Lecture capture podcasts: differential student use and performance in ...

Education Tech Research Dev DOI 10.1007/s11423-015-9406-5 RESEARCH ARTICLE

Lecture capture podcasts: differential student use and performance in a large introductory course Adrienne E. Williams1 Diane K. O’Dowd3

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Nancy M. Aguilar-Roca2

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 Association for Educational Communications and Technology 2015

Abstract Video ‘‘podcast’’ recordings of lectures are popular with students, but are often associated with a decrease in attendance and little increase in performance. Assessment has generally focused on the class as a whole, potentially masking benefits to different subgroups. In this study, conducted in 2 sections of a large active-learning undergraduate introductory biology class with daily podcasts, average attendance remained high (89.5 %). More than 50 % of the students used podcasts but less than 3 % of the variance in actual minus predicted exam performance was correlated with the number of podcasts viewed. Podcast use also varied significantly with gender and ethnicity but even within high use subgroups (females and Asians) less than 6 % of the variance in exam performance was correlated with the number of podcasts viewed. These data suggest that lecture capture, even for the students who attend class and use them heavily, do not increase learning gains. Alternative uses for video are discussed. Keywords

Podcasts  Video  Lecture capture  Gender  Ethnicity  Active learning

& Adrienne E. Williams [email protected] Nancy M. Aguilar-Roca [email protected] Diane K. O’Dowd [email protected] 1

Department of Developmental and Cell Biology, UC Irvine, 2011 BioSci 3, Irvine, CA 92697-2300, USA

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Department of Ecology and Evolutionary Biology, UC Irvine, 321 Steinhaus Hall, Irvine, CA 92697-2525, USA

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Department of Developmental and Cell Biology, UC Irvine, 4221 McGaugh Hall, Irvine, CA 92697-2300, USA

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Introduction A common use of digital video in higher education is ‘‘lecture capture,’’ or the regular archiving of the live lecture that students can access after class (Educause 2008; Lane 2006; Malan 2007). It is theorized that students will use metacognition to recognize their comprehension of the material is incomplete (Bransford et al. 2000) and re-watch lectures to increase understanding (Evans 2008; McKinney et al. 2009; Walls et al. 2010). There is evidence that students learn more effectively via dual channel learning that contains both visual and audio stimulus, and cannot learn with high levels of cognitive load (Mayer 2002). It is theorized that studying via lecture capture utilizes dual channel learning and reduces cognitive load by allowing pauses (Traphagan et al. 2010; Lori 2011). This could make studying lecture capture videos more effective than re-reading the text or notes. The majority of experimental evidence indicates, however, that lecture capture does not increase exam averages and can reduce student attendance. Much of this research analyzes the effectiveness of lecture capture on the class as a whole; none has looked to see if student subgroups such as women or under-represented minority students might show significant benefits. Universities would be more interested in supporting the technology for lecture capture if regular learning gains could be found for even a subset of students. Below are summarized the current experimental findings associated with student learning and lecture capture videos. This study on student use of these videos by gender and ethnicity in a large, introductory biology course is then presented.

The terms ‘‘podcast’’ vs ‘‘lecture capture’’ vs ‘‘lecture podcast’’ Historically, a podcast is an audio or video digital file that is assigned an RSS feed for automatic upload to a mobile device. These traditional podcasts are episodically and regularly distributed. The lecture capture videos at our university are called podcasts inside the learning management system, but are not automatically distributed. Students must go to the class website and click the link for each desired recording. A review of the literature shows the terms ‘‘lecture capture,’’ ‘‘podcast,’’ ‘‘webcast,’’ and ‘‘m-learning’’ have been used to describe the same sort of digital video lecture recordings. The terms ‘‘lecture capture video’’ and ‘‘lecture podcast’’ are therefore used interchangeably in this paper, while recognizing that the videos are not podcasts as normally defined.

