Using Eye Tracking Technology to Analyse Cognitive Load in Multichannel Activities in University Students

Figures & data

Table 1. List of metrics in eye tracking, their meaning and relationship with analysis of learning strategies.

Metric	Acronym	Unit measure	Meaning of the metric	Relationship to cognitive and metacognitive strategies
Fixation count	FC	Count	A larger number of fixations on a stimulus can indicate that the learner has less knowledge about the task or difficulty in discriminating relevant vs. non-relevant information. It is a measure of global search.	An indicator of the application of metacognitive strategies of acquisition (Román Sánchez & Gallego Rico, 2008) or according to Veenman (2017) orientation strategies. These refer to the ‘what’ in the resolution process.
Fixation duration	FD	Miliseconds	It is an indicator of the learner’s degree of interest and reaction times. Longer durations are usually associated with deeper cognitive processing and greater effort. The duration of fixations provides information about the search process.
Average duration of fixation	AFD	Count	Longer fixations refer to the learner spending more time analysing and interpreting the information content within the Areas of Interest (AOI). An average duration is between 200 ms-260ms.
Saccade count	SC	Count	This refers to the number of saccades in each stimulus. The greater the amplitude of the saccade, the lower the cognitive effort. However, it can also refer to problems in understanding information.	More saccades suggests greater use of targeting strategies (as classified by Veenman, 2017).
Gaze point	GP	X and Y axes of position in the stimulus.	A chain of eye positions relative to the coordinates of the position axes in a stimulus. The length of the scan path provides information on reaction times in tasks without predetermined duration.	Studying the learner’s behavioural patterns will facilitate the development of guidance from the teaching staff in order to achieve the most personalised learning possible.
Pupil diameter	PD	Millimeters	The mean pupil diameter is collected for all fixations within an AOI over a time interval.	It may provide information about the level of attention or interest in the information provided by the stimulus. A larger pupil diameter may be related to a greater cognitive load as well as to the difficulty of processing a task.
Number of visits or revisits	V	Count	Refers to the number of visits within an AOI during a time interval.	It can be an indicator of attention and interest in a piece of information. Or it may be an indicator of the difficulty of the information for the learner.

Table 2. Descriptive statistics of the participants with respect to the variables age and gender.

Number	Women			Men
N	n	Mage	SDage	n	Mage	SDage
42	38	22.39	2.44	4	22.75	2.4

Note: N = Total number of participants; n = Disaggregated number of participants; M_age = Mean of age; SD_age = Standard deviation of age.

Figure 1. Stimuli used in eye tracking in the study.

Table 3. Stimuli applied in the study and their relationship with cognitive load.

Stimulus	Task	Intrinsic cognitive load	Extrinsic cognitive load	Germane load
Stimulus 1	Read instructions	Intermediate	Visual without SRL	Reading process Understanding the information to execute the task
Stimulus 2	View an image	Intermediate	Visual without SRL	Image recognition process and its relation to theoretical subject matter elements
Stimulus 3	Practical laboratory with SRL	High	Visual and auditory with SRL Highlights of relevant information Includes diagrams.	- Attention - Understanding - Conceptual interrelationship
Stimulus 4	Read acknowledgements	Low	Visual without SRL	Reading process

Table 4. Skewness and kurtosis analysis of the independent variable.

	N	Minimum	Maximum	M	SD	Skewness		Kurtosis
						S	S error	S	S error
Prior knowledge. Test 1	42	6.00	10.00	8.54	1.16	−0.48	0.37	−0.59	0.72

Note. N: participant numbers; M: Mean; SD: Standard deviation; S: statistical; S error: Standard error.

Figure 2. Steps in the study procedure.

Figure 3. Relationship between RQs and research design.

Table 5. One-factor fixed-effects ANOVA (participation in the early intervention case study virtual laboratory vs. non-participation).

