Can we predict students attention from a classroom video? We present our system for analysing movement of students in classroom during a lecture. We go into details about the technical side for analysing motion of people with limited visibility, and we present preliminary results which show how the neighbourhood and location in the classroom affect individual movement

Dillenbourg, Pierre

Raca, Mirko

Tormey, Roland

Infoscience - École polytechnique fédérale de Lausanne

Student motion and it’s potential as a classroomperformance metricMirko Raca, Roland Tormey, Pierre DillenbourgCHILI Lab, Ecole polytechnique federale de Lausanne (EPFL),EPFL-CHILI, RLC D1 740 (Rolex Learning Center), Station 20, CH-1015 Lausanne{mirko.raca,roland.tormey,pierre.dillenbourg}@epfl.chhttp://chili.epfl.ch/Abstract. Can we predict students attention from a classroom video?We present our system for analysing movement of students in classroomduring a lecture. We go into details about the technical side for analysingmotion of people with limited visibility, and we present preliminary re-sults which show how the neighbourhood and location in the classroomaffect individual movement.Keywords: classroom, orchestration, motion, attention, location1 IntroductionA major difference between a novice and experienced lecturer is how they adjusttheir presentation to the audience [3]. A whole body of work on orchestration[6][7] is dedicated to helping teachers perform well in the classroom by helpingthem become a reflective practitioner [16]. But how is the teacher to judge theeffect of his lecture on an unfamiliar group of listeners?Our system analyses the behaviour of audience of the classroom [17]. Topresent motion as a valid indicator of audience response, we draw a parallel withprevious works on studying interaction between students in the classroom, andshow how our method relates to previous findings while remaining unobtrusiveto the learning process.1.1 Theoretical BackgroundLiterature which focuses on teacher behaviour is rich. From practical advice [5]to detailed analysis of teacher’s usage of space [15], the importance of teachersrole in the process of learning has been re-confirmed throughout the literature[8][11]. The theory of orchestration [6][7] which formulates the presentation ofknowledge as only one of the many duties of the teacher attempts to demonstratethe many facets of teaching process. An equally important question about thestate of the classroom audience - the students - was discussed from a numberof sides: modelling student goals [2], how the group behaviour is affecting thestudents [9], how relevant is the position of the student [13] and how it affectsinteraction [1]. Also, general studies on behaviour in groups provide us with2 EC TEL’13 - 3rd International Workshop on Teaching Analyticsuseful concepts of audience inhibition, diffusion of responsibility [14] and socialloafing [10] which can also be observed in a typical classroom.In this paper we focus on social influence in the audience (e.g. that peoplelook at others for guidance [14]) and how the position of a person in the classroomis affecting his or hers behaviour in terms of motion. The conclusions previouslystated in [1] and [13] show the effect of classroom location in terms of verbalactivity and participation. Inspired by the concepts of unobtrusive measurements[19], we are evaluating how these conclusions can be supported by the newmeasurement of body motion in the classroom.2 Method2.1 Motion AnalysisAnalysis of the motion is based on tracking feature points in the video [4].Our setup consists of three cameras used for coverage of the students and oneobserving the teachers actions. Initial steps of analysis - synchronization of videostreams from all sources and annotating visible regions in which students resideduring the lecture, are described in [17].Our main challenges in the process of extracting a measurement of motionfor further analysis were i) inter-personal occlusions, ii) perspective distortion,iii) normalization of the amount of movement recorded from a single person intoa comparable measurement between several persons.a) b) c)Fig. 1. Motion grouping. a) Individual motion vectors shown as purple arrows b)Grouped motion into motion tracks c) Marked student areas and centres of Gaussianprobabilities of each studenti) Inter-personal occlusions are handled by taking several pre-processingsteps before assigning the motion to a person. The main idea is that by groupingthe motion vectors into motion tracks, we can more reliably assign the wholetrack to a single person, instead of taking each motion vector as an isolatedmeasurement.Steps of the process are illustrated in Figure 1. Raw motion vectors areshown in Fig.1a as purple arrows who’s intensities add to the amount of motionof one person at one time instance. Motion vectors (v) are next grouped intotracks (T ) which consist of ”cloud“ of motion vectors over several frames. Thecriteria for grouping is based on