=Paper=
{{Paper
|id=Vol-3120/paper5
|storemode=property
|title=An Analysis of Reading Process based on Real-time Eye-tracking Data with Web-camera--Focus on English Reading at Higher Education Level
|pdfUrl=https://ceur-ws.org/Vol-3120/paper5.pdf
|volume=Vol-3120
|authors=Xiu Guan,Chaojing Lei,Yingfen Huang,Yu Chen,Hanyue Du,Shuowen Zhang,Xiang Feng
}}
==An Analysis of Reading Process based on Real-time Eye-tracking Data with Web-camera--Focus on English Reading at Higher Education Level==
https://ceur-ws.org/Vol-3120/paper5.pdf
An analysis of reading process based on real-time eye-tracking
data with web-camera——Focus on English reading at higher
education level
Xiu Guan 1, Chaojing Lei 1 , Yingfen Huang 1 , Yu Chen 1 , Hanyue Du 1 , Shuowen Zhang 1
and Xiang Feng 1
1
Shanghai Engineering Research Center of Digital Educational Equipment, East China Normal University,
China
Abstract
Reading skill, an important and complex cognitive ability, is one of the target skills for talent
training. Especially with the trend of online learning becoming more and more prevalent,
digital reading literacy is a necessary skill for learners. Therefore, many researchers started to
focus on study reading process, and study methods are diverse, such as by collecting eye
movement data. However, most researches were limited to scope of laboratory, so it is difficult
to achieve low-cost, large-scale, non-invasive and objective reading process data collection
and analysis. Therefore, this research developed a platform using web camera of laptop and
open-source eye tracking library (webgazer.js) to get physiological indicators based on eye
tracking data, which can be used to analyze reading process and explore the impact of the
reading performance. This research also used inferential statistics and machine learning
methods to quantitatively characterize and analyze the relationship between reading behavior
and reading performance. This study’s conclusions can help make subsequent targeted
adjustments and interventions to improve learners' reading performance, and prove the
usability for collecting eye-tracking data by platform with web-camera.
Keywords 1
Text reading, Eye tracking, Web-camera
1. Introduction
Reading skill is an important and complex cognitive ability, and is always regarded as one of the
most significant skills for learning and living in future life. Especially with the prevalence of online
learning, digital reading literacy is an indispensable skill for everyone to study online[1]. The PISA,
which is internationally authoritative, has focused on the evaluation of reading literacy for a long time.
Since 1997, reading literacy evaluation was prepared to be included in PISA by OECD(Organization
for Economic Co-operation and Development). And then, reading skills were regarded as one of the
most important abilities that future talents should have in the PISA 2000. In the relevant report of PISA
2018 reading training was regarded as an important way to improve reading literacy[2], and digital
reading literacy was particularly highlighted[3]. However, most of current researches on reading skills
were resultant in measurement and evaluation of reading skills, ignoring the interpretation and analysis
of the reading process, that is, the internal mechanism and principle of the reading process is not yet
clear[4]. Traditional research methods are difficult to get non-invasive and objective reading process
data collection and analysis, which will influence the objectivity and reliability of the analysis results,
such as self-report. Therefore, researches begun to analyze the reading process based on physiological
indicators, such as eye tracking data, electroencephalogram (EEG), and so on[5].
As far as existing research is concerned, eye tracking has been widely used, and was regarded as an
effective tool for analysis of reading process[6, 7]. It because that 80%-90% of human information is
Proceedings of the 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior, March 21-22, 2022
https://sites.google.com/view/lak22datachallenge
©️ 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
obtained through the human visual system in general[8], especially reading during the learning
process[9]. Existing studies often use eye tracking data for the studies of cognitive processes such as
reading[10]. The analysis , to a certain extent, can reflect participants’ attention distribution and
instantaneous cognitive processing during cognitive processing process[11], which can also be visually
presented and characterized[9, 12, 13] for interventions and adjustments provide a data basis. However,
eye tracking often needs expensive instruments, equipment and professional venues to support. For
online learning, laptop screen mount devices can be carried out much easier for the collection and
analysis of eye tracking data[11]. In order to achieve a more comprehensive analysis, research tools
need to be developed.