Literature review The effect of lecture podcast on student attendance and learning has been examined in several recent studies. This review focuses on studies that also measured student performance or student attendance. Making lecture recordings available in a nursing program resulted in reduced attendance and had no significant effect on student performance (Kemp et al. 2010). Faculty who taught a psychology course without and then with lecture capture found students reported studying longer hours when podcasts were available, but there was no change in course grade (Ford et al. 2012). In several courses using lecture podcasts over multiple years at one institution, 55 % of surveyed faculty reported decreased attendance in years podcasts were provided (Settle et al. 2011). There was also a slight decrease in the percentage of passing grades when compared to years in which podcasts were not available. A three-year study of podcast use by medical students showed little regular use, but higher use was associated with lower scores on Board exams (McNulty et al. 2011). One

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study ran simultaneous sections of an introductory geology class with and without lecture podcast access (Traphagan et al. 2010). Students with access to podcasts had significantly lower attendance than those without access. However, students who were absent but watched the podcasts did better than those who were absent but did not watch. One study in an undergraduate pharmacology course did not see any decrease in attendance when lecture podcasts were introduced in the second semester and there was a significant improvement on final exam score (Bollmeier et al. 2010). This study suggests that adding lecture podcasts in a way that does not also reduce attendance levels may increase student performance.

Minimal research on lecture capture use by different demographic groups It is possible that there are specific groups for whom access to lecture podcasts will be particularly beneficial. There are well established differences in how students of different gender, ethnicity or preparation level study and learn, but optional and online resources are newer and have not been fully studied. A recent review of podcast papers from 2001 to 2011 makes no mention of use differences by gender or ethnicity (Kay 2012). An examination of 31 articles on lecture capture or podcasts found three that reported differences in the use of optional online resources based on demographics. Survey data in a chemistry class indicated that female students were more likely than male students to multitask while listening to optional audio recordings of the lecture (Ruedas-Rama and Orte 2011). In 14 online courses surveyed at one institution female students indicated that supplementary podcasts hold their attention longer and are more relevant than male students (Bolliger et al. 2010). In a communications theory class, non-white students were shown to be less likely to re-view posted videos that were also shown in class (Dupagne et al. 2009). None of these studies however looked at the lecture capture type of podcast, and none compared the course performance of heavy users versus occasional or non-users.

Experimental design The focus of this study was to determine if there were subgroups of students, particularly students from disadvantaged backgrounds including underrepresented minorities (URMs), in a large introductory biology course whose performance was correlated with use of lecture podcasts. The main research questions were: 1. 2. 3. 4.

Can podcasts be added without negatively affecting attendance? Is the level of podcast use correlated with increased performance in the course? Is the timing of podcast use correlated with increased performance in the course? Do specific student subgroups use podcasts differently and if so does this correlate with improvements in performance in the class?

Method Methodology This study used student login data from the video viewing site, clicker participation data from class records, student demographic data from the registrar, and exam scores. A multiple regression model that includes student demographics was used to control for

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student preparation level and calculate a predicted performance in the class for every student. This predicted performance was subtracted from each actual student performance as measured by exam scores. The ‘‘actual–predicted performance’’ value was compared to podcast views using correlation analysis.

Context and participants Two sections of first-year introductory biology ‘‘DNA to Organisms’’ were team-taught by the third author (DOD) and another instructor in Fall 2011. The course is 10 weeks in length, and was 92.7 % first-quarter freshman students. Most students intend to major in biology. On the first day of class students were notified that their coursework and demographic data would be used in aggregate for a research study. Students under 18 and those who did not want their data included were given opt-out information. All data collection was given human subjects approval via the University’s IRB. All analyses combined data from students in both sections. The population used for the study consisted of 835 students, 67.0 % female. Ethnicity data is indicated in Table 1. The two course sections had week 5 exams on the same day, an hour apart. The final exams were 2 days apart. The exams were written to have similar questions, and had similar averages. Exam scores were totaled, and converted to Z scores within each section using the following calculation: Z score ¼ ðXc  XsÞ=SD where Xc is the class mean, Xs is the student score, and SD is the class standard deviation. Plus or minus one Z score is equivalent to ± one standard deviation from the class mean.

Creating lecture podcasts All lectures were recorded during class time using Camtasia Relay software on the instructor’s laptop. Lectures were captured by opening the recording software, clicking ‘‘Start’’ at the beginning of the lecture and then ‘‘Stop’’ and ‘‘Upload’’ at the end of the lecture. This produces an online flash video that shows the instructor’s slides and is overlayed with the instructor’s teaching audio. Access to the video required students to log in with their university ID, and no downloadable mp4 or mp3 files were given. Each lecture had several minutes of active learning. The acquisition software continued to run during these minutes of the class, even though there was no lecturing to listen to or study.

