Using transparent whiteboards to boost learning from online STEM lectures

Accepted Manuscript Using transparent whiteboards to boost learning from online STEM lectures Andrew T. Stull, Logan Fiorella, Morgan J. Gainer, Richard E. Mayer PII:

S0360-1315(18)30039-3

DOI:

10.1016/j.compedu.2018.02.005

Reference:

CAE 3301

To appear in:

Computers & Education

Received Date: 16 May 2017 Revised Date:

31 January 2018

Accepted Date: 4 February 2018

Please cite this article as: Stull A.T., Fiorella L., Gainer M.J. & Mayer R.E., Using transparent whiteboards to boost learning from online STEM lectures, Computers & Education (2018), doi: 10.1016/ j.compedu.2018.02.005. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Using Transparent Whiteboards to Boost Learning from Online STEM Lectures Andrew T. Stull*, Logan Fiorella†, Morgan J. Gainer*, and Richard E. Mayer* University of California, Santa Barbara †

University of Georgia

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Submitted: May 16, 2017

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*

Resubmitted: February 1, 2018

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Author Note

Andrew T. Stull, Department of Psychological and Brain Sciences, University of California, Santa Barbara; Logan Fiorella, Department of Educational Psychology, University of Georgia; Morgan J. Gainer, Department of Chemistry and Biochemistry, University of

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California, Santa Barbara; Richard E. Mayer, Department of Psychological and Brain Sciences, University of California, Santa Barbara.

Correspondence should be addressed to Andrew T. Stull, Department of Psychological

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and Brain Sciences, University of California, Santa Barbara, CA 93106. Phone: 805-636-9769;

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E-mail: [email protected].

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Using Transparent Whiteboards to Boost Learning from Online STEM Lectures

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Submitted: May 16, 2017

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Resubmitted: February 1, 2018

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Abstract Research is needed to understand how best to design online videos that foster learning.

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This study explored the effects of using transparent whiteboards, which allow the instructor to stand behind a transparent glass board and face the students to write and draw while providing a concurrent explanation of the material. Specifically, the affordances of transparent whiteboard

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lessons might better follow design principles of multimedia learning and foster social agency compared to conventional whiteboard lessons, thereby promoting learning. In two experiments,

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college students viewed a 20-minute Organic Chemistry video lecture with the instructor using either a conventional whiteboard or a transparent whiteboard. Results indicated that students who viewed transparent whiteboard lessons performed better on immediate posttests (Experiment 1 and 2) at interpreting the configuration of spatial diagrams and at explaining key concepts. Students viewing transparent whiteboard lessons also reported more positive ratings of

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their lecture experience. However, Experiment 2 indicated that the benefits of learning from transparent whiteboards did not persist on a delayed posttest. Overall, this study provides the

instruction.

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first systematic investigation of the effects of using transparent whiteboards in video-based

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Keywords: multimedia learning; social agency; representational competence; STEM learning; online learning

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Using Transparent Whiteboards to Boost Learning from Online STEM Lectures 1. Introduction

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What is the best way to integrate words (e.g., what the lecturer says) and visuals (e.g., what the lecturer writes on the board) in video lectures in science, technology, engineering, and mathematics (STEM) disciplines? In a typical video lecture using conventional whiteboards,

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instructors often turn their back to the audience when they write or draw on the board and may be facing the board as they explain what they are writing or drawing. A disadvantage of this

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approach is that students cannot see the instructor’s face or hand while material is being written or drawn on the board. In contrast, imagine a scenario in which the instructor faces the class, so students can see the instructor’s face and hand as he or she writes and draws on the board. This scenario is accomplished by using transparent whiteboards, in which the instructor stands behind a large pane of glass facing the video camera while he or she writes or draws on the glass, and

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the camera reverses the instructor’s writing and drawing so that it is legible for the audience. The present study examines the potential of using transparent whiteboards to deliver

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video-based lectures, with the goal of advancing our understanding of multimedia design principles and social presence in online learning. Specifically, we compared student learning-

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outcomes from video lectures in organic chemistry supported by either a transparent whiteboard or a conventional whiteboard. With transparent whiteboards, instructors face the class as they create visualizations (e.g., by drawing maps, diagrams, or formulae) while providing a concurrent oral explanation of the material. Because the video camera reverses the image for the students, the instructor can face the students throughout the lesson (as shown in the left panel of Figure 1). In contrast, a conventional whiteboard requires the instructor to turn his or her back to the students when writing or drawing (as shown in the right panel of Figure 1). The main goal of

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the present study is to test the efficacy of this popular and trending system for delivering online lectures, and in doing so, extend existing theories of how students learn from multimedia lessons and to suggest research-based principles for improving the design of video-based instruction in

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STEM.

The proposed value of transparent whiteboards is expected to be particularly relevant for learning in STEM disciplines, in which there is a heavy demand on using complex spatial

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representations, known to be difficult to understand for many students (Wu & Shah, 2004). This prediction is grounded in a growing body of research and theory related to principles of

environments (Mayer, 2014b).

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multimedia learning (Mayer, 2009) and learning from social cues in multimedia learning

This study also relates to a growing interest within higher education to offer online lectures, although surprisingly little research has investigated research-based principles for the

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design of online video lectures. Video lectures are an important component of online instructional methods, as well as most technology-driven curricula (Means, Toyama, Murphy, Bakia, & Jones, 2009). For example, massively open online courses (MOOCs) have seen

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explosive growth in the last few years, with several prominent universities and commercial institutions offering numerous courses that record registrations in the millions (Breslow,

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Pritchard, DeBoer, Stump, Ho, & Seaton, 2013), although completion rates are typically less than 10% (Bonk, Lee, Reeves, & Reynolds, 2015; Shah, 2016). Furthermore, the effort to “flip” classrooms (i.e., to off-load lectures to video and dedicate class time to guided problem solving and learning activities) is also gaining much attention among instructors who wish to explore ways to engage students in classroom settings, but a common criticism is that video lectures are

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not engaging (Nielsen, 2012). The present study explores a new method for fostering engagement and learning from video lectures. 1.1 Instructional media vs. instructional methods

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The history of education is rich with unfulfilled claims that new media would

revolutionize the way students learn—including learning via motion pictures, radio, educational television, and computer-programmed instruction (Cuban, 1986, 2001). As the development of

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new technology continues to accelerate during the 21st century, similar claims are made

regarding learning from the Internet, computer games and simulations, mobile devices (such as

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smartphones and tablets), and online courses (such as MOOCs). However, there is no evidence that the instructional medium per se contributes to learning outcomes (Clark & Feldon, 2014). As Clark (1994, 2012) has argued, learning is caused by the instructional methods rather than by the instructional media used to present material to students. For example, learning the same

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material from a textbook or from a computer-based lesson is not on its own likely to result in different learning outcomes. At the same time, some media may offer unique affordances for the application of effective instructional methods. Computer-based instruction, for instance, allows

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for the provision of immediate, individualized feedback—a highly effective instructional method that is not easily afforded by a textbook. In short, the choice of which medium to use depends on

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the extent to which it makes use of instructional methods that promote learning. Thus, the focus of the present study is not on comparing and evaluating particular learning technologies (i.e., media comparison); rather, we propose that transparent whiteboard technologies may make use of effective instructional methods not present in typical video-based lessons (such as conventional whiteboard lessons), thereby fostering cognitive processing appropriate for meaningful learning (Mayer, 2002).

