Using Student Action Recognition to Enhance the Efﬁciency of

Tele-education

Eleni Dimitriadou

and Andreas Lanitis

1,2

Visual Media Computing Lab, Deptartment of Multimedia and Graphic Arts, Cyprus University of Technology, Cyprus

CYENS - Centre of Excellence, Nicosia, Cyprus

Keywords:

Action Recognition, Deep Learning, Tele-education.

Abstract:

Due to the COVID-19 pandemic, many schools worldwide are using tele-education for class delivery. How-

ever, this causes a problem related to students’ active class participation. We propose to address the problem

with a system that recognizes student’s actions and informs the teacher accordingly, while preserving the

privacy of students. In the proposed action recognition system, seven typical actions performed by students at-

tending online courses, are recognized using Convolutional Neural Network (CNN) architectures. The actions

considered were deﬁned by considering the relevant literature and educator’s views, and ensure that they pro-

vide information about the physical presence, active participation, and distraction of students, that constitute

important pedagogical aspects of class delivery. The action recognition process is performed locally on the

device of each student, thus it is imperative to use classiﬁcation methods that require minimal computational

load and memory requirements. Initial experimental results indicate that the proposed action recognition sys-

tem provides promising classiﬁcation results, when dealing with new instances of previously enrolled students

or when dealing with previously unseen students.

1 INTRODUCTION

A major issue related to tele-education is the level of

concentration of students during the course delivery

process (Moubayed et al., 2020). This is especially

true for primary school students, who ﬁnd it more dif-

ﬁcult to concentrate and attend a class delivered on-

line (Putri et al., 2020). To make the situation worse,

in many countries the use of cameras during the de-

livery of online courses to primary school students is

forbidden as a means of preserving student privacy,

and since the teacher has no optical contact with the

students, it is very common that student’s lose con-

centration during the course delivery process.

The purpose of this study is to provide a com-

puter vision-based tool that can be used for examining

the behavioral participation and behavioural disaffec-

tion of primary school students’ during teleconferenc-

ing. Behavioral participation involves: effort, atten-

tion, initiation of action, involvement, intensity, per-

sistence, and absorption, while behavioral disaffec-

tion involves: withdrawal, giving up, passivity, being

inattentive, distracted, not engaged mentally and not

prepared (Skinner et al., 2008, 2009). In line with the

taxonomy listed above, we aim to develop a method

for recognising student’s actions related to student’s

attention to the lesson, their absence from the lesson,

their active participation, and their distraction.

To deal with this problem, we propose to employ

a machine vision-based approach that can be used for

monitoring student’s actions during the class deliv-

ery process and report to the instructor the status and

level of concentration of the students. For this pur-

pose, we have used different network architectures in-

cluding faster R-CNN, SqueezeNet, GoogleNet, and

Inception-v3. Figure 1 shows a block diagram of

the proposed student monitoring system. An impor-

tant aspect of this approach is that images of students

captured during the process are processed locally and

only information about the actions of the students is

transmitted to the instructor so that privacy issues are

not violated. When the proposed system is used in

real classes, it is assumed that both parents and stu-

dents should give their prior consent, ensuring that

no ethical considerations related to privacy protection

arise. Since the computation is done locally on the

computers or smart devices of each student, it is im-

perative to adopt approaches that require minimum

computational power, to ensure the smooth opera-

tion of the system regardless the speciﬁcations of the

equipment that each student uses for the purpose of

tele-education. Although the recognition of student’s

actions has been addressed in the literature before, to

the best of our knowledge, the proposed formation of

Dimitriadou, E. and Lanitis, A.

Using Student Action Recognition to Enhance the Efﬁciency of Tele-education.

DOI: 10.5220/0010868200003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP, pages

543-549

ISBN: 978-989-758-555-5; ISSN: 2184-4321

543

the application within the domain of tele-education

has not been considered before.

Local

Processor

Action

Status

Attending

Writing

Hand Raising

Instructor

Students

Figure 1: Schematic diagram of the proposed student mon-

itoring system during online courses.

In the remainder of the paper, we present a liter-

ature review on the topics of Computer vision in ed-

ucation and present the methodology adopted in our

study. In Section 4, we present the results of the

experimental evaluation followed by plans for future

work and concluding comments.

