterizes their way of learning. Respondents are allowed to choose more than one option if necessary or omit a question if no responses apply. As a result of evaluation of an individual’s responses to the questionnaire, a person might have a single learning preference known as unimodal, or more than one learning preference known as multimodal (see Figure 2 for conceptual model).
Subcategories of multimodal learning preference include bi-modal (having two learning preference) tri-modal (having three learning preference) or quad-modal (having four learning preference).
Evaluation of computer use, knowledge and skills. Self-Evaluation of Technology Use survey is used to measure participants’ computer use, knowledge and skills prior to the course. In this survey, participants were asked to evaluate their skills on some technology tools (e.g. internet search, word processing, e-mail, electronic library etc.) and asked about their prior experience on distance education and web-based courses.
Evaluation of student learning. Students’ knowledge acquisition was evaluated using the results of six quizzes that they took throughout the semester. Students were allowed to retake the quizzes if they want to improve their grades. In this study, each student’s initial quiz scores were used.
Procedures Following their acceptance into the program, a consent form along with the background information form was sent to the students. Students who returned a signed consent form to the researcher received a survey package one week before the semester began. This package included the VARK Learning Style Questionnaire and SelfEvaluation of Technology Use. Participants were given one week to complete and return the survey package to the researcher. Quiz scores of participants were obtained from the course instructor.