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1.2 Applying multimedia principles to whiteboard lessons Multimedia learning involves learning from lessons that contain words (i.e., written or spoken) and pictures (e.g., illustrations, diagrams, graphs, or charts) (Mayer, 2009). In a

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transparent whiteboard or a conventional whiteboard lecture, words are primarily presented via the instructor’s oral explanation and pictures are presented via the instructor’s dynamic creation of visuals (such as diagrams) on the board. According to the cognitive theory of multimedia

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learning (Mayer, 2009, 2014a), learners have a very limited processing capacity that they must use to engage in cognitive processing necessary for learning, which includes selecting the most

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relevant words and pictures from a lesson, organizing them into a coherent cognitive structure in working memory, and integrating it with prior knowledge activated from long-term memory, as shown in Figure 2.

Accordingly, instruction should serve to (1) minimize cognitive processing that is

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irrelevant to the instructional goal—or what is referred to as extraneous processing, (2) manage cognitive processing necessary for initially representing the material—or what is referred to as essential processing (and corresponds to the cognitive process of selecting)—and, (3) foster

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cognitive processing necessary for making sense of the material—or what is referred to as generative processing (and corresponds to the cognitive processes of organizing and

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integrating). Research on learning from multimedia has provided empirical support for several instructional principles designed to serve each of these goals (Mayer, 2009, 2014a). We propose that transparent whiteboard lessons and conventional whiteboard lessons inherently differ in the extent to which they follow three of these principles—the signaling principle, the temporal contiguity principle, and the segmenting principle, as summarized in Table 1 (Mayer & Fiorella, 2014; Mayer & Pilegard, 2014).

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According to the signaling principle, students learn better when a multimedia lesson contains verbal or visual cues to direct cognitive processing toward the most relevant information, thereby reducing extraneous processing (Mayer & Fiorella, 2014). Verbal cues

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include emphasizing key words in speech or using “pointer” words (e.g., “first, second, third…”, “however,” “in contrast,” etc.) and visual cues include the use of arrows, pointing, highlighting, numbering, or headings (e.g., Mautone & Mayer, 2001). A recent review by Mayer and Fiorella

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(2014) reports positive support for adding signaling features to multimedia lessons in 24 of 28 experimental comparisons, yielding a small-to-medium effect size of d = 0.41. This research

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suggests that signaling may be most useful for complex learning materials (Jeung, Chandler, & Sweller, 1997) and for students with low prior knowledge (Naumann, Richter, Flender, Christmann, & Groeben, 2007).

According to the temporal contiguity principle, students learn better from a multimedia

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lesson when the instructor’s spoken words are presented at the same time as the corresponding instructional visuals, rather than before or after the visuals are presented (Mayer & Fiorella, 2014). Following the temporal contiguity principle helps reduce extraneous processing because

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students are able to process words and pictures simultaneously in working memory instead of being forced to hold onto one representation before being presented with the other. In their

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review, Mayer and Fiorella (2014) found support for the temporal contiguity principle in 9 of 9 experimental comparisons, yielding a large effect size of d = 1.22. This effect appears to be strongest for lessons presenting complex material (Ginns, 2005) and for fast-paced lessons that are not under the learner’s control (Michas & Berry, 2000). According to the segmenting principle, students learn better from a multimedia lesson when it is presented in manageable parts rather than as a continuous unit (Mayer & Pilegard,

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2014). Breaking down material into parts helps manage essential processing because it allows learners to process each unit of the lesson before moving on to the next part of the lesson instead of being forced to process all of the information at once. A recent review by Mayer and Pilegard

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(2014) found positive support for the segmenting principle in 10 of 10 experimental tests,

yielding a median effect size of d = 0.79. Segmenting is particularly effective when the lesson content is complex and when the lesson presentation rate is fast-paced (Mayer, Dow, & Mayer,

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2003).

How do the signaling, temporal contiguity, and segmenting principles relate to learning

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from conventional whiteboard lessons? According to a recent study by Fiorella and Mayer (2016a), drawing diagrams on a conventional whiteboard can make use of each principle to some extent: the instructor’s hand directs students’ attention to the relevant visual information (i.e., the signaling principle); the instructor’s speech is synchronized with concurrently drawing the

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corresponding diagrams (i.e., the temporal contiguity principle); and the act of creating the diagrams inherently breaks down the material into parts (i.e., the segmenting principle). Indeed, a series of experiments by Fiorella and Mayer (2016a) found that observing the instructor draw

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diagrams during a concurrent oral explanation of the Doppler effect was more effective than listening to the same explanation while viewing equivalent static (i.e., already-drawn) diagrams.

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Importantly, the instructor stood to the side of the board so students could see what was being drawn, and the instructor attempted to face the class during much of the verbal explanation. 1.3 Potential instructional affordances of transparent whiteboards Given its inherent similarities to a conventional whiteboard lesson (as used in Fiorella & Mayer, 2016a), transparent whiteboards presumably afford each of the multimedia principles in much the same way. However, there are two major differences between the two media that

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suggest transparent whiteboards may offer unique learning benefits. First, conventional whiteboard lessons cause the problem of occlusion—that is, information being drawn on the board (as well as information that is already drawn on the board) is often temporarily blocked

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from the students’ view by the instructor’s body, arm, and hand. Consequently, occlusion during a lesson can violate the signaling, temporal contiguity, and segmenting principles—that is,

students are no longer cued to the relevant information, students are presented with the words

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and corresponding graphics at different times (because students must wait for the graphics to become visible again), and the segmentation of the material is no longer made explicit to

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students. Although instructors using conventional whiteboards can minimize occlusion, it is often difficult and even unavoidable when using a conventional whiteboard without training and extensive practice. In contrast, transparent whiteboard lessons do not present the problem of occlusion because the instructor is oriented behind the material presented on the board

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throughout the lesson. This allows students to spend more time attending to the relevant information (i.e., selecting)—such as watching a diagram as it is being drawn by the instructor— and it allows students to spend more time building connections among the different elements of

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information (i.e., organizing and integrating)—such as between the instructor’s words and a diagram being drawn, or between a diagram being drawn and diagrams that are already drawn.