Table 1 Student ethnicity in Fall 2011 Ethnicity

%

Asian

66.6

Under-represented minoritiesa

19.7

Caucasian

12.4

Other (decline to state, international)b a

1.2

URM include African American, Hispanic/Latino, Native American ethnicities

b

Students who did not indicate their ethnicity or were international students were not included in analyses that compared ethnicity, but were included in other analyses

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Lecture podcasts were usually posted by 4 p.m. after the 12 p.m. and 1 p.m. lectures on Monday, Wednesday and Friday. The 12 p.m. lecture was most commonly posted, unless there was a problem with the recording. Both sections shared a class website and had equal access to the lecture recordings. The availability of the podcasts was announced on the first day of class, and a link to each podcast was posted on the home page of the class website.

Measurement of attendance Clicker data was collected from all students as a normal part of the course. The days with full participation by a student was used as a measurement of attendance.

Actual and predicted performance data The following demographic information on each student from the university Registrar: gender, date of matriculation, declared major, SAT scores, AP Biology score, and ethnicity. Actual exam Z scores The total exam score for each student was converted to a Z score for their section. Predicted exam Z scores Previous studies by our group and others have demonstrated a variety of demographic factors and pre-college preparation can be predictive of student performance (Freeman et al. 2007; Aguilar-Roca et al. 2012). Using a multiple regression analysis and backward elimination of variables technique (Freeman et al. 2007; Zar 2010), three pre-college academic indicators were identified as being significantly related to class performance: Math SAT, Reading SAT, and AP Biology score [ 3 (R2 = 0.436). Therefore to compare performance of students with different levels of pre-course academic achievement the following equation was used to generate a Predicted Z score for each student Predicted Z score ¼ 3:978 þ 0:002673  ½Reading SAT þ 0:003883  ½Math SAT þ 0:6675  ½AP Bio: Performance: The discussion will focus on the actual minus the predicted Z-score. A positive value indicates that performance was higher than predicted and a negative value indicates that performance was lower than predicted.

Coding of podcast use After the class had ended, the Office of Instructional Technology provided a timestamp and user ID for every ‘‘click-through’’ visit to a lecture podcast during the quarter. There were 11,554 total visits by 627 different people for 28 total podcasts. An examination of the raw podcast use showed some students had clicked on the same podcast multiple times in one study session. Repeat visits within a 60-min period were treated as a single visit. After removing the 37 non-enrolled individuals (mostly teaching assistants and tutors) and 19 students who opted out of participation in the study, there were 571 podcast users included in the data set. Some students did not have full demographic records on file—they were removed for the analyses that required this information. For instance, the analysis that included use of SAT scores to predict performance excluded data from the 26 students without SAT scores, generating a total of 545 students for that analysis.

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Statistical analysis All statistics were measured using GraphPad InStat 3 (GraphPad Software 2009).

Results Is average daily attendance reduced by access to podcasts? Average daily attendance was high (89.5 %) and not significantly different than attendance reported from previous years (Aguilar-Roca et al. 2012) when podcasts were not available (Fig. 1a). The large lectures were taught in an active learning style, with 15–25 min of each 50 min class period devoted to clicker questions, small group discussion, physical demonstrations of biological principles, and short writing assignments. While the lecture capture audio continued to run during these activities, the benefit gained by the student who is working on these activities during class is not captured by the software. The data indicate that providing an active learning environment and minimal point incentive via clickers (\2 % of grade) is sufficient to maintain robust attendance even when podcasts for every lecture are available.

Is use of lecture recordings correlated with improved exam performance? The number of study participants in the two classes was 835, and 545 (65 %) watched at least one podcast during the quarter. In a survey at the end of the course, students reported using podcasts for several different reasons, including extra review opportunities and for making up content due to absence. The most common reason was because they were confused by or couldn’t keep up with lecture content (data not shown). To determine if there was a correlation between podcast use and an improvement in learning as measured by exam performance, a calculation of the predicted minus Actual exam Z score was made for each student, to normalize between students entering with different levels of preparation. A correlation analysis of the number of podcasts watched versus exam performance for each student revealed a significant but very small relationship: less than 3 % of the variance in performance was associated with number of podcasts watched (Fig. 1b, solid line).

Is there an association between timing of podcast use and exam performance? The podcast views per day varied tremendously over the course of the quarter (Fig. 2). Spikes in the number of views were highly associated with in-class exam dates: quizzes (week 2 and 8), midterm (week 5) and final exams (FA and FB exams in week 11). Views of the podcasts during the 48 h before an exam accounted for 44.6 % of all views, and 29.7 % of the views occurred within 24 h of the exams. Podcast users who first accessed podcasts early in the course ([48 h before the midterm) performed significantly better than predicted in the course based on a positive Actual minus Predicted exam Z score but the magnitude of increase was small (0.1 SD, Fig. 2b). In contrast, podcast users who did not access their first podcasts until just before the midterm had a mean Actual Predicted exam Z scores that was negative (Fig. 2b).