In general, the contributions of this research are summarized as follows: (a) We build a platform for
the reading process to realize eye tracking based on web-camera, and then explore the relationship
between reading behavior indicators and reading performance based on statistical analysis methods and
machine learning methods. The results obtained can explore the laws that exist between reading
behavior and reading performance. (b) By conclusions obtained in this research with the conclusions of
existing research, it can further prove the reliability of the eye tracking platform for the reading process
constructed by this research. The platform can use for low-cost, large-scale, and convenient collection
of eye tracking data during the reading process in subsequent research, and further explore the
relationship between reading behavior and reading performance. The rest of the paper is structured as
follows: Section 2 introduces background and related work. And then, Section 3 shows methodology
and process of our experiment. Moreover, Section 4 presents our results. Finally, Section 5 concludes
and proposes directions for future work.
2. Background and Related Work
2.1. Reading
Reading skill is an important ability for higher education learners, because it is an important
influencing factor for future work and lifelong learning[14]. For example, the development of reading
ability was emphasized in the European Commission, which can help individuals achieve personal
development and integration with society[15]. For example, studies have shown a correlation between
the level of development of reading skills and future socioeconomic status[16], that is, reading ability
refers to a kind of ability that is very important for learning, work, social life, and so on. So, with the
help of complex cognitive activities such as meta-cognition to understand text, and then it can realize
the screening and details of information, related inferences, and problem solving[16]. Researchers in
related fields, such as OECD, believe that reading can effectively help learners improve their reading
skills[18]. Therefore, it is crucial to use learning analytic for help educational practitioners achieve
targeted interventions and moderation.
2.2. Eye tracking
Eye tracking technology is currently widely used in the study of complex cognitive processes such
as reading. It is mainly supported by eye-mind-assumption[19], and that is to say, gaze can represent
what the brain is processing. Therefore, researchers regarded eye tracking data as indicators that can
reflect the individual's timely cognitive processing[20]. With the aid of eye tracking technology, the
reader can be quantified and visualized in the cognitive process. The focus, attention, and scanning
patterns of the system are measured to reflect the timeliness of cognitive processing in the process[21].
So, eye tracking technology is more convenient and feasible for studying complex cognitive activities
such as reading. The main reason is that eye tracking technology can be integrated into existing digital
learning equipment to collect eye tracking data in natural situations in a non-intrusive way [21], such
as a web camera based on a laptop, which can be used as a device to realize eye tracking. Compared
with eye movement activity assessment glasses (also called mobile eye trackers), and eye trackers
embedded within virtual reality headsets, laptop-based eye-tracking devices have slightly insufficient
accuracy[13], but it can avoid the interference and high cost caused by new equipment. In addition, it
is a relatively convenient solution for data collections of eye tracking in the digital learning process[11].
Additionally, the existing eye tracking data is often used in business, medical and other contexts[22],
but there are no effective learning analysis related research results to support how to use these data to
improve learning performance in the education field[22]. Therefore, we can develop one eye-tracking
platform based on web-camera of laptop.
2.3. Analysis of reading based on eye tracking
From the perspective of data, indicators of eye tracking data are meaningful for the analysis of
complex cognitive activities such as reading. This can be supported by eye-mind hypothesis, which was
present in former content. Based on this theory, we can know that if one person focuses on an area over
certain time, they probably have difficult to understand the information, or was bored with information
in this area. In existing researches, indicators can be divided into two categories, namely, visual
representations such as heat maps, and quantitative representations such as annotation rates[21]. Based
on these data, Area of Interest (AOI) can be identified during the individual cognitive activity. This
means that the learner may have cognitive difficulties or higher interest in the corresponding area[24].
For example, in the process of designing and optimizing the learning platform, the relevant data of eye
tracking can be used as auxiliary decision-making information[10]. Another common indicator, which
can reflect the difficulty of understanding is regressions[11]. It reflects learners frequently review
former information to help understanding all information, when they is processing difficult information.
These data indicators have relatively mature calculation methods in related researches[8], and are
widely used in reading-related research, such as the measurement of reading literacy ability[25, 26].
Related research shows that experts may behave in the reading process with the characteristics of shorter
fixation duration, less fixation and retrospect[4].
