Cognitive Ability, Cognitive Style, and Learning Preferences for Multimedia Learning in the Visual and Verbal Domains Natalie Toomey Duquesne University Doctoral Program in Instructional Technology and Leadership United States
[email protected] Abstract: This paper is a report of the findings of a pilot study designed to observe trends and optimize a measurement model for the determination of cognitive ability, cognitive style, and learning preferences on the visualizer-verbalizer scale and compare these characteristics with choices of multimedia learning format options. Findings suggest an overall preference for and selection of mixed multimedia formats. Differences in selections of multimedia formats between males and females as well as lack of clear inclusion of an audio option in questions directed towards the verbalizer dimension indicate a need for adjustment to the survey model.
Introduction Technology-enabled learning has become a staple of the 21 st century classroom. As a part of technology-enabled learning, online learning has steadily expanded in popularity as well as availability over the years and has reached a point of growth exceeding that of its face-to-face learning counterpart (Allen & Seaman, 2010). In the online classroom, students are heavily if not exclusively reliant on digital media in their interactions with course content and learning materials (Clark, 2005). This digital media facilitates online learning while also allowing for a high degree of flexibility in how information can be presented to learners, ranging from simple text to image, audio and video files, animations, and other forms of interactive media (Anderson, 2008). Theories of multimedia learning, specifically the multimedia learning hypothesis, indicate that learning is optimized when individuals are presented with mixed formats of materials in the form of images and text (Mayer, 2005). This concept is derived from cognitive load theory, which explores how humans process and, in turn, learn new information (Sweller, 2005). Cognitive architecture is theorized to exist as memory structures that work together to take in and process information. These structures consist of the sensory memory which allows us to detect new information, the working memory where we actively process novel information, and the long term memory where our accumulated knowledge is stored. The process of learning then relies on the ability to detect new information via sensory input, process this information in the working memory, and finally the transfer of this information into the long-term memory. Limitations arise however, as the working memory has a limited capacity for the intake of novel information (Sweller, 2005). With the findings that the working memory is divided into two channels: a channel for processing visual information and a channel for processing phonological or speech based information (Baddeley, 1992), multimedia learning indicates that learning increases when both working memory sub-channels are used in the presentation of novel information (Sweller, 2005). The combination of these concepts then form the multimedia principle which, corresponding to the multimedia learning theory, states that learning outcomes are best achieved when combinations of images and text are used. This principle additionally states that learning is further enhanced by the presentation of images in conjunction with narration (Fletcher & Tobias, 2004). Given these conceptualizations of cognitive structure with regard to working memory and information intake and how these are impacted by variations of multimedia presentations, it is worthwhile to consider how students’ cognitive and learning characteristics might factor into perceptions of multimedia and how selections of materials correspond to individual characteristics as well as the multimedia principle and multimedia learning theory (Irani, Telg, Sherler, & Harrington, 2003; Leutner & Plass, 1998).
The Study
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Today’s online learning technologies allow instructors easy creation and use of varied multimedia learning materials. These materials often include text, images, audio tracks and combinations thereof. Consequently, online students have potential access to a wide selection when choosing materials for their own learning. Given this potential variety, considerations of what motivates learner selections and how these selections may correspond with innate learner characteristics may be useful in order to better inform instructional design. The study was initiated with the assumption that the visualizer-verbalizer hypothesis could provide a strategy for measuring learner characteristics via the concept that some individuals learn better through words while others learn better through pictures (Mayer & Massa, 2003). The study will eventually measure participant characteristics on this visualizer-verbalizer scale and compare the results with choices and selfexpressed preferences for online multimedia materials. During a pilot study phase, the aim was on observation of overall trends in order to optimize the survey instrument for future experimental settings and study. During the pilot study, a modified version of Mayer and Massa’s (2003) original instrument was created to measure cognitive ability, cognitive style, and learning preferences along the visualizer-verbalizer scale. This 10-measure instrument consisted questions measuring cognitive ability (two question items and one timed vocabulary test), visual-spatial ability (one timed authentic task and one verbal-spatial ability questionnaire), cognitive style (two learning style questionnaires), and learning preference (two learning scenario/preference questionnaires and one computer-based behavior test). The results of these measures were then compared with selections of multimedia options in a simulated learning task to determine what, if any, correspondences existed. Participants for this pilot study were recruited from the general student population during the spring term of 2013 at a medium-size private university in a northeastern state of USA. Flyers were posted on campus advertising a request to participate in an online survey and a $20 incentive was offered for full completion. Participation was anonymous and the only criteria for participation were to be an active student, age 18 or over. Given the relatively small number of participants (51), descriptive statistics were used to provide an overview of each measure as well as explain trends and provide comparisons between measures. The data was also used to analyze the overall quality of the model based on participant responses and determine any alterations needed for the purpose of future study.