2 LITERATURE REVIEW

Computer vision is often used to help educators to an-

alyze videos and images of students as a means of

acquiring important information regarding students.

Within this context several researchers addressed the

problem of automated student attendance registration.

Chowdhury et al. (2020), proposed an automatic stu-

dent attendance system based on face recognition us-

ing Convolutional Neural Networks (CNN). The pro-

posed system can detect and recognize multiple stu-

dents’ faces from a real-time video stream obtained

by a static camera located in a classroom. Mery

et al. (2019), used deep networks to implement an

automated student attendance system that identiﬁes

students in crowded classrooms using images cap-

tured by a smartphone camera, while Chintalapati

and Raghunadh (2013) suggested an automated at-

tendance management system based on face detection

and recognition algorithms to detect the students who

are entering the classroom. Instead of using a static

camera, Gupta et al. (2018), used a rotating camera to

capture images of students, followed by the detection

of students’ faces using an Inception-v3 CNN.

Computer vision methods have been used to help

educators to keep control of all the students in phys-

ical classes (Radlak et al., 2015; Bebis et al., 2003;

Rashmi et al., 2021). Ashwin and Guddeti (2020)

proposed a system to analyze students’ body postures,

gestures and facial expressions to investigate engage-

ment. Three states of student’s engagement were ex-

amined: boredom, engaged and neutral. Ngoc Anh

et al. (2019) present a system to monitor the behaviour

of students in the classroom. Various computer vi-

sion techniques such as face embedding, gaze esti-

mation, face detection and facial landmark detection

were used for analyzing images. Similarly, Thomas

and Jayagopi (2017) proposed a machine learning al-

gorithm to determine students’ engagement in a class-

room by analyzing students’ head position, direction

of eye gaze and facial expressions. Furthermore,

Yang and Chen (2011) presents a system that pro-

vides feedback regarding student actions that utilizes

eye and face detection to determine if the students

are active or not. Recently, Tran et al. (2021) de-

veloped a system regarding video face recognition in

collaborative learning environments. Their proposed

system could recognize multiple students’ faces from

different angles in a single image frame using static

cameras in the classroom. Similarly, Teeparthi et al.

(2021) developed a method for detecting multiple stu-

dents’ hands in a single image frame in a collabora-

tive learning environment. Their proposed method in-

cluded transfer learning and Faster R-CNN as their

baseline method using time-projections, clustering

and small region removal. In all approaches stated

above ﬁxed cameras were used for capturing images.

Instead of using a single camera, Li et al. (2019)

describes the use of four cameras, which were ﬁxed

on the wall (front and back of classroom), to record

the students’ actions from various viewpoints. Fur-

thermore, they studied 15 different types of student’s

actions, six of which are similar with the actions

considered in our work. To evaluate the proposed

model, four types of algorithms were used: IDT (Im-

proved Dense Trajectory) used with Support Vector

Machines, and CNN implemented with VGG-16 and

Inception-v3 architectures. Unlike the work of Li

et al. (2019), in our work we use images captured

from a single laptop camera rather than employing

multiple ﬁxed cameras.

Recently, Rashmi et al. (2021), proposed a sys-

tem, that recognizes multiple actions of students given

an image frame captured by a CCTV camera. The

dataset of student’s actions included the actions of

sleeping, eating, using phone, discussion and being

engaged. The YOLOv3 framework was used for ob-

ject detection and students’ action recognition. While

this study is related to our work, in our study we rec-

ognize the actions of a student in a single image frame

during the online courses using a camera pointing at

each student. As a result, the overall efﬁciency of the

approach is optimized, as there is no need to include

the computationally expensive YOLO algorithm that

would have prevented the real-time operation of the

system on less powerful machines.

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

544

The work of Bian et al. (2019), is closely related

to our work since they consider the problem of facial

expression recognition during the delivery of on-line

courses. They use facial expression algorithms based

on the VGG16 network and different CNN architec-

tures. In our work, instead of focusing on the analysis

of facial expressions, we consider the recognition of

actions that relate to the physical presence (Raddon,

2006), active participation (Pratton and Hales, 1986),

and distraction (Baron, 1986) as indicated in the rele-

vant literature.

3 METHODOLOGY

The main steps of the proposed methodology include

the steps of User Evaluation, Data Collection, and Ac-

tion Recognition. In the following sections, we de-

scribe each step of the methodology.