proximity, direction similarity and intensity ofthe vectors. For visualisation purposes, a set of cloud centres from several framesStudent motion and it’s potential as a classroom performance metric 3are connected into a track which is shown in Fig.1b. Finally the entire track isassigned to the student of highest probability (gf ) (principle can be formalizedas gf = arg maxg∑∀v∈T p(v|g)). Each student (g) has a Gaussian distributioncentred on the position of his head (depicted in Fig.1c). The entire track isassessed over every center (i.e. student) and motion is assigned to the studentwith the highest probability. Fitting the Gaussian distributions produced regionsin which a motion vector will be assigned to a specific person.In cases where a student was occluded on more than 80% of tracked areathe movements were undistinguishable from the person in front of him/her. De-pending on the quality of the measurements for the person in front, either oneor both students were removed from further processing.Taking into account that our primary interest was motion between neigh-bours, it is important to notice that this method was designed so that i) amotion occurring between two students would not be assigned to both studentsii) large motions spanning several tracked areas would be assigned to a singleperson, and not to a group of people.ii) Perspective distortion To compensate for the perspective effect, thenumber of points remains constant over all annotated tracked areas. Also, theintensity of the vector is normalized by the diagonal of the student region.iii) Normalizing the amount of motion Normalizing the motion of aperson has proven to be difficult. We based our normalization on two premisesi) student is on average sitting still during the class ii) student has at least onefull-body movement in the recorded footage (e.g. pose shift). To scale this toa range of 0-100% motion, we take the median value of movement intensity asthe 5% motion (which corresponds to small motion/sitting still being registeredas 5% motion), and we verify that given this basic motion intensity the studentreaches 100% motion at least once during the class. Motion which registers abovethe threshold of 100% is clipped to the maximum value.The final motion intensity over time can be visualized as shown in Figure 2b.2.2 ProcedureWe base our results on analysis of two classes, described in Table 1. The teach-ers were two experienced lecturers. The lectures were given in different times ofthe day - one being in the morning, the other in the late afternoon time, andin different rooms. We observed each lecture for the duration of 45 minutesClass no. Class size Analysed Female ratio Rows No. of benches1 38 29 36.8% 6 72 18 14 22.2% 4 5Table 1. Basic information about analysed classeswith 4 interruptions. During this time at a random interval (average duration10 minutes) the students filled out a questionnaire sampling their activities.We are aware that our request from the students to fill out the questionnaireabout attention interrupts their work, so the interruption was treated as a simu-lated synchronised activity and marked as a ”question-asked“ period. Activities4 EC TEL’13 - 3rd International Workshop on Teaching Analyticsduring those periods were not taken into consideration when analysing studentbehaviour.Even though we initially consider both classes comparable, small number ofstudents in the second classroom rendered conclusions from that observationstatistically invalid. We did use it to show that there is a consistent trend inboth cases.A major influence on the behaviour of students in the classrooms plays thegeometry of the arrangement [18] which differs in the two cases. Since the ge-ometry of the room changes the student’s perception of the speaker, due toproxemics, we decided against normalizing the space in the way it was done for[1], which would make the two classes comparable.3 ObservationsWe compared the number of synchronized movements during the class period.Synchronized movement is considered in a dyadic context, and is in this casedefined as body movement with more than 30% intensity from each of the twopersons being compared. Persons can move at the precisely same moment orwithin the range of ±4 seconds from each other, in which case the person whomoves first has to be visible to the second person, and is considered as thestimulus.a) b)Fig. 2. a) Organization of classroom zones and units of measurements, top of theimage represents the front of the classroom. v,h - vertical and horizontal spacing be-tween students, 1 uod (unit of distance) in our current setup. s - between-row spacing,1 uod. d - distance between the professor (center-front of the classroom) and the anal-ysed student. Light-blue zone represents the visible students for student at the location3rd row, 4th seat. Darker-blue rectangle around the student represents his immediateneighbourhood. b) Example of co-movement on a movement intensity graphic of twopersons. Student 1 shifted hers