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Beyond the problems associated with occlusion, conventional whiteboard lessons and

transparent whiteboard lessons also likely differ in the extent to which they provide social cues. Past research suggests that social cues such as using words presented in conversational rather than formal style (e.g., Kang & Tversky, 2016; Kartal, 2010; Mayer, Fennell, Farmer, & Campbell, 2004), or showing the instructor’s gesture, eye-contact, or facial expressions (e.g., Lusk & Atkinson, 2007; Mayer & DaPra, 2012; van Gog, Verveer, & Verveer, 2014) can

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promote learning. According to social agency theory (Mayer, 2014b), social cues activate a social response in the learner and create a sense of partnership (i.e., social agency) between the instructor and the learner, which motivates students to engage in generative processing.

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A transparent whiteboard may be more effective in motivating students to make sense of the material because it provides important social cues that are mostly hidden in conventional whiteboard lessons. For example, a transparent whiteboard allows instructors to face students

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and provide eye-contact (via video) throughout the lesson, whereas conventional whiteboard methods require instructors to repeatedly turn their back to students and thus lose eye-contact.

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Consequently, a transparent whiteboard may help establish a sense of social agency between the students and instructor, whereas conventional whiteboard lessons may be perceived as less personalized (Mayer, 2014b). Similarly, with transparent whiteboards, students are always able to see where the instructor is looking, whether at the camera or at material drawn or being drawn

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on the board. This is important because research suggests that people tend to follow the eyegaze of others (Langton, Watt, & Bruce, 2000), and that this form of gaze guidance could potentially benefit learning (van Gog et al., 2014). In other words, the eye gaze of the instructor

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may help support the signaling, temporal contiguity, and segmenting principles by cueing students when and where to look throughout the lesson. As a result, students are able to more

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effectively and efficiently attend to and build connections among the relevant information. Overall, the unique social cues provided by a transparent whiteboard suggest that it may be a more effective method for delivering video-based instruction than conventional whiteboard methods—by fostering student engagement in appropriate cognitive processing during learning, ultimately leading to better meaningful learning outcomes.

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1.4 The present study To compare the benefit of conventional whiteboard and transparent whiteboard methods, we examined how students in organic chemistry learn from video lectures utilizing these two

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lecture methods. Organic chemistry is a domain rich in representations and one where there are heavy demands on students to integrate oral, written, and drawn representations to understand complex spatial concepts (Harle & Towns, 2010). Spatial representations in organic chemistry

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are the lingua franca—placing high demands on students’ spatial ability (Wai, Lubinski, &

Benbow, 2009; Wu & Shah, 2004), and causing many students to struggle (Fiorella & Mayer,

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2016b; Stull, Gainer, Padalkar, & Hegarty, 2016; Stull & Hegarty, 2016; Stull, Hegarty, Dixon, & Stieff, 2012).

Figure 3 shows three representations of the same molecule. The focus of the lecture was to teach students about how 3D space can be represented in 2D, how internal rotations around

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carbon-carbon bonds produce different spatial configurations of a molecule, and how intramolecular interactions influence the stability (i.e., relative energies) of the possible configurations. For example, as shown in Figure 4, in the simplest case of ethane the Newman

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projection helps the student understand that, as the carbon-carbon bond rotates, the hydrogens bonded to neighboring carbons are brought closer in space until they reach a point at which they

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are directly in-line with one another (known as an eclipsed configuration; see Figure 4c). This configuration is less stable than when the hydrogens on neighboring carbons are farthest apart (known as a staggered configuration; see Figure 4b). Due in part to the increased interactions between neighboring hydrogens, the eclipsed configuration is a less stable configuration than the staggered configuration. Determining the relative stability of ethane’s different configurations is straightforward but it becomes progressively more difficult with larger molecules having larger

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bonded subgroups. Therefore, students must not only be able to translate between structural diagrams, they must also be able to determine accurately the stability of different rotational

places high cognitive demands on students.

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configurations for a given molecule. Explaining such complex spatial concepts is difficult and

We predicted that transparent whiteboard methods would result in better learning

outcomes than conventional whiteboard methods because they better support the multimedia

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principles of signaling, segmenting, and temporal contiguity, and because they provide important social cues that foster social agency. Experiment 1 tested this hypothesis when learners were

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assessed on an immediate test, whereas Experiment 2 tested this hypothesis when learners were assessed on both immediate and delayed (one-week) tests. 2. Experiment 1

To assess the effectiveness of transparent whiteboards as a method of promoting learning,

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Experiment 1 compared student performance on measures of diagrammatic translation, spatial

organic chemistry. 2.1 Methods

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reasoning, and conceptual understanding after viewing a 20-minute video lecture on a topic in

2.1.1 Participants and design

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The participants were 65 undergraduate students at a research university in the western

United States who had previously completed the third of three general chemistry classes, within which they had been introduced to the basic concepts necessary for understanding the material covered by the lecture. Fifty-five students remained in the analysis after four were dropped for failing to follow directions, and six were dropped for having pretest scores greater than two standard deviations from the mean (i.e., pretest accuracy of 73% or better). These latter

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participants were excluded from the analysis because their high performance suggested extensive knowledge of the study topic and little room for improvement. The experiment followed a pretest-posttest design with two levels of a single between-

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subjects variable (intervention: conventional or transparent). Where appropriate, posttest

performance was analyzed with ANCOVA using pretest and individual difference scores as covariates when assumptions of ANCOVA were met. There were 29 students (9 men) in the

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conventional group and 26 students (7 men) in the transparent group. The mean age of the

participants was 19.15 (SD = 1.00). Students received $20 for their participation. The study was

Institutional Review Board (IRB). 2.1.2 Materials and apparatus

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approved by and conducted in accordance to ethical guidelines and approval of the authors’

The study materials included two 20-minute video lectures (one with the instructor using

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a conventional whiteboard and one with the instructor using a transparent whiteboard), one 16item pretest and one 16-item posttest, a short-answer concept test sheet, a post-lesson survey, a

instruction sheets.

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demographic questionnaire, and a spatial ability test. There also were a consent form and task

The intervention involved participants watching a 20-minute video lecture about organic

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chemistry with the instructor using either a conventional whiteboard or a transparent whiteboard. This length is typical of online courses, which we attempted to simulate in this study. The lecture incorporated verbal descriptions, drawn diagrams, and manipulative models to demonstrate how ethane (a 2-carbon chain molecule; see Figure 4) and butane (a 4-carbon chain molecule; see Figure 6) are represented in 2D. Students were asked to attend to the lecture without taking written notes. The focus of the lecture was to introduce structural (e.g., how a

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diagram depicts 3D space) and functional concepts (e.g., how intra-molecular forces affects a molecule’s stability and, therefore, 3D shape). The instructor, who is one of the authors, has three years of experience teaching organic chemistry to college students in classroom and online

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settings.