3.1 User Evaluation

Prior to the application development, the proposed

system and its operation was presented to a group

of teachers, students and parents, who provided feed-

back related to the class monitoring tool (Dimitriadou

and Lanitis, 2021). Data were collected through semi-

structured interviews since this method constitutes the

most appropriate tool for recording detailed opinions

(Barriball and While, 1994).

The analysis of the feedback received shows that

the participants were enthusiastic about the system of

recognizing students’ actions in online lessons. With

regards to the disadvantages, the educators and par-

ents characterized the application as excellent in con-

trast with the students, where the majority declared

that they would have difﬁculties in accepting the ap-

plication of the system as it will prevent them from

engaging in activities not related to the class delivery.

At the end of the interviews, most of the participants

found the proposed application complete, while they

were given the chance to suggest ways to enrich the

system with additional features. More details related

to the analysis of results of the user evaluation are pre-

sented in (Dimitriadou and Lanitis, 2021).

Based on the feedback received from the stake-

holders, the development process was carried out, as

exempliﬁed in the following subsections.

3.2 Data Collection

Data have been collected using a laptop camera, from

15 primary school students while they were using a

laptop in a style that is usually adopted during the at-

tendance of on-line courses from their homes. Stu-

dents participating in the experiment were asked to

perform the actions presented in Table 1. All ac-

tions considered are related to the three categories of

“Physical Presence”, “Active Participation” and “Dis-

traction”. Furthermore, the deﬁnition of the exact ac-

tions considered were based on suggestions of several

active educators, engaged in tele-education.

Table 1: Taxonomy of student actions considered.

Categories Classes Description

Physical Absent Not being present in the camera point of view.

Presence

Attending Looking towards the camera.

Active Hand Raising Raising the left or right hand to request permission to talk.

Participation Writing Student writing notes on a pad.

Telephone Call Using a smartphone for making a call.

Distraction Using Phone Using a smartphone for texting or playing games.

Looking Looking at different directions rather than

Elsewhere looking towards the screen of the laptop.

For each action stated in table 1, a 10 second

video was recorded depicting each student perform-

ing the action. For each student, two set of videos

have been recorded (Session A and Session B), in

which the students wear different clothes while the

background was different for each session and stu-

dent. An example of typical images depicting differ-

ent actions considered are shown in Figure 2. Cur-

rently, the dataset contains 194 videos with a total of

9700 image frames, where Session A has 5200 frames

and Session B 4500 frames. We are still in the pro-

cess of collecting additional data, and once the data

collection is completed, we plan to make the dataset

publicly available.

(a)

(b)

(c)

(e)

(f)

(d)

(g)

Figure 2: Typical students’ images regarding the seven ac-

tions.(a) Telephone Call (b) Attending (c) Hand Raising (d)

Looking Elsewhere (e) Writing (f) Using Phone (g) Absent.

3.3 Action Recognition

Action recognition is performed using deep networks.

We trained and evaluated the performance of several

deep network architectures that include a GoogleNet

(Szegedy et al., 2015), SqueezeNet (Iandola et al.,

2016), Inception-v3 (Szegedy et al., 2016) and faster

R-CNN (Ren et al., 2015).

To solve the problem of limited data, we used

data augmentation to augment images in the train-

ing dataset. In the present work, augmentation algo-

Using Student Action Recognition to Enhance the Efﬁciency of Tele-education

545

rithm included reﬂection, rotation, scale and transla-

tion of all training images. During the training pro-

cedure, transfer learning was adopted where starting

from a pre-trained version of the networks trained us-

ing images from the ImageNet database (www.image-

net.org), the network weights of certain layers are

adapted based on the train data and the classiﬁcation

task in question. To train the networks, we used the

Adam optimizer (Kingma and Ba, 2014) with a mo-

mentum value of 0.9 and weight decay of 0.0001 to

minimize a cross-entropy loss function. In the train-

ing options, we set the learning rate 0.0001, deﬁned

the size of batch 11 while training involved 15 epochs.

