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Mouse tracking serves as an alternative to eye tracking in measuring the learning process in education because of its affordability. Moreover, mouse tracking does not require extra hardware, as in the case of eye tracking, because it is a feature in personal computers by default. Therefore, it is possible to implement mouse tracking in a massive open scale. However, mouse tracking has only been implemented in a laboratory setting to date, ostensibly because of the associated extremely high running costs. Nonetheless, there is no available data to support the claim of high resource costs, which has resulted in much speculation among implementers. In general, the implementation of mouse tracking in a non-laboratory environment is still rare. Therefore, the authors developed an application to investigate real-time mouse tracking online. It was implemented on the Moodle learning management system and tested on an online quiz session accessed abroad. Additionally, the application can handle tracking on mobile devices. In this work, the main resources that include CPU, network, RAM, and storage costs were measured when mouse tracking was used. These results can serve as a reference for network and server administrators during future implementation of this technique. It was determined that the characteristics of mouse activities were dynamic in that occasional surges and lulls were observed. Additionally, this article also discussed the advantage of real-time and online implementation to regular online implementation and showed that there is a possibility of implementing mouse tracking on a large scale if mouse tracking data are not aggregated and transmitted as a single data package.
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Education and Information Technologies manuscript No.
(will be inserted by the editor)
Implementation of Real-Time Online Mouse Tracking
on Overseas Quiz Session
From Server Administrator Point of View
Received: date / Accepted: date
Abstract Mouse tracking serves as an alternative to eye tracking in measuring
the learning process in education because of its affordability. Moreover, mouse
tracking does not require extra hardware, as in the case of eye tracking, because
it is a feature in personal computers by default. Therefore, it is possible to im-
plement mouse tracking in a massive open scale. However, mouse tracking has
only been implemented in a laboratory setting to date, ostensibly because of the
associated extremely high running costs. Nonetheless, there is no available data
to support the claim of high resource costs, which has resulted in much specu-
lation among implementers. In general, the implementation of mouse tracking in
a non-laboratory environment is still rare. Therefore, the authors developed an
application to investigate real-time mouse tracking online. It was implemented on
the Moodle learning management system and tested on an online quiz session ac-
cessed abroad. Additionally, the application can handle tracking on mobile devices.
In this work, the main resources that include CPU, network, RAM, and storage
costs were measured when mouse tracking was used. These results can serve as a
reference for network and server administrators during future implementation of
this technique. It was determined that the characteristics of mouse activities were
dynamic in that occasional surges and lulls were observed. If mouse tracking data
are not aggregated and transmitted as a single data package, then mouse tracking
can be implemented on a large scale.
Keywords mouse tracking ·online ·real time ·large open implementation ·
resource cost
1 Introduction
With the recent advances in technology, including the Internet, information can be
searched and published with few restrictions. With the advancement in informa-
tion communication technology (ICT), more activities are being conducted online.
Individuals no longer spend hours staring at computer screens after work or class;
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instead, they often use their mobile devices to stay online irrespective of the time
or their physical location. (Dentzel, 2013). Unlike in the past where individuals
were limited to local newspapers, televisions, and textbooks, people can now eas-
ily search and choose the information they want using advanced search engines
such as Bing, DuckDuckGo, Google, and Yahoo. In the case of social networking
services (SNS) such as Facebook, Instagram, Line, and Twitter, individuals can
get the latest news, interact with one another, open discussions, and share in-
formation. Users can enjoy entertainment such as viewing photographs, listening
to music, and watching videos using services such as Dailymotion, Metacafe, and
Youtube. In addition, video games are also available. Shopping is also facilitated
by online merchants such as Aliexpress and Amazon, where individuals can order
items and have them delivered. All of these online activities can be performed
using a computer device connected to the Internet.
Education has also benefitted from the Internet and courses can be delivered
blended (Paturusi et al., 2012) or fully online (Wen and Ros´e, 2014). A variety
of learning and teaching activities can be performed outside the classroom, for
example, the reading of learning material, discrete discussion in forums, submis-
sion of assignments, and the performance of exercises (Linawati et al., 2017). This
greatly reduces the burden on both students and teachers. The number of higher
education institutions that provides online education is increasing, and it is only
a matter of time before primary and secondary schools (Sopu et al., 2016) follow
this model. Implementing an online course is currently much easier than in the
past because of the advent of learning management systems (LMS) where most
processes are automated (Kakasevski et al., 2008) without the need for advanced
knowledge on computers and web programming (Chourishi et al., 2011); only com-
puter literacy is a prerequisite. The next step after implementing online courses is
the implementation of massive open online courses (MOOC) (Drake et al., 2015).
Unlike regular online courses, everyone can join MOOC, which is not limited to
students of certain institutions.
With the option of numerous online activities, many studies have become in-
terested in analyzing these activities in an area known as online analytics. Online
analytics records who, where, and when associated with online activities. The
most popular metrics are total traffic, source traffic, bounce rate (the rate of peo-
ple immediately leaving the page after visiting), and conversion rate (whether the
page fulfills its purpose) (Bluehost, 2016). Whether it is a public website or an
online course, the concept is almost the same. On a public website, the number
of page views, comments, ratings, image views, videos played, and items bought
are recorded. Based on page views only, a variety of analyses can be performed.
A page view can predict a user’s demographic (Hu et al., 2007), characterize an
audience in terms of preference for news, multimedia, games, or adult content
(Kumar and Tomkins, 2010). It can also predict whether a user is at risk on visit-
ing malicious websites (Canali et al., 2014). Page views also provide hints on how
to improve a website’s design, for example moving popular webpage links to the
header (Khan et al., 2018). There are already many web analytics software avail-
able such as Google Analytics, Open Web Analytics, Piwik, and Cloudflare (spa,
2019). In online courses, logins, learning materials viewed, discussions, assignments
submissions, quizzes attempt, and grades are recorded (moo, 2016). They can re-
veal for example a student’s level of activity (Nandi et al., 2011), how difficult
the quizzes are for students (Nakano et al., 2005; Usagawa et al., 2006), and even
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identify failing students (Fungai and Usagawa, 2016). Usually, these data are used
to measure the learning performance of students (Wen and Ros´e, 2014).
Although those popular metrics can measure learning performance, there is a
limit when it comes to measuring the actual learning process (Zushi et al., 2012).
As such, the what, when, and where can be measured in detail, but not the how
(Purnama et al., 2016a). To obtain more detailed measurements, it is necessary
to record the time spent viewing a page (Li and Tsai, 2017). To obtain detailed
information, the time spent viewing each section of a page can be recorded (Koh
et al., 2018). One of the most common approaches is to divide a page into subpages
(Lee et al., 2009) or to insert tracker codes into sections of the page (Purnama
et al., 2016b). More powerful approaches involve eye-tracking (Pernice, 2017) and
mouse tracking (Henrie et al., 2015; Koh et al., 2018), which can provide more
information than just time spent viewing particular sections.
