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An Active Viewing Framework for Video-Based Learning Dodson, Samuel; Roll, Ido; Fong, Matthew; Yoon, Dongwook; Harandi, Negar M.; Fels, Sidney 2018

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An Active Viewing Framework for Video-Based LearningSamuel DodsonUniversity of British Columbiadodsons@mail.ubc.caIdo RollUniversity of British Columbiaido.roll@ubc.caMatthew FongUniversity of British Columbiamfong@ece.ubc.caDongwook YoonUniversity of British Columbiayoon@cs.ubc.caNegar M. HarandiUniversity of British Columbianegarm@ece.ubc.caSidney FelsUniversity of British Columbiassfels@ece.ubc.caABSTRACTVideo-based learning is most effective when studentsare engaged with video content; however, the literaturehas yet to identify students’ viewing behaviors andground them in theory. This paper addresses this needby introducing a framework of active viewing, which issituated in an established model of active learning todescribe students’ behaviors while learning from video.We conducted a field study with 460 undergraduatesin an Applied Science course using a video player de-signed for active viewing to evaluate how students en-gage in passive and active video-based learning. Theconcept of active viewing, and the role of interactive,constructive, active, and passive behaviors in video-based learning, can be implemented in the design andevaluation of video players.Author Keywordsactive viewing, video-based learning, annotationINTRODUCTIONPrevious work has found that students’ active engage-ment can improve their learning outcomes [10, 13], es-pecially when compared with passive engagement [2,8]. Active learners make use of affordances in learn-ing objects and environments in order to control theirlearning. They regulate their learning in accordancewith their knowledge and goals [11]. The concept of ac-tive learning has not yet been applied to video-basedlearning. With a framework of active viewing, we iden-tify potential affordances for learning with video.This paper introduces and operationalizes the conceptof active viewing as follows. First, we introduce theconcept and ground it in a constructivist view of learn-ing. Second, we describe a study with a video playerPermission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than theauthor(s) must be honored. Abstracting with credit is permitted. To copy otherwise, orrepublish, to post on servers or to redistribute to lists, requires prior specific permissionand/or a fee. Request permissions from permissions@acm.org.L@S 2018, June 26–28, 2018, London, United Kingdom© 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM.ISBN 978-1-4503-5886-6/18/06. . . $15.00DOI: https://doi.org/10.1145/3231644.3231682that facilitates active viewing by supporting interac-tion with visual and textual representations of videocontent and personalization with highlights and notes.This study, which was conducted for seven weeks inan undergraduate Applied Science class of 460 stu-dents, provides in vivo data from students using videoto prepare for lectures, homework, and exams. Usingthe data collected in our study, we describe how stu-dents use the video player affordances in order to takecontrol of their learning. Third, we suggest that whenprovided with a video player designed for active view-ing, students engage in different behaviors to supporttheir learning. This paper provides a foundation for de-signing and evaluating video players to support video-based learning by filling a gap in the literature on stu-dents’ engagement with video.ACTIVE VIEWING FRAMEWORKThe current literature on video-based learning lackssufficient understanding of how students’ behaviors arerelated to their engagement with video content. Con-sequently, this paper works towards a framework ofactive viewing by describing students’ behaviors usingthe ICAP framework [2], which distinguishes betweenthe interactive, constructive, active, and passive cat-egories of behaviors. Several video-based learning be-haviors, and students’ motivations for using them, aredescribed in Table 1. The behaviors were identified byreviewing the literature, drawing on active learning inother media, and from our observations of, and conver-sations with, students.Active viewing includes interactive, constructive, andactive behaviors, which treat video as a non-linearmedium. These behaviors are described by Chi andWylie [3]. Interactive behaviors involve collaborating,cooperating, and communicating with instructors andpeers in video. The interactive category is based ona socio-constructive understanding of learning, whichemphasizes learning as a socially-driven process. Sev-eral systems have supported peer-to-peer interactionswith video content. Bargeron et al.’s MRAS offeredtemporally-anchored discussion threads between stu-dents [1]. Dorn et al.’s TrACE went a step further bysupporting spatially-anchored discussion threads [5]. ATable 1. Behaviors