4 EXPERIMENTAL

INVESTIGATION

From the initial stages of the experimental evaluation,

it was evident that the faster R-CNN architecture was

the least appropriate network for the proposed appli-

cation framework, due to the computational load re-

quired during the classiﬁcation stage, that would pre-

vent the real time operation of the system on stu-

dent’s machines. Therefore, the experimental evalu-

ation was focused on the comparative evaluation of

the GoogleNet, SqueezeNet and Inception-v3 archi-

tectures.

Based on the taxonomy in Table 1, results are re-

ported both for the seven-class classiﬁcation problem,

where the exact student action is recognized, and for

the three-class classiﬁcation problem where student’s

actions are classiﬁed into the states of “Physical Pres-

ence”, “Active Participation” and “Distraction”. Two

experiments were performed :

4.1 Experiment 1: Using New Instances

of Previously Enrolled Students

For this experiment image frames from Session A

were used for training the system and image frames

from Session B (see section 3.2) for testing the sys-

tem, allowing in that way the assessment of the abil-

ity of the proposed system to recognize actions in new

videos of students who provided training data.

The results obtained from the proposed CNN

architectures for the performance evaluation of the

seven-class classiﬁcation problem are presented in

Table 2. The errors of the GoogleNet, SqueezeNet

and Inception-v3 are 0.022, 0.059 and 0.374 respec-

tively.

GoogleNet and SqueezeNet are proved as the most

suitable networks in the classiﬁcation of the students’

actions in Experiment 1, in contrast to Inception-v3.

However, the SqueezeNet needs less computational

time and memory requirements in comparison with

the other networks. The low performance measures

in Inception-v3 are due to the fact that more data are

required for training.

Table 2: Accuracy metrics for experiment 1.

Performance Measures GoogleNet SqueezeNet Inception-v3

Accuracy(%) 97.81 94.15 62.61

Recall 0.964 0.910 0.601

Precision 0.964 0.926 0.510

F1-score 0.963 0.912 0.542

MCC* 0.960 0.907 0.479

Cohen’s k 0.910 0.761 0.345

Classiﬁcation time (s) 0.089 0.039 0.258

Memory requirements (MB) 21.3 2.60 77.1

*MCC=Matthews Correlation Coefﬁcient

Based on the results it is evident that the algorithm

can classify students’ actions almost with perfect ac-

curacy for GoogleNet. From the confusion matrix for

the seven-class problem, all test images were recog-

nized correctly in class 1 (Absent), class 4 (Looking

Elsewhere) and class 6 (Using phone), while for class

2 (Attending), class 3 (Hand Raising), class 5 (Tele-

phone Call) and class 7 (Writing) the accuracy is over

90%. Figure 3 represents the ROC curve for each

class for the GoogleNet.

Figure 3: ROC curves for GoogleNet for the seven-class

classiﬁcation problem for experiment 1.

The impact of the action recognition system is

negative when networks misclassify images of ac-

tive participation as distraction with high error rates.

Based on the confusion matrix for the seven-class

problem, the networks GoogleNet, SqueezeNet, and

Inception-v3 mostly misclassify the class “Writing”

as “Using Phone”, with percentages of 0.6%, 1.8%,

and 2.7%, respectively. Furthermore, SqueezeNet

misclassiﬁes 1.8% of the “Attending” images as “Us-

ing Phone”. Overall, the errors rates of the algorithms

are minimal. However, more training data and net-

work tuning are required to minimize the errors that

could give to the teacher a wrong impression about a

student’s behaviour.

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

546

By aggregating the seven actions into three more

general categories the GoogleNet, SqueezeNet and

Inception-v3 acheive accuracies of 98.66%, 95.74%,

and 73.45% respectively. Figure 4, illustrates the con-

fusion matrix in the GoogleNet network which clas-

siﬁes the three general categories related to student

status. The class related to student physical pres-

ence is perfectly classiﬁed while the active partici-

pation and distraction categories have an accuracy of

96.3% and 99.6% respectively. Based on the results,

we conclude that the GoogleNet can recognize with

increased accuracy the behavioural disaffection in-

stead of behavioural participation of primary school

students’, which is important for educators to know

during teleconferencing.

Accuracy: 98.66%

100.0%

0.0%

96.3%

231

0.4%

0.0%

3.8%

99.6%

568

Physical Presence Active Participation Distraction

Predicted Class

Physical Presence

Active Participation

Distraction

True Class

Figure 4: Confusion matrix for GoogleNet for the three-

class classiﬁcation problem for experiment 1.

