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Research Study Conducted Spring 2012 [research conducted at six MCCVLC colleges] How Do Online Faculty Course Designers Apply UD Principles to Courses?

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Presentation on theme: "Research Study Conducted Spring 2012 [research conducted at six MCCVLC colleges] How Do Online Faculty Course Designers Apply UD Principles to Courses?"— Presentation transcript:

1 Research Study Conducted Spring 2012 [research conducted at six MCCVLC colleges] How Do Online Faculty Course Designers Apply UD Principles to Courses?

2 6 MCCVLC Colleges Participated About 120 online faculty at all six colleges were emailed to participate and 33 of those “designers-by-assignment” responded. Results were typical to all six colleges; no one college had atypical responses or results, so results are likely to be transferrable to all the MCCVLC member colleges.

3 Rationale: Online learner attrition is higher than in traditional classes. Attrition is higher in community colleges, and distance education in particular "sees a larger number of students who fail to persist than traditional courses" (Stanford-Bowers, 2007, pp. 15, 93). Causes of this attrition remains uncertain(Angelino, Williams, & Natvig, 2007; Cross, 2008; Gleason, 2004; Groleau, 2004; Moody, 2004; Morris, Wu, & Finnegan, 2005; Parker, 1999; Tyler-Smith, 2006; Stanford- Bowers, 2007).

4 Attrition and Course Design Extraneous Cognitive Load is a major factor affecting performance of all “outlier” learners, and possibly most learners overall. “It is believed that attending to and explaining how some learners may be affected by cognitive overload and the development of strategies to deal with it will reduce early attrition, improve retention and enhance learning outcomes” (Tyler-Smith, 2006, “Conclusion”).

5 Who are “outlier” learners? Over age 35; people seeking new careers, advancement in current careers, single parents, etc.; often working while attending classes. ADD / ADHD adults wanting to work at their own pace. People with learning challenges that prefer to work outside a traditional classroom: dyslexia, visual challenges, auditory challenges, etc. Even ESL students.

6 Why Universal Design? Application of UD principles to online course designs would emphasize a focus on learning designs equally suitable and effective for all learners. UD can meet learning needs of “outlier” learners.

7 Universal Design can Help Universal design (UD) is defined as "the design of products and environments to be usable by all people, to the greatest extent possible, without adaptation or specialized design” (Universal Design Alliance, 2011, "What is universal design," para. 2).

8 UD Principles for Online Course Design Elias (2010, “Eight UID principles”) proposed eight UID principles for use in education. Six apply to online course designs: equitable use flexible use simple and intuitive design design using perceptible information tolerance for error low physical and technical effort

9 6 UD Principles for Online Defined Equitable use: The design is useful and accessible for people with diverse abilities and in diverse locations. The same means of use should be provided for all students, identically whenever possible or in an equivalent form when not. Flexible use: The learning design accommodates a wide range of individual abilities, preferences, schedules, and levels of connectivity. Provide the learners with choice in methods of use. Simple and intuitive: The course interface design is easy to understand, regardless of the user’s experience, knowledge, language skills, technical skills, or current concentration level; it should eliminate unnecessary complexity. Perceptible information: The design communicates necessary information effectively to the user, regardless of ambient conditions or the student’s sensory abilities. Tolerance for error: The design minimizes hazards and adverse consequences of accidental or unintended actions. Low physical and technical effort: The design can be used efficiently and comfortably and with minimal physical and mental fatigue.

10 My Research with Six MCCVLC Colleges How much do faculty course designers (designers-by-assignment) know about UD, and which principles of UD do they typically apply in their online course designs?

11 Definitions of UD 33 participants were asked to define UD according their current understanding: About one-third could give an approximate definition of universal design. A little over one-third had general understanding of universal design. The remainder had no familiarity with UD.