of active and passive viewing, cate-gorized using the ICAP framework.Interactive BehaviorsCommunicating with others.Cooperating with others.Collaborating with others.Constructive BehaviorsMaking connections between learning objects.Taking notes to record sense making.Highlighting video content for future use.Tagging video content for future use.Active BehaviorsBrowsing for general information.Searching for specific information.Triaging between learning objects, such as a videoand a textbook.Re-watching specific video content.Pausing video content to reflect on the video contentor engage in another viewing behavior.Passive BehaviorsPlaying video content.recent trend is to extract useful information by aggre-gating users’ interactions with video content. For ex-ample, Glassman et al.’s Mudslide used user-generatedhighlights to identify areas of confusion or importancewithin videos [7]. Constructive behaviors, such astaking notes, are used to record meaning making.Through these activities, learners construct their ownmeaning in a way that expands and extends beyondthe video itself. This also includes making connectionsbetween concepts. Active behaviors include, but arenot limited to, browsing, searching, pausing, changingplayback speed, and re-watching video content. Previ-ous work suggests that annotating may support text-based learning processes and outcomes [9, 12]; however,there are few studies investigating whether or not an-notating provides similar benefits when learning withvideo [4]. In contrast to these categories of behaviorsthat make up active viewing, passive viewing simplyinvolves watching the video linearly, without interac-tion [3]FIELD STUDY OF VIDEO-BASED LEARNINGTo explore how students learn with video, we con-ducted a field study using ViDeX [6], a video playerdesigned for active viewing, in an undergraduate Ap-plied Science course of 460 students. Students use ofViDeX was logged for seven weeks and then analyzed.Students were provided with 14 optional videos (8 ± 3minutes) in which their instructor lectures over a deckof slides. During the seven-week study, 219 of the stu-dents (48%) used ViDeX at least once. In this paper,we focus on their interactions with ViDeX, acknowl-edging that active learning can also use additional re-sources, such as paper or electronic notebooks. The logdata were supplemented by an online questionnaire.The 112 respondents were asked about their video-Figure 1. ViDeX with student-generated highlightsand tags. The Player is displayed at the top left, theFilmstrip at the middle left, the note taking area atthe bottom left, and the Transcript at the right. Stu-dents can select intervals for highlighting using eitherthe Filmstrip or the Transcript. At the bottom of theFilmstrip, a histogram displays the amount of times astudent has watched specific parts of the video.based learning practices, including how and why theyhighlighted and took notes using ViDeX. Students werenot compensated for participating in the study.ViDeX: A Video Player for Active ViewingTo fully support video-based learning, video playersshould provide tools that go beyond traditional play-back features. Unfortunately, the majority of videoviewers only support passive and active behaviors,which are not as effective for learning as constructiveand interactive behaviors. The version of ViDeX usedin this study was designed to support a broader set ofbehaviors, as described in Table 1, than most videoplayers. ViDeX has four main components: the Player,the Filmstrip, the Transcript, and the note taking area,as shown in Figure 1.Students can use ViDeX to interact with and person-alize video content. The Filmstrip and Transcript pro-vide students with visual and textual representations ofthe video content, respectively. The Filmstrip supportsvisual navigation of video content through thumb-nails. Students can hover their cursor over a thumb-nail to preview the visual content of a specific intervalof video content. The Transcript enables navigationof the textual content of a video. The Transcript al-lows students to read ahead or behind the playhead. Atell-how video, with much narration and little demon-stration, results in a useful Transcript and a less use-ful Filmstrip, whereas a show-how video, with muchdemonstration and little narration, results in a usefulFilmstrip and a less useful Transcript.To further support active viewing behaviors, ViDeX al-lows for highlighting and note taking. After clicking onand dragging across either the Filmstrip or the Tran-script to select an interval of visual or textual content,students can choose a color to highlight the selected in-terval. If students would like to make annotations thatare more verbose than a highlight, they can write notesthat are then linked to a specific time in the video.ResultsWe analyzed the ViDeX log data and found that stu-dents’ most frequent behaviors aligned with our defi-nition of passive and active viewing: