Eye-Tracking Study on the Gender Effect Towards Cognitive Processes During Multimedia Learning
Abstract
Multimedia learning is defined as the process of forming a knowledge mental model from words and pictures. It is important to measure cognitive process during multimedia learning. Differences in learners’ capabilities can be investigated through cognitive processes to improve the learning process. However, conventional methods such as interviews or behavioural assessment do not provide an objective measurement of cognitive processes during multimedia learning. Some advance methods to measure cognitive processes takes into account learner’s eye movement during learning process. In such a case, eye-tracking can be used as an alternative method to measure cognitive processes because eye movement has become a major part of human cognitive function. Another issue is related to the learners with different gender, which might have different styles of interaction with the source of information. Unfortunately, the effect of gender disparities in multimedia learning has not been widely studied. To address this research gap, this study examines the effect of gender differences based on eye-tracking metrics during multimedia learning. Based on the experimental results, `time until first fixation` on the text-type area of interest (AOI), `number of fixations` on the image type AOI, and `transition` from text-type AOI to image-type as well as `transition` between Image AOIs provided notable distinctions for each gender group (p < 0.05). It was found that male learners preferred to access information from images. In contrast, female learners tended to do a thorough inspection on textual and pictorial information during multimedia learning. This study can be used as an alternative method for collecting cognitive process indicators in multimedia learning.
References
R.E. Mayer, “Cognitive Theory of Multimedia Learning,” in The Cambridge Handbook of Multimedia Learning, R.E. Mayer, Ed., Cambridge, UK: Cambridge University Press, 2014, pp. 43–71, doi: 10.1017/CBO9781139547369.005.
A. Paivio, Mental Representations: A Dual Coding Approach. Oxford, UK: Oxford University Press, 1990, doi: 10.1093/acprof:oso/9780195066661.001.0001.
R.E. Mayer and R. Moreno, “Nine Ways to Reduce Cognitive Load in Multimedia Learning,” Educ. Psychol., Vol. 38, No. 1, pp. 43–52, 2003, doi: 10.1207/S15326985EP3801_6.
A.D. Baddeley, Essentials of Human Memory. London, UK: Psychology Press, 1999.
J. Sweller, Instructional Design in Technical Areas. Camberwell, Australia: ACER Press, 1999.
E. Korobova, I. Kardovich, M. Konysheva, and D. Mironova, “Cognitive Activity: Philosophical Analysis, Psychological and Pedagogical Characteristics,” SHS Web Conf., 2018, pp. 1–6, doi: 10.1051/SHSCONF/20185001083.
APA Dictionary of Psychology, “Cognitive process,” access date: 20-Jan-2023, https://dictionary.apa.org/cognitive-process.
K. Scheiter and A. Eitel, “The Use of Eye Tracking as a Research and Instructional Tool in Multimedia Learning,” in Eye-Tracking Technology Applications in Educational Research, C. Was, F. Sansosti, B. Morris, Eds., Hershey, PA, USA: IGI Global, 2017, ch. 8, pp. 143–164, doi: 10.4018/978-1-5225-1005-5.
R.E. Mayer, “Using Multimedia for E-Learning,” J. Comput. Assist. Learn., Vol. 33, No. 5, pp. 403–423, Jun. 2017, doi: 10.1111/jcal.12197.
P. Rodrigues and P.J. Rosa, “Eye-Tracking as a Research Methodology in Educational Context: A Spanning Framework,” in Early Childhood Development: Concepts, Methodologies, Tools, and Applications, Vol. 3, Information Resources Management Association, Ed., Hershey, PA, USA: IGI Global, 2017, ch. 13, pp. 269–294, doi: 10.4018/978-1-5225-7507-8.
T. Ujbanyi, J. Katona, G. Sziladi, and A. Kovari, “Eye-Tracking Analysis of Computer Networks Exam Question Besides Different Skilled Groups,” 2016 7th IEEE Int. Conf. Cogn. Infocommun. (CogInfoCom), 2016, pp. 277–282, doi: 10.1109/CogInfoCom.2016.7804561.