Eye tracking is arguably one of the most accurate methods for recording the
viewing activity of users, but the financial cost is very high, thereby confining
the technology to lab environments (Lai et al., 2013). Although mouse tracking is
not as accurate, the financial cost is low in comparison. No additional devices are
needed to perform mouse tracking, which can be implemented by anyone who has
a computer. Most people own computers and with the increasing availability of
the Internet, it is possible to perform massive scale mouse tracking implementa-
tion (Huang et al., 2011). Therefore, mouse tracking can be implemented outside
of the laboratory in places such as classrooms, online learning, and websites. Re-
cently, web mouse tracking software such as Mouseflow, ClickTale, ClickHeat, and
Sessioncam has emerged (NT, 2015).
Unfortunately, another obstacle must be overcome before widespread imple-
mentation of mouse tracking can be achieved. This is related to the resource cost,
especially for personal implementation. It is rumored that the resource cost for
maintaining mouse tracking (eye tracking as well) is notoriously high, and there-
fore classified as Big Data (Sin and Muthu, 2015). However, the rumors were
not confirmed. Furthermore, mouse tracking resource cost was never discussed
in detail. Leiva and Huang (2015) state that a mouse swipe from left to right
can generate hundreds of cursor coordinates and a mouse activity over a minute
can generate 1 MB (megabyte) of data. That is the only information related to
mouse tracking resource cost that was presented in their article. Discussions on
these matters are very few, which has been unhelpful to implementers. Therefore,
this work investigates the popular resource costs of mouse tracking including a
computer processing unit (CPU), data rate, random access memory (RAM), and
storage. A real-time online mouse tracking application was developed that can
be implemented on any website. In this case, it was demonstrated on a quiz ses-
sion on Moodle LMS. The source code is available on Github (Authors, 2019a).
The implementation and resource measurement took place for three events: solo
measurements in a laboratory, local testing by five people of a quiz session in a
laboratory, and an overseas quiz session by students of classroom size in Mongolia,
accessing a server in Japan.
This article is divided into six chapters. The first chapter is the introduction
that includes background information on online activities and popular analytics,
mouse tracking, and the problem of mouse tracking implementation as previously
described. The second chapter is the literature review in which the results of several
interesting studies in the field of eye tracking and mouse tracking are presented.
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Mainly, this chapter shows that there are promising results, which have generated
interest and excitement, in these fields. The last part of this chapter discusses
the state-of-the-art of this work. The third chapter is the system overview that
discusses the real-time online mouse tracking application, developed as a part of
this work, and mainly considers its operation and features. The fourth chapter is
the experiment and implementation that explains the hardware and tools used,
subjects, and the procedures of the experiment and their implementation. The
fifth chapter presents the results and discussion on the mouse tracking data that
was collected, sample analysis, and the resource costs from both calculated and
profiled measurements. The sixth chapter is the conclusion, which summarizes the
main findings.
2 Literature Review
2.1 Eye Tracking
Rayner (1998) reviewed many articles regarding eye movements spanning from
1971–1998 and claimed that eye tracking technologies existed since then. The most
fundamental aspect of eye movements are fixation and saccade, where fixation is
the process of fixing the gaze to a certain region of interest (ROI), and saccade is
the process of moving the gaze to another ROI. However, it is up to the examiner
to interpret eye movements, for example, eye movements can provide information
about the user’s attention, interest, and state of mind. Eye tracking has been
researched in the field of pattern recognition whether in a non-digital environment
(Holmqvist and Wartenberg, 2005; Holsanova et al., 2006) or digital environment
(Hy¨on¨a et al., 2002; Liu, 2005; Duggan and Payne, 2011; Jarodzka et al., 2017),
search engine result page (SERP) (Rodden and Fu, 2007; Rodden et al., 2008;
Huang et al., 2011, 2012), web evaluation and usability (Ehmke and Wilson, 2007;
Buscher et al., 2009; Tzafilkou and Protogeros, 2017; Hsu et al., 2018), and visual
search (Rayner, 2009; Dragunova et al., 2017).
In the category of learning, Lai et al. (2013) reviewed eye movement research in
seven themes including pattern of information processing, effects of instructional
design, reexamination of existing theories, individual differences, effects of learning
strategies, patterns of decision making, and conceptual development. They con-
cluded that the eye-tracking method provides a promising channel for educational
researchers to connect learning outcomes to cognitive processes. Many educational
researchers gained interest in the application of eye tracking in the process of learn-
ing and teaching, especially in online learning, which can make up for the lack of
emotional connection between students and teachers (Cantoni et al., 2012). For
example, eye tracking can capture signs of comprehension difficulties, cognitive
stress, or tiredness of students during online learning, which a good teacher is able
to perform during face-to-face learning.
In e-learning, eye tracking has been integrated into the online framework where
the eye tracking hardware captures eye movement on the client, transmits the eye
movement data to the server, then processes the data for direct analysis or to
implement interactivity. Finally, the data are kept in storage.
One of the earliest eye tracking implementation research in e-learning and on-
line learning was Adaptive E-Learning via the Eye Tracking (AdELE) frame-
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work (Barrios et al., 2004; Pivec et al., 2006). The framework provides adaptive,
integrated, and real-time eye tracking during e-learning processes. Real-time
eye tracking can yield spontaneous interaction or interactivity in students. For
example, it can detect tiredness and instruct the user to take a break.
Wang et al. (2006) integrated eye tracking to an emphatic software agent (ESA)
in online education. Eye tracking captures the state of awareness of the learners
and responds accordingly. For example, when the learner views a topic and
interest is detected, the ESA provides positive feedback. When the learner
looks away often, the ESA queries the learner on their level of interest.
Calvi et al. (2008) developed e5Learning (enhanced exploitation of eyes for
effective e-learning) which is a system that consists of three main components:
(1) monitoring of accessed screen, areas/history recorder where the author can
set the ROI to capture the extent of gazing, (2) contextual content generator
whereby a certain content is visible after the user spends a certain amount of
time on an ROI, (3) emotion recognizer.
Wei et al. (2009) integrated eye tracking into traditional Adaptive and Per-
sonalized e-Learning Systems (AeLS). Usually, the user’s learning profile is
generated based on questionnaires. In eye tracking based AeLS, eye tracking
is used to capture the interest of users to generate the customized learning
profiles.
Ivanovi´c et al. (2017) integrated eye tracking into Programming Tutoring Sys-
tem (Protus) which is an intelligent system that can adapt content based on
the learning style of the user. Similar to the work of Wei et al. (2009), the
learning styles of the users are usually determined via questionnaires. In this
case, eye tracking is utilized to identify the users’ learning styles.
Other than being integrated into the online learning framework, eye tracking
is often utilized without adaptability and interactivity, simply as a tool to ana-
lyze specific characteristics of the learners and to perform post actions based on
this analysis (Rakoczi and Pohl, 2012; Lupu and Ungureanu, 2013). Examples
of utilizations include obtaining the cognitive (Eger, 2018) and emotional state
of the users (Cantoni et al., 2012), evaluation of instructional design (Jarodzka
and Brand-Gruwel, 2017; Yang et al., 2018) and user interface design (UID) (Ra-
makrisnan et al., 2012; Chivu et al., 2018), pattern recognition (Alhasan et al.,
2018; Parikh and Kalva, 2018), strategic patterns (Tsai et al., 2012; Busjahn et al.,
2014), etc.