4.2 Experiment 2: Recognizing Actions

of Previously Unseen Students

All images of eight students (from both session A and

Session B) were used for training the system and im-

ages of the remaining seven students were used for

testing the system, so that recognition performance is

tested on previously unseen students.

Table 3 presents the performance measures ob-

tained from the suggested CNN architectures for the

seven-class classiﬁcation problem. For Experiment 2,

the errors of GoogleNet, SqueezeNet, and Inception-

v3 are 0.057, 0.127 and 0.551, respectively.

We observe from Table 3 that in Experiment 2,

GoogleNet and SqueezeNet have better performance

than Inception-v3 which requires more data in or-

der to be efﬁcient. The computational time for

SqueezeNet network is inﬁnitesimal and half the time

required for GoogleNet.

GoogleNet achieved the highest correct recogni-

tion rates among all networks considered. From the

confusion matrix for GoogleNet for the seven-class

Table 3: Accuracy metrics for experiment 2.

Performance Measures Googlenet Squeezenet Inception-v3

Accuracy(%) 94.32 87.32 44.91

Recall 0.913 0.820 0.540

Precision 0.939 0.812 0.369

F1-score 0.911 0.804 0.361

MCC* 0.911 0.790 0.325

Cohen’s k 0.768 0.482 0.555

Classiﬁcation time (s) 0.082 0.038 0.234

Memory requirements (MB) 21.3 2.60 77.1

*MCC=Matthews Correlation Coefﬁcient

problem the images were recognized correctly in class

1 (Absent), class 4 (Looking Elsewhere), class 5

(Telephone Call) and class 6 (Using Phone). For the

remaining classes, class 3 (Hand Raising), class 2 (At-

tending) and class 7 (Writing) have accuracy 98.8%,

78.6% and 61.4% respectively. Figure 5 represents

the ROC curve for Experiment 2 for each class for the

GoogleNet.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

False Positive Rate

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

True Positive Rate

ROC curves

Absent

Attending

Hand Raising

Looking Elsewhere

Telephone Call

Using Phone

Writing

Figure 5: ROC curves for GoogleNet for the seven-class

classiﬁcation problem for experiment 2.

For the three-class classiﬁcation problem the

GoogleNet, SqueezeNet and Inception-v3 achieve

correct recognition rates of 94.32%, 92.46% and

65.13% respectively. GoogleNet proved to be the net-

work with the highest classiﬁcation accuracy, like in

the case of Experiment 1. Based on the confusion

matrix (Figure 6) of GoogleNet, physical presence

and distraction are perfectly classiﬁed while the ac-

tive participation category has an accuracy of 80.8%.

Overall, the classiﬁcation rates of GoogleNet and

SqueezeNet are satisfactory, despite the fact that we

deal with images of previously unseen students.

Our results suggest that the GoogleNet mostly

misclassiﬁes the classes “Writing” and “Attending”

with phone usage by 3.4% and 1.9%, respectively.

We observe that in both Experiments, GoogleNet con-

fuses the aforementioned classes with minimal er-

rors. Otherwise, SqueezeNet mostly overestimates

the phone usage of students during active participa-

tion by 3.4%. On the other hand, Inception-v3 over-

estimates phone usage and class “Looking elsewhere”

during active participation by 6.4% and 13%, respec-

tively. Error percentages for the three networks are

higher in Experiment 2 than in Experiment 1, since

Using Student Action Recognition to Enhance the Efﬁciency of Tele-education

547

the size of the train set in Experiment 1 is larger

than the size in Experiments 2. In the future, in or-

der to minimize the error rates, the overall experi-

mental evaluation will be implemented using several

recorded videos of online lessons of larger duration.

Accuracy: 94.32%

100.0%

0.0%

80.8%

181

0.0%

19.2%

100.0%

527

Physical Presence Active Participation Distraction

Predicted Class

Physical Presence

Active Participation

Distraction

True Class

Figure 6: Confusion matrix for GoogleNet for the three-

class classiﬁcation problem for experiment 2.

5 CONCLUSION AND FUTURE

WORK

In our study, we developed a deep network model

which can provide to educators real-time video analy-

sis of young students, providing in that way informa-

tion about the behavioral participation and behavioral

disaffection of their students during the delivery of

online courses.