The video lessons were recorded in the campus video studio. Video equipment included a tripod mounted Sony HDR-FX1 XDCAM digital video camera with an integrated video

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inverter. The transparent whiteboard was a 58” x 36” sheet of tempered ½ low iron polished glass that was bordered on three sides with High Power LED light strips. The conventional

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whiteboard was a standard 6 x 4 ft dry erase whiteboard. Expo Neon marker pens were used by the instructor to write and draw. Both video lessons were delivered through a desktop computer linked to a ceiling-mounted LCD video projector. A screenshot from each version of the video lesson is shown in Figure 1.

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The pretest and posttest each included 16 unique problems of four types presented on 16 sheets of 8.5 x 11 inch paper. The first set of four problems (Draw Any) asked students to translate a given diagram of a molecule (i.e., either Dash-wedge or Newman projection; such as

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shown in Figure 3) into any configuration of the other kind of diagram of the same molecule. The second set of four problems (Order) gave students four Newman projections with different

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configurations of the same molecule and asked them to mark the order of the configurations from highest to lowest in terms of stability. The third set of four problems (Match) gave students Newman projections of four different configurations of the same molecule but the students were asked to match each configurations to its position on a graph of stability. The fourth set of four problems (Draw Specific) gave students a Dash-Wedge diagram and asked them to draw a Newman projection of a specific configurations (i.e., of highest or lowest stability), again of the

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same molecule. In order to control for question difficulty, the molecules in the posttest were mirror images (i.e., enantiomers) of those problems used in the pretest. All of the molecules used in the problems had two chiral carbons (i.e., carbons that are bonded to four different atoms

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or groups of atoms) linked by a single bond. The four types of problems are illustrated in Figure 5 and test performance was combined across all 16 items into an Aggregate score.

The concept test sheet contained two short-answer concept questions that tested

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knowledge of important concepts presented in the video lesson. The first question asked

students to explain how and why the stability of a molecule changes as the central carbon-carbon

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bond within a molecule is rotated to form different configurations (e.g., staggered and eclipsed configurations). The second question asked students to explain how to identify two common staggered configurations (i.e., anti and gauche staggered configurations). Figure 6 illustrates the concepts that were addressed by these questions. Each question was scored on a 3-point scale

administered as a posttest.

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based on the completeness and accuracy of their answer. These concept questions were only

The post-lesson survey was on an 8.5 x 11 inch sheet of paper containing 10 Likert-scale

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items that asked students to rate their experience with the lesson. These items included the questions: “I felt that the subject matter was difficult,” which was reverse coded in the analysis,

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and “Please rate the amount of mental effort you put into understanding the material,” “I enjoyed learning this way,” “I would like to learn this way in the future,” “I feel like I have a good understanding of the material,” “After this lesson, I would be interested in learning more about the material,” “I found the lesson to be useful to me,” “I felt like the instructor was working with me to help me understand the material,” “I found the instructor’s teaching style engaging,” and “I felt motivated to try to understand the material,” and “Please rate the amount of effort you put

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into understanding the material.” The possible ratings ranged from 1, for strongly disagree, to 7, for strongly agree. An average score was calculated for the total survey measure. The demographic questionnaire was an 8.5 x 11 inch sheet of paper that contained

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prompts soliciting self-reported demographic information (i.e., age, sex, date of birth, major course of study, years in college, handedness, and colorblindness) as well as specific information about how many chemistry classes students had taken.

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The Vandenberg and Kuse (1978) Mental Rotation Test (MRT) was administered as a paper-based test of spatial ability, which consisted of 20 items administered in two 3-minute

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blocks of ten items. Each item consisted of a row of five block figures. The left-most figure was the goal figure. Two of the four figures to the right of the goal figure were different from the goal figure by a rotation. The other two figures could not be rotated into congruence with the goal figure. The participant’s task was to mark the two figures that could be rotated to match the

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goal figure. Each item was worth four points; a participant’s score was calculated by adding one

or false alarm. 2.1.3 Procedure

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point for each correct selection or each correct rejection, and subtracting one point for each miss

Students were randomly assigned to treatment groups except that men and women were

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assigned alternately to one of the two intervention groups to ensure gender balance across the conditions. Students were tested in groups of up to 10 at a time in a classroom, and were seated in desk chairs facing a 6 x 5 ft screen at the front of the room. Students were first given basic instructions, which included examples of the two kinds of diagrams with reminders for understanding the conventions of each diagram and the nature of the task. After they read the instructions, participants completed the pretest problems.

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Following the pretest problems, the students received either the conventional or transparent whiteboard video corresponding to their assigned group. Next, all individuals completed the posttest problems. After the posttest, students were asked to answer two short-

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answer concept questions to test their understanding of key concepts presented in the video

lesson. Lastly, students completed the post-lesson survey, demographic questionnaire, and the Mental Rotation Test. The total duration of the study was approximately 60 minutes.

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The total proportion of correct solutions to all four types of problems served as the score for the pretest and posttest. Responses to Order and Match problems were coded as correct only

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if the student correctly ordered or matched all of the provided conformers in the requested order or to the correct position on the given graph. Drawing performance from pretest and posttest data for 18 participants (31% of the sample) was coded independently by two researchers and inter-rater reliability was high for both Draw Any (Cohen’s Kappa = 0.92), Draw Specific

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(Cohen’s Kappa = 1.00), as well as the concept test (Cohen’s Kappa = 0.95) questions. Discrepancies were resolved by consensus. Individual difference measures for mental rotation ability (Cronbach’s alpha = 0.93), and post-lesson survey (Cronbach’s alpha = 0.88) were found

2.2 Results

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to be internally consistent.

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2.2.1 Did the groups differ on basic characteristics? A preliminary issue is whether the groups differed on basic characteristics. As

summarized in Table 2, there were no statistically significant differences between the conventional and transparent groups in grade point average (GPA), age, years in college, or pretest score, but the average mental rotation test score was statistically higher for the transparent

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whiteboard group than for the conventional whiteboard group. Thus, performance on the mental rotation test was evaluated as a covariate in the analysis. 2.2.2 Did students learn better from video lectures using transparent rather than

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conventional whiteboards?