Tests	Timepoint	Group 1 n = 31	Group 2 n = 11
		M(SD)	M(SD)	df	F	p	η^2
Test 1	Fourth week of the semester	8.52 (1.21)	8.60 (1.10)	(3,40)	0.04	0.85	0.001
Test 2	Seventh week of the semester	9.28 (1.10)	8.83 (1.41)	(3,40)	1.17	0.29	0.03
Project preparation	Throughout the semester	1.77 (0.13)	1.76 (0.11)	(3,40)	0.20	0.66	0.005
Project presentation	Week eight of the semester	1.43 (0.11)	1.30 (0.08)	(3,40)	12.91	0.001*	0.24
Co-evaluation	Throughout the semester	1.61 (0.44)	1.46 (0.58)	(3,40)	8.95	0.005*	0.18
Total scores	End of semester	8.91 (0.75)	8.18 (0.75)	(3,40)	7.65	0.009*	0.16

Note. Group 1 = students who participated in the early intervention case study resolution laboratory with eye tracking monitoring; Group 2: students who did not participate in the early intervention case study resolution laboratory with eye tracking; M: mean; SD: standard deviation; df: degrees of freedom; η²: eta-squared.

Table 6. One-factor fixed-effects ANOVA (level of prior knowledge).

Parameters	Group 1 n = 4	Group 2 n = 15	Group 3 n = 12
	M(SD)	M(SD)	M(SD)	df	F	p	η^2
FC Stimulus 1_AOI_R	26.50 (13.43	19.27 (15.30)	27.33 (15.31)	(2,31)	1.74	0.19	0.11
FC Stimulus 2_AOI_R	7.75 (1.71)	8.53 (2.17)	8.75 (2.45)	(2,31)	0.30	0.74	0.02
FC Stimulus 3_ AOI_NR	53.00 (30.00)	56.13 (22.11)	54.92 (27.71)	(2,31)	0.03	0.97	0.002
FC Stimulus 3_ AOI_R	2141.75 (270.75)	2196.40 (354.73)	2239.75 (314.13)	(2,31)	0.14	0.87	0.01
FC Stimulus 4_AOI_R	9.25 (2.50)	8.13 (3,18)	9.17 (2.21)	(2,31)	0.56	0.58	0.04
FD Stimulus 1_AOI_R	5.28 (3.12)	3.71 (1.92)	5.49 (83.64)	(2,31)	1.42	0.26	0.09
FD Stimulus 2_AOI_R	2.69 (0.18)	2.61 (0.21)	2.64 (0.14)	(2,31)	0.27	0.76	0.02
FD Stimulus 3_AOI_NR	10.56 (4.25)	11.63 (4.89)	12.07 (6.88)	(2,31)	0.11	0.90	0.01
FD Stimulus 3_AOI_R	562.95 (50.33)	537.70 (73.92)	564.92 (74.30)	(2,31)	0.54	0.59	0.04
FD Stimulus 4_AOI_NR	1.87 (0.52)	1.57 (0.73)	1.96 (0.53)	(2,31)	1.33	0.28	0.09
SC Stimulus 1_AOI_R	9.50 (2.38)	10.50 (5.16)	13.42 (5.84)	(2,31)	1.36	0.28	0.09
SC Stimulus 2_AOI_R	4.25 (1.50)	4.07 (1.59)	4.08 (1.73)	(2,31)	0.02	0.98	0.001
SC Stimulus 3_AOI_NR	376.75 (160.11)	405.00 (115,54)	390.67 (98.50)	(2,31)	0.22	0.81	0.02
SC Stimulus 3_AOI_R	5.00 (2.16)	4.93 (2.17)	4.75 (2.10)	(2,31)	0.11	0.89	0.008
SC Stimulus 4_AOI_R	5 (2.16)	4.93 (2.17)	4.75 (2.09)	(2,31)	0.30	0.97	0.002
No. Visit Stimulus 1_AOI_R	3.00 (1.419	2.87 (0.92)	2.83 (1.27)	(2,31)	0.03	0.97	0.002
No. Visit Stimulus 2_AOI_R	1.00 (0.00)	1.20 (0.41)	1.00 (0.00)	(2,31)	1.81	0.18	0.11
No. Visit Stimulus 3_AOI_NR	36.40 (20.92)	34.40 (13.37)	32.50 (17.41)	(2,31)	0.11	0.90	0.008
No. Visit Stimulus 3_AOI_R	226.75 (38.81)	192.83 (35.78)	192.83 (35.77)	(2,31)	1.38	0.27	0.09
No. Visit Stimulus 4_AOI_R	1.25 (0,50)	1.47 (0.52)	1.58 (0.67)	(2,31)	0.51	0.61	0.04
PD Stimulus 1_AOI_R	3.19 (0.46)	3.03 (0.36)	3.18 (0.55)	(2,31)	0.44	0.65	0.03
PD count Stimulus 2	3.22 (0.48)	3.11 (0.37)	3.33 (0.45)	(2,31)	0.95	0.40	0.06
PD Stimulus 3_AOI_NR	2.73 (0.37)	2.65 (0.23)	2.80 (0.29)	(2,31)	1.01	0.35	0.72
PD Stimulus 3_AOI_R	2.75 (0.35)	2.65 (0.24)	2.82 (0.31)	(2,31)	1.22	0.31	0.08
PD Stimulus 4_AOI_R	2.96 (0.43)	2.82 (0.29)	2.96 (0.32)	(2,31)	0.72	0.50	0.05