Until now, eye tracking has yet to be implemented on a large scale because
of hardware limitations. Almost all eye tracking articles cited herein are based
on experiments in laboratory environments where separate and usually expensive
eye tracking hardware is utilized. Most of these articles express confidence in the
eventual reduction in cost and affordability of eye tracking hardware and the ex-
pectation that eye tracking will be implemented on a large scale in the future. In
recent years, few researchers have attempted to fulfill these expectations, for exam-
ple, Sungkur et al. (2016) and Zheng and Usagawa (2018) developed eye tracking
in web cameras. As almost all modern laptops are equipped with a web camera,
and most people including students own laptops, eye tracking in a web camera is
a key aspect in the quest for large-scale implementation.
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2.2 Mouse Tracking
Although most researchers prefer eye-tracking data, many mouse tracking articles
have noted that mouse tracking is a viable alternative because eye tracking tech-
nology is too expensive and inconvenient (Cooke, 2006). There are investigators
who have attempted to correlate mouse tracking to eye tracking by utilizing ex-
ploratory studies (Rodden et al., 2008), correlation analysis (Chen et al., 2001;
Rodden et al., 2008; Voßk¨uhler et al., 2008; Liebling and Dumais, 2014; Demˇsar
and C¸¨oltekin, 2017), or prediction (Guo and Agichtein, 2010; Johnson et al., 2012;
Huang et al., 2012; Navalpakkam and Churchill, 2012) to demonstrate the inaccu-
racy involved in correlation mouse tracking data to eye tracking data. In contrast,
there is also active research that treats mouse tracking data independently (Naval-
pakkam and Churchill, 2012). There are also other rare studies that attempt to
direct the eye gaze to the mouse cursor by restricting the user’s field of vision,
thereby coupling the mouse tracking data with the eye tracking data (Tarasewich
et al., 2005; Lagun and Agichtein, 2011; Maruya et al., 2015; Kim et al., 2017).
Similar to eye tracking, mouse tracking is also conducted in the area of pattern
recognition, search engine result page (SERP) (Rodden and Fu, 2007; Rodden
et al., 2008; Guo and Agichtein, 2008; Huang et al., 2011, 2012; Lagun et al., 2014;
Arapakis and Leiva, 2016), web evaluation and usability (Arroyo et al., 2006;
Navalpakkam and Churchill, 2012; Manson et al., 2012), and education.
In the field of education, recent articles emphasize the need to record the time
spent on a learning activity to obtain the user’s behavior patterns (Li and Tsai,
2017). Koh et al. (2018) emphasize the need to record the time spent on a particular
section of a learning activity because the time spent on an entire page does not
reflect the actual learning time given that the time spent on different sections
varies. The authors further stated that mouse trajectories and scrolling can be
used to determine the time spent on a particular section, although the capability
of mouse tracking is more than simply being able to determine the time spent on
a particular section. Mouse tracking can be used record the trajectories, velocities,
and many other variables of the mouse’s cursor that can be used to measure many
things including cognitive load (Rheem et al., 2018). However, the data generated
by mouse tracking can be overwhelming to examine, although this is no longer a
major problem because there are many data mining and visualization applications
that can be used to extract meaningful information from the data of the users
(Poon et al., 2017).
The earliest article on mouse tracking implementation was presented by Mueller
and Lockerd (2001), whereas in e-learning, the earliest article was presented around
the year 2012.
Zushi et al. (2012) implemented a web application that can record mouse tra-
jectories for an English jumbled-word question where the learners are asked to
match the jumbled-words with the appropriate Japanese translation. Their con-
tributions were: (1) recording mouse events including durations, coordinates,
and events, (2) recording the events specifically for their system including En-
glish words activated, English words selected into a group, and drag & drop, (3)
replay and visualizations of mouse trajectories and events, and (4) interpreta-
tion of the mouse data and statistical analysis such as correlation analysis and
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clustering. In the end, they achieved their objective of analyzing the learning
process whereas other systems were only able to analyze the learning outcome.
Rodrigues et al. (2013) also emphasize the learning process and non-intrusive
data gathering, but their research focused on the identification of the stress
levels of students in e-learning. They implemented mouse and keyboard track-
ing in Moodle where they captured the click accuracy, click duration, amount
of movement, mouse movement, mouse clicks, and keyboard strokes. They at-
tempted to create a controlled experiment where one group was set to be
normal, while the other group was set to be stressed. In the end, they obtained
mouse and keyboard profile comparisons for the stressed and non-stressed users
and established that stressed users exhibited a drastic increase in all actions
including key down, key up, mouse down, mouse up, mouse wheel, and mouse
movement.
A similar study was performed by Salmeron-Majadas et al. (2014) but with dif-
ferent indicators including affective states (degree of valence, for example plea-
sure versus displeasure and degree of arousal for example high versus low acti-
vation) and behavior changes using the Self-Assessment-Manikin scale. Their
article also stated that mouse and keyboard tracking methods are non-intrusive
and low cost. Their objective was to generate automatic affective states and
behavior change identifier from mouse and keyboard logs by exploiting machine
learning.
Harrati et al. (2016) also utilize mouse and keyboard tracking on Moodle,
although their purpose was not to measure the learning process but to measure
the usability of Moodle, whether it is easy to use or not. They stated that using
the System Usability Scale (SUS) was insufficient for measuring the usability
of Moodle. Therefore, they added the number of clicks, task duration, cursor
distance, and completion rate to their measurements. The authors collected
mouse and keyboard data of lecturers as they performed tasks on Moodle
including login, visiting course section, visiting module upload section, and
uploading a module.
As shown in the preceding, there are many interesting works on mouse track-
ing; however, very few have investigated the implementation and resource costs
associated with the process, which has caused implementers to doubt the feasibil-
ity of large-scale implementation. Huang et al. (2011) conducted a massive scale
mouse tracking on Microsoft’s Bing search engine. By reducing the amount of
mouse trajectories recorded their massive scale experiment succeeded. However,
they only discussed the data analysis afterward and neglected to consider the re-
source costs. Leiva and Huang (2015) and Mart´ın-Albo et al. (2016) addressed the
issue but their discussion quickly shifted to the solution, which is primarily based
on compression methods. To date, there are no articles, except this, that consider
the resource costs of mouse tracking implementation.
3 Real-time Online Mouse Tracking System Overview
3.1 Framework
The mouse tracking application developed by the authors was designed to run
online and in real-time. Online means that the mouse tracking is run remotely via
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the Internet where the client runs the mouse tracking application when browsing
a webpage and the associated data is sent to the server. Real-time means that the
mouse tracking data is continuously sent by the client to the server during the
mouse tracking process. Overall, this can be seen in the real-time online mouse
tracking framework on Fig. 1.