The primary research question concerns whether students learn better from video lectures using transparent whiteboards than from video lectures covering identical content using

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conventional whiteboards. The top row of Table 3 shows the mean posttest score (and standard deviations) for each group. To test this research question, posttest scores were compared

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between the two lecture interventions with an ANCOVA using pretest scores and mental rotation scores as covariates. After controlling for pretest performance, F(1,51) = 13.93, MSE = 0.46, p < .001, and mental rotation scores, F(1,51) = 0.01, MSE < 0.01, p < .95, posttest scores were statistically better for those receiving the transparent whiteboard lesson than those receiving the

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conventional whiteboard lesson, F(1,51) = 4.16, MSE = 0.14, p < .05, d = 0.64. These results support the prediction that the lecture intervention with a transparent whiteboard supports improved learning over watching a lecture with traditional whiteboard methods.

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The bottom row of Table 3 shows the mean score (and standard deviation) on the concept test for the two groups. Consistent with the results for the posttest scores, the transparent group

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scored significantly better on the concept test, F(1,53) = 15.13, MSE = .65, p < .001, d = 0.92, than did the conventional group. These results support the prediction that transparent whiteboard methods promote learning from video lectures better than receiving conventional whiteboard methods.

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2.2.3 Did students report more positive ratings of their lecture experience for video lectures using transparent rather than conventional whiteboards? Another important question concerns whether students reported more positive ratings of

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their lecture experience for video lectures using transparent rather than conventional

whiteboards. An ANOVA revealed that students in the transparent whiteboard condition (M = 5.20, SD = 0.85) had a significantly higher overall rating on the lecture experience survey

whiteboard condition (M = 4.33, SD = 1.09).

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measure, F(1,53) = 10.60, MSE = 10.32, p < .01, d = 0.88, than did students in the conventional

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To explore these data further, we then compared the two groups on each of the individual survey questions (as summarized in Table 4). Concerning social partnership, students in the transparent group reported a significantly greater sense of rapport with the instructor (d = 0.62) and thought the instructor was significantly more engaging (d = 0.77) than students in the

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conventional group. These results support a key prediction that the transparent whiteboards allow the instructor to be better able to build a sense of social partnership with the audience – by allowing the class to see the instructor’s facial expression when writing on the board.

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Concerning engagement, students rated the transparent whiteboard lesson as requiring significantly more mental effort (d = 0.62) than the conventional lesson. Importantly, students’

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perceptions of the difficulty of the material did not differ, which suggests that transparent whiteboard methods encourage more engagement without increasing perceived difficulty. These results support the prediction that transparent whiteboards foster greater engagement than conventional whiteboards. Concerning motivation to learn, the transparent whiteboard lesson was significantly better at promoting enjoyment (d = 0.71), willingness to participate in future lessons with this

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method (d = 0.60), and motivation to learn the material (d = 1.04) compared than the conventional whiteboard lesson. However, the two groups did not differ significantly on ratings of understanding the material, desire to learn more, finding the lesson useful, or interest in

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learning more although all four ratings favored the transparent group. Overall, these results are consistent with the prediction that the transparent group will report higher levels of motivation and satisfaction.

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3. Experiment 2

Experiment 1 demonstrated that receiving a lecture delivered by transparent whiteboard

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methods leads to improved learning over conventional whiteboard methods on an immediate test. In line with Shavelson and Towne's (2002, p. 4) recommendation that scientific research in education should "replicate and generalize across studies," a reasonable next step is to determine whether the results can be replicated on an immediate test as in Experiment 1 and whether the

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results are also resilient over time as measured by a delayed test. To address these questions, Experiment 2 compared learning outcomes when students were tested immediately after a

delay. 3.1 Methods

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transparent whiteboard lecture or a conventional whiteboard lecture as well as after a 7-day

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3.1.1 Participants and design

The participants were 63 college students recruited from the same population as in

Experiment 1. Fifty-two students remained in the analysis (Age: M = 18.8, SD = 0.72) after 7 were dropped for not following directions, and 2 were dropped for having pretest scores greater than two standard deviations from the mean (i.e., pretest accuracy of 70% or better). The high

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pretest participants were excluded from the analysis because their high performance suggested knowledge of the study topic and little room for improvement. The study followed a pretest-posttest design with two levels of a single between-subjects

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variable (intervention: conventional or transparent). Where appropriate, posttest performance was analyzed with ANCOVA using pretest and individual difference scores as covariates when assumptions of ANCOVA were met. There were 27 (9 men) in the conventional and 25 (7 men)

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in the transparent group. Students received $30 for their participation. The study was approved

Review Board (IRB). 3.1.2 Materials and apparatus

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by and conducted in accordance to ethical guidelines and approval of the authors’ Institutional

The materials and videos were identical to those in Experiment 1, but with two additions. First, an Abstract Reasoning Test from the Differential Aptitudes Test (Bennett, Seashore, &

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Wesman, 1974) was administered as a test of general reasoning ability (40 items, 10-minute time limit) and served as an additional control variable (along with spatial reasoning ability, as in Experiment 1). Each item of the test illustrated a set of four graphics that formed an abstract

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sequential relationship and a list of five items from which the students were asked to pick the next graphic that would logically continue the sequence. Each item was worth one point and a

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participant’s score was calculated by adding one point for a correct selection, and subtracting a quarter point for an incorrect selection. Second, a delayed posttest was added that had identical problem types as in the immediate posttest but utilized alternative configurations of the same molecules (i.e., stereo isomers) used for the pretest and immediate posttest.

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3.1.3 Procedure The procedure was identical to Experiment 1 with two exceptions. First, the delayed posttest was administered seven days following the lecture and the immediate posttest. Second,

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the concept questions, lecture experience survey, demographic questionnaire, mental rotation test, and abstract reasoning test were administered after the delayed posttest, which was seven days following the lecture rather than immediately after the lecture.

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The pretest and posttest were scored in the same manner used in Experiment 1. Drawing performance from pretest and posttest data for 18 participants (33% of the sample) was coded

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independently by two researchers and inter-rater reliability was high for both Draw Any (Cohen’s kappa = 0.90), Draw Specific (Cohen’s kappa = 0.96), and Concept (Cohen’s kappa = 0.81) questions. Discrepancies were resolved by consensus.

Individual difference measures for mental rotation ability, (Cronbach’s alpha = 0.91), and

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abstract reasoning ability (Cronbach’s alpha = 0.92), and post-lesson survey, (Cronbach’s alpha = 0.83) were found to be internally consistent. 3.2 Results

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3.2.1 Did the groups differ on basic characteristics? As summarized in Table 5, there were no statistically significant differences between the

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groups in age, grade point average, years in college, mental rotation ability, abstract reasoning ability, or pretest score. We conclude that the two groups did not differ on basic characteristics before viewing the video lecture.