P.V. Yulianandra, S. Wibirama, and P.I. Santosa, “Examining the Effect of Website Complexity and Task Complexity in Web-Based Learning Management System,” 2017 1st Int. Conf. Inform., Comput. Sci. (ICICoS), 2017, pp. 119–124, doi: 10.1109/ICICOS.2017.8276348.
E. Alemdag and K. Cagiltay, “A Systematic Review of Eye Tracking Research on Multimedia Learning,” Comput., Educ., Vol. 125, pp. 413–428, Oct. 2018, doi: 10.1016/j.compedu.2018.06.023.
Z. Sharafi, T. Shaffer, B. Sharif, and Y.-G. Guéhéneuc, “Eye-Tracking Metrics in Software Engineering,” 2015 Asia-Pacific Softw. Eng. Conf. (APSEC), 2015, pp. 96–103, 10.1109/APSEC.2015.53.
H.-C. Liu, “Investigating the Impact of Cognitive Style on Multimedia Learners’ Understanding and Visual Search Patterns: An Eye-Tracking Approach,” J. Educ. Comput. Res., Vol. 55, No. 8, pp. 1053–1068, Jan. 2018, doi: 10.1177/0735633117697020.
M.A. Just and P.A. Carpenter, “A Theory of Reading: From Eye Fixations to Comprehension,” Psychol. Rev., Vol. 87, No. 4, pp. 329–354, Jul. 1980, doi: 10.1037/0033-295X.87.4.329.
S. Zander, S. Wetzel, T. Kühl, and S. Bertel, “Underlying Processes of an Inverted Personalization Effect in Multimedia Learning--An Eye-Tracking Study,” Front Psychol., Vol. 8, pp. 1–9, Dec. 2017, doi: 10.3389/fpsyg.2017.02202.
A.I. Molina, Ó. Navarro, M. Ortega, and M. Lacruz, “Evaluating Multimedia Learning Materials in Primary Education Using Eye Tracking,” Comput. Stand., Interfaces, Vol. 59, pp. 45–60, Aug. 2018, doi: 10.1016/j.csi.2018.02.004.
N. Tsianos et al., “Eye-Tracking Users’ Behavior in Relation to Cognitive Style within an E-Learning Environment,” 2009 Ninth IEEE Int.l Conf. Adv. Learn. Technol., 2009, pp. 329–333, doi: 10.1109/ICALT.2009.110.
T.J. Mehigan, M. Barry, A. Kehoe, and I. Pitt, “Using Eye Tracking Technology to Identify Visual and Verbal Learners,” 2011 IEEE Int. Conf. Multimed., Expo, 2011, pp. 1–6, doi: 10.1109/ICME.2011.6012036.
G.E. Raptis et al., “Using Eye Gaze Data and Visual Activities to Infer Human Cognitive Styles,” UMAP ’17: Proc. 25th Conf. User Model. Adapt., Personalization, 2017, pp. 164–173, doi: 10.1145/3079628.3079690.
M. Koć-Januchta et al., “Visualizers Versus Verbalizers: Effects of Cognitive Style on Learning with Texts and Pictures--An Eye-Tracking Study,” Comput. Human Behav., Vol. 68, pp. 170–179, Mar. 2017, doi: 10.1016/j.chb.2016.11.028.
J. Meyers-Levy and D. Maheswaran, “Exploring Differences in Males’ and Females’ Processing Strategies,” J. Consumer Res., Vol. 18, No. 1, pp. 63–70, Jun. 1991, doi: 10.1086/209241.
P. Cafferata and A.M. Tybout, “Gender Differences in Information Processing: A Selectivity Interpretation,” in Cognitive and Affective Responses to Advertising, P. Cafferata and A.M. Tybout, Eds., Lexington, MA, USA: Lexington Books, 1989.