Findings The primary goal of this pilot study was to assess and improve design of a test instrument intended to determine participant cognitive ability, cognitive style, and learning preference on a visualizer-verbalizer scale and to compare these results with actual selections of online multimedia learning options. A total of 51 individuals participated in the pilot study, 24 males and 27 females. Results for eight of the 10 test measures are presented in Table 1 which lists the mean scores, standard deviations, and sample sizes.
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Measure 1. SAT Math 2. SAT Critical Reading 3. Sat Writing 4. Vocabulary Test 5. Paper Folding Test 6. Verbal-Spatial Ability Rating 7. Santa Barbara Learning Style Questionnaire 8. Visual-Verbal Learning Style Rating
M 619.77 618.18 595.91 8.32 5.46 3.75 18.58
Males SD 100.86 81.92 81.28 1.67 2.17 0.99 3.18
N 24 23 23 24 24 24 24
M 569.52 625.24 610.24 7.86 4.30 3.48 19.93
Females SD 87.43 102.84 96.36 2.20 2.00 0.89 1.47
N 23 23 23 27 27 27 27
3.42
1.93
24
4.04
1.53
27
Table 1: Measures 1- 4 represent cognitive ability, measures 4 and 5 spatial ability, measures 6 and 7 cognitive style and measure 8 learning preference. Measures 6-8 are rated on scales of 0 to 8, 0 to 36, and 0 to 6 respectively with values below each scale median representing verbal preferences and values above the median representing visual or spatial preferences. In addition to the items listed in table one, two measures were used to determine participants’ learning preference. In the first measure consisting of five questions of visual or verbal learning presentation options, participants consistently selected the visual option with the exception of question 4, “Which format do you prefer for following instructions on how to set time on a stopwatch?” Table 2 shows the overall results of this measure and a marked shift from visual to verbal selection for question 4 for female participants. Learning Scenario
Number of selections
30 25 20 15
M-Vis
10
M-Verb F-Vis
5 0 M-Vis M-Verb F-Vis V-Verb
V-Verb Q1 22 2 26 1
Q2 20 4 24 3
Q3 20 4 21 6
Q4 16 8 11 16
Q5 20 4 23 4
Table 2: Results of the Learning Scenario Questionnaire: M-Vis represents males selecting visual help, M-Verb: males selecting verbal help, F-Vis: females selecting visual help, and F-Verb: females selecting verbal help For the second measure of learning preferences, participants were presented with a learning scenario about the weather. Three options were offered: text only, image and text, and image and narration as help options to provide additional information. A total of 30 participants, 15 male and 15 female selected the image
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E-Learn 2013 and narration option as most preferred, while 37 participants, 17 males and 20 females, selected the text only option as the least preferred. The final measure used the same weather learning scenario to observe user selections of the above help options. Four questions were presented for which users were free to select text only, image and text, or image and narration. The results of this Multimedia Learning Preference Test are shown in Table 3. Participant selections are generally consistent with self-expressed preferences. Males showed consistent preferential selection of the image and narration option. Females preferential selected both the image and text and image and narration options for questions 1 and 2, shifting towards the image and text option for questions 3 and 4.