*p < 0.05. Note. FC: Fixation count; FD: Fixation duration; SC: Saccade count; No: Number; PD: Pupil diameter; AOI_R: Relevant AOI; AOI_NR: Non-relevant AOI; Group 1 = students with scores of satisfactory (5–6.9) on the prior knowledge test; Group 2: students with intermediate scores (7–8.9) on the intermediate knowledge test; Group 3: students with outstanding scores (9–10) on the intermediate knowledge test; M: mean; SD: standard deviation; df: degrees of freedom; η²: eta-squared.

Table 7. One-factor fixed effects ANOVA (degree of knowledge acquired) on eye tracking parameters (FC, FD, SC, No. and PD) for stimulus 3 in AOI_NR and R.

Parameters	Group 1 n = 4	Group 2 n = 4	Group 3 n = 7	Group 4 n = 17
	M(SD)	M(SD)	M(SD)	M(SD)	df	F	p	η^2
FC Stimulus 3_AOI_NR	69.00 (36.57)	62.50 (29.83)	43.57 (12.23)	52.82 (24.96)	(3,31)	1.05	0.39	0.10
FC Stimulus 3_AOI_R	1933.00 (140.10)	2384.25 (187.95)	2224.25 (187.95)	2207.94 (346.81)	(3,31)	1.46	0.25	0.14
FD Stimulus 3_AOI_NR	14.85 (6.84)	15.79 (8.78)	8.82 (2.40)	10.59 (5.02)	(3,31)	2.12	0.12	0.18
FD Stimulus 3_AOI_R	564.86 (117.60)	564.95 (72.76)	568.80 (64.74)	538.93 (62.16)	(3,31)	0.40	0.75	0.04
SC Stimulus 3_AOI_NR	6.50 (3.87)	5.50 (3.00)	5.00 (1.73)	5.29 (2.99)	(3,31)	0.25	0.86	0.03
SC Stimulus 3_AOI_R	337.50 (58.19)	477.00 (160.49)	410.00 (112.97)	386.06 (99.36)	(3,31)	1.25	0.31	0.12
No. Visit Stimulus 3_AOI_NR	41.75 (22.47)	37.25 (22.90)	26.43 (6.73)	33.18 (14.98)	(3,31)	0.91	0.45	0.09
No. Stimulus 3_AOI_R	187.50 (41.22)	181.25 (58.26)	218.71 (48.76)	211.47 (31,63)	(3,31)	1.11	0.36	0,11
PD Stimulus 3_AOI_NR	2.85 (0.27)	2.61 (0.17)	2.56 (0.15)	2.76 (0.27)	(3,31)	1.51	0.23	0.14
PD Stimulus 3_AOI_R	2.90 (0.29)	2.63 (0.18)	2.57 (0.31)	2.72 (0.28)	(3,31)	1.68	0.19	0.15

*p < 0.05. Note. FC: Fixation Count; FD: Fixation Duration; SC: Saccade Count; No: Number; PD: Pupil Diameter; AOI_R: relevant AOI; AOI_NR: not relevant AOI; Group 1 = students with scores of 7 out of 10 on the comprehension knowledge test; Group 2: students with scores of 8 out of 10 on the comprehension knowledge test; Group 3: students with scores of 9 out of 10 on the comprehension knowledge test; Group 4: students with scores of 10 out of 10 on the comprehension knowledge test; M: mean; SD: standard deviation; df: degrees of freedom; η²: eta-squared.