Analyzing reading from the perspective of learning content can be approached from two different
levels. First of all, vocabulary is an important influencing factor and predictive factor for text reading[4].
This is mainly because in the reading process not only a certain vocabulary is required, but the ability
to integrate context, collocation, and grammar[29] for more comprehensive and accurate understanding
is necessary as well. Secondly, focus on the global text, the learner's mastery and understanding of the
full text are based on the analysis results of the learner's scan path and other data in the reading process.
Among them, the subject's first fixation time, regressions and other related indicators can reflect the
different reading strategies of learners[30]. This is a research perspective of reading analysis that has
attracted more attention.
Therefore, the main research question of this study is whether the reading behavior reflected by the
indicators collected by self-developed reading platform for eye-tracking in the reading process obtained
by eye tracking will have a significant impact on the effect of reading? Based on above analysis, we
formed two hypotheses: (a) H1: There is a significant correlation between some reading process
behavior indicators and reading performance. (b) H2: Some indicators with specific meanings have a
significant impact on academic performance`.
3. Method
This research designed and implemented an experiment to explore., and the details of the
experimental process are shown in the figure 1. This research used eye tracking technology to obtain
learners' reading behavior data during English reading tests. Participants speak English as a second
foreign language. Finally, the subjects were interviewed, which means to know if there are some
difficulties during the test and help verify our conclusion[6, 31]. The sample consisted of 35 higher
education learners from a college in East China. They use English as a second foreign language and
have basic English reading ability and logical reasoning ability. The effective sample size is 32. Before
the experiment, we obtained the informed consent of the subjects. One English reading test of CET 4
were selected as the reading materials, and can see more in Figure2 and 3.
Figure 1: Experimental process and details
Figure2: Example of video recording and system screen recording interface during the experiment
Figure3: Reading text before and after highlighting (the different color represent different items and
coresponding text)
The experiment uses a laptop computer with a 46.67-cm (14 in.) diagonal display, a 720pHD camera
(about 920,000 pixels), which used for eye-tracking devices during the experiment to realize non-
intrusive data collection. And, an eye tracking library, webgazer.js (https://webgazer.cs. brown.edu/),
can predict the user's eye-gaze location[32]. During reading process, the system can automatically get
the positions of the pdf window on the screen, the current page number, and the distance between the
top of the current page and the top of the current pdf window in real time. And then system can judge
the eye-gaze position predicted by webgazer.js to fall within which line, and, that is to say, the content
read by the user at the current moment can be predicted. Based on these, we develop a software called
Readgazer. We used HTML5, Flask back-end framework and MongoDB database to develop such a
system, and used python 3.9 and SPSS to analyze data. The average error with the best model in the
experiment was at about 130 pixels. In order to combine the reader's eye-gaze position with the text
content in the document, we use pdf.js (https://github.com/mozilla/ pdf.js/) to render pdf documents.
So, we can get basis to compute eye-tracking indicators.
4. Results
The reading test selected in this study includes five questions in total (T1, T2, T3, T4 and T5). Since
the types and difficulty of the questions are different, the correlation and influence of them will be
explored respectively. For the acquired data, SPSS 21.0 is used for Inferential Statistical Analysis, and
use algorithm in machine learning to do data mining, and further analyze the relationship between
reading behavior and reading performance. Eye-tracking indicators are shown in table 1.
Table 1:
Eye-tracking indicators for reading process
Indicators Interpretation of indicators Remark
Frequency of fixation on page i (The number of
freq_i
fixations on all rows on page i)
Fixation rate on page i (Number of fixations on
pagefreq_on_rate_i
page i/total number of fixations on all pages) There are a total of 3
Frequency of fixation in line j on page i (The pages of reading
freq_i_j content and
number of fixations on row j of page i)
questions, each page
Fixation rate in line j on page i (Gaze rate on line
freq_rate_i_j can be arranged with
j on page i)
23 lines of text, and
Fixation rate on page i (Number of fixations on the last page is only
pagefreq_onpage_rate_i
page i/total number of fixations on page i) 10 lines (a total of 56
Fixation rate on all pages (Gaze rate on pdf, the lines of reading
pagefreq_onpage_rate_total number of fixations on the pdf / the number of content and
fixations on the overall web interface.) questions).