seating position (blue line), 2 seconds later, neighbour-ing student 2 (marked in green) also started re-adjusting herself.We compared the average number of synced movements between pairs sittingimmediately next to each other (marked as the dark-blue region in Fig.2a) or not.Student motion and it’s potential as a classroom performance metric 5The null hypothesis can be rejected with a two-sampled t-test (p ≤ 0.05). Neigh-bouring pairs had higher probability of synchronized moving ( First class: µ =76.54, σ = 32.47; Second class: µ = 63.33, σ = 24.33), than a randomly pickedpair (First class: µ = 54.43, σ = 15.6492; Second class: µ = 44.88, σ = 18.42).We also noticed no significant difference in number of synchronized momentsbetween the pair from a visible student population (marked as the light-blueregion in Fig 2a) and the rest of the student population.We also tested the influence of teachers proximity to the movement of thestudents: the further away students are from the front-center of the classroom(the point which is the closest to the teacher in both cases, represented as mea-surement d in Fig 2a) the less active they are (Kendall correlation is -0.284(p = 0.03) for Class 1; for Class 2 is -0.172 (p = 0.45)). The distance measure-ment is approximate, and is using 1 unit of distance (uod) per row and seatdifference (passages between rows are taken into consideration also as 1 uod).Because of the small sample we used Kendall correlation for analysing the sam-ples and we have seen the same trend in both cases (Figure 3), even tough thecorrelation was insignificant for the second classroom.a) b)Fig. 3. Correlation between distance from teacher and motion intensity in classes 1 and2. a) Class 1. 28 students in sample. Kendall correlation -0.284 (p = 0.03) b) Class 2.13 students in sample. Kendall correlation -0.172 (p = 0.45)4 ConclusionWe have shown interesting correlations of movement with previous work onposition and class activity from [1] and [13]. We see the teacher and the teachingmaterial as the dominant “signal” in the classroom. With our conclusion thatstudents are most likely to “sync” with the person next to them, we show thatit is not the only relevant signal, and that the immediate neighbourhood alsohas a significant influence on students attention, and also on students in-classperformance.In our future work we would like to explore further the phenomenon which wecall the “distraction ripples” - assuming the transitivity of the motion-syncing,we would like to capture the spreading of influence of one class-member to peoplearound him. We are also interested to correlate how well do these “ripples” spread6 EC TEL’13 - 3rd International Workshop on Teaching Analyticsin high-attention and low-attention groups of students, in order to make a newmetric of class attention. We feel that the future reflective practitioners wouldbenefit from a system that will provide an objective metric of performance. Wesee the system as being especially useful for novice teachers who might be primedto see cooperative students [12].Acknowledgements This work has been sponsored by the ProDoc SNF Grant,project PDFMP1 135108.References1. R. S. Adams. Location as a feature of instructional interaction. Merrill-PalmerQuarterly of Behavior and Development, 15(4):309–321, 1969.2. J. D. Allen. Classroom management: Students perspectives, goals, and strategies.American Educational Research Journal, 23(3):437–459, 1986.3. H. Borko and C. Livingston. Cognition and improvisation: Differences in mathe-matics instruction by expert and novice teachers. American educational researchjournal, 26(4):473–498, 1989.4. J.-Y. Bouguet. Pyramidal implementation of the affine lucas kanade feature trackerdescription of the algorithm. Intel Corporation, 2001.5. J. Campbell, P. DeBlois, and D. Oblinger. Academic analytics: A new tool for anew era. Educause Review, 42(4):40, 2007.6. P. Dillenbourg and P. Jermann. 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Macmillan, 2011.13. M. Koneya. Location and interaction in row-and-column seating arrangements.Environment and Behavior, 8(2):265–282, 1976.14. B. Latane´, S. Nida, et al. Ten years of research on group size and helping. Psy-chological Bulletin, 89(2):308–324, 1981.15. F. Lim, K. L. O’Halloran, and A. Podlasov. Spatial pedagogy: mapping meaningsin the use of classroom space. pages 235–251, May 2012.16. A. Pollard and J. Collins. Reflective teaching. Continuum International PublishingGroup, 2005.17. M. Raca and P. Dillenbourg. System for assessing classroom attention. In Proceed-ings of the Third International Conference on Learning Analytics and Knowledge,pages 265–269. ACM, 2013.18. R. Sommer. Studies in personal space. Sociometry, 22(3):247–260, 1959.19. E. J. Webb, D. T. Campbell, R. D. Schwartz, and L. Sechrest. Unobtrusive mea-sures, volume 2. SAGE Publications, Incorporated, 1999.

Student motion and it's potential as a classroom performance metric

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