Fig. 1. Real-time Online Mouse Tracking Framework. The Framework is divided
into two sides where one side is the client and the other side is the server. The
client and the server are connected via the Internet. The server contains the front
end, which is usually the representation side of the website, and back end where
background processing and data storing occurs. There is a browser on the client
equipped with client-side programming. The arrow presents the direction of the
processes and the number presents the order of the processes
The mouse tracking process is illustrated on Fig. 1. Firstly, the client requests
the webpage from the server. Secondly, the server sends the webpage mainly in Hy-
pertext Markup Language (HTML) and Cascading Style Sheets (CSS) embedded
with the mouse tracking code written in client-side programming language. The
client-side programming language used in this work is jQuery, which is a JavaScript
(JS) library designed to simplify HTML Document Object Model (DOM) tree
traversal and manipulation, as well as event handling, CSS animation, and Asyn-
chronous JS and XML (Ajax) (jsf, 2019). Thirdly, the client’s browser views the
retrieved webpage (HTML and CSS). Fourthly, the client’s browser executes the
mouse tracking code. The clients actually have full control over the mouse tracking
process because the programming is client-side based. However, they are usually
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unaware of this because the mouse tracking runs in the background. They would
have to thoroughly inspect the background area to control the mouse tracking pro-
cess. However, most clients do not attempt to perform this task. This is the reason
why mouse tracking is considered non-intrusive. Fifthly, the mouse tracking code
contains post methods to transmit the data to the server. The author designed
the mouse tracking code to transmit data on each event (clicks, moves, scrolls,
etc.) immediately (real-time). Sixthly, the server using server-side programming
language receives the post data and connects to the database. In this work, the
server-side programming language used is Hypertext Preprocessor (PHP) because
Moodle and most other LMS are written in PHP. Finally, the mouse tracking data
is stored on the database in form of Structured Query Language (SQL).
3.2 Application
The mouse tracking application developed as part of this work is a standalone
application that can be implemented either on the server or on the client. In the
case of the former, the mouse tracking code is incorporated into the webpage. A
webpage mainly contains HTML, CSS, and JS. A more direct approach is to inject
the mouse tracking code in the JS code. Another approach is to create a plugin
for a certain content management system (CMS) or LMS. In this work, a Moodle
mouse tracking plugin was developed, which can be in the form of an admin plugin,
theme plugin, or a block plugin shown as shown in Fig. 2. A theme plugin usually
applies to entire Moodle pages managed by the administrator while a block plugin
applies to selected pages usually managed by managers and teachers.
Fig. 2. Mouse Tracking Plugin on Moodle. The figure shows examples of mouse
tracking implemented as a block plugin (in blue) and theme plugin (in red).
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The mouse tracking Moodle plugin was implemented on the authors’ labora-
tory server, which can be accessed on https://md.hicc.cs.kumamoto-u.ac.jp. The
authors planned to publish the Moodle plugin on Moodle’s website in the future.
To implement the application on the client, the mouse tracking code is incorpo-
rated into the browser’s code. This can be achieved by direct insertion or plugin
installation. Fig. 3 shows a mouse tracking browser extension installed on Google’s
Chrome Browser. The authors plan to publish the extension in Chrome stores and
other online stores.
Fig. 3. Mouse Tracking Chrome extension. The mouse tracking extension is visible
on the extension bar. The user can open the configuration window by clicking the
icon and config. the events (clicks, moves, scrolls) to record.
Implementation on a server is more efficient because the mouse tracking code is
only installed on the server, whereas implementation on the client requires instal-
lation on each client. However, server implementation limits the mouse tracking
process to the server’s website only. The authors were able to identify users as
they visited or left the website but were unable to perform tracking once the users
left the website. In comparison, client implementation facilitates the recording of
every detail of the browser activity of users, including mouse tracking on all visited
websites.
3.3 Features
In this section, the main features of the real-time online mouse tracking application
are reviewed in detail. The guide for writing the code is available on jQuery’s
website (jsf, 2019). Tracking is divided into the main event logging and other
information loggings. A simple keyboard logging was also implemented. For mobile
devices, a mouse is rarely used, therefore tracking of scrolls, touches, and zooms
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is preferred. Fig. 4 shows a demonstration of the loggings that are available online
(Authors, 2019a).
Fig. 4. Mouse tracking demonstration. MouseX shows “1102,” which is the hori-
zontal axis value of the mouse cursor position, while MouseY shows “3137,” which
is the vertical axis value. ScrollLeft shows “17,” which is the horizontal axis value
of the horizontal scroll bar position, while ScrollTop shows “2653,” which is the
vertical axis value of the vertical scroll bar position. KeyboardPress shows “c,”
which was pressed. MouseClick shows “left click,” which was pressed. Zoom showed
“100%,” which is the default. It should be noted that all the coordinates are rela-
tive to the entire document and not the screen or the window. It is also seen that
the top vertical value axis of the image is not zero. The zero value is still far above
where scrolling up is necessary.
3.3.1 Event Loggings
Mouse Click
The recording of mouse clicks acquires data on the depression and release of
the mouse buttons, along with the coordinate of the occurrence in pixels. The
events can be left, middle, and/or right click. Before the recording of mouse
trajectories, mouse click recordings were practiced. Currently, the recording of
mouse clicks is still more popular than mouse trajectories.
Mouse Move
Mouse move is the same as mouse trajectories. The recording of mouse moves
acquires data on the coordinate (horizontal axis and vertical axis) of the mouse
cursor in pixels. Mouse move can also be combined with mouse press, which
results in mouse drag. Among other event loggings, mouse move consumes the
highest resource.
Scroll
The recording of scrolls acquires data on the current topmost and left most
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scroll position in pixels. Some users do not frequently move the mouse cur-
sor but move the scroll often, in which case the scroll data is more valuable
than the mouse movement data. This is particularly true for users of mobile
devices where there is no mouse cursor, fewer touches, and a greater reliance
on scrolling.
Keyboard press
The recording of keyboard press acquires data on the buttons pressed by the
users. Like mouse click, keyboard press recordings were practiced before mouse
trajectory recordings. Currently, keyboard recordings are more popular than
mouse trajectory recordings. However, keyboard press recordings are notori-
ous for misuse and malicious activities such as password thief. As such, extra
care regarding privacy must be exercised when implementing keyboard press
recordings; for example, the non-implementation of keyboard press recordings
on login pages.
Zoom
The recording of zoom acquires data on the long-shot or close-up activity of
users as a percentage value. The formula is the window dimension divided by
the screen dimension (explained in other loggings: sections) multiplied by one
hundred percent. Users usually utilize zooming when an element is too big or
too small. In addition, zooming is most frequently used on mobile devices.
Touch
The recording of touch acquires the press and release data of the touch screen,
along with the coordinate of the occurrence. The events can be touched, un-
touched, and interrupted. Touch recordings are exclusively for touch devices;
mostly mobile devices. A demonstration is shown in Fig. 5.
Touch Move
The recording of touch move acquires data on movement during the touch
event along with the coordinate of the occurrence. It is the same as touch drag
because the movement can only be recorded when the user touches the touch
screen. In addition, touch move occurs mostly during the dragging of an object.