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3.2.2 Did students learn better from video lectures using transparent rather than conventional whiteboards on an immediate test? We predicted that students who received the transparent whiteboard video lecture would

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show better performance than students in the conventional group on both the immediate and delayed posttests as well as on the concept questions. The left side of Table 6 present the mean scores for the two groups on the immediate posttest in Experiment 2. To test this hypothesis,

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posttest scores were compared between the two lecture interventions with an ANCOVA. After controlling for pretest performance, F(1,47) = 3.00, MSE = 0.06, p = .09, mental rotation scores,

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F(1,47) = 4.48, MSE = 0.09, p = .04, and abstract reasoning scores, F(1,47) = 2.39, MSE = 0.05, p = .13, posttest scores were statistically better for those receiving the transparent whiteboard lesson than those receiving the conventional whiteboard lesson, F(1,47) = 4.16, MSE = 0.08, p < .05, d = 0.64.

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These results replicate those from Experiment 1 and suggest that transparent whiteboard methods were better than conventional whiteboard methods when students are asked to reason

reasoning ability.

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about spatial diagrams, after controlling for individual differences in mental rotation and abstract

3.2.3 Did students learn better from video lectures using transparent rather than

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conventional whiteboards on a delayed posttest? We also predicted that the transparent group would score higher on the delayed test than

the conventional group. The right side of Table 6 lists the mean scores for the two groups on the delayed posttest in Experiment 2. To test this hypothesis, posttest scores were compared between the two lecture interventions with an ANCOVA. After controlling for pretest performance, F(1,47) = 6.72, MSE = 0.20, p = .01, mental rotation scores, F(1,47) = 0.17, MSE <

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0.01, p = .73, and abstract reasoning scores, F(1,47) = 5.37, MSE < 0.16, p = .03, posttest scores were not statistically different between those receiving the transparent whiteboard lesson and those receiving the conventional whiteboard lesson, F(1,47) = 0.11, MSE < 0.01, p = 0.75.

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Finally, the bottom row of Table 6 shows the mean score (and standard deviation) on the delayed concept test for the two groups. Accuracy on the delayed concept test was not

statistically different between the two groups, F(1,50) = 1.26, MSE = .05, p = .27. Thus, the

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results of this delayed test do not replicate the benefit observed for an immediate test in

Experiment 1. It is possible that taking the immediate posttest served as a learning experience -

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consistent with the testing effect (Fiorella & Mayer, 2015; Roediger & Karpicke, 2006) - which helped equalize the learning outcomes of the two groups on the delayed test. 3.2.4 Did students using transparent whiteboards give better ratings for video lectures than students using conventional whiteboards?

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Another issue concerns whether students in the transparent group rate their learning experience more positively than those in the conventional group (as in Experiment 1). Table 7 summarizes the mean rating (and standard deviation) for each group on each of the 10 post-

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questionnaire items. An ANOVA revealed that students in the transparent whiteboard condition (M = 4.58, SD = 0.97) were not significantly different on the combined lecture experience survey

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measure, F(1,49) = 0.36, MSE = 0.31, p = .55, than students in the conventional whiteboard condition (M = 4.42, SD = 0.90). In addition, we compared each of the survey questions to further investigate difference in

the students’ subjective experience with the two interventions. Concerning social partnership, the transparent group report significantly higher ratings than the conventional group on thinking that the instructor was more engaging (d = 0.88). Consistent with Experiment 1, these results

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provide partial support for the key prediction that using transparent whiteboards allows the instructor to build a sense of social partnership with the audience. However, unlike Experiment 1 the groups did not differ significantly on any of the other self-report items, including those

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tapping engagement, motivation, and affect. Given that students completed the survey after a 7day delay, students may have answered the survey in reference to the proximal (i.e., delayed testing) rather than the distal (i.e., lecture intervention) activities.

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In summary, students in Experiment 2 who received transparent whiteboard lectures showed better performance on immediate testing (replicating Experiment 1) but not for delayed

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testing. Additionally, students in Experiment 2 reported more positive ratings on items involving social partnership with the instructor (replicating Experiment 1) but not on other items. 4. Discussion 4.1 Empirical contributions

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This study provides the first systematic investigation of the potential benefits of transparent whiteboards for fostering learning from video lectures, compared to conventional whiteboard lessons. First, across two experiments, students who viewed an online video lesson

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performed better on an immediate test of learning if the video involved transparent rather than conventional whiteboards. Further, across two experiments, students who viewed an online

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video lesson rated a higher level of social partnership with the instructor if the video involved transparent rather than conventional whiteboards. Finally, in one experiment, there was no significant difference between the groups on a delayed test given one week after initial learning and testing.

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4.2 Theoretical implications These results are generally consistent with our hypothesis that transparent whiteboards better adhere to multimedia learning principles that encourage learners to engage in the cognitive

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processes of selecting and organizing presented material, which leads to improved learning. This may be because transparent whiteboards avoid occluding essential information from the lesson. Furthermore, students’ self-reported lecture experiences support our prediction that transparent

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whiteboard methods promote social partnership between the student and the instructor by

providing important social cues, such as eye contact and eye gaze. However, findings from

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Experiment 2 suggest that the transparent whiteboard lesson did not better foster the deeper cognitive processing required for persistent learning. One explanation is that the transparent whiteboard lesson fostered greater engagement during the initial learning phase but did not require students to engage in the effortful retrieval processes that tend to foster long-term

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learning (i.e., the testing effect; e.g., Roediger & Karpicke, 2006). In fact, both groups completed an immediate posttest after initial learning (i.e., pretest and immediate test), which may have attenuated any unique learning benefits associated with the transparent whiteboard

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lesson, thereby diminishing differences on a delayed test. Another possible explanation for the generally low performance on delayed testing may be due to a lack of incentive on the part of the

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students to engage deeply with the test, especially given that they had already solved similar problems a week before.

4.3 Practical implications

This study is in line with recent evidence on the important role of instructor presence in video-based instruction (e.g., Fiorella, Van Gog, Hoogerheide, & Mayer, in press). Findings from the present study suggest that students learn better on an immediate test when they can see

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the instructor's facial expressions and hand-writing and drawing movements, which are not occluded by the instructor, as the material is explained. When facing the class, instructors offer social cues and gaze guidance cues to support social engagement and direct student attention. A

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practical implication is that instructors should try to face the class when they talk, avoid talking when facing away from the audience, and avoid occluding written or drawn information on the board when teaching. When instructors engage students and avoid occluding material as it is

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written or drawn on the board, instructors adhere to important multimedia learning principles, such as segmenting, signaling, and temporal contiguity and students are better able to select and

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organize this material for improved learning. For online lessons, transparent whiteboards may better afford facing students throughout the lesson than conventional whiteboards. However, it is important to caution that the present study only found support for this prescription based on immediate tests in a controlled lab study. Additional work is needed to determine whether

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transparent whiteboards can better foster learning on delayed tests (with intervening immediate tests) in authentic learning situations.