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Fig. 1 Effect of multiple podcasts watched under conditions of high attendance. a Course attendance in 2012 when podcasts were available was not significantly different than the previous year without podcasts. b Relationship between number of different podcasts watched and Actual–Predicted Z score for all students who watched at least one podcast. Data fit with a linear regression (n = 545, p = 0.0003, R2 = 0.0236)

Fig. 2 Relationship between the number of different podcasts watched, timing of first podcast use, and exam performance (Actual–Predicted Z score). a Podcast use per 12 h spiked on days before quizzes and exams. b Students who first visited a podcast video more than 48 h before the midterm (n = 354) performed better than predicted in the course (?0.11 SD), a significant difference compared to students (n = 191) who watched their first podcast just before (\48 h) the midterm who performed worse than predicted (-0.14 SD) T test, p \ 0.0001

Behavior and performance of different demographic subgroups The number of podcasts watched per student ranged from 0 to 28 with a mean of 5.8 ?/1 SEM (total number of students = 835, including non-watchers). However there were interesting differences in the viewing behavior of several different subgroups. Female students watched significantly more podcasts compared to male students (Fig. 3a, Mann– Whitney, p \ 0.0001). Asian students watched significantly more podcasts than Caucasian students (Fig. 3b, p \ 0.05 Kruskal–Wallis, Dunn’s Multiple Comparisons). The number of podcasts watched by URM students was intermediate. Students who watched 13 or more

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Fig. 3 Different student groups were more likely to be heavy podcast users. a Females (n = 559) watched significantly more podcasts than males (n = 276), Mann–Whitney, p \ 0.0001. b Asian students podcast use was significantly different than Caucasian or URM students (Kruskal–Wallis, p = 0.012, with post hoc test showing a significant difference with Caucasians (p \ 0.05). c In females, the regression of Actual minus Predicted score to podcast use was significant (p = 0.010) but the r2 was only 0.052. d In Asian students, the regression of Actual minus Predicted score to podcast use was not significant (p = 0.10)

podcasts in the groups were identified as heavy users. For females and Asians, a separate correlation analysis of Actual–Predicted exam Z score as a function of number of podcasts watched was run. Only the female students showed a significant relationship between the number of videos watched and Actual–Predicted exam Z score, but the variance predicted by podcast use was again low at 5.2 % (Fig. 3c, d). Finally, there was no significant correlation between predicted exam Z score and number of podcasts watched for lessprepared students with lower predicted Z scores.

Discussion In this study, individual student use of lecture capture podcasts was examined in order to determine if there was a correlation between podcast use and improved performance. Consistently high attendance levels allowed an evaluation of this resource without the confound of low class attendance often associated with use of lecture capture (Kemp et al. 2010; Traphagan et al. 2010; Wieling and Hofman 2010). The data indicate that when attendance is high, there is little benefit in terms of summative learning gains of providing lecture podcasts, even for students who are heavy users.

Early and regular users A small subset of students in the class watched podcasts on a regular basis, viewing them as they were posted. For this group of students the exam Z scores were higher than

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predicted suggesting that this usage pattern may contribute to their learning of the course material, or that they have the metacognitive skill to recognize their comprehension of the material needed support earlier in the class. However the effect size was very small (\0.03). In contrast, the students who first viewed the podcasts directly before the first exam had Z scores that were lower than predicted. This is consistent with the procrastination behavior that is typical of many college students (as reviewed in Steel 2007). Students who procrastinate are less likely to use deep cognitive and meta-cognitive strategies when studying (Wolters 2003). Because they are used at the last minute, the videos likely provide only a surface-level cognitive rehearsal strategy (Weinstein and Mayer 1986). Supporting these results, students who report low levels of self-efficacy for self-regulation also demonstrate increased procrastination during learning and have lower course grades (Klassen et al. 2008).

