Assessing the Effects of Segmentation and Signaling in Tegrity Lecture-Capture Videos and Students’ Learning Outcomes
Ryan Treadwell, Mohamed Ibrahim and Rebecca Callaway Arkansas Tech University United States
[email protected] [email protected] [email protected] Abstract Segmentation and signaling design principles based on the cognitive theory of multimedia learning (CTML) will be applied to Tegrity lecture videos inside of a Blackboard learning module. Students in a Business Statistics course will participate for a grade (n = ~60). Participants will be randomly placed in an experimental group (SS) or control group (non-SS). The SS group will view four Tegrity lectures that have been segmented and signaled with a bulleted list of the key concepts at the beginning and a summary at the end of each segment. The SS group will sequentially progress through a learning module, and the non-SS group will cover the same material without any design principles applied. Participants will be assessed based on retention and transfer knowledge after all modules have been completed. Based on the results of previous studies, we hypothesize that the use of segmentation and signaling design principles on Tegrity videos will enhance student learning. 1. Objectives College enrollment has dramatically increased as a result of the importance of a degree in today’s job market. Online courses offer the convenience of earning college credits without having to step onto a campus and have influenced enrollment numbers. In fall 2009, colleges experienced the largest annual increase in online enrollment, as more than twenty-one percent of college students were taking at least one online class. One million more students took an online class than in 2008, bringing the total to around 5.6 million. During the fall 2010 term, this number rose again to more than 6.1 million (Lytle, 2011). Most online learning environments have some type of learning management system (LMS) to keep content organized such as Moodle, Desire2Learn, and Blackboard. One of the growing components of learning management systems is online video, and a key part of online video within the LMS is lecture capture such as echo360, Kaltura, and Tegrity. Tegrity is a cloud-based lecture capture solution that allows instructors and students to capture audio, video and computer screen activity. Tegrity packages the components into a single “session” and automatically uploads the recording for viewing. Tegrity can be used in classrooms, from home or office computers, or from an iPad or iPhone. While products such as Tegrity allow students to review content and learn at their own convenience, there is a tendency for instructors to create lengthy videos without
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considering design aspects that might optimize student learning. According to cognitive scientists, we should design multimedia based on best learning practices such as signaling, segmentation, weeding, synchronizing, and individualization (Mayer & Moreno, 2003). Many studies observe the effects of differing multimedia designs on illustrations, animation, and simulations (Brünken, Steinbacher, Plass, & Leutner, 2002; Mayer & Moreno, 1998; Schnotz & Rasch, 2005), but very little research has examined the effects of design principles on Tegrity video. Therefore, the purpose of this study is to assess students’ learning outcomes by applying segmentation and signaling to Tegrity videos in an online module. 2. Theoretical Framework The Cognitive Theory of Multimedia Learning (Mayer, 2001) borrows important aspects of various learning theories in explaining the learning processes and working memory involved in various multimedia formats. Mayer’s framework takes into consideration that information can be encoded through an auditory or verbal channel, but each channel has a limited capacity of working memory and can become overloaded (Reed, 2006). Extraneous and intrinsic cognitive load must be reduced for information to be processed efficiently and effectively to maximize learning outcomes. Cognitive researchers have reported specific design principles to reduce cognitive load and increase efficiency of working memory such as the placement of text and images (Mayer & Moreno, 1998), blending auditory and visual channels (Craig, Gholson, & Driscoll, 2002), and signaling and segmentation (Mayer & Moreno, 2003). Signaling is the technique providing cues to the learner about how to select and organize the material being presented (Meyer, 1975). Signaling reduces cognitive load by limiting the demands on working memory (Mautone & Mayer, 2001) and can be achieved through various ways such as using titles and headings to label the dominant themes in a passage (Lorch, 1989; Meyer, 1975) or by providing previews and overviews before or after a passage highlighting the major topics or general organization (Britton, Glynn, Meyer, & Penland, 1982; Lorch, 1989; Meyer, 1975). Signaling has been implemented in previous studies using speech, written text, and animation (Mautone & Mayer, 2001). The cognitive load placed on the visual channel in animation closely relates to online video and the load required from the visual channel. Therefore, this study will focus on signaling due to the similarities between animation and online video. Segmentation is the principle in which a presentation is broken down into segments to allow the learner ample time to organize selected words and images from the segment before the next segment begins (Mayer & Moreno, 2003). It is possible for breaks to be implemented automatically or by user control (Mayer & Chandler, 2001). Results of segmenting animation indicate that students understand a multimedia explanation better in segments than as a continuous presentation (Mayer & Chandler, 2001). Once again, animation places a high cognitive load on the visual channel and is comparable to online video and its similar requirements. This study will focus on segmentation as a key component due to the similarities between animation and online video.