3.3.2 Other Loggings
Tab Status
The tab status shows whether the user is actively viewing the tab or not. It is
identified by either mouse cursor position, touch, and focus whether they occur
inside or outside of the tab. In Li and Tsai (2017)’s article, their traditional
recording system cannot identify whether the users are active on the tab or
not. However, this was possible in this investigation because mouse tracking
was utilized.
Name
The users have the option of providing their names or remaining anonymous.
Email
The users have the option of providing their emails or remaining anonymous.
Screen Size
The screen size returns the height and width size of the monitor in units of
pixels.
Window Size
The window size returns the height and width size of the applications (not
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Fig. 5. Tracking demonstration on a mobile device. Although touch was not shown
in Fig. 4, in this figure, touch showed “touched” where a finger was in contact with
the screen. The coordinate of the touch is shown on MouseX and MouseY as “336”
and “311,” respectively
the monitor, which is the screen) in pixels. The application in this case, is the
browser. When the window size is larger than the screen size, this means that
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the user has zoomed in, and conversely, when the window size is smaller than
the screen size, this means that the user has zoomed.
Current Uniform Resource Locator (URL)
The current URL returns the web address of the tab, i.e., the webpage that
the user is currently opening.
Internet Protocol (IP) Address
The IP Address returns the public IP address used by the user.
Date
The date returns the day, month, year, and time of the occurrence of an event.
Duration
The duration returns the length of time for the event. It is calculated as the
date difference between the current event and the previous event.
3.3.3 Other Features
Sampling Rate
The unit of recordings is events per time or in this case, it is the number
of events generated per seconds. By default, the maximum number of events
(clicks, moves, scrolls, etc.) per seconds depends on the capability of the users
and the computer hardware. However, the frequency of the recording may be
too high for either the client or the server. Therefore, this feature is designed
to limit the number of events per seconds, which can reduce the resource costs
of the tracking.
Data to File
The data are sent from the client to the server through the post method and
the server stores the data in specific formats such as .csv, .json, and .txt.
Data to Database
The data is sent from the client to the server using the post method and the
server stores the data in a database such as MySQL, Oracle, PostgreSQL, and
MongoDB.
User Control Menu
Shows a configuration menu, which allows the user to control the recording
process. It allows them to choose which loggings to record.
Demo Bar
Shows a demo bar that illustrates the operation of the real-time online mouse
tracking application, which is represented by the green sticky header bar that
shows the mouse positions, mouse scrolls, keyboard types, and zooms in Fig.
4 and Fig. 5.
4 Experiment and Implementation
The real-time online mouse tracking application was installed on the author’s Moo-
dle server. Three mouse tracking experiments were performed during which the
clients participated in a ten-question quiz session on the server. The resource costs
were then measured. The data rate of the network was measured using a tool
called Wireshark. The default values for the CPU, RAM, and storage monitor-
ing are available from the server’s operating system (OS) which is Ubuntu 18.04
LTS server. The server is equipped with an Intel(R) Core(TM) i7-6800K CPU @
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3.40GHz (with SSE4.2) CPU, 32 GB of DDR4 RAM, 10 TB of hard drive, and an
allocated 2 MBps network.
4.1 P2P Experiment
Fig. 6. P2P real-time mouse tracking experiment. The right laptop has a Moodle
server installed with mouse tracking codes, while the left laptop has Ubuntu Desk-
top OS installed. The role of the latter is to access the Moodle server on the right
laptop using a browser and perform one click. The right laptop received the click
event and stored it on the database while measuring the network cost of the click
event.
The first experiment was point-to-point (P2P) as illustrated in Fig. 6 where one
client accessed the server directly without using the Internet. In this experiment, a
laptop was directly connected to the server on an isolated P2P network to obtain
clean data. The empirical data rate of one event (single click) was measured. As
clean data were obtained, it was possible to derive a theoretical mouse tracking
data rate. The other resource costs were not measured because the authors did not
possess the necessary hardware, software, and knowledge to measure such small
events.
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Fig. 7. Local real-time online mouse tracking experiment. Five clients accessed
the Moodle server from the Internet and participated in ten-question quiz ses-
sions while mouse and keyboard activities were recorded. The Moodle server also
measured the resource costs.
4.2 Local Experiment
The second experiment was a local experiment illustrated in Fig. 7 where five
clients accessed the server through the Internet. This experiment was conducted
inside the author’s laboratory. Five lab members including the main author tested
the mouse tracking application and answered 10 questions during the quiz session.
A resource costs profile of the five users was generated. There were no limits to
the number of events per second that the clients were allowed to produce.
4.3 Overseas Implementation
The third experiment was an overseas implementation illustrated on Fig. 8 where
44 clients in Mongolia accessed the server in Japan. Unlike the previous experi-
ment, this was a real implementation where students from the School of Engineer-
ing and Applied Science, National University of Mongolia participated in a real
quiz session on the server in the Human Interface Cyber Communication Labora-
tory, Kumamoto University. In this case, there was also no limitation in terms of
events per second on the clients. Apart from determining the resource costs profile
for the real quiz session, useful mouse tracking data was obtained. Although this
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Fig. 8. Overseas real-time online mouse tracking implementation. Forty-four clients
from the National University of Mongolia, separated into two groups, accessed
the Moodle server at Kumamoto University in turns through the Internet and
participated in a ten-question quiz session while mouse and keyboard activities
were recorded. The Moodle server also measured the resource costs.
work discusses the mouse tracking data and demonstrates some simple analysis,
further analyses are out of the scope of this investigation.
5 Result and Discussion
5.1 Theoretical Calculation from P2P Empirical Data
Table 1 shows the resource cost of the authors’ real-time online mouse tracking
application for one event on the server, for a variety of information. This data
applies to the P2P experiment in which the client performed one click on the
Moodle server where mouse tracking was activated. As more information was in-
cluded in the event, the data rate increased. The data rate revealed an increase
of approximately 12 bytes when new information was added. This behavior is ex-
pected because an increase in the amount of information results in an increase in
the post data size. For example, in Table 1 there was a significant increase in the
data rate when the variable “date” and “content url” were included because they
contain more characters compared to other variables. The authors also attempt to
measuring CPU and RAM activity but the change was negligibly small. Although
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Table 1. The resource cost of one event posted to the server. The rows before the
last row are the types of information, and the last row shows the data rate of the
submitted post. (Authors, 2019b)
NO
ID
Name
Email
X
Y
leftclick
rightclick
middleclick
keyboardtype
scrollx
scrolly
zoom
touch
touchmove
tab
duration
date
content url
windowsheight
windowswidth
screenheight
screenwidth
Data rate (kB) 3.11 3.14 3.14 3.2 3.2 3.22 3.25 3.29 3.43 3.56 3.64 3.72
the result is limited to this application only, similar results are expected for other
existing applications.