Female and Asian students as heaviest users Reports of lecture capture have not previously described how student use varies by demographic group. As introductory biology is an important first course for the STEM student, it was of particular interest to determine if there was a difference in utilization of podcasts by different subgroups and if usage behavior could be correlated with improvement in course performance. The results indicate that women and Asian students were the heaviest users of the lecture podcasts. The high use by females is consistent with a study reporting that female medical students are more likely to watch instructional videos embedded in their online coursework than male students (Romanov and Nevgi 2007). However, even in the subpopulation of heavy users there was little or no correlation between use and performance in this class.

Discussing study habits with students In this introductory biology class, exam questions were grouped into three different categories based on Bloom’s Taxonomy: Knowledge/Comprehension (40 %), Application/ Analysis (40 %), and Synthesis/Evaluation (10 %) (see Crowe et al. 2008 for a discussion of Bloom’s Taxonomy in exam questions). Simple memorization of lecture material is not sufficient to answer *50 % of the exam questions (Bransford et al. 2000). A regular portion of class time is spent working with students to apply content to new situations in order to practice higher-taxonomy thinking. While the data indicate that lecture podcasting is not an effective study tool in this type of learning environment, they do provide an opportunity to discuss with students how real learning occurs. As a result of this study, the author who is the main instructor (DOD) continued to provide lecture podcasts, but with caveats. She clearly articulates to students that watching the videos can be useful in clarifying specific questions but is only a first step in studying, and she regularly reinforces the use of more effective study strategies in class.

Podcasts for non-native English speakers and students with disabilities Many public universities have seen large increases in the number of disabled students, international students and English language learners due to changing enrollment criteria. Surveys of students with disabilities have found 81 % feel lecture podcasts are useful

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(Vajoczki et al. 2010). It will be interesting to determine if international students will find captioned lecture podcasts similarly useful and if use will result in improved performance. A newly instituted program in this university is making lecture podcasts with closed captioning in introductory biology classes. Future work will assess the effectiveness of this resource in helping students process course content.

Lecture capture as feedback for instructors As video analysis tools continue to improve, there are more opportunities for instructors to learn from student use of class resources. For several years, videotaping of teaching has been used to provide feedback to teachers about their techniques and effectiveness (see Bryan and Recesso 2006; Santagata et al. 2007 for examples). Lecture capture videos provide new opportunities for analytics, such as audience retention metrics that indicate sections of the video most often watched. This can provide valuable feedback to the instructor about the parts of the lecture that were found most confusing by students.

Creating condensed videos for flipped classes Until recently podcast use in higher education was used primarily for either revision materials (lecture podcasts) or supplementary material (TED talks or other ‘‘extra’’ resources) (Fernandez et al. 2009; Leijen et al. 2009). However, the development of easy to use recording and delivery software has made other options feasible. In the ‘‘flipped class’’ model, mini-lecture videos are created by the instructor and are watched by students before class, with class time devoted to problem solving and practicing deeper critical thinking skills (Brame 2012). The same simple software used for lecture capture was used by one author (DOD) to create a small number of pre-class videos and when coupled with class time devoted to problem solving this resulted in improved student learning gains on the topics covered (Moravec et al. 2010). The first author (AEW) has used the slightly more advanced version of the software and the institution’s podcast delivery service to flip her entire introductory biology course. Current research has indicated slight increases in student activity scores (Kim and Chen 2011), engagement (McLaughlin et al. 2013, 2014), and grades (Pierce 2013) in fully flipped courses. So while optional access to recordings of a full lecture do not produce learning in students, the technology associated with this system has made it easier to create and deliver short content videos that have been associated with improvements.

Conclusions and future research This study shows no compelling evidence that just providing lecture capture podcasts improves student exam performance. This is an important finding, as many universities are adding capture technology and video storage to their courses. This comes at significant cost and not insignificant issues of copyright protection and ADA compliance. Future studies aimed at increasing student use at times other than directly before the exam or measuring use of captioned videos by international students will be important in determining if this resource can be utilized more effectively to improve student learning outcomes. Acknowledgments This research was funded by a Professor grant to DOD from the Howard Hughes Medical Institute. The data of student use of video podcasts was gathered and provided by the Office of Instructional Technology at our institution.