Table 3: Multimedia Learning Preference Test: M-Text represents males selection of text only option, F-Text: female selection of text only option, M-Image/Text: males selection of the image and text option, F-Image/Text: female selection of the image and text option, M-Image/Narr: males selection of the image narration option, and F-Image/Narr: females selection of the image narration option
Conclusions The overall purpose of this study was to assess overall trends and patterns within the test instrument for the purpose of optimization for future research. It was noted that males expressed a higher spatial ability than females consistent with the findings of the motivational study (Mayer & Massa, 2003). Males also had higher SAT Math scores than females. Females, however, exhibited slightly higher scores on the SAT Critical Reading and Writing sections than males. An interesting discovery was made in one of the learning scenario questionnaires; throughout the learning scenario questionnaires, participants indicated their preference for visual help, but in one of the question on mechanical manipulation of an object, they switched their learning preference to verbal help. While it can be speculated that the change might have been influenced by the question’s subject matter (too mechanical), future versions of this measure would benefit from the inclusion of additional, similar as well as different subject matter questions to further explore this trend. Additionally, in the selections of multimedia help, female participants showed a shifting preferential selection from image and narration to image and text while males tended to consistently choose the image and narration option. This would point to a possible shift in preferences for female participants which might indicate a need to re-survey participants on their multimedia preference following the selections section to determine more clearly any shift in preference, given that choice of multimedia help for female participants initially were consistent with self-expressed preferences. Overall, this study has provided useful data to inform the creation of an adjusted survey instrument for
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References Allen, I. E. & Seaman, J. (2010). Class differences: Online education in the United States, 2010. U.S.A: Babson Survey Research Group. Anderson, T. (2008). Towards a theory of online learning. In T. Anderson (Ed.), The Theory and Practice of Online Learning, Second Edition (p. 45-70). Edmonton, Alberta: AU Press. Retrieved from http://books.google.com/books Baddeley, A. (1992). Working memory. Science, 255, 556-559. doi:10.1126/science.1736359 Clark, R. C. (2005). Multimedia learning in e-courses. In R. E. Mayer (Ed.), The Cambridge Handbook of Multimedia Learning (p. 589-616). New York: Cambridge University Press. Fletcher, J. D. & Tobias, S. (2005). The multimedia principle. In R. E. Mayer (Ed.), The Cambridge Handbook of Multimedia Learning (p. 117-134). New York: Cambridge University Press. Irani, T., Telg, R., Scherler, C., & Harrington, M. (2003). Personality type and its relationship to distance education students course perceptions and performance. Quarterly Review of Distance Education, 4(4), 445453. Kolloffel, B. (2012). Exploring the relation between visualizer-verbalizer cognitive styles and performance with visual or verbal learning material. Computers & Education, 58. 697-706. doi:10.1016/j.compedu.2011.09.016 Leutner, D., & Plass. J. L. (1998). Measuring learning styles with questionnaires versus direct observation of preferential choice behavior in authentic learning situations: the visualizer/verbalizer behavior observations scale (VV-BOS). Computers in Human Behavior. 14(4), 543-557. doi:10.1016/S0747-5632(98)00023-5 Mayer, R. E. (2005a). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.) Cambridge Handbook of Multimedia Learning (p. 31-48). New York: Cambridge University Press. Mayer, R. E. & Massa, L. J. (2003). Three facets of visual and verbal learners: Cognitive ability, cognitive style, and learning preference. Journal of Educational Psychology, 95(4), 833-846. doi: 10.1037/0022-0663.95.4.833 Sweller, J. (2005). Implications of cognitive load theory for multimedia learning. In R. E. Mayer (Ed.), The Cambridge Handbook of Multimedia Learning (p. 19-30). New York: Cambridge University Press.
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