The addition of new data does not appear to significantly increase the data
rate; however, this addition will be consequential as the number of users increases,
especially when they perform many activities. The rate of mouse tracking activ-
ities is measured in events per second or frequency in hertz (Hz). Although the
frequency of mouse movement and scrolling is high, usually, the rate does not ex-
ceed 70 events per second or 70 Hz (Rheem et al., 2018). Based on the empirical
data obtained from Table 1, it is possible to estimate the data rate of the soon to
be implemented mouse tracking. The first step in this process is to determine the
number of events generated by users per second. Then the data rate is identified
in Fig. 9 and multiplied by the number of users. The results revealed that it is
possible for 1 MB of data to be generated from mouse activity in one minute (Leiva
and Huang, 2015). From Fig. 9, when a user constantly generates five events per
second (5 Hz), the data generated can reach 1 MB in approximately one minute.
As previously stated, the real-time online mouse tracking application has a fea-
ture to limit the maximum number of events per second generated by a user. This
can be set after allocating the network bandwidth of the mouse tracking process.
Assuming that data are recorded for all available variables in mouse tracking if
there are 22 users, and the network allocated to mouse tracking is 2 megabytes per
second (MBps), the mouse tracking application should be limited to 25 events per
seconds. However, this calculation is not realistic and is only relevant for measuring
the worst-case scenario, whereby smooth implementation and not optimal resource
usage is intended. This is because the events generated per second by users are
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Fig. 9. A plot of data rate generated by a user based on the events generated per
second. The horizontal axis represents the events per second or frequency in hertz
and the vertical axis represents the data rate in kilobytes per second. The different
colored lines represent the number of variables included (refer to Table 1).
dynamic and not static. For example, there are instances when the mouse cur-
sor is not moved as the user stops scrolling to read. Likewise, there are instances
when users move the mouse cursor and scroll to search for information. There
are also occasions when users drag and drop objects during interactive activities.
Consequently, users do not generate a fixed number of events per second.
5.2 Profile Measurement from Local Experiment
Table 2. The resource cost for local experiments and overseas implementation of
the server. CPU usage in percentage, RAM usage in megabytes, and data rate in
kilobytes were measured for both experiments. From the measurements, statistical
metrics were derived including the minimum, maximum, median, average, and
standard deviation.
Statistical Local Mouse Tracking Experiment Overseas Mouse Tracking Implementation
Metrics CPU (%) RAM (MB) Data Rate (kB) CPU (%) RAM (MB) Data Rate (kB)
Minimum 0 1576.07 543 0 1080.09 416
Maximum 18 1850.83 312990 22 2417.06 837632
Median 9.79 1746.82 47846.5 10.42 2025.87 115992
Average 10 1767.74 51302.77 22 2106.62 104525.91
Standard Deviation 1.42 66.05 25959 4.84 272.13 71029.98
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To obtain more realistic data and to perform reliable calculation, profile mea-
surements should be acquired. The main question that is considered in profile
measurement is “how often do users move their mouse and scroll?” The profile
measurement used in this work is resource monitoring during local mouse track-
ing processes by five users followed by statistical analysis on the time series data.
Although the data required is the average events per second during the mouse
tracking processes, it is more convenient to immediately measure the average re-
source costs.
Fig. 10. CPU usage time series during local mouse tracking experiment by five
users. The horizontal axis is the time of day and vertical axis is the CPU usage
percentage.
Unlike the P2P experiments, the local experiment measures not only the mouse
tracking but also all the other processes, which includes accessing the Moodle page
and answering 10 questions. During this experiment, the CPU percentage Fig. 10,
the RAM usage Fig. 11, and the data rate Fig. 12 were rarely zero, indicating that
idle activity was uncommon. For the local experiment with five users, the CPU
percentage usage was an average of 10%, the RAM usage was an average of 1.7
GB, and the data rate was an average of 51 kB. This indicates that there was a
reserve capacity for more users. It should be noted that the initial CPU percentage
and RAM usage by the OS were 0% and 1.4 GB, respectively.
An interesting result is shown in Fig. 12 for the data rate. During the quiz
session at approximately 17:02:40-17:14:20 (11 minutes and 40 seconds, or 700
seconds), a table size of 6.1 MB with approximately 16287 rows (equivalent to
16287 events) and 17 columns were generated (note that the number of columns
is less than the number of introduced variables in Table 1 because during this
time, tracking for mobile device had not been developed. In addition, the total
data transmission may not be equal to the size of the stored data on the database
because of other factors such as the transmission methods, unoptimized applica-
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Fig. 11. RAM usage time-series during local mouse tracking experiment with five
users. The horizontal axis is the time of day and vertical axis is the RAM usage
in megabytes.
Fig. 12. Data rate during local mouse tracking experiment for five users. The
horizontal axis is the time of day and the vertical axis is the data rate in bytes
per second.
tion, and other factors apart from mouse tracking such as data transmitted when
loading Moodle pages). Interestingly, the average events per second (16287 events
divided by 700 seconds) was 23 or 23 Hz. If this data is plotted on Fig. 9, a result
78 kBps is obtained which is not far from the actual measurement of 51 kBps in
Table 2.
Can a single mouse swipe produce hundreds of mouse coordinates (Leiva and
Huang, 2015)? The answer is “yes,” is we examine the spikes in Fig. 12. The highest
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spike occurred between 17:07:28-17:07:29 when 98 queries per second were received
by the database. If plotted in Fig. 9, the result of 335 kBps is obtained which is
close to the actual measurement where the maximum data rate from Table 2 is
312 kBps (unfortunately, name and email identification was not available at the
time. Therefore, the identity of the users and the number of them who performed
a large number of events is unknown). This shows that the speculation seems to
be justified in the case where users are able to generate a high number of events
per second momentarily. In other words, more than one user can perform many
activities; mainly, simultaneous mouse moves and scrolls. The upper spikes are
very large, as Table 2 indicates that the difference between median and maximum
is large compared to the difference between median and minimum. Moreover, the
spikes indicate that a very high level of activities only occurs momentarily and
not constantly. Network and server administrators observed that mouse activities
potentially generate a large amount of data. However, there was concern that this
high level of activity is constant. The presented results clearly demonstrate that
high-level mouse activity is mostly temporary.
5.3 Overseas Implementation between Mongolia and Japan
5.3.1 Mouse Tracking Data and Sample Analysis
Fig. 13. Screenshot of mouse tracking data of students from National University
of Mongolia who attempted a quiz session on a Moodle server at Kumamoto Uni-
versity.
The authors were able to obtain mouse tracking data from 44 students in the
School of Engineering and Applied Science, National University of Mongolia, for
an online quiz session on the server in the Human Interface Cyber Communication
Laboratory, Kumamoto University. Fig. 13 shows a screenshot of the mouse track-
ing data in form of a table. The table size is approximately 145 MB, containing
393585 rows and 22 columns. Are the rumors that mouse tracking produces a no-
toriously large amount of data true? The answer to this question is “yes.” A half
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Fig. 14. Sample visualization of mouse tracking data from Fig. 13 where all the
coordinates from 44 students are plotted. The left image represents mouse click
visualization with triangles for left clicks, squares for middle clicks, and pentagons
for right clicks. The middle image represents mouse movement visualizations where
increasing overlap of the coordinates is represented by a darker color. The right
image represents a heatmap visualization where yellow indicates 5 seconds and red
indicates over 10 seconds.
year Moodle log data with a similar number of students was only approximately
300 kB, while the mouse tracking data represented in Fig. 13 was 145 MB after 3
hours and 30 minutes.