Initial results demonstrate high classiﬁcation rates

for the three class and seven class classiﬁcation prob-

lems, both when dealing with new images of previ-

ously enrolled students or when dealing with previ-

ously unseen students. The results indicate the feasi-

bility of this approach that can be used as the basis for

implementing an integrated system that helps educa-

tors to monitor actions of participants during remote

class delivery. An important aspect of the system is

the fact that the privacy of students is protected since

teachers receive only real-time information about stu-

dents’ actions rather than receiving images of the stu-

dents. To eliminate any additional ethical consid-

erations regarding student privacy, in the ﬁnal sys-

tem students and parents will be thoroughly informed

about the operation of the action recognition system,

and they will be required to approve its use during the

delivery of on-line courses.

Since in the proposed system, the action recog-

nition process will take place locally, on the device

of each student, it is imperative to use classiﬁcation

methods that require minimal computational load. In

the ﬁnal system implementation, students who pos-

sess devices with low computational capabilities will

be using the SqueezeNet network for classiﬁcation, as

in that case the computational and memory require-

ments are minimized, allowing real-time operation,

and a reasonable recognition accuracy. Otherwise, if

the students have a powerful device, then it is more

appropriate to use the GoogleNet network so that we

take advantage of the increased recognition accuracy

achieved by this architecture.

In future work, we aim to explore different net-

work architectures, extend the dataset to include more

students and additional actions, perform an exhaus-

tive user evaluation with all stakeholders, and in-

vestigate practical issues of integrating the proposed

action recognition system in tele-education teaching

sessions.

ACKNOWLEDGEMENTS

We would like to thank all volunteers who partic-

ipated in the interviews and all student who pro-

vided training data. This project was partially funded

by the European Union’s Horizon 2020 Research

and Innovation Programme under Grant Agreement

No 739578 and the Government of the Republic of

Cyprus through the Directorate General for European

Programmes, Coordination and Development.

REFERENCES

Ashwin, T. and Guddeti, R. M. R. (2020). Impact of in-

quiry interventions on students in e-learning and class-

room environments using affective computing frame-

work. User Modeling and User-Adapted Interaction,

30(5):759–801.

Baron, R. S. (1986). Distraction-conﬂict theory: Progress

and problems. Advances in experimental social psy-

chology, 19:1–40.

Barriball, K. L. and While, A. (1994). Collecting data us-

ing a semi-structured interview: a discussion paper.

Journal of Advanced Nursing-Institutional Subscrip-

tion, 19(2):328–335.

Bebis, G., Egbert, D., and Shah, M. (2003). Review of com-

puter vision education. IEEE Transactions on Educa-

tion, 46(1):2–21.

Bian, C., Zhang, Y., Yang, F., Bi, W., and Lu, W. (2019).

Spontaneous facial expression database for academic

emotion inference in online learning. IET Computer

Vision, 13(3):329–337.

Chintalapati, S. and Raghunadh, M. (2013). Automated at-

tendance management system based on face recogni-

tion algorithms. In 2013 IEEE International Confer-

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

548

ence on Computational Intelligence and Computing

Research, pages 1–5. IEEE.

Chowdhury, S., Nath, S., Dey, A., and Das, A. (2020). De-

velopment of an automatic class attendance system

using cnn-based face recognition. In 2020 Emerging

Technology in Computing, Communication and Elec-

tronics (ETCCE), pages 1–5. IEEE.

Dimitriadou, E. and Lanitis, A. (2021). Student action

recognition as a means of supporting remote learning.

In ICERI2021 Proceedings, 14th annual International

Conference of Education, Research and Innovation,

pages 8175–8180. IATED.

Gupta, S. K., Ashwin, T., and Guddeti, R. M. R. (2018).

Cvucams: Computer vision based unobtrusive class-

room attendance management system. In 2018 IEEE

18th International Conference on Advanced Learning

Technologies (ICALT), pages 101–102. IEEE.

Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K.,

Dally, W. J., and Keutzer, K. (2016). Squeezenet:

Alexnet-level accuracy with 50x fewer parame-

ters and¡ 0.5 mb model size. arXiv preprint

arXiv:1602.07360.

Kingma, D. P. and Ba, J. (2014). Adam: A

method for stochastic optimization. arXiv preprint

arXiv:1412.6980.