W.K. Darley and R.E. Smith, “Gender Differences in Information Processing Strategies: An Empirical Test of the Selectivity Model in Advertising Response,” J. Advert., Vol. 24, No. 1, pp. 41–56, 1995, doi: 10.1080/00913367.1995.10673467.
V. Bonomo, “Gender Matters in Elementary Education Research-Based Strategies to Meet the Distinctive Learning Needs of Boys and Girls,” Educ. Horiz., Vol. 88, No. 4, pp. 257–264, 2010.
N. Barber, T. Dodd, and N. Kolyesnikova, “Gender Differences in Information Search: Implications for Retailing,” J. Consumer Mark., Vol. 26, No. 6, pp. 415–426, Sep. 2009, doi: 10.1108/07363760910988238.
S. Saha and S. Halder, “He or She: Does Gender Affect Various Mode of Instructional Visual Design?” J. Res. Women, Gend., Vol. 7, No. 1, pp. 47–58, 2016.
M. Heo and N. Toomey, “Learning with Multimedia: The Effects of Gender, Type of Multimedia Learning Resources, and Spatial Ability,” Comput., Educ., Vol. 146, pp. 1–12, Mar. 2020, doi: 10.1016/j.compedu.2019.103747.
B.A. Sargezeh, N. Tavakoli, and M.R. Daliri, “Gender-Based Eye Movement Differences in Passive Indoor Picture Viewing: An Eye-Tracking Study,” Psychol. Behav., Vol. 206, pp. 43–50, Jul. 2019, doi: 10.1016/j.physbeh.2019.03.023.
C. Wang, Y. Chen, S. Zheng, and H. Liao, “Gender and Age Differences in Using Indoor Maps for Wayfinding in Real Environments,” ISPRS Int. J. Geoinf., Vol. 8, No. 1, pp. 1–20, Dec. 2018, doi: 10.3390/ijgi8010011.
Q.-X. Qu and F. Guo, “Can Eye Movements Be Effectively Measured to Assess Product Design?: Gender Differences Should Be Considered,” Int. J. Ind. Ergonom., Vol. 72, pp. 281–289, Jul. 2019, doi: 10.1016/j.ergon.2019.06.006.
J.C. Hung and C.C. Wang, “The Influence of Cognitive Styles and Gender on Visual Behavior During Program Debugging: A Virtual Reality Eye Tracker Study,” Hum.-Centric Comput., Inf. Sci., Vol. 11, pp. 1–19, May 2021, doi: 10.22967/HCIS.2021.11.022.
A.G.P. Sidhawara, S. Wibirama, and T.B. Adji, “Classification of Visual-Verbal Cognitive Style in Multimedia Learning Using Eye-Tracking and Machine Learning,” 2020 6th Int. Conf. Sci., Technol. (ICST), 2020, pp. 1–5, doi: 10.1109/ICST50505.2020.9732880.
G. Nishimura, “Déjà Vu: Classification of Memory Using Eye Movements,” 2015. Access date: 20-Jan-2023. [Online], https://www.doc.ic.ac.uk/teaching/distinguished-projects/2015/g.nishimura.pdf.
J. Holsanova, N. Holmberg, and K. Holmqvist, “Reading Information Graphics: The Role of Spatial Contiguity and Dual Attentional Guidance,” Appl. Cogn. Psychol., Vol. 23, No. 9, pp. 1215–1226, Dec. 2009, doi: 10.1002/acp.1525.
L. Mason, P. Pluchino, M. C. Tornatora, and N. Ariasi, “An Eye-Tracking Study of Learning from Science Text with Concrete and Abstract Illustrations,” J. Exp. Educ., Vol. 81, No. 3, pp. 356–384, Apr. 2013, doi: 10.1080/00220973.2012.727885.
L. Mason, M.C. Tornatora, and P. Pluchino, “Do Fourth Graders Integrate Text and Picture in Processing and Learning from an Illustrated Science Text? Evidence from Eye-Movement Patterns,” Comput., Educ., Vol. 60, No. 1, pp. 95–109, Jan. 2013, doi: 10.1016/j.compedu.2012.07.011.