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The mouse tracking data contains so much information that a separate report is
required to discuss the characteristics of the data and the types of possible analysis.
There are many different types of analysis and discussions on mouse tracking data
and several examples were reviewed earlier. In this report, sample visualization
based on heatmaps and the trails of the mouse tracking data is presented, as shown
in Fig. 14. As expected, left clicks occurred more often for selection of the questions.
However, there were also left clicks associated with some of the questions and the
visualization shows that the left clicks were dragged. This can be interpreted as
highlighting the questions by the students. Middle clicks occurred most frequently
for question four; however, the reason for this occurrence is not clear. Right clicks
were most common on the top of the page, where some students probably decided
to explore the available features. As expected, there were numerous trails such that
it seemed that there were too much to visualize all at once. The heatmap indicates
that most of the students placed the mouse cursor on the questions and choices.
There were also few students who placed the mouse cursor outside the questions.
Probably, these were individuals who preferred to keep the mouse cursor away
from the text while reading. Further analyses are outside the scope of this work.
5.3.2 Resource Costs of the Mouse Tracking Process
Fig. 15. CPU percentage time series during mouse tracking implementation be-
tween National University of Mongolia and Kumamoto University. The horizontal
axis represent the time of day and vertical axis is the CPU usage percentage.
Similar to the local mouse tracking experiment with five users, the resource
costs were also measured, allowing us to determine whether large-scale implemen-
tation is possible or not. Although 44 students attempted the quiz, the session
was divided into two sessions and each session contained only 22 students. The
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Fig. 16. RAM usage time series during mouse tracking implementation between
National University of Mongolia and Kumamoto University. The horizontal axis
represents the time of day and vertical axis is the RAM usage in megabytes.
Fig. 17. Data rate during mouse tracking implementation between National Uni-
versity of Mongolia and Kumamoto University. The horizontal axis is the time of
day and the vertical axis represents the data rate in bytes per second.
students were informed that the first session would start at 12:00, followed by a
break at approximately 14:00. The second session started a few minutes later and
finishes at 15:30. As such, the entire process took 3 hours and 30 minutes (12600
seconds). The three Figures Fig. 15, Fig. 16, and Fig. 17 seems relevant to the
informed schedule where a decrease in the graph was observed at 14:00 for a few
minutes. The number of events generated during this time (12600 s) was 393585.
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The average number of events per second was 31 (393585 divided by 12600) or 31
Hz. When 31 Hz is plotted in Fig. 9, the result of 115 kB is obtained, which is
close to the measured average data rate of 105 kB in Table 2.
Is large-scale mouse tracking implementation possible? This is possible if re-
source usage is balanced and distributed. Implementing real-time mouse tracking
is a better choice than implementing non-real-time tracking. For example, if mouse
tracking is first accumulated and subsequently submitted all at once, this will cause
a bottleneck at the server. Fig. 15, Fig. 16, and Fig. 17 would not show constant
usage but would show idle activities at the beginning, which would become con-
stantly high in the middle. This is arguably an inefficient use of resource. Real-time
implementation helps to evenly distribute the transmission of the mouse tracking
data.
Compared to the local mouse tracking experiment with the five users, the
resource costs are expected to increase because more users (22) were involved in
this implementation, but there were more unexpected findings. The unexpected
aspect is that the standard deviation is very high. As such, not only are there many
positive spikes, but there are also many negative spikes, which further indicates
that the number of events per second generated by the users is very dynamic. It
should be noted that there was no limitation on the number of events per second
that the students were allowed to generate. Based on Table 1 and Fig. 9, the
data rate should increase in excess of 5 MBps for the worst-case scenario where
22 students simultaneously generate 70 events per second. However, this scenario
never occurred as shown in Fig. 17, indicating high dynamics, and the very low
probability of the worst-case scenario.
In Table 2, not only does the standard deviation increase indicating high dy-
namics, the distance between the median and maximum also increases, as rep-
resented by the taller spikes. The highest spike occurred at 14:28:40 when 228
events were submitted to the server and surprisingly, this was attributed to only
two users. This occurrence either contradicts the assumption of the authors that
a user can generate up to 70 events per second or there was a delay in trans-
mission, and the submitted events were incorrectly aggregated. When 228 events
per second are plotted in Fig. 9, the result 849 kBps is obtained, which is close
to the actual measurement in Table 2, where the maximum data rate during this
implementation was 837 kBps.
6 Conclusion and Future Work
The first conclusion is that the online mouse tracking application was successfully
implemented. The overseas quiz that was session monitored with real-time mouse
tracking at the National University of Mongolia to Kumamoto University was
successfully conducted and at present, mouse tracking is still running on the server.
The mouse tracking data containing mouse clicks, mouse movements, and mouse
scrolls was obtained, but the analysis of this data will be challenging because of the
large size. Additionally, this demonstrates the possibility of tracking on a mobile
device using scroll, touch, and zoom events.
Are the rumors concerning high resource cost in mouse tracking true? Can a
single swipe generate hundreds of mouse coordinates? Does mouse tracking over
a minute generate in excess of a megabyte of data? Based on the result of this
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investigation, the answer to these questions is “yes.” In that case, is mouse track-
ing implementable on a large scale? The answer is also “yes.” One server with
its specification highlighted in the Experiment and Implementation section was
able to handle a classroom of users, and resource monitoring showed that there
was much reserve capacity. Other institutions or corporations should have no diffi-
culty in implementing mouse tracking because they typically have big data centers
(large number of networks and servers, and distributed resources). For example, a
corporation such as Google should not encounter difficulties, although this might
be different for technologically challenged entities.
The second conclusion is that mouse tracking is implementable if resource usage
is distributed. In this work, the mouse tracking data were transferred in real-time
to evenly distributed resource usage, instead of aggregating the data and trans-
mitting them together, which may cause bottlenecks. Unfortunately, the nature
of mouse tracking is such that it is difficult to predict. As such, it is challenging
to determine resource allocation. The data acquired as part of this work showed
the high dynamic characteristic of mouse activities, as reflected in the high stan-
dard deviations observed during monitoring of resource usage. When 22 students
attempted the quizzes, the resource usage peaks were very high, but only tem-
porarily. This was identified as spikes. Both upper spikes and lower spikes were
observed, where upper spikes indicate momentary high-level activity and lower
spikes indicate the opposite.
If the amount of available resource is limited, then the resource cost of mouse
tracking can be reduced. The mouse tracking application developed in this work
can limit the number of events per second or frequency. Additionally, it can exclude
unnecessary data. Moreover, even prior to this mouse tracking resource usage
investigation, research on the compression of mouse tracking data already existed.
This opens many paths for future work. Although real-time implementation
assisted in the distribution of resource usage, the characteristics of the resource
usage data showed how mouse tracking can potentially destabilize the system.