Frequency of regressions(RS) among pages. Such
page_forward_back as looking back from page 2 to page 1. (The total
frequency of page backwards)
4.1. Analysis of reading based on eye tracking
Correlation and regression analysis results of eye-tracking indicators and reading performances are
shown in Table2. In this study, the main of correlation analysis was to filter indicators, which has
significant relation with score of each item. Then, logistic regression analysis can use these indicators
to identify key indicators, which can have significant relationship with reading performance for each
item. For reading performance of T2, there is no significant correlation with all reading behavior
indicators, which means that no reading behavior indicator can significantly influence T2 reading
performance.
Table 2:
Correlation between eye-tracking indicators and reading performance (there just present some
indicators have significant relationship with reading performance)
Correlation analysis Logistic regression analysis
95% C.I. for
Items Correlation Hosmer & Nagelkerke Percentage
Indicators p Items(Z) B S.E Wals df Sig. Exp (B) EXP(B)
coefficient Lemeshow R2 accuracy
Lower Uper
freq_1_3 0.362* pagefreq_
pagefreq_onpag onpage_r 1.337 0.767 3.044 1 0.081 3.809 0.848 17.114
0.359*
T1 e_rate_1 0.016 0.308 0.222 59.40% ate_total
pagefreq_onpag
0.372* Constant -0.47 0.449 1.094 1 0.296 0.625
e_rate_total
page_forward_b
-0.355* freq_1_19 -1.042 0.521 3.999 1 0.046 0.353 0.127 0.979
ack
T3 freq_1_19 -0.378* 0.020 0.335 0.206 62.50%
freq_1_20 -0.376* Constant -0.187 0.388 0.231 1 0.631 0.83
freq_2 -0.351*
freq_rate_1_12 0.379*
freq_rate_1_13 0.350*
freq_3_5 -1.089 0.492 4.907 1 0.027 0.336 0.128 0.882
freq_1_20 -0.380*
T4 freq_1_21 -0.405* 0.014 0.508 0.264 81.30%
freq_2_12 -0.380*
freq_3_4 -0.388* Constant 1.542 0.522 8.709 1 0.003 4.672
freq_3_5 -0.443*
freq_2_2 0.360*
freq_rate
freq_2_8 0.356* 1.096 0.519 4.464 1 0.035 2.992 1.083 8.267
_2_8
T5 freq_rate_2_8 0.369* 0.002 0.733 0.432 71.90%
freq_3_1 0.400* freq_3_2 1.461 0.676 4.678 1 0.031 4.312 1.147 16.215
freq_3_2 0.405* Constant 0.367 0.448 0.669 1 0.413 1.443
Take T1 as an example to explain the data analysis results in Table2. From table 2, we can find that
there are three indicators significantly correlated with the performance of learners on the first question.
Based on this, results of logistic regression using the selected indicators indicate that
“pagefreq_onpage_rate_total” can predict the learner's correct rate on the first question. Although, p
value less than 0.05, and “Hosmer & Lemeshow” value more than 0.05, which means that the logistic
analysis result of T1 with responding indicators is statistically significant. Nagelkerke R2 is 0.222,
which shows that this logistic regression model can explain 22.2% of change in dependent variable.
Percentage accuracy represents that 59.4% results of predict are correct. But, sig value of
pagefreq_onpage_rate_total is 0.081 more than 0.05, which means that the prediction of this indicator
is not statistically significant. Based on this, we can conclude that the higher the proportion of the
participant’s annotation frequency to the overall text, the more concentrated the participant’s attention,
and therefore the better the participant’s answering performance on T1.It should be noted that in order
to be able to compare the difference in importance between different indicators in the same logistic
regression analysis result, the study standardized all indicators by Z-score before performing logistic
regression analysis. For example, the logical analysis result of T5 shows that freq_rate_2_8 has a greater
influence on T5's reading performance prediction than freq_3_2.