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References Aguilar-Roca, N. M., Williams, A. E., & O’Dowd, D. K. (2012). The impact of laptop-free zones on student performance and attitudes in large lectures. Computers & Education, 59(4), 1300–1308. Bolliger, D. U., Supanakorn, S., & Boggs, C. (2010). Impact of podcasting on student motivation in the online learning environment. Computers & Education, 55(2), 714–722. Bollmeier, S. G., Wenger, P. J., & Forinash, A. B. (2010). Impact of online lecture-capture on student outcomes in a therapeutics course. American Journal of Pharmaceutical Education, 74(7), 1–6. Brame, C. J. (2012). Flipping the classroom. Nashville: Center for Teaching, Vanderbilt University. Retrieved from http://cft.vanderbilt.edu/teaching-guides/teaching-activities/flipping-the-classroom/. Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn. Washington, DC National Academy Press. Retrieved from http://www.csun.edu/*SB4310/How%20People%20Learn.pdf. Bryan, L. A., & Recesso, A. (2006). Promoting reflection among science student teachers using a web-based video analysis tool. Journal of Computing in Teacher Education, 23(1), 31–39. Crowe, A., Dirks, C., & Wenderoth, M. P. (2008). Biology in bloom: Implementing Bloom’s Taxonomy to enhance student learning in biology. CBE Life Science Education, 7(4), 368–381. doi:10.1187/cbe.0805-0024. Dupagne, M., Millette, D. M., & Grinfeder, K. (2009). Effectiveness of video podcast use as a revision tool. Journalism & Mass Communication Educator, 64(1), 54–70. Educause. (2008). 7 Things you should know about lecture capture. EDUCAUSE. Retrieved from http:// www.educause.edu/library/resources/7-things-you-should-know-about-lecture-capture. Evans, C. (2008). The effectiveness of m-learning in the form of podcast revision lectures in higher education. Computers & Education, 50(2), 491–498. doi:10.1016/j.compedu.2007.09.016. Fernandez, V., Simo, P., & Sallan, J. M. (2009). Podcasting: a new technological tool to facilitate good practice in higher education. Computers & Education, 53(2), 385–392. doi:10.1016/j.compedu. 2009.02.014. Ford, M. B., Burns, C. E., Mitch, N., & Gomez, M. M. (2012). The effectiveness of classroom capture technology. Active Learning in Higher Education, 13(3), 191–201. doi:10.1177/1469787412452982. Freeman, S., O’Connor, E., Parks, J. W., Cunningham, M., Hurley, D., Haak, D., & Wenderoth, M. P. (2007). Prescribed active learning increases performance in introductory biology. CBE Life Science Education, 6(2), 132–139. doi:10.1187/cbe.06-09-0194. Kay, R. H. (2012). Exploring the use of video podcasts in education: A comprehensive review of the literature. Computers in Human Behavior, 28(3), 820–831. Kemp, P. K., Myers, C. E., Campbell, M. R., & Pratt, A. P. (2010). Student perceptions and the effectiveness of podcasting in an Associate Degree Nursing program. Teaching and Learning in Nursing, 5(3), 111–114. doi:10.1016/j.teln.2010.01.006. Kim, J., & Chen, C.-Y. (2011). The influence of integrating pre-online lecture videos in classrooms: A case study. In World conference on E-learning in corporate, government, healthcare, and higher education (Vol. 2011, No. 1, pp. 244–249). Klassen, R. M., Krawchuk, L. L., & Rajani, S. (2008). Academic procrastination of undergraduates: Low self-efficacy to self-regulate predicts higher levels of procrastination. Contemporary Educational Psychology, 33(4), 915–931. doi:10.1016/j.cedpsych.2007.07.001. Lane, C. (2006). UW podcasting: Evaluation of year one. Office of Learning Technologies, University of Washington. Retrieved from http://catalyst.Washington.Edu/research_development/papers/2006/ podcasting_year1.Pdf. ¨ ., Lam, I., Wildschut, L., Robert-Jan Simons, P., & Admiraal, W. (2009). Streaming video to Leijen, A enhance students’ reflection in dance education. Computers & Education, 52(1), 169–176. Lori, A. (2011). Podcasting, Cognitive Theory, and Really Simple Syndication: What is the potential impact when used together? Journal of Educational Multimedia and Hypermedia, 20(3), 219–234. Malan, D. J. (2007). Podcasting computer science E-1. ACM SIGCSE Bulletin, 39(1), 389–393. Mayer, R. E. (2002). Multimedia learning. Psychology of Learning and Motivation, 41, 85–139. McKinney, D., Dyck, J. L., & Luber, E. S. (2009). iTunes University and the classroom: Can podcasts replace professors? Computers & Education, 52(3), 617–623. McLaughlin, J. E., Griffin, L. M., Esserman, D. A., Davidson, C. A., Glatt, D. M., Roth, M. T., & Mumper, R. J. (2013). Pharmacy student engagement, performance, and perception in a flipped satellite classroom. American Journal of Pharmaceutical Education, 77(9), 196. doi:10.5688/ajpe779196. McLaughlin, J. E., Roth, M. T., Glatt, D. M., Gharkholonarehe, N., Davidson, C. A., Griffin, L. M., & Mumper, R. J. (2014). The flipped classroom: A course redesign to foster learning and engagement in a health professions school. Academic Medicine, 89(2), 236–243. doi:10.1097/ACM. 0000000000000086.