Figure 4. Elbow method in the k-means algorithm on FC, FD, SC, visits and pupil diameter data. Note. 1. Elbow method in FC, 2. Elbow method in FD, 3. Elbow method in SC, 4. Elbow method in Visits, 5. Elbow method in pupil diameter.

Table 8. Final cluster centres and ANOVA for FC.

	Cluster 1 n = 21	Cluster 2 N = 10	df	F	p
Stimulus 1 AOI_R	22	25	1, 29	0.35	0.56
Stimulus 2 AOI_R	9	8	1, 29	3.44	0.07
Stimulus 3 AOI_NR	53	60	1, 29	0.48	0.50
Stimulus 3 AOI_R	2375	1852	1, 29	43.10	0.00*
Stimulus 4 AOI_R	9	8	1, 29	1.21	0.28

*p < 0.05. Note. AOI_R: relevant AOI; AOI_NR: non-relevant AOI.

Table 9. Final cluster centres and ANOVA for FD.

	Cluster 1 n = 15	Cluster 2 N = 16	df	F	p
Stimulus 1 AOI_R	4.7	4.52	1, 29	0.03	0.87
Stimulus 2 AOI_R	2.62	2.65	1, 29	0.17	0.68
Stimulus 3 AOI_NR	9.37	13.81	1, 29	5.80	0.02*
Stimulus 3 AOI_R	608.44	498.11	1, 29	48.72	0.00*
Stimulus 4 AOI_R	1.79	1.72	1, 29	0.09	0.76

*p < 0.05. Note. AOI_R: relevant AOI; AOI_NR: non-relevant AOI.

Table 10. Final cluster centres and ANOVA for SC.

	Cluster 1 n = 12	Cluster 2 N = 20	df	F	p
Stimulus 1 AOI_R	13	12	1, 29	0.23	0.64
Stimulus 2 AOI_R	5	4	1, 29	4.33	0.04*
Stimulus 3 AOI_NR	6	5	1, 29	0.16	0.69
Stimulus 3 AOI_R	511	328	1, 29	66.66	0.001*
Stimulus 4 AOI_R	5	4	1, 29	0.25	0.62

*p < 0.05. Note. AOI_R: relevant AOI; AOI_NR: non-relevant AOI.

Table 11. Final cluster centres and ANOVA for No. of Visits.

	Cluster 1 n = 16	Cluster 2 N = 15	df	F	p
Stimulus 1 AOI_R	3	2	1, 29	4.48	0.04*
Stimulus 2 AOI_R	1	1	1, 29	0.42	0.52
Stimulus 3 AOI_NR	39	28	1, 29	4.20	0.04*
Stimulus 3 AOI_R	239	172	1, 29	64.26	0.001*
Stimulus 4 AOI_R	2	1	1, 29	2.10	0.16

*p < 0.05. Note. AOI_R: relevant AOI; AOI_NR: non-relevant AOI.

Table 12. Final cluster centres and ANOVA on pupil diameter.

	Cluster 1 n = 10	Cluster 2 n = 8	Cluster 3 n = 13	df	F	p
Stimulus 1 AOI_R	2.67	3.73	3.06	1, 28	80.09	0.001*
Stimulus 2 AOI_R	2.76	3.76	3.22	1, 28	86.03	0.001*
Stimulus 3 AOI_NR	2.43	3.06	2.73	1, 28	46.23	0.001*
Stimulus 3 AOI_R	2.42	3.07	2.75	1, 28	50.40	0.001*
Stimulus 4 AOI_R	2.55	3.26	2.90	1, 28	58.64	0.001*

*p < 0.05. Note. AOI_R: relevant AOI; AOI_NR: non-relevant AOI. One missing value was detected.