4.4 Limitations and future directions

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There were limitations to this research, which should be addressed in future studies. First, we had hoped to create a learning scenario that closely replicated learning demands of an

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actual online lecture course. However, unlike in an actual course, students in the present study were not provided with any direct incentives to invest the effort required to develop a deep understanding of the material. Additionally, unlike typical online lessons, for practical reasons our participants observed the lecture as a group in a classroom setting. Future work should test the effects of different lecture video formats in more authentic online learning contexts. Similarly, the lesson was much shorter than a typical in-person classroom lecture (i.e., 20

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minutes) and prerequisite knowledge was significant, which is why we selected participants with prior chemistry experience. Future work might observe learning performance in two matched samples over an extended period of time to assess the long-term potential. In order to avoid a

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possible testing effect, it would be better to administer the delayed test without administering an immediate test.

We note that the supplemental materials (i.e., concept questions, experience survey,

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questionnaire) were collected immediately after the lecture in Experiment 1 but they were collected 7 days following the lecture, in Experiment 2. It is likely that the users’ lecture

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experience ratings were attenuated over this delay. However, students in Experiment 2 did still report that the lecturer was more engaging and that they had more rapport with the instructor, supporting our suggestion that transparent whiteboard methods support social partnership. Future studies should be consistent in collecting questionnaire data about the lesson in the same

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session as the lesson rather than after a delay.

The instructor in the present study used a hand-held model to illustrate the drawn representations and to explain the spatial topic (Stull et al., 2016). Although a common method

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for teaching this specific topic, use of these models necessitated having the instructor turn toward the audience to demonstrate how the 3D models represented the 2D diagrams. Inclusion of these

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learning aids may have dampened the effect of the transparent whiteboard methods because use of a model for direct demonstration to the audience supported segmenting, temporal contiguity, and signaling principles, and social partnership. For example, to demonstrate how to draw different conformers as Newman projections (see Figure 3), the instructor faced the audience (social partnership), pointed between the 3D and 2D representations (signaling), explained the model manipulations as they were performed (temporal contiguity), and related the different

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components of the model to the different features of the diagrams (segmenting). Therefore, the presence of hand-held models as demonstration aids may have elevated performance in the conventional whiteboard lesson, making it more difficult to detect the unique benefits of

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transparent whiteboards. Future research should conduct more precise comparisons between the two lecture conditions.

Another limitation of the present study is that we are unable to make strong conclusions

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regarding the specific cognitive and motivational mechanisms responsible for the observed

immediate benefits of transparent whiteboard lessons. The two lesson formats were designed to

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be identical in every way (e.g., lesson script, diagrams, pace, use of models) except the type of whiteboard the instructor used. We identified two inherent differences between these two methods – namely, occlusion (in the case of conventional whiteboards) and the provision of social cues (in the case of transparent whiteboards). These two differences were expected to

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influence the extent to which the whiteboard conditions adhered to multimedia principles. The present study provides preliminary support for the idea that transparent whiteboards may promote learning because they better foster social partnership. However, further work is needed

cues.

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5. Conclusion

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to specify how this relates to the cognitive processes underlying multimedia principles and social

In conclusion, students who viewed an online video lesson using a transparent

whiteboard performed better on an immediate test of learning and rated higher levels of social partnership with the instructor, although these advantages were not observed for delayed testing, which warrants further investigations. This project contributes to a better understanding of effective practices for designing and delivering online instruction. Transparent whiteboard

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technology is gaining interest among educators, although empirical research that validates and explains its benefit is currently limited. Understanding how this technology contributes to learning in the modern online and hybrid classroom will help to enrich student learning and to

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inform teachers about effective design of asynchronous lessons throughout K-12 and college.

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Table 1: Signaling, Temporal Contiguity, and Segmenting Principles

Description

Instructional Goal

Signaling

Use visual or verbal cues to direct learners’ attention toward the most relevant information. Present words and their corresponding pictures simultaneously rather than one before or after the other. Break down complex material into more manageable parts rather than present the material all at once.

Reduce extraneous processing Reduce extraneous processing

Temporal contiguity

Manage essential processing

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Segmenting

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Principle

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Table 2. Summary of group equivalence for Experiment 1. Transparent M SD N

ANOVA F (MSE) p

GPA

3.21 0.46 27

3.32 0.33 26

1.12 (0.18) .29

Age

19.00 0.85 29

19.12 1.03 26

Years in College

2.00 0.76 29

2.08 0.98 26

Mental Rotation

31.47 16.41 29

42.01 18.41 26

5.04 (1522.2) .03

Pretest

0.16 0.18 29

0.21 0.16 26

0.844 (0.03) .36

0.207 (0.18) .65 0.108 (0.08) .74

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Conventional M SD N

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Table 3. Means and standard deviations for learning outcome measures by two groups in

Conventional M SD

Transparent M SD

Posttest

0.53 0.22

0.66 0.19

Conceptual

0.44 0.21

0.65 0.21

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Problems

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Experiment 1. Values are reported as mean proportion correct.

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Table 4. Mean ratings (and standard deviations) on post-questionnaire by two groups in Experiment 1. The first nine items used a scale from 1 (Strongly Disagree) to 7 (Strongly

Conventional M SD

Transparent M SD

I felt that the subject matter was difficult.

4.24 1.41

4.50 1.27

I enjoyed learning this way.

4.14 1.55

5.12 1.18

I would like to learn this way in the future.

4.10 1.72

5.04 1.37

2.21 .03*

I feel like I have a good understanding of the material.

4.28 1.41

4.77 1.18

1.40 .17

After this lesson, I would be interested in learning more about the material.

4.55 1.55

5.27 1.34

1.83 .07

I found the lesson to be useful to me.

5.24 1.41

5.77 1.11

1.53 .13

I felt like the instructor was working with me to help me understand the material.

4.48 1.75

5.54 1.68

2.28 .03*

0.62

I found the instructor's teaching style engaging.

3.79 1.86

5.12 1.58

2.82 <.01*

0.77

4.17 1.75

5.69 1.05

3.84 <.01*

1.04

4.66 1.59

5.50 1.03

2.31 .03*

0.62

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Please rate the amount of mental effort you put into understanding the material.

t-test t p

Effect Size d

0.71 .48

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2.61 .01*

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I felt motivated to try to understand the material.

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Question

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Agree). The tenth item used a scale from 1 (Very Low) to 7 (Very High).