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A. E. Williams et al. McNulty, J. A., Hoyt, A., Chandrasekhar, A. J., Espiritu, B., Gruener, G., Jr, Price, R., & Naheedy, R. (2011). A three-year study of lecture multimedia utilization in the medical curriculum: Associations with performances in the basic sciences. Medical Science Educator, 21(1), 29–36. doi:10.1007/ BF03341591. Moravec, M., Williams, A., Aguilar-Roca, N., & O’Dowd, D. K. (2010). Learn before lecture: a strategy that improves learning outcomes in a large introductory biology class. CBE Life Science Education, 9(4), 473–481. Pierce, R. (2013). Student performance in a flipped class module. Society for Information Technology & Teacher Education International Conference, 2013(1), 942–954. Romanov, K., & Nevgi, A. (2007). Do medical students watch video clips in eLearning and do these facilitate learning? Medical Teacher, 29(5), 490–494. Ruedas-Rama, M. J., & Orte, A. (2011). Using text-to-speech generated audio files for learning chemistry in higher education. Eurasian Journal of Physics and Chemistry Education, 4(1), 65–77. Santagata, R., Zannoni, C., & Stigler, J. W. (2007). The role of lesson analysis in pre-service teacher education: An empirical investigation of teacher learning from a virtual video-based field experience. Journal of Mathematics Teacher Education, 10(2), 123–140. Settle, A., Dettori, L., & Davidson, M. J. (2011). Does lecture capture make a difference for students in traditional classrooms. In Proceedings of the 16th annual joint conference on Innovation and technology in computer science education (pp. 78–82). New York, NY: ACM. doi:10.1145/1999747. 1999772. Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential selfregulatory failure. Psychological Bulletin, 133(1), 65–94. doi:10.1037/0033-2909.133.1.65. Traphagan, T., Kucsera, J. V., & Kishi, K. (2010). Impact of class lecture webcasting on attendance and learning. Educational Technology Research and Development, 58(1), 19–37. Vajoczki, S., Watt, S., Marquis, N., Holshausen, K., Vajoczki, S., Watt, S., & Holshausen, K. (2010). Podcasts: Are they an effective tool to enhance student learning? A Case Study. Journal of Educational Multimedia and Hypermedia, 19(3), 349–362. Walls, S. M., Kucsera, J. V., Walker, J. D., Acee, T. W., McVaugh, N. K., & Robinson, D. H. (2010). Podcasting in education: Are students as ready and eager as we think they are? In Computers & education (Vol. 54, No. 2, pp. 371–378). New York: Macmillan Company. Weinstein, C. E., & Mayer, R. E. (1986). The teaching of learning strategies. Handbook of Research on Teaching, 3, 315–327. Wieling, M. B., & Hofman, W. H. A. (2010). The impact of online video lecture recordings and automated feedback on student performance. Computers & Education, 54(4), 992–998. Wolters, C. A. (2003). Understanding procrastination from a self-regulated learning perspective. Journal of Educational Psychology, 95(1), 179–187. doi:10.1037/0022-0663.95.1.179. Zar, J. H. (2010). Biostatistical analysis (5th ed.). Upper Saddle River, NJ: Pearson-Prentice Hall.

Adrienne E. Williams is a lecturer and biology education researcher at the University of California, Irvine, and was the Co-Director of the HHMI-UCI Professor Program. She studies the effects of new teaching technologies on student success in large biology lectures. Nancy M. Aguilar-Roca is a teaching professor at the University of California, Irvine. During this research she was a research associate with the HHMI-UCI Professor Program and is particularly interested in improving the success of under-represented minority students in science. Diane K. O’Dowd is a Professor of Developmental and Cell Biology and is the Vice Provost for Academic Personnel at the University of California, Irvine. She is an HHMI Professor and maintains an active research interest in improving evidence-based instruction in large research universities.

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