12 Reported Use of UD in Design: Top 3 UD Principle N Rept’d Used Rept’d Unused Reasons Equitable Design 20155 Applied: Need to be “fair” to all students. Not Applied: Possible student disabilities; lack of faculty training; CMS limitations. Flexible Use in Design 18144 Applied: Accommodate students’ schedules; keep students engaged; provide content in varied ways for different learning styles. Not Applied: One stated: “[There is] one method for everyone [and all] must master the content.” Simple and Intuitive Design 17 Applied: Just do as well as possible with tools. Five noted challenges trying to adjust course designs or include usage guides for students experiencing difficulties with the CMS/LMS interfaces or tools.

13 Reported Use of UD in Design: Bottom 3 UD Principle N Rept’d Used Rept’d Unused Reasons Perceptible Information in Design 19136 Applied: Make ‘design’ take back seat to information; lower student frustration to facilitate focus on content (not technology). Not Applied: Course is largely text-based; unsure how to apply. Tolerance for Error in Design 168 8 [diff.] Applied: Most gave no reasons. Not Applied vs. Difficult to Apply: CMS limitations; often required to override CMS or other tech-problems on behalf of students. Low Phys./Tech. Effort in Design 17125 Applied: Most just reported they “try” to apply OR they rely on CMS pre-sets for usability; one mentioned designing for non-traditional / possible minor disabilities; make things easy to read for everyone. Not Applied vs. Difficult to Apply: Course is very writing-intensive (at computer); course content requires ‘mental rigor’ (at computer); students should expect ‘mental fatigue’ when learning.

14 Verification of Applications of Equitable Design Frequency of response data reporting any reference to specific need-related concerns among learners for this UD principle:

15 Verification of Applications of Low Physical/Technical Effort in Design 4 participants explained accommodative designs connected to vision problems. Frequency of response data reporting any reference to specific need-related concerns among learners for this UD principle:

16 General Conclusions 1. Survey responses indicate much unfamiliarity with or incomplete levels of understanding about UD among designers-by-assignment. 2. Most designers-by-assignment cited only equitable use, flexible use, and simple and intuitive use as the UD principles they typically apply to their online course designs. 3. Only about half of respondents selected perceptible information, tolerance for error, and low physical/technical effort as UD principles they try to apply.

17 Detailed Conclusions Follow-interviews and course observations showed that respondents assigned individualized valuations and interpretations of what each UD principle actually means, as seen in the following examples: A PowerPoint presentation was cited as design to meet the UD principle guidelines for flexible use Course menu areas and portlet headers/content were cited as examples of simple and intuitive design

18 Loose Interpretations of UD Equitable – taken to mean “design components are the same for everyone,” but not to mean designing to address varied possible needs. Flexible – assumed, as long as students could access materials from multiple points or adjust fonts on their own computers, etc. Simple and intuitive – usually connected to navigational elements only, such as labels. Perceptible – typically seen as ease-of-use within or by CMS structure, not as sensory-aware design.

19 While these design elements do illustrate the application of those UD principles at basic levels, they do not firmly establish that designers-by-assignment are applying UD principles in specific ways that could address “outlier” learners’ needs.

20 My study emphasized over-40 learners, a large “outlier” cohort dealing with: Decreased visual acuity affects reading text online (Humes et al., 2007) Decreased hearing (Souza & Boike, 2006, p. 146) Lowered speed and accuracy in motor skills affects both typing and response selections (McMahan & Sturz, 2006; Smith, Sharit, & Czaja, 1999) Slower cognitive processing speed for both content and task requirements (Buchler, Hoyer, & Cerella, 2008; Kray & Lindenberger, 2000; Mallo, Nordstrom, Barterls, & Traxler, 2007)

21 Fast Fact: In 2007-2008, the American Association of Community Colleges (2011) reported that at least 15 percent of all community college learners were over age 40. MCCVLC Research Responses: 22 of 33 stated online learners over the age of 40 presented course design challenges specifically due to their low levels of technology skills. Referring to needs of over-40-learners: Only four mentioned possible visual needs; one mentioned possible learning disabilities; one mentioned possible hearing problems; none mentioned slower cognitive processing or motor skills.