0.71

0.60

0.49

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Table 5. Summary of group equivalence for Experiment 2. Transparent M SD N

ANOVA F (MSE) p

GPA

3.26 0.38 27

3.34 0.39 25

0.64 (0.10) .43

Age

18.74 0.66 27

18.84 0.75 25

Years in College

1.37 0.57 27

1.40

Mental Rotation

31.04 15.41 27

33.35 20.04 25

0.22 (70.05) .64

Abstract Reasoning

10.28 6.29 27

11.35 4.91 25

0.46 (14.92) .50

Pretest

0.15 0.13 27

0.15 0.13 25

0.01 (<0.01) .93

0.26 (0.13) .61

SC

0.03 (0.01) .87

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0.71 25

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Conventional M SD N

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Table 6. Means and standard deviations on learning outcome measures by two groups in

Immediate Posttest

Delayed Posttest

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Experiment 2.

Conventional M SD

Transparent M SD

Conventional M SD

Transparent M SD

Posttest

0.34 0.15

0.43 0.17

0.31 0.17

0.33 0.21

Conceptual

0.33 0.21

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0.26 0.19

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Problems

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Table 7. Mean ratings (and standard deviations) on post-questionnaire by two groups in Experiment 2. The first nine items used a scale from 1 (Strongly Disagree) to 7 (Strongly

Transparent

M SD

M SD

I felt that the subject matter was difficult.

4.85 1.32

4.88 1.33

I enjoyed learning this way.

4.00 1.49

4.54 1.25

1.39 .17

I would like to learn this way in the future.

4.07 1.54

4.33 1.76

0.56 .58

I feel like I have a good understanding of the material.

3.78 1.50

3.79 1.41

0.03 .97

After this lesson, I would be interested in learning more about the material.

5.00 1.39

4.54 1.82

1.02 .31

I found the lesson to be useful to me.

5.22 1.28

4.83 1.71

0.92 .36

I felt like the instructor was working with me to help me understand the material.

4.52 1.50

5.17 1.49

1.54 .13

0.43

I found the instructor's teaching style engaging.

3.96 1.37

5.22 1.47

3.12 <.01*

0.88

I felt motivated to try to understand the material.

5.15 1.35

4.79 1.84

0.79 .43

Please rate the amount of mental effort you put into understanding the material.

5.37 1.08

5.46 1.10

0.29 .78

EP

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t-test

Effect Size d

Conventional

t p

0.06 .95

SC

M AN U

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Question

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Agree). The tenth item used a scale from 1 (Very Low) to 7 (Very High).

0.39

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she creates and reveals the written and drawn explanations.

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Figure 1: Video-based lectures using a Learning Glass show the face of the instructor as he or

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Figure 2: The Cognitive Theory of Multimedia Learning (Mayer, 2009, 2014a)

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(b)

CH3

(c)

H

H NH2

HO H

OH

CH3

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(a)

HO

OH

NH2 H

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Figure 3. Three structural representations of an organic molecule. (a) A concrete (ball-and-

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stick) model, (b) a Dash-Wedge diagram (side-view), and (c) a Newman diagram (end view) of the same organic molecule depicted in the ball-and-stick model. The 3D model represents space directly. The two diagrams are 2D representations that use different conventions to depict threedimensional information in the two dimensions of the printed page. A Dash-Wedge diagram (b), uses solid lines, dashed wedges (i.e., dashes), and solid wedges (i.e., wedges) to represent spatial

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positions of a molecule’s component parts (i.e., subgroup). In this representation, solid lines are in the plane of the page, dashes are behind the plane of the page, and wedges are in front of the plane of the page. Carbon is assumed present at any intersection or termination of lines, unless it

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is explicitly draw otherwise. A Newman projection (c) is used to represent the molecule from a side-on perspective, obtained by ‘sighting-down’ one of potentially many carbon-carbon bonds.

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In the carbon-carbon bond of interest, the carbon that is nearest to the viewer is depicted at the intersection of the three lines. The second carbon of the carbon-carbon bond is occluded by the first but represented as the circle in the Newman diagram. (Originally published in Stull, et al, 2013)

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(b)

Staggered

(c)

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(a)

Eclipsed

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Figure 4. Staggered and eclipsed conformers of ethane. The carbon-carbon bond rotates

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allowing ethane to move between staggered and eclipsed conformers. The staggered conformer (a and b) has side-groups staggered between the front and rear carbons. This is not obvious in a dash-wedge diagram (a) but is easily visible in a Newman projection (b) when viewed down the carbon-carbon bond. Rotating the carbon-carbon bond 60 degrees produces the eclipsed conformer (c), which has overlapping side-groups when viewed down the carbon-carbon bond.

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Staggered conformers have greater stability (i.e., lower relative energy) than eclipsed conformers

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(i.e., higher relative energy).

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B.

C.

D.

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A.

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Figure 5. Representative problems of the four types used in both the pretest and posttest. A. The Draw Any type of question asked students to draw the same molecule in a different

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diagrammatic format. B. The Order type of question asked students to mark the order of each conformer from highest to lowest relative energy. C. The Match type of question asked students to match each conformer to its position on the relative energy graph. D. The Draw Specific type of question asked students to draw a translation of the same molecule but of a specific conformer. A and D were combined as Drawing problems and B and C were combined as Reasoning problems.

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(b)

(c)

(d)

Anti Staggered

Eclipsed

Gauche Staggered

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(a)

Highest energy

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Lowest energy

Eclipsed

Figure 6. Four conformers of butane (above) differ by a rotation around the carbon-carbon

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bond. Conformers (a) and (c) are staggered and conformers (b) and (d) are eclipsed. Conformers (b), (c), and (d) differ from conformer (a) by a rotation of 60°, 120°, and 180°, respectively. The position and size of the side-groups influences the relative energy of a conformer, which differ in relative energy. The lowest energy staggered conformer (a) is also referred to as the anti conformer and the highest energy staggered conformer (c) is referred to as

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a gauche conformer. The highest energy conformer (d) is eclipsed and has its largest side groups

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closest together when looking down the carbon-carbon bond.

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1

Acknowledgements This research was partially supported by grants from the National Science Foundation (1252346 and 1561728) and the Spencer Foundation. We also thank Jessieann Hibbard, Robert

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Lewis, Will Mehring, and Yessica Santana for their assistance in conducting this study. In addition, we are grateful to Russell Shannon for his insights and skills recording the video lessons and R. Daniel Little for his advice, suggestions, and contributions throughout this

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project.

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Highlights

conventional whiteboard video lessons. •

Conceptual knowledge performance was better following transparent rather than conventional whiteboard video lessons.

•

Social partnership with the instructor was higher following transparent rather than conventional whiteboard video lessons.

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There was no noticeable difference between the two lecture methods on testing after a one

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week delay.

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•

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Spatial problem-solving performance was better following transparent rather than

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•