Li, X., Wang, M., Zeng, W., and Lu, W. (2019). A stu-

dents’ action recognition database in smart classroom.

In 2019 14th International Conference on Computer

Science & Education (ICCSE), pages 523–527. IEEE.

Mery, D., Mackenney, I., and Villalobos, E. (2019). Stu-

dent attendance system in crowded classrooms using a

smartphone camera. In 2019 IEEE Winter Conference

on Applications of Computer Vision (WACV), pages

857–866. IEEE.

Moubayed, A., Injadat, M., Shami, A., and Lutﬁyya, H.

(2020). Student engagement level in an e-learning en-

vironment: Clustering using k-means. American Jour-

nal of Distance Education, 34(2):137–156.

Ngoc Anh, B., Tung Son, N., Truong Lam, P., Le Chi,

P., Huu Tuan, N., Cong Dat, N., Huu Trung, N.,

Umar Aftab, M., Van Dinh, T., et al. (2019). A

computer-vision based application for student be-

havior monitoring in classroom. Applied Sciences,

9(22):4729.

Pratton, J. and Hales, L. W. (1986). The effects of active

participation on student learning. The Journal of Ed-

ucational Research, 79(4):210–215.

Putri, R. S., Purwanto, A., Pramono, R., Asbari, M., Wi-

jayanti, L. M., and Hyun, C. C. (2020). Impact of

the covid-19 pandemic on online home learning: An

explorative study of primary schools in indonesia. In-

ternational Journal of Advanced Science and Technol-

ogy, 29(5):4809–4818.

Raddon, A. (2006). Absence as opportunity: Learning out-

side the institutional space and time. Journal of Fur-

ther and Higher Education, 30(02):157–167.

Radlak, K., Frackiewicz, M., Szczepanski, M., Kawulok,

M., and Czardybon, M. (2015). Adaptive vision

studio—educational tool for image processing learn-

ing. In 2015 IEEE Frontiers in Education Conference

(FIE), pages 1–8. IEEE.

Rashmi, M., Ashwin, T., and Guddeti, R. M. R. (2021).

Surveillance video analysis for student action recog-

nition and localization inside computer laboratories of

a smart campus. Multimedia Tools and Applications,

80(2):2907–2929.

Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster

r-cnn: Towards real-time object detection with region

proposal networks. Advances in neural information

processing systems, 28:91–99.

Skinner, E., Furrer, C., Marchand, G., and Kindermann,

T. (2008). Engagement and disaffection in the class-

room: Part of a larger motivational dynamic? Journal

of educational psychology, 100(4):765.

Skinner, E. A., Kindermann, T. A., and Furrer, C. J. (2009).

A motivational perspective on engagement and dis-

affection: Conceptualization and assessment of chil-

dren’s behavioral and emotional participation in aca-

demic activities in the classroom. Educational and

psychological measurement, 69(3):493–525.

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.,

Anguelov, D., Erhan, D., Vanhoucke, V., and Rabi-

novich, A. (2015). Going deeper with convolutions.

In Proceedings of the IEEE conference on computer

vision and pattern recognition, pages 1–9.

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wo-

jna, Z. (2016). Rethinking the inception architecture

for computer vision. In Proceedings of the IEEE con-

ference on computer vision and pattern recognition,

pages 2818–2826.

Teeparthi, S., Jatla, V., Pattichis, M., Pattichis, S.-C., and

Lopez Leiva, C. (2021). Fast hand detection in collab-

orative learning environments? International Confer-

ence on Computer Analysis of Images and Patterns.

Thomas, C. and Jayagopi, D. B. (2017). Predicting stu-

dent engagement in classrooms using facial behavioral

cues. In Proceedings of the 1st ACM SIGCHI interna-

tional workshop on multimodal interaction for educa-

tion, pages 33–40.

Tran, P., Pattichis, M., Celedon-Pattichis, S., and

Lopez Leiva, C. (2021). Facial recognition in collab-

orative learning videos. International Conference on

Computer Analysis of Images and Patterns.

Yang, S. and Chen, L. (2011). A face and eye detection

based feedback system for smart classroom. In Pro-

ceedings of 2011 International Conference on Elec-

tronic & Mechanical Engineering and Information

Technology, volume 2, pages 571–574. IEEE.

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