22 Recommendation Online Faculty want to provide quality instruction to their students, but may lack knowledge and training to design their courses effectively for “outlier” learners like over-40 learners, ADHD learners, challenged and special needs learners, ESL learners. 1. Provide online faculty course designers with information about the learning needs of “outlier” learners. 2. Provide online faculty course designers with training and tools needed to address and meet the needs of “outlier” learners.

23 References American Association of Community Colleges. (2011). Fast facts. Retrieved from Angelino, L. M., Williams, F. K., & Natvig, D. (2007). Strategies to engage online students and reduce attrition rates. Journal of Educators Online, 4(2). Retrieved from Buchler, N. G., Hoyer, W. J., & Cerella, J. (2008). Rules and more rules: The effects of multiple tasks, extensive training, and aging on task-switching performance. Memory & Cognition, 36(4), 735-748. doi:10.3758/MC.36.4.735 Cross, D. B. (2008). Pre-entry characteristics: A study in the use of an internet- based self-assessment survey for predicting persistence in adult online education. (Doctoral Dissertation). Retrieved from ProQuest Dissertations and Theses. (3291953) Elias, T. (2010). Universal design principles for Moodle. The International Review of Research in Open and Distance Learning, 11(2). Retrieved from irrodl/article/view/869/1575 Gleason, B. J. (2004). Retention issues in online programs: A review of the literature. Second AIMS International Conference on Management, December 28-31, 2004. Retrieved from Groleau, D. G. (2004). An analysis of barriers to adult learner persistence in online and face-to-face courses. (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses. (3132736)

24 Humes, L. E., Burk, M. H., Coughlin, M. P., Busey, T. A., & Strauser, L. E. (2007). Auditory speech recognition and visual text recognition in younger and older adults: Similarities and differences between modalities and the effects of presentation rate. Journal of Speech, Language, and Hearing Research, 50(2), 283-303. doi:10.1044/1092- 4388(2007/021) Kray, J., & Lindenberger, U. (2000). Adult age differences in task switching. Psychology and Aging, 15(1), 126-147. doi:10.1037/0882-7974.15.1.126 Mallo, J., Nordstrom, C., Bartels, L., & Traxler, A. (2007). Electronic performance monitoring: The effect of age and task difficulty. Performance Improvement Quarterly, 20(1), 49- 63. doi:10.1111/j.1937-8327.2007.tb00431.x McMahan, S., & Sturz, D. (2006). Implications for an aging workforce. Journal of Education for Business, 82(1), 50-55. doi:10.3200/JOEB.82.1.50-55 Moody, J. (2004). Distance Education: Why Are the Attrition Rates so High?. Quarterly Review of Distance Education, 5(3), 205-210. Retrieved from Morris, L. V., Wu, S., & Finnegan, C. L. (2005). Predicting Retention in Online General Education Courses. American Journal of Distance Education, 19(1), 23-36. doi:10.1207/s15389286ajde1901_3

25 Parker, A. (1999). A study of variables that predict dropout from distance education. International Journal of Educational Technology, 1(2). Retrieved from Smith, M. W., Sharit, J., & Czaja, S. J. (1999). Aging, motor control, and the performance of computer mouse tasks. Human Factors, 41(3), 389. doi:10.1518/001872099779611102 Souza, P. E., & Boike, K. T. (2006). Combining temporal-envelope cues across channels: Effects of age and hearing loss. Journal of Speech, Language, and Hearing Research, 49(1), 138-49. Retrieved from Stanford-Bowers, D. E. (2007). Online persistence in community college distance education: Perceptions of major stakeholders. (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses. (3245295) Tyler-Smith, K. (2006). Early attrition among first time e-learners: A review of factors that contribute to drop-out, withdrawal, and non-completion rates of adult learners undertaking e-learning programmes. Journal of Online Learning and Teaching, 2(2). Retrieved from smith.htm Universal Design Alliance. (2011). What is universal design? Retrieved from

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