In general, through the correlation analysis and logistic regression analysis of each topic, we can
find that: (a) For a relatively simple topic such as T1, as long as the participant can ensure concentration
during the reading process There is a great possibility that the answer is correct. (b) For topics such as
T3 and T4 that indicate the approximate area of the corresponding article content, the more participants
pay attention to the corresponding content or topic of the reading article, it means that the participant
has processed more information in that area. The processing process, that is, the subject may have
doubts, so the subject's performance on the corresponding question will be worse. (c) For T5 topics that
do not indicate the approximate area of the topic corresponding to the content of the article, the higher
the participant’s attention to the topic and the corresponding text content in the text, the more
information is processed and processed at the corresponding location. There is no proof of a specific
text content area. After the correct content can be recognized, the subject will further confirm the text
content and the topic. Therefore, the more subject focus on corresponding area, the more correct may
be.
4.2. Machine learning analysis
In order to be able to more clearly characterize the predictive effect of behavior indicators on reading
performance, the decision tree and random forest algorithm in machine learning is used in this study to
further determine the impact of behavior indicators on the prediction of reading performance results
under different circumstances.
When it comes to the data mining of machine learning, behavioral indicators in the reading process
can make more accurate decisions for reading performance. The results are summarized in Figure 4,
class 0/1 mean not correct/correct answers. Take a branch of T2's decision tree model as an example to
explain in detail. Decision tree model of T2 is shown in the Figure 4, and a relatively high accuracy
(acc=0.745) is obtained after three-fold cross-validation. As can be seen from the figure, if
freq_rate_2_19 less than or equal with 0.004 and freq_rate_1_3 less than 99.0, it can judge that subject
may get correct answer for T2. Further, if freq_rate_2_19 more than 0.004, and pagefreq_on_rate_2
less than or equal with 0.166, which means that answer for T2 has a great possibility that it is right
correct. Therefore, from the decision tree, we can predict the possible answer of the subjects on the
corresponding questions based on the indicators under different threshold conditions.
Figure 4: Decision tree models for each topic with acc value greater than 0.5
And then, in order to judge and identify the importance of indicators, random forest models were
constructed in this study. The results are shown in the Figure 5. The importance of the index is
calculated by using the Leave-One-Out Cross Validation method to construct the training set and the
test set, and then obtain the importance of 32 random forest models and the corresponding indexes, and
then take the average of the 32 importance, and use this as the basis Sort all the indicators and find the
10 most important indicators corresponding to the missing questions. Among them, the acc value
representing the accuracy of the model is marked in Figure 5. From Figure 5, we can further get
information as follows. Relatively speaking, the index of the title and the corresponding article content
area is more important for prediction of reading performance. Take T4 for example, we can find that
freq_rate_2_12 is the most important indicator for prediction of T4. Freq_rate_2_12 is the aera of T2,
which means that students foucs on T2 may influence the performance of T4. So, we can further study
the reson why cause this result. From this result, we can further get information about the indicators’
importance for each item.
Therefore, the more focus on text content related to topic, the more cognitive attention allocated on
it, and that is to say, there may be confusions for participants; Relatively low fixation rate on the topic
and text indicates that there is relatively little cognitive processing on the topic and there is no confusion.
So, the behavioral data during reading can be used to predict the corresponding reading performance
after learning analysis.
Figure 5: The top10 indicators of each random forest model for each item
5. Discussion and Conclusion
Based on the above analysis, we found that the more focus on content or topic, the worse reading
performance would be, which is also supported by the existing studies. When it comes to the reason,
the higher values of the indicators, such as freq_2_19, represent participants' cognitive processing of
reading on the corresponding content, which means that there may be cognitive processing difficulties.
Therefore, indicators with larger value associated with poorer reading performance. This is consistent
with the conclusion of existing studies. For learners with weak reading skills, they will pay more
attention on information processing, which can represent learners' efforts, and can also be indicators to
reveal reading difficulties. Therefore, future studies can use our developed reading platform, which can
collect eye-tracking data. And then, in order to improve reading performance, it needs to be considered
that how to intervene and adjust from key eye-tracking indicators. The significance of this study is that
the developed system can well capture the user's reading behavior data during reading process, which
can be the basis of studying reading performance improvement, and quantify the influence relationship
between behavior indicators and reading performance.