Figure 5. Visualisation of the clusters in the parameters FC, FD, SC, visits and pupil diameter in each participant. Note: FC: Fixation count; FD: Fixation duration; SC: Saccade count; V: Number of visits; PD: Pupil diameter.

Table C1. Relationship between the RQs proposed in this study, the experimental studies basis for this study and the findings of this study.

Relationship with the RQs	Experimental baseline studies	Findings from the experimental baseline studies	Limitations identified in the experimental baseline studies	Relationship of the present study to the experimental baseline studies	Limitations of the present study	Future lines of intervention of the present study
RQ1, RQ2, RQ3, RQ4	Alemdag, E., & Cagiltay, K. (2018). A systematic review of eye tracking research on multimedia learning. Computers and Education, 125, 413–428. https://doi.org/10.1016/j.compedu.2018.06.023	Eye tracking technology is beginning to be used, especially by university students learning in multimedia environments, for the detection of differences in information processing and the use of cognitive and metacognitive strategies. The use of multi-channel materials implies a higher cognitive load.	No limitations but suggestions for future studies are noted on eye tracking technology The study supports the use of eye-tracking technology in the field of educational research to study learning processes in detail rather than simply observing the learning outcomes. They also point to the importance of including self-regulated materials either through voice or through diagrams and pictures to compensate for differences in prior knowledge.	In this study, a self-regulated video task was designed in a voice-over format that also included diagrams and images. No effects of prior knowledge on learning outcomes were found in the students to whom this laboratory was applied.	The laboratory was implemented in a limited time. However, applying this technology on a one-to-one basis to 31 students took three weeks of intervention.	Ideally, the learner should have an instrument that allows him/her to test his/her performance throughout the learning process.
RQ3 and RQ4	Andrzejewska, M., & Stolińska, A. (2016). Comparing the difficulty of tasks using eye tracking combined with subjective and behavioural criteria. Journal of Eye Movement Research, 9(3), 1–16. https://doi.org/10.16910/jemr.9.3.3	Fixation time increases, and the number of fixations decreases when the cognitive load is high. It is important to determine the cognitive load for each learner on a task-by-task basis to make grading criteria.	No contributions.	In this study, significant differences were found between students in the grouping clusters. Students grouped in cluster 1 had more FC, FD and in SC and made more visits to information in relevant vs. non-relevant areas.	The authors worked with a specific task of short duration and with a selected sample of health science students.	Future studies will delve deeper into the learning patterns of each student and will study the differences between students in cluster 1 vs. those in cluster 2 regarding the analysis of the scan path figures (tracking paths used).
RQ2 and RQ3	Asadi, A., Saeedpour-Parizi, M. R., Aiken, C. A., Jahanbani, Z., Houminiyan Sharif Abadi, D., Simpson, T., & Marchant, D. (2022). Effects of attentional focus and cognitive load on novice dart throwing: Evidence from quiet eye duration and pupillary responses. Human Movement Science, 103015. https://doi.org/10.1016/j.humov.2022.103015	An increase in pupil diameter is related to an increased cognitive load.	No contributions.	Significant differences in pupil diameter were found in the AOI-R and AOI_NR stimuli. Students grouped in cluster 2 had a larger pupil diameter in all stimuli.	The authors worked with a specific task of short duration and with a selected sample of health science students.	Future studies will address the behavioural profile of students on the platform in the different groups to establish hypotheses as to why there is a higher cognitive load in some students than in others.
RQ1 and RQ4	Molenaar, I., de Mooija, S., Azevedo, R., Bannertc, M., Järveläe, S., & Gašević, D. (2023). Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data. Computers in Human Behavior, 139, 107540. https://doi.org/10.1016/j.chb.2022.107540	Technologies can help to detect, diagnose and improve the SRL process through the use of multi-channel resources. Specifically, the use of Artificial Intelligence techniques opens up a range of possibilities in the field of educational technology.	Few studies use multi-modal or multi-channel cut off tasks. The data analyses applied are often straightforward. Few studies use IA techniques. The use of technology that supports SRL together with AI analysis will facilitate the improvement of SRL in virtual environments.	This study provides a multi-channel material developed from SRL. This task has been applied within an eye- tracking environment that has modelled its development. It also applies unsupervised machine learning techniques of cluster analysis to study the results.	The authors worked with a specific task of short duration and with a selected sample of health science students.	Future studies will extend the time of task development with SRL. It will be applied to students from other branches of knowledge. Other supervised machine learning techniques will be included to study the results.