M.-L. Lai et al., “A Review of Using Eye-Tracking Technology in Exploring Learning from 2000 to 2012,” Educ. Res. Rev., Vol. 10, pp. 90–115, Dec. 2013, doi: 10.1016/j.edurev.2013.10.001.
B. Park, A. Korbach, and R. Brünken, “Do Learner Characteristics Moderate the Seductive-Details-Effect? A Cognitive-Load-Study Using Eye-Tracking,” J. Educ. Technol., Soc., Vol. 18, No. 4, pp. 24–36, Oct. 2015.
K. Holmqvist et al., Eye Tracking: A Comprehensive Guide to Methods and Measures. Oxford, UK: Oxford University Press, 2011.
J.L. Turner, Using Statistics in Small-Scale Language Education Research: Focus on Non-Parametric Data. Oxfordshire, UK: Routledge, 2014.
G.W. Corder and D.I. Foreman, Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011.
P.W. Glimcher, “The Neurobiology of Visual-Saccadic Decision Making,” Annu. Rev. Neurosci., Vol. 26, pp. 133–179, Mar. 2003, doi: 10.1146/annurev.neuro.26.010302.081134.
F.J.M. Moss, R. Baddeley, and N. Canagarajah, “Eye Movements to Natural Images as a Function of Sex and Personality,” PLoS One, Vol. 7, No. 11, pp. 1–9, Nov. 2012, doi: 10.1371/journal.pone.0047870.
H.-C. Liu and H.-H. Chuang, “An Examination of Cognitive Processing of Multimedia Information Based on Viewers’ Eye Movements,” Interact. Learn. Environ., Vol. 19, No. 5, pp. 503–517, 2011, doi: 10.1080/10494820903520123
P.A. O’Keefe et al., “Learning from Multiple Representations: An Examination of Fixation Patterns in A Science Simulation,” Comput. Hum. Behav., Vol. 35, pp. 234–242, Jun. 2014, doi: 10.1016/j.chb.2014.02.040.
P. Merritt et al., “Evidence for Gender Differences in Visual Selective Attention,” Pers. Individ. Differ., Vol. 43, No. 3, pp. 597–609, Aug. 2007, doi: 10.1016/j.paid.2007.01.016.
D.F. Halpern, “A Cognitive-Process Taxonomy for Sex Differences in Cognitive Abilities,” Curr. Dir. Psychol. Sci., Vol. 13, No. 4, pp. 135–139, Aug. 2004, doi: 10.1111/j.0963-7214.2004.0029.
F.L. Coward, S.M. Crooks, R. Flores, and D. Dao, “Examining the Effects of Gender and Presentation Mode on Learning from a Multimedia Presentation,” Multidiscip. J. Gend. Stud., Vol. 1, No. 1, pp. 48–69, Feb. 2012, doi: 10.4471/generos.2012.03.
M.A. Rau, J.E. Michaelis, and N. Fay, “Connection Making Between Multiple Graphical Representations: A Multi-Methods Approach for Domain-Specific Grounding of an Intelligent Tutoring System for Chemistry,” Comput., Educ., Vol. 82, pp. 460–485, Mar. 2015, doi: 10.1016/j.compedu.2014.12.009.
V. Bauhoff, M. Huff, and S. Schwan, “Distance Matters: Spatial Contiguity Effects as Trade-Off Between Gaze Switches and Memory Load,” Appl. Cogn. Psychol., Vol. 26, No. 6, pp. 863–871, Dec. 2012, doi: 10.1002/acp.2887.
K. Goodrich, “The Gender Gap: Brain-Processing Differences Between the Sexes Shape Attitudes about Online Advertising,” J. Advert. Res., Vol. 54, No. 1, pp. 32–43, Mar. 2014, doi: 10.2501/JAR-54-1-032-043.
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