However, the study also has some shortcomings. Firstly, since the system is developed by research
team members based on the open-source code and tools, the accuracy in operation is difficult to compare
with the current mature system or eye-tracking tools. This system will be modified and improved in the
future. In other words, the accurancy of this system need to be improve, which lead the results of this
research to have a certain deviation, so we will improve the system. Secondly, due to limited human
resources, the number of subjects is small, so there is less data. Therefore, it is difficult to conduct the
high-precision machine learning data mining, and the precision of the decision tree and random forest
models is poor. The sample size will be further increased in subsequent studies to get more valuable
conclusions.
6. Acknowledgements
This research was planneed and implemented at East China Normal University, in China. The
research is made available under a Research on the Model, Automatic Measurement and Intervention
on Academic Well-being in Online Learning (21ZR1419100), which is supported by the 2021 Shanghai
Science and Technology Plan.
7. References
[1] M. Grubb, Predictors of High School Student Success in Online Courses. Ph.D. thesis, Faculty of
the College of Education University of Houston, (2012). UMI Number: 3462834.
[2] OECD, PISA 2018 Assessment and Analytical FrameworkPISA, 2019. URL:
https://doi.org/10.1787/b25efab8-en.
[3] OECD, Literacy Skills for the World of Tomorrow: Further Results from PISA 2000, 2003. URL:
http://www.SourceOECD.org.
[4] Krstic, K., et al., All good readers are the same, but every low-skilled reader is different: an eye-
tracking study using PISA data. European Journal of Psychology of Education 33 (2018) 521-541.
doi: https://doi.org/10.1007/s10212-018-0382-0.
[5] Gwizdka, J., et al., Temporal dynamics of eye-tracking and EEG during reading and relevance
decisions. Journal of the Association for Information Science and Technology, 68(10) (2017)
2299-2312. doi: https://doi.org/10.1002/asi.23904.
[6] Kennedy, A., Book Review: Eye Tracking: A Comprehensive Guide to Methods and Measures.
Quarterly Journal of Experimental Psychology, 69(3) (2016) 607-609. ISBN: 9780199697083.
[7] Jarodzka, H., I. Skuballa and H. Gruber, Eye-Tracking in Educational Practice: Investigating
Visual Perception Underlying Teaching and Learning in the Classroom. Educational Psychology
Review, 33 (2021) 1-10. doi: https://doi.org/10.1007/s10648-020-09565-7.
[8] Zehui, et al., Online Learners' Reading Ability Detection Based on Eye-Tracking Sensors. Sensors,
16(9) (2016) 1457. doi: https://doi.org/10.3390/s16091457.
[9] Keith, R., Eye movements and attention in reading, scene perception, and visual search. Quarterly
journal of experimental psychology (2006), 62(8) (2009) 1457-1506. doi:
10.1080/17470210902816461.
[10] Berkman, M.I., Eye Tracking in Virtual Reality, in Encyclopedia of Computer Graphics and
Games, N. Lee, N. Lee Editors. 2018, Springer International Publishing: Cham. (2019) 1-8. doi:
https://doi.org/10.1007/978-3-319-08234-9_170-1.
[11] Gorbunovs, A., The Review on Eye Tracking Technology Application in Digital Learning
Environments. Baltic Journal of Modern Computing, 9(1) (202) 1-24. doi:
https://doi.org/10.22364/bjmc.2021.9.1.01.
[12] Rayner, K., Eye Movements and Visual Cognition: Scene Perception and Reading. Springer
Verlag, New York Berlin Heidelberg London Paris, 1992.
[13] Raschke, M., T. Blascheck and M. Burch, Visual Analysis of Eye Tracking Data, in Handbook of
Human Centric Visualization, New York, Springer (2013) 391-409. doi: 10.1007/978-1-4614-
7485-2_15.
[14] Sinatra, G.M. and S.H. Broughton, Bridging Reading Comprehension and Conceptual Change in
Science Education: The Promise of Refutation Text. Reading Research Quarterly, 46(4) (2011)
374-393. doi: 10.1002/RRQ.005.
[15] EEUROPEAN COMMISSION Directorate-General for Education and Culture., European report
on the quality of school education : sixteen quality indicators, Luxembourg: Office for Official
Publications of the European Communities (2001).
[16] Cremin, T., et al., Building Communities of Engaged Readers: Reading for pleasure, Abingdon:
Routledge, 2014.