i) all participantsplayed one or more videos; ii) the playback speed waschanged by 60 participants (27%) on average of 10times each; and iii) 139 participants (63%) re-watchedone or more videos on average 12 times each (see Ta-ble 2).Students also regularly engaged in constructive behav-iors. Forty participants (18%) highlighted, creating 333highlights in total. The highlighting behaviors of par-ticipants were idiosyncratic, and highlights varied incolor, temporal length, and positions within the videos.We asked the survey respondents about the ease of useand usefulness of highlighting with ViDeX. Fifty-fourpercent (54%) of respondents agreed or strongly agreedthat highlighting was easy, while 7% disagreed orstrongly disagreed. Forty-three percent (43%) agreedor strongly agreed that highlighting was useful, while20% disagreed or strongly disagreed.The highlights may have been used to support otheractive viewing behaviors. Sixty-seven percent (67%)of highlights were the destination of a seek, suggest-ing that highlights helped participants re-watch spe-cific parts of video. The number of highlights directlysought is likely greater than 67%, as we only counteda seek when a user clicked within the span of a high-light. Of the highlights that were sought to, the me-dian number of times they were sought to was two(M = 3.2, SD = 3.8), suggesting that participantsrevisited confusing or important parts of videos mul-tiple times. The temporal length of highlights thatwere sought to were larger than those that were notre-watched (median=7.5 seconds and median=5.6seconds, respectively). A Welch-Two-Sample t-testfound that this difference is statistically significant(t = 3.102, df = 197.12, p = .002).Twenty-six (12%) participants created one or morenotes, creating 83 notes in total. The median notelength was 21 words (M = 31.0, SD = 28.5). Fif-teen of the 40 participants that highlighted (38%) alsotook one or more notes. Fifty percent (50%) of surveyrespondents agreed or strongly agreed that note takingwith ViDeX was easy, while 18% disagreed or stronglydisagreed. Thirty-nine percent (39%) of participantsagreed or strongly agreed that note taking was useful,while 25% disagreed or strongly disagreed.To assess whether or not there was a difference be-tween highlighting and note taking, we compared theseresults with those reported earlier on highlighting. Mc-Nemar’s test was used to determine if there was a dif-Table 2. Logged behaviors, including the percentage ofstudents that engaged in each behavior and the meannumber of times they did so. Interactive behaviors arenot reported on in this paper, as design of the systemdid not yet specifically support cooperation, collabora-tion, and communication at the time of data collection.Behavior % of Students Mean OccurrencesPassiveWatching Video 100% 74 times eachActiveChanging Play-back Speed27% 10 times eachRe-watching 63% 12 times eachPausing 97% 71 times eachConstructiveHighlighting 18% 8 times eachNote Taking 12% 3 times eachference in the ease of use of highlighting and note tak-ing (p = .112) and the usefulness of highlighting andnote taking (p = .721); however, no statistically signifi-cant differences were found.DISCUSSIONThe results suggest that students make use of videoplayer affordances for active viewing. Participants reg-ularly engaged in active and constructive behaviors.It is important not to label students as active or pas-sive viewers based on the frequency of their behaviorsalone. Passive behaviors can, for example, support in-teractive, constructive, and active behaviors. Conse-quently, students often engage in active and passiveviewing behaviors when learning from video. We sus-pect that students are using these behaviors for mul-tiple reasons. They may try to maximize efficiency byminimizing the amount of time they spend watchingvideos. Students may also try to personalize the videocontent to their needs. Active viewing is used by stu-dents to make decisions about which parts of the videothey need to watch.ViDeX provides visual and textual representations ofvideo content — visual in the form of the Filmstripand the Player and textual in the form of the Tran-script. We found that students highlight mainly usingthe Transcript, possibly because they highlight tex-tual, tell-how information or because students maybe used to highlighting text. Instances of highlight-ing, using either the Filmstrip or the Transcript, andsubsequent seeking to the highlighted video contentusing the other interface was common. This suggeststhat students were able to use the Filmstrip and theTranscript selectively. We anticipate that over time,students will develop strategies that take advantageof the affordances provided by different interfaces andrepresentations of video content. Additional researchis needed to fully explore how these new strategiesemerge as students develop active viewing practices.CONCLUSIONThis paper describes the beginning of an