The use of load balancing techniques can help stabilize the implementation. To
achieve the minimum system requirement for mouse tracking, more experiments
with different machine specification needs to be conducted. In addition, resource
measurement on the client-side needs should be conducted to achieve the minimum
system requirement for the client. Even though the developed mouse tracking
application was able to limit activity level recording, the settings are still manually
inputted. Adaptive settings are required for optimal usage. Although it was useful
to conduct overseas implementation, more users and longer implementations are
required to further evaluate the viability of real-time online mouse tracking.
Acknowledgements The authors are very grateful to Muhammad Bagus Andra, Hamidullah
Sokout, Irwansyah, and members of the School of Engineering and Applied Science, National
University of Mongolia, for participating in the experiment. The authors would also like to
thank Masayoshi Aritsugi, Hendarmawan, Hamidullah Sokout, Alhafiz Akbar Maulana, and
Sari Dewi for inspiring this research topic. A special thanks to Muhammad Bagus Andra and
Ni Nyoman Sri Indrawati for suggesting some interesting ideas. The authors would also like
to thank Fahd Ouassarni for providing suggestions with respect to compressing the mouse
tracking application codes. Finally, the authors would like to thank Alvin Fungai for initiating
this research and for his assistance in proofreading.
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Funding
Part of this work was supported by JSPS KAKENHI Grant-in-Aid for Scientific
Research 25280124 and 15H02795.
Conflict of interest
The authors declare that they have no conflict of interest.
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... • Mouse tracking web browser plugin and client side programming script [31,32]. ...
... The implementation of mouse tracking is based on DOM events, specifically mouse, touch, and user interface (UI) events which are actions that occurs as a result of the user's mouse actions or as result of state change of the user interface or elements of a DOM tree [37]. Our previous work [31] uses jQuery to access the DOM API and Purnama and Usagawa J Big Data (2020) 7:27 receives information that are related to mouse, touch, and UI events. They can be stored into default dynamic variables or in an ArrayBuffer for enhanced performance. ...
... Finally the information is either stored locally or sent to a server using hyper text transfer protocol (HTTP) post method. Traditionally, the information is transmitted all at once at the end of the session, but in our study [31], we found that it is better to transmit them in real-time without delay. The difference between offline, regular online, and real-time online mouse tracking is shown in Fig. 2. ...
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Abstract Pageview is the most popular webpage analytic metric in all sectors including blogs, business, e-commerce, education, entertainment, research, social media, and technology. To perform deeper analysis, additional methods are required such as mouse tracking, which can help researchers understand online user behavior on a single webpage. However, the geometrical data generated by mouse tracking are extremely large, and qualify as big data. A single swipe on a webpage from left to right can generate a megabyte (MB) of data. Fortunately, the geometrical data of each x and y point of the mouse trail are not always needed. Sometimes, analysts only need the heat map of a certain area or perhaps just a summary of the number of activities that occurred on a webpage. Therefore, recording all geometrical data is sometimes unnecessary. This work introduces preprocessing during real-time and online mouse tracking sessions. The preprocessing that is introduced converts the geometrical data from each x and y point to a region-of-interest concentration, in other words only heat map areas that the analyzer is interested in. Ultimately, the approach used here is able to greatly reduce the storage and transmission cost of real-time online mouse tracking.
... • Mouse tracking web browser plugin and client side programming script [31] [32]. Some commercial and open source software programs are as follows: ...
... The implementation of mouse tracking is based on DOM events, specifically mouse, touch, and user interface (UI) events which are actions that occurs as a result of the user's mouse actions or as result of state change of the user interface or elements of a DOM tree [37]. Our previous work [31] uses jQuery to access the DOM API and receives information that are related to mouse, touch, and UI events. They can be stored into default dynamic variables or in an ArrayBuffer for enhanced performance. ...
... Finally the information is either stored locally or sent to a server using hyper text transfer protocol (HTTP) post method. Traditionally, the information is transmitted all at once at the end of the session, but in our study [31], we found that it is better to transmit them in real-time without delay. The difference between offline, regular online, and real-time online mouse tracking is shown in Figure 2. [31]. ...
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Full-text available
Pageview is the most popular webpage analytic metric in all sectors including blogs, business, e-commerce, education, entertainment, research, social media, and technology. To perform deeper analysis, additional methods are required such as mouse tracking, which can help researchers understand online user behavior on a single webpage. However, the geometrical data generated by mouse tracking are extremely large, and qualify as big data. A single swipe on a webpage from left to right can generate a megabyte (MB) of data. Fortunately, the geometrical data of each x and y point of the mouse trail are not always needed. Sometimes, analysts only need the heat map of a certain area or perhaps just a summary of the number of activities that occurred on a webpage. Therefore, recording all geometrical data is sometimes unnecessary. This work introduces preprocessing during real-time and online mouse tracking sessions. The preprocessing that is introduced converts the geometrical data from each x and y point to a region-of-interest concentration, in other words only heat map areas that the analyzer is interested in. Ultimately, the approach used here is able to greatly reduce the storage and transmission cost of real-time online mouse tracking.
... The authors of this paper want to track mouse movements in order to diagnose "technology acceptance items for students when interacting with a web-based tutoring system during a web development course" (Tzafilkou & Protogeros, 2020), this means the author is finding a correlation with technology and online tutoring [11]. Despite that their ultimate goal is different from us, we are both trying to track user mouse movements to analyze their behavior. ...
... The authors of this paper are tracking mouse movements, but on overseas quizzes [13]. Their mouse tracking system was "implemented on the Moodle learning management system and tested on an online quiz session accessed abroad" (Purnama et al., 2020). They were also able to record their data in real-time. ...
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This study employed an eye-tracking machine to record the process of peer assessment. Each web page was divided into several regions of interest (ROIs) based on the frame design and content. A total of 49 undergraduate students with a visual learning style participated in the experiment. This study investigated the peer assessment attitudes of the participants and found that they possessed highly positive attitudes towards and understanding of peer assessment. After comparing the results of the peer assessments and evaluation by experts, high consistency occurred when the design of the web page was concise; however, the consistency decreased when the web page content was too diverse. After comparing the peer assessment attitudes of the participants and their web page design scores, it was found that the web pages with concise designs attracted the visual-style students' attention more, and that there was a significant negative correlation for those students who possessed a more negative attitude toward peer assessment. In addition, the study further analyzed the visual-style students' serial behaviors in the peer assessment process for each web page design. After comparing the evidence of each student's eye movements and his/her evaluation results, it was found that the students who gave higher or lower scores had different eye movements. For the website scored as having the best design, the fixations and behaviors of the assessors giving higher scores were highly consistent with those of the experts, implying that the few assessors giving lower scores were relatively poor at peer assessment. On the contrary, for the website which was scored as having the worst design, the fixations and behaviors of the assessors giving lower scores were highly consistent with those of the experts. Consequently, from the eye fixation hotspot evidence, when the students were more concentrated on the peer assessment, their evaluated results were closer to those of the two experts. Finally, the study found that the eye fixation hotspots were the same as the key points planned by the student designers of the website which scored the highest, which provided the student designers with additional important eye-tracking feedback from the peer assessment activities. © 2018, International Forum of Educational Technology and Society.
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