	Liu, H. C. (2021). Using Eye-Tracking Technology to Explore the Impact of Instructional Multimedia on CFL Learners’ Chinese Character Recognition. The Asia-Pacific Education Researcher, 30, 33–46. https://doi.org/10.1007/s40299-020-00512-2	The features of the task presentation determine the cognitive load on learners. Variations can be found depending on the learning profile of each learner.	It is oriented towards future studies analysing the effects of voice as an aid in learning materials.	The use of a self-regulated video has been shown to be an important resource for increasing student learning outcomes.	The work has been done with a specific task (a short self-regulated video).	Future studies will analyse the indicators of cognitive load in the different resources implemented in the self-regulated video (underlining, outlining and voice).
RQ1 and RQ3	Pi, Z., & Hong, J. (2016). Learning process and learning outcomes of video podcasts including the instructor and PPT slides: A Chinese case. Innovations in Education and Teaching International, 53(2), 135–144. https://doi.org/10.1080/14703297.2015.1060133	The teacher’s PowerPoint™ presentation facilitated the acquisition of declarative, but not procedural knowledge. Videos that included procedural knowledge learning increased the cognitive load. However, students who participated in learning with these materials through visual tracking reported better learning outcomes. Tasks that implement information through various channels have a higher cognitive load on the learner.	These authors conducted the study in the laboratory, not in natural learning contexts. They also explored the learning of a single concept "attachment".	In this study, the authors conducted it in a light and temperature-controlled context but it was a classroom, not a laboratory. Also, the video used in the study included procedural knowledge for the execution of a practical. However, it also found improvements in the learning outcomes of the students who had participated in the laboratory and had been monitored with eye tracking. It has been found that the task (stimulus 3, a-virtual laboratory) had a higher cognitive load on the students.	The authors worked with a specific task of short duration and with a selected sample of health science students.	The use of eye-tracking technology in natural learning contexts will be applied and included as a standard resource in the classroom. The aim is to monitor student’s learning in the performance of different learning tasks.
RQ3	Ponce, Héctor R., Mayer, R. E., Loyola, M. S., López, M. J., & Méndez, E. E. (2018). When two computer-supported learning strategies are better than one: An eye-tracking study. Computers and Education, 125, 376–388. https://doi.org/10.1016/j.compedu.2018.06.024.	The highlighting of information makes it easier for students to understand. The use of eye-tracking technology made it possible to examine cognitive processing during learning and to compare the results with learning outcomes.	The study was carried out over a short period and on a single type of task, which was text reading. It is suggested that similar studies should be carried out on other tasks and the importance of longer periods is noted.	This work also found better learning outcomes in the students who participated in the laboratory. The video they watched contained highlighted information. However, it has limitations that Ponce et al. (2018) also note in their work.	The authors worked with a specific task of short duration and with a selected sample of health science students.
RQ3	Ponce, H. R., Mayer, R. E. & Méndez, E. E. (2022). Effects of Learner-Generated Highlighting and Instructor-Provided Highlighting on Learning from Text: A Meta-Analysis. Educational Psychology Review, 34, 989–1024 https://doi.org/10.1007/s10648-021-09654-1	Highlighting teacher-generated materials enhances the learning of university students. If these materials are accompanied by pictures or diagrams, the learning potential is increased.	One limitation of the effect of underlining may be the students’ lack of familiarity with this type of material.	In this paper, we have found evidence of differences in information processing in the clusters of students found.	This study worked with a specific task of short duration and with a selected sample of health science students.	Future studies will analyse the cognitive load experienced by students in different tasks (underlined, non-underlined, with outlines, without outline support, with voice support, without voice support, etc.).