[17] Kirsch, I., et al., Reading for change: Performance and engagement across countries: Results from
PISA 2000. 2002, URL: http://lst-iiep.iiep-unesco.org/cgi-
bin/wwwi32.exe/[in=epidoc1.in]/?t2000=019225/(100), 2002.
[18] OECD, Are students who enjoy reading better readers? 2011, URL:
https://doi.org/10.1787/9789264095250-28-en.
[19] Just, M.A. and P.A. Carpenter, A theory of reading: from eye fixations to comprehension.
Psychological review, 87(4) (1980) 329-354. doi: 0033-295X/80/8704-0329$00.75.
[20] RAYNER, K., Eye movements in reading and information processing : 20 years of research.
Psychological bulletin, 124(3) (1998) 372-422. doi: 0033-2909/98/$3.00.
[21] Tóthová, M. and M. Rusek, Použití eye-trackingu v analýze učebnic pro přírodovědné předměty:
přehledová studie. Scientia in educatione, 12(1) (2021) 63-74. doi:
https://doi.org/10.14712/18047106.1932.
[22] Davies, A., et al., Computational Methods for Analysis of Visual Behavior using Eye-tracking.
Measuring Behavior, (2016).
[23] Giorgio, B., et al., Group vs Individual Web Accessibility Evaluations: Effects with Novice
Evaluators. Interacting with Computers, 28(6) (2016) 843-861. doi: 10.1093/iwc/iww006.
[24] Scheiter, K. and A. Eitel, The Use of Eye Tracking as a Research and Instructional Tool in
Multimedia Learning. In Advances in Educational Technologies and Instructional Design,
Germany, IGI Global, 2016. 143-164. doi: 10.4018/978-1-5225-1005-5.ch008.
[25] Zehui, Z., An Emotional and Cognitive Recognition Model for Distance learners Based on
Intelligent Agent—— The Coupling of Eye Tracking and Expression Recognition Techniques.
Mod. Dist. Educ. Res, (5) (2013) 100-105. doi: 10. 3969 /j. issn. 1009 －5195. 2013. 05. 013.
[26] Zehui, Z., L. Ting and M. Zicheng, Distance Learning Supporting Services Based on Virtual
Assistant and Its Technical Difficulties. Modern Distance Education Research, (06) (2014) 95-
103+111. doi: 10.3969/j.issn.1009-5195.2014.06.011.
[27] Isnin, I. and N.M. Jaafar, Reading Glossed Passages in English to Improve Comprehension: An
Eye Tracking Study. GEMA Online Journal of Language Studies, 21(3) (2021) 125-139. doi:
http://doi.org/10.17576/gema-2021-2103-07.
[28] Haynes, M., READING IN A SECOND LANGUAGE: MOVING FROM THEORY TO
PRACTICE. William Grabe. New York: Cambridge University Press, 2009. Pp. xv + 467. Studies
in Second Language Acquisition, 32(4) (2010) 648-650. doi:
https://doi.org/10.1017/S0272263110000355.
[29] Sulaiman, N., K. Salehuddin and R. Khairudin, Reading English Academic Texts: Evidence from
ESL Undergraduates’ Eye Movement Data. 3L The Southeast Asian Journal of English Language
Studies, 26(1) (2020) 60-78. doi: http://doi.org/10.17576/3L-2020-2601-05.
[30] Hyönä, J., R. Lorch and M. Rinck, Eye Movement Measures to Study Global Text Processing. The
Mind's Eye: Cognitive and Applied Aspects of Eye Movement Research, North-Holland, Elsevier
Science, 2003. doi: 10.1016/B978-044451020-4/50018-9.
[31] Ishimaru, S. and A. Dengel, ARFLED: ability recognition framework for learning and education.
UbiComp '17: Proceedings of the 2017 ACM International Joint Conference on Pervasive and
Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable
Computer, (2017) 339-343. doi: https://doi.org/10.1145/3123024.3123200.
[32] Papoutsaki, A., et al., WebGazer: Scalable Webcam Eye Tracking Using User
Interactions.Proceedings of the Twenty-Fifth International Joint Conference on Artificial
Intelligence - IJCAI 2016, AAAI Press / International Joint Conferences on Artificial Intelligence
press, New York, USA, 2016 3839-3845. URL: http://ijcai-16.org/.