active view-ing framework, grounded in the ICAP framework. Asvideo players are re-designed to better support activeviewing, new affordances for interactive, constructive,and active behaviors are a priority. We believe stu-dents will make use of these affordances to suit theirstyle of learning and preferences, similar to how somestudents annotate their textbooks liberally while othersdo not. Furthermore, as students make use of activeviewing affordances in video players, they will developnew strategies to support their video-based learningwhich will, in turn, lead to new behaviors that can beadded to our active viewing framework. Taking this astep further, video content may then be re-imaginedwith the aim of creating more active experiences of en-gaging with video.Future WorkOur analysis treated instances of constructive, active,and passive viewing behaviors as independent of eachother. Future work could investigate sequences of be-haviors, or video-based learning strategies. For exam-ple, what behaviors do students engage in before andafter highlighting? Identifying common video-basedlearning strategies could then further inform the designof educational video players with the affordances stu-dents need to effectively study from video. Methods formeasuring learning processes and outcomes in relationto active viewing also need to be investigated.Video is used in addition to, and sometimes in place of,traditional lectures, introducing a paradigm shift thatfundamentally changes the way instructors teach andstudents learn. By providing mechanisms that makeuse of familiar text-based interactions and apply themto video, as well as create completely novel affordancestuned to the unique properties of videos, we have theopportunity to elevate video-based learning to be aninteractive medium. By studying active viewing, wehope to better understand video-based learning anddesign video viewers to help students fully realize thepotential of video as a medium for learning.AcknowledgementsThis work was funded by the University of BritishColumbia Teaching and Learning Enhancement Fund,Natural Sciences and Engineering Research Council ofCanada and Microsoft Corporation.REFERENCES1. David Bargeron, Jonathan Grudin, Anoop Gupta,Elizabeth Sanocki, Francis Li, and ScottLeetiernan. 2002. Asynchronous collaborationaround multimedia applied to on-demandeducation. Journal of Management InformationSystems 18, 4 (2002), 117–145.2. Michelene T H Chi. 2009.Active-constructive-interactive: A conceptualframework for differentiating learning activities.Topics in Cognitive Science 1, 1 (2009), 73–105.3. Michelene T H Chi and Ruth Wylie. 2014. TheICAP framework: Linking cognitive engagement toactive learning outcomes. Educational Psychologist49, 4 (2014), 219–243.4. Samuel Dodson, Ido Roll, Matthew Fong,Dongwook Yoon, Negar M. Harandi, and SidneyFels. 2018. Active viewing: A study of videohighlighting in the classroom. In Proceedings of the2018 Conference on Human InformationInteraction & Retrieval. ACM, New York, NY,237–240.5. Brian Dorn, Larissa B Schroeder, and AdamStankiewicz. 2015. Piloting TrACE: Exploringspatiotemporal anchored collaboration inasynchronous learning. In Proceedings of the 18thACM Conference on Computer SupportedCooperative Work & Social Computing. ACM, NewYork, NY, 393–403.6. Matthew Fong, Samuel Dodson, Xueqin Zhang, IdoRoll, and Sidney Fels. 2018. ViDeX: A platform forpersonalizing educational videos. In Proceedings ofthe 18th ACM/IEEE Joint Conference on DigitalLibraries. ACM, New York, NY, 331–332.7. Elena L Glassman, Juho Kim, AndrésMonroy-Hernández, and Meredith Ringel Morris.2015. Mudslide: A spatially anchored census ofstudent confusion for online lecture videos. InProceedings of the 33rd Annual ACM Conferenceon Human Factors in Computing Systems. ACM,New York, NY, 1555–1564.8. Richard R Hake. 1998. Interactive-engagementversus traditional methods: A six-thousand-studentsurvey of mechanics test data for introductoryphysics courses. American Journal of Physics 66, 1(1998), 64–74.9. Catherine C Marshall. 1998. Toward an ecology ofhypertext annotation. In Proceedings of the NinthACM Conference on Hypertext and Hypermedia.ACM, New York, NY, 40–49.10. Michael Prince. 2004. Does active learning work?A review of the research. Journal of EngineeringEducation 93, 3 (2004), 223–231.11. Ido Roll and Philip H Winne. 2015. Understanding,evaluating, and supporting self-regulated learningusing learning analytics. Journal of LearningAnalytics 2, 1 (2015), 7–12.12. Michele L Simpson and Sherrie L Nist. 1990.Textbook annotation: an effective and efficientstudy strategy for college students. Journal ofReading 34, 2 (1990), 122–129.13. Dongsong Zhang, Lina Zhou, Robert O Briggs, andJay F Nunamaker. 2006. Instructional video ine-learning: Assessing the impact of interactivevideo on learning effectiveness. Information &Management 43, 1 (2006), 15–27.

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