RQ1 and RQ3	Orru, G., & Longo, L. (2019). The Evolution of Cognitive Load Theory and the Measurement of Its Intrinsic, Extraneous and Germane Loads: A Review. In L. Longo and M. C. Leva (Eds.), Human Mental Workload: Models and Applications (pp. 23–48). Springer International Publishing.	Variations in the perception of cognitive load depend on the type of task and the cognitive effort each learner requires to complete it.			This study worked with a specific task of short duration and with a selected sample of health science students.	Future studies will analyse the indicators of cognitive load in the different resources implemented in the self-regulated video (underlining, outlining and voice).
RQ1 and RQ2	Souchet, A. D., Philippe, S., Lourdeaux, D., & Leroy, L. (2022). Measuring Visual Fatigue and Cognitive Load via Eye Tracking while Learning with Virtual Reality Head-Mounted Displays: A Review. International Journal of Human-Computer Interaction, 38(9), 801–824. https://doi.org/10.1080/10447318.2021.1976509	Cognitive load refers to the number of resources the learner has to process in working memory. Visual fatigue can be measured by the number of blinks and cognitive load by pupil diameter. The use of eye-tracking technology facilitates the collection of these metrics.	The authors reflect that there are few studies that refer to the use of technology during real learning situations. They also point out that it would be interesting to include techniques such as EEG that would provide more metrics that could more accurately support the detection of cognitive load. However, visual fatigue seems to be measured by blinks and cognitive load by pupil dilation. The most intense periods are at the beginning of learning.	The use of a self-regulated video in the laboratory may have mitigated the effects of visual fatigue detection and cognitive load on the level of learning outcomes. However, mean effect values on pupil diameter were detected in the relevant and non-relevant AOIs.	The authors worked with a specific task of short duration and with a selected sample of health science students.	It would be interesting to apply monitoring technology to all proposed learning tasks in a natural learning environment.
RQ1 and RQ3	Tong, S., & Nie, Y. (2022). Measuring Designers’ Cognitive Load for Timely Knowledge Push via Eye Tracking. International Journal of Human-Computer Interaction, 1–14. https://doi.org/10.1080/10447318.2022.2057898	The detection of cognitive load on certain tasks through non-invasive measurement techniques such as eye tracking is a promising indicator for improving performance.	No limitations are identified, but challenges such as the following: measuring multimodal cognitive load from behavioural analysis measures in virtual environments based on mouse clicks, keystrokes, etc. The idea is to obtain real-time feedback.	Students who participated in the laboratory, where eye-tracking technology was applied, obtained better learning results. Feedback on one’s own performance through the acquisition of measurement parameters gives the student feedback on their performance. It helps them to focus on the strengths and weaknesses of their intervention.	The laboratory was implemented in a limited time. However, applying this technology on a one-to-one basis to 31 students took three weeks of intervention.	Ideally, the learner should have an instrument that allows him/her to test his/her performance throughout the learning process.
RQ1	Wiedbusch, M. D., Kite, V., Yang, X., Park, S., Chi, M., Taub, M., & Azevedo, R. (2021). A Theoretical and Evidence-Based Conceptual Design of MetaDash: An Intelligent Teacher Dashboard to Support Teachers’ Decision Making and Students’ Self-Regulated Learning. Frontiers in Education, 6, 1–13. https://doi.org/10.3389/feduc.2021.570229	The monitoring of students’ cognitive and metacognitive processes in the learning process through ALT is referential for the teacher’s decision-making in order to facilitate learning outcomes, as these resources provide information about the cognitive load of each student in each task.	They do not point out any limitations but only refer to the fact that this approach is the future of the teaching-learning process and that there are few studies on it.	The monitoring of the students’ performance during the laboratory has provided indicators of the students’ learning process by detecting differences and elements that could be reinforced.	Only one task has been worked on in a short period.	It would be important to increase the time spent using monitoring systems such as eye tracking in more tasks. However, this would require technology that is easier to use in natural contexts and also a system for analysing and interpreting the data to provide teachers with real-time information about their students’ processing.

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