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3. Methods This study will use a quasi-experimental design to assess the effects of segmentation and signaling on students’ retention and transfer of knowledge in a statistics class using online Tegrity video. The video will be placed in an online learning module using the Blackboard course management system. Students’ prior knowledge will be taken into account and included as a covariate. Participants will be randomly assigned into two groups using Blackboard’s random selection tool for group membership: a segmented and signaled video group and a group that uses the original instructional video without any design principles applied. Each module will be offered for two days to each respective group for credit. Participants: Based on prior enrollment in this course, we expect 35-40 males and 20-25 female participants in all sections. The average student age is anticipated to be 23 years old; the majority of students are juniors, and over 95% majoring in a business-related degree. Instrumentation: Blackboard’s course management system will house all assessments and surveys, as well as each learning module for the groups. Each module will be released using the Adaptive Release tool within Blackboard, which only allows students to access content based on criteria set by the instructor. Assessments will consist of a 20-question (five knowledge retention, fifteen transfer knowledge) multiple choice and short-answer pre-test, and a 20-question (five knowledge retention, fifteen transfer knowledge) multiple choice and short answer post-test. All tests and assessments will be developed by the course instructor. Materials: The Tegrity videos used for the experiment module will cover introductory topics for the Business Statistics course. The experiment module will include four segmented Tegrity videos with distinct titles that have been signaled with a bulleted list of the ideas covered in the video at the beginning and a summary at the end of each segment of the key concepts. Video one discusses distinguishing between quantitative and qualitative variables, and helps students identify what kind of analysis to use. The video also helps students compute means, standard deviations, and proportions of percentages in groups depending on what type of analysis is to be used. The second video covers problem characteristics and teaches students how to set up a data set in a data mining computer program. Video three helps students identify samples and populations so students can describe sample data and then describe characteristics about the larger population of a sample. Finally, video four covers the setting of hypotheses and teaches students how to read a set of problems and then write a hypothesis for each. The nonmodified module will include the same videos un-segmented and un-signaled. Procedure: Students will be randomly assigned to either the modified or non-modified group in Blackboard. Students complete a demographic survey, the pre-tests, review the video(s) in the respective modules, and complete the post-tests within a two-day time frame. Each step of this process will be released based on the completion of the previous item.
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Data Analysis: MANCOVA will be used to assess the video design effects of segmentation and signaling (independent variables) on retention and transfer knowledge (dependent variable); prior knowledge will be included as covariate. 4. Data Sources This study will attempt to answer the following questions: Research Question 1: Does applying segmentation and signaling to a Tegrity video improve the overall learning outcomes for novice students in a business statistics course? Research Question 2: Does applying segmentation and signaling to a Tegrity video have a greater effect on knowledge retention or transfer knowledge? 5. Results and conclusions Previous studies focusing on improving learning from complex material such as illustrations and animation (Boucheix & Guignard, 2005; Mautone & Mayer, 2001; Mayer & Chandler, 2001), online learning modules (Ibrahim & Callaway, 2012), and video (Moreno, 2007) encourage the use of segmentation and/or signaling. Therefore, it is anticipated that students in the SS group will show significant improvement in learning outcomes compared to the control group, thus supporting segmenting and signaling of Tegrity videos. 6. Educational importance of the study The proposed study is significantly important for improving learning in online learning environments. Very few studies examined design principles in online instructional video, especially Tegrity video inside of a learning management system like Blackboard. As more students enroll in web-based classes, it is crucial that we find the best strategies to enhance student learning within LMS’s utilizing lecture capture. Because the main focus of higher education is enhancing students’ higher-level learning, this study is an attempt to improve the delivery of Tegrity videos and consequently help students with low prior knowledge better-understand complex content and improve the transfer of knowledge in